From 424b15583a2edc00ff31c66057db1742117959a4 Mon Sep 17 00:00:00 2001
From: AlexBowring <A.bowring@warwick.ac.uk>
Date: Tue, 2 Jun 2020 16:57:31 +0100
Subject: [PATCH] files for amended ds109 FSL analyses

---
 .../cope1.feat/stats/cluster_count.csv        | 121 +++
 .../cope1.feat/stats/euler_chars.csv          | 121 +++
 .../permutation_test/cluster_count.csv        | 121 +++
 .../permutation_test/euler_chars.csv          | 121 +++
 ds109/FSL/SCRIPTS/level3_amended.fsf          | 552 +++++++++++
 ds109/FSL/SCRIPTS/permutation_test_amended.sh |   6 +
 ds109/FSL/SCRIPTS/sub-01_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-01_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-01_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-02_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-02_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-02_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-03_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-03_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-03_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-08_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-08_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-08_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-09_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-09_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-09_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-10_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-10_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-10_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-11_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-11_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-11_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-14_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-14_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-14_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-15_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-15_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-15_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-17_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-17_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-17_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-18_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-18_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-18_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-21_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-21_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-21_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-22_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-22_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-22_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-26_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-26_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-26_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-27_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-27_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-27_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-28_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-28_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-28_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-30_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-30_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-30_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-31_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-31_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-31_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-32_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-32_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-32_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-43_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-43_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-43_run-02_level1_amended.fsf  | 538 +++++++++++
 ds109/FSL/SCRIPTS/sub-48_level2_amended.fsf   | 366 +++++++
 .../SCRIPTS/sub-48_run-01_level1_amended.fsf  | 538 +++++++++++
 .../SCRIPTS/sub-48_run-02_level1_amended.fsf  | 538 +++++++++++
 figures/ds109_notebook_amended.ipynb          | 913 ++++++++++++++++++
 figures/lib/download_data_amended.py          | 178 ++++
 scripts/ds109_bold_maps_amended.m             |  82 ++
 scripts/ds109_euler_chars_amended.m           |  28 +
 .../lib/template_ds109_FSL_level1_amended.fsf | 538 +++++++++++
 scripts/process_ds109_FSL_amended.py          |  72 ++
 75 files changed, 33135 insertions(+)
 create mode 100644 ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/cluster_count.csv
 create mode 100644 ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/euler_chars.csv
 create mode 100644 ds109/FSL/LEVEL2_amended/permutation_test/cluster_count.csv
 create mode 100644 ds109/FSL/LEVEL2_amended/permutation_test/euler_chars.csv
 create mode 100644 ds109/FSL/SCRIPTS/level3_amended.fsf
 create mode 100755 ds109/FSL/SCRIPTS/permutation_test_amended.sh
 create mode 100644 ds109/FSL/SCRIPTS/sub-01_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-01_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-01_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-02_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-02_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-02_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-03_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-03_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-03_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-08_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-08_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-08_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-09_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-09_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-09_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-10_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-10_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-10_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-11_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-11_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-11_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-14_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-14_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-14_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-15_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-15_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-15_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-17_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-17_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-17_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-18_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-18_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-18_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-21_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-21_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-21_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-22_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-22_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-22_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-26_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-26_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-26_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-27_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-27_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-27_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-28_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-28_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-28_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-30_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-30_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-30_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-31_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-31_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-31_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-32_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-32_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-32_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-43_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-43_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-43_run-02_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-48_level2_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-48_run-01_level1_amended.fsf
 create mode 100644 ds109/FSL/SCRIPTS/sub-48_run-02_level1_amended.fsf
 create mode 100644 figures/ds109_notebook_amended.ipynb
 create mode 100644 figures/lib/download_data_amended.py
 create mode 100644 scripts/ds109_bold_maps_amended.m
 create mode 100644 scripts/ds109_euler_chars_amended.m
 create mode 100644 scripts/lib/template_ds109_FSL_level1_amended.fsf
 create mode 100644 scripts/process_ds109_FSL_amended.py

diff --git a/ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/cluster_count.csv b/ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/cluster_count.csv
new file mode 100644
index 0000000..f39362b
--- /dev/null
+++ b/ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/cluster_count.csv
@@ -0,0 +1,121 @@
+-6,3
+-5.9,3
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+-1.7,9
+-1.6,14
+-1.5,16
+-1.4,23
+-1.3,33
+-1.2,32
+-1.1,30
+-1,37
+-0.9,42
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diff --git a/ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/euler_chars.csv b/ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/euler_chars.csv
new file mode 100644
index 0000000..d17ad9d
--- /dev/null
+++ b/ds109/FSL/LEVEL2_amended/group.gfeat/cope1.feat/stats/euler_chars.csv
@@ -0,0 +1,121 @@
+-6,2
+-5.9,2
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diff --git a/ds109/FSL/LEVEL2_amended/permutation_test/cluster_count.csv b/ds109/FSL/LEVEL2_amended/permutation_test/cluster_count.csv
new file mode 100644
index 0000000..954703a
--- /dev/null
+++ b/ds109/FSL/LEVEL2_amended/permutation_test/cluster_count.csv
@@ -0,0 +1,121 @@
+-6,4
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diff --git a/ds109/FSL/LEVEL2_amended/permutation_test/euler_chars.csv b/ds109/FSL/LEVEL2_amended/permutation_test/euler_chars.csv
new file mode 100644
index 0000000..41ff4ce
--- /dev/null
+++ b/ds109/FSL/LEVEL2_amended/permutation_test/euler_chars.csv
@@ -0,0 +1,121 @@
+-6,2
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+-3.5,7
+-3.4,7
+-3.3,8
+-3.2,11
+-3.1,7
+-3,7
+-2.9,8
+-2.8,11
+-2.7,10
+-2.6,10
+-2.5,14
+-2.4,22
+-2.3,21
+-2.2,22
+-2.1,14
+-2,18
+-1.9,10
+-1.8,5
+-1.7,2
+-1.6,4
+-1.5,-3
+-1.4,-5
+-1.3,-2
+-1.2,1
+-1.1,-5
+-1,-19
+-0.9,-23
+-0.8,-27
+-0.7,-40
+-0.6,-31
+-0.5,-27
+-0.4,-22
+-0.3,-46
+-0.2,-42
+-0.1,-29
+0,-8
+0.1,-13
+0.2,-13
+0.3,-14
+0.4,-34
+0.5,-20
+0.6,-27
+0.7,2
+0.8,1
+0.9,-3
+1,30
+1.1,21
+1.2,27
+1.3,43
+1.4,40
+1.5,33
+1.6,35
+1.7,35
+1.8,18
+1.9,13
+2,20
+2.1,17
+2.2,47
+2.3,42
+2.4,25
+2.5,24
+2.6,29
+2.7,32
+2.8,28
+2.9,30
+3,38
+3.1,38
+3.2,35
+3.3,35
+3.4,35
+3.5,33
+3.6,38
+3.7,34
+3.8,34
+3.9,37
+4,36
+4.1,36
+4.2,35
+4.3,33
+4.4,43
+4.5,39
+4.6,41
+4.7,42
+4.8,39
+4.9,40
+5,35
+5.1,29
+5.2,25
+5.3,22
+5.4,22
+5.5,18
+5.6,19
+5.7,16
+5.8,18
+5.9,15
+6,16
diff --git a/ds109/FSL/SCRIPTS/level3_amended.fsf b/ds109/FSL/SCRIPTS/level3_amended.fsf
new file mode 100644
index 0000000..5c8c181
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/level3_amended.fsf
@@ -0,0 +1,552 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL2_amended/group"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 21
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 21
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 2
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-21/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-10/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (3)
+set feat_files(3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-01/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (4)
+set feat_files(4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-30/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (5)
+set feat_files(5) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-26/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (6)
+set feat_files(6) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-14/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (7)
+set feat_files(7) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-02/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (8)
+set feat_files(8) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-32/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (9)
+set feat_files(9) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-09/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (10)
+set feat_files(10) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-18/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (11)
+set feat_files(11) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-08/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (12)
+set feat_files(12) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-03/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (13)
+set feat_files(13) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-11/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (14)
+set feat_files(14) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-15/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (15)
+set feat_files(15) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-28/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (16)
+set feat_files(16) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-27/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (17)
+set feat_files(17) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-22/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (18)
+set feat_files(18) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-31/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (19)
+set feat_files(19) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-48/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (20)
+set feat_files(20) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-43/combined.gfeat/cope1.feat"
+
+# 4D AVW data or FEAT directory (21)
+set feat_files(21) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-17/combined.gfeat/cope1.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1.0
+
+# Higher-level EV value for EV 1 and input 3
+set fmri(evg3.1) 1.0
+
+# Higher-level EV value for EV 1 and input 4
+set fmri(evg4.1) 1.0
+
+# Higher-level EV value for EV 1 and input 5
+set fmri(evg5.1) 1.0
+
+# Higher-level EV value for EV 1 and input 6
+set fmri(evg6.1) 1.0
+
+# Higher-level EV value for EV 1 and input 7
+set fmri(evg7.1) 1.0
+
+# Higher-level EV value for EV 1 and input 8
+set fmri(evg8.1) 1.0
+
+# Higher-level EV value for EV 1 and input 9
+set fmri(evg9.1) 1.0
+
+# Higher-level EV value for EV 1 and input 10
+set fmri(evg10.1) 1.0
+
+# Higher-level EV value for EV 1 and input 11
+set fmri(evg11.1) 1.0
+
+# Higher-level EV value for EV 1 and input 12
+set fmri(evg12.1) 1.0
+
+# Higher-level EV value for EV 1 and input 13
+set fmri(evg13.1) 1.0
+
+# Higher-level EV value for EV 1 and input 14
+set fmri(evg14.1) 1.0
+
+# Higher-level EV value for EV 1 and input 15
+set fmri(evg15.1) 1.0
+
+# Higher-level EV value for EV 1 and input 16
+set fmri(evg16.1) 1.0
+
+# Higher-level EV value for EV 1 and input 17
+set fmri(evg17.1) 1.0
+
+# Higher-level EV value for EV 1 and input 18
+set fmri(evg18.1) 1.0
+
+# Higher-level EV value for EV 1 and input 19
+set fmri(evg19.1) 1.0
+
+# Higher-level EV value for EV 1 and input 20
+set fmri(evg20.1) 1.0
+
+# Higher-level EV value for EV 1 and input 21
+set fmri(evg21.1) 1.0
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Group membership for input 3
+set fmri(groupmem.3) 1
+
+# Group membership for input 4
+set fmri(groupmem.4) 1
+
+# Group membership for input 5
+set fmri(groupmem.5) 1
+
+# Group membership for input 6
+set fmri(groupmem.6) 1
+
+# Group membership for input 7
+set fmri(groupmem.7) 1
+
+# Group membership for input 8
+set fmri(groupmem.8) 1
+
+# Group membership for input 9
+set fmri(groupmem.9) 1
+
+# Group membership for input 10
+set fmri(groupmem.10) 1
+
+# Group membership for input 11
+set fmri(groupmem.11) 1
+
+# Group membership for input 12
+set fmri(groupmem.12) 1
+
+# Group membership for input 13
+set fmri(groupmem.13) 1
+
+# Group membership for input 14
+set fmri(groupmem.14) 1
+
+# Group membership for input 15
+set fmri(groupmem.15) 1
+
+# Group membership for input 16
+set fmri(groupmem.16) 1
+
+# Group membership for input 17
+set fmri(groupmem.17) 1
+
+# Group membership for input 18
+set fmri(groupmem.18) 1
+
+# Group membership for input 19
+set fmri(groupmem.19) 1
+
+# Group membership for input 20
+set fmri(groupmem.20) 1
+
+# Group membership for input 21
+set fmri(groupmem.21) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group activation"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Display images for contrast_real 2
+set fmri(conpic_real.2) 1
+
+# Title for contrast_real 2
+set fmri(conname_real.2) "group deactivation"
+
+# Real contrast_real vector 2 element 1
+set fmri(con_real2.1) -1.0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Mask real contrast/F-test 1 with real contrast/F-test 2?
+set fmri(conmask1_2) 0
+
+# Mask real contrast/F-test 2 with real contrast/F-test 1?
+set fmri(conmask2_1) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/permutation_test_amended.sh b/ds109/FSL/SCRIPTS/permutation_test_amended.sh
new file mode 100755
index 0000000..92ee5e3
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/permutation_test_amended.sh
@@ -0,0 +1,6 @@
+cd /storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL2_amended/permutation_test
+
+fslmerge -t contrasts /storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-*/combined.gfeat/cope1.feat/stats/cope1.nii.gz
+randomise -i contrasts -o OneSampT -d /storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL2_amended/permutation_test/../group.gfeat/design.mat -t /storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL2_amended/permutation_test/../group.gfeat/design.con -x -c `ptoz 0.005` -n 10000 -m /storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL2_amended/permutation_test/../group.gfeat/cope1.feat/mask.nii.gz
+fslmaths OneSampT_clustere_corrp_tstat1 -thr 0.95 -bin -mul OneSampT_tstat1 05FWECorrected_OneSampT_pos_exc_set
+fslmaths OneSampT_clustere_corrp_tstat2 -thr 0.95 -bin -mul OneSampT_tstat2 05FWECorrected_OneSampT_neg_exc_set
diff --git a/ds109/FSL/SCRIPTS/sub-01_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-01_level2_amended.fsf
new file mode 100644
index 0000000..cf631ca
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-01_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-01/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-01/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-01/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-01_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-01_run-01_level1_amended.fsf
new file mode 100644
index 0000000..738b77c
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-01_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-01/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-01_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-01_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-01_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-01_run-02_level1_amended.fsf
new file mode 100644
index 0000000..5eb248d
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-01_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-01/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-01_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-01_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-01_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-02_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-02_level2_amended.fsf
new file mode 100644
index 0000000..18b9639
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-02_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-02/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-02/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-02/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-02_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-02_run-01_level1_amended.fsf
new file mode 100644
index 0000000..ba9bc50
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-02_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-02/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-02_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-02_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-02_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-02_run-02_level1_amended.fsf
new file mode 100644
index 0000000..b195ef1
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-02_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-02/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-02_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-02_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-02_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-03_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-03_level2_amended.fsf
new file mode 100644
index 0000000..e24ce1e
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-03_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-03/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-03/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-03/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-03_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-03_run-01_level1_amended.fsf
new file mode 100644
index 0000000..a20c216
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-03_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-03/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-03_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-03_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-03_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-03_run-02_level1_amended.fsf
new file mode 100644
index 0000000..d6f12ae
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-03_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-03/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-03_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-03_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-03_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-08_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-08_level2_amended.fsf
new file mode 100644
index 0000000..ecc3114
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-08_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-08/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-08/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-08/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-08_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-08_run-01_level1_amended.fsf
new file mode 100644
index 0000000..15ff31f
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-08_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-08/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-08_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-08_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-08_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-08_run-02_level1_amended.fsf
new file mode 100644
index 0000000..26c7f76
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-08_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-08/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-08_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-08_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-08_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-09_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-09_level2_amended.fsf
new file mode 100644
index 0000000..66d17ac
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-09_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-09/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-09/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-09/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-09_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-09_run-01_level1_amended.fsf
new file mode 100644
index 0000000..688e33f
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-09_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-09/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-09_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-09_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-09_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-09_run-02_level1_amended.fsf
new file mode 100644
index 0000000..b1f8859
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-09_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-09/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-09_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-09_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-09_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-10_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-10_level2_amended.fsf
new file mode 100644
index 0000000..b9a6259
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-10_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-10/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-10/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-10/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-10_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-10_run-01_level1_amended.fsf
new file mode 100644
index 0000000..70d1ad7
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-10_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-10/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-10_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-10_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-10_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-10_run-02_level1_amended.fsf
new file mode 100644
index 0000000..6e16228
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-10_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-10/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-10_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-10_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-10_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-11_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-11_level2_amended.fsf
new file mode 100644
index 0000000..56b15f6
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-11_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-11/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-11/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-11/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-11_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-11_run-01_level1_amended.fsf
new file mode 100644
index 0000000..f07ffdf
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-11_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-11/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-11_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-11_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-11_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-11_run-02_level1_amended.fsf
new file mode 100644
index 0000000..79d85db
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-11_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-11/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-11_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-11_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-11_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-14_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-14_level2_amended.fsf
new file mode 100644
index 0000000..9929ad7
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-14_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-14/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-14/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-14/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-14_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-14_run-01_level1_amended.fsf
new file mode 100644
index 0000000..fa331b3
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-14_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-14/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-14_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-14_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-14_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-14_run-02_level1_amended.fsf
new file mode 100644
index 0000000..03799ed
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-14_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-14/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-14_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-14_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-14_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-15_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-15_level2_amended.fsf
new file mode 100644
index 0000000..06104e5
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-15_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-15/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-15/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-15/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-15_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-15_run-01_level1_amended.fsf
new file mode 100644
index 0000000..5944e97
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-15_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-15/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-15_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-15_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-15_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-15_run-02_level1_amended.fsf
new file mode 100644
index 0000000..50555f7
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-15_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-15/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-15_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-15_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-15_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-17_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-17_level2_amended.fsf
new file mode 100644
index 0000000..b7adac4
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-17_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-17/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-17/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-17/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-17_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-17_run-01_level1_amended.fsf
new file mode 100644
index 0000000..8b1e95f
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-17_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-17/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-17_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-17_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-17_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-17_run-02_level1_amended.fsf
new file mode 100644
index 0000000..9c8eaac
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-17_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-17/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-17_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-17_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-17_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-18_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-18_level2_amended.fsf
new file mode 100644
index 0000000..84f6cc1
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-18_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-18/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-18/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-18/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-18_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-18_run-01_level1_amended.fsf
new file mode 100644
index 0000000..6837d45
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-18_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-18/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-18_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-18_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-18_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-18_run-02_level1_amended.fsf
new file mode 100644
index 0000000..3adbb30
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-18_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-18/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-18_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-18_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-18_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-21_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-21_level2_amended.fsf
new file mode 100644
index 0000000..fbeb513
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-21_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-21/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-21/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-21/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-21_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-21_run-01_level1_amended.fsf
new file mode 100644
index 0000000..58e4548
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-21_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-21/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-21_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-21_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-21_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-21_run-02_level1_amended.fsf
new file mode 100644
index 0000000..c0e26b9
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-21_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-21/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-21_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-21_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-21_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-22_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-22_level2_amended.fsf
new file mode 100644
index 0000000..128822f
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-22_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-22/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-22/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-22/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-22_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-22_run-01_level1_amended.fsf
new file mode 100644
index 0000000..87131cf
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-22_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-22/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-22_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-22_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-22_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-22_run-02_level1_amended.fsf
new file mode 100644
index 0000000..9da598c
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-22_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-22/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-22_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-22_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-22_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-26_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-26_level2_amended.fsf
new file mode 100644
index 0000000..04d189f
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-26_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-26/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-26/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-26/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-26_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-26_run-01_level1_amended.fsf
new file mode 100644
index 0000000..9fd32f5
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-26_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-26/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-26_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-26_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-26_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-26_run-02_level1_amended.fsf
new file mode 100644
index 0000000..ef9d101
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-26_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-26/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-26_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-26_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-26_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-27_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-27_level2_amended.fsf
new file mode 100644
index 0000000..bd84b86
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-27_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-27/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-27/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-27/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-27_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-27_run-01_level1_amended.fsf
new file mode 100644
index 0000000..8251656
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-27_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-27/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-27_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-27_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-27_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-27_run-02_level1_amended.fsf
new file mode 100644
index 0000000..e0f8432
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-27_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-27/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-27_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-27_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-27_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-28_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-28_level2_amended.fsf
new file mode 100644
index 0000000..53b764a
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-28_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-28/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-28/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-28/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-28_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-28_run-01_level1_amended.fsf
new file mode 100644
index 0000000..e3b4f64
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-28_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-28/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-28_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-28_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-28_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-28_run-02_level1_amended.fsf
new file mode 100644
index 0000000..e2645d2
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-28_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-28/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-28_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-28_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-28_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-30_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-30_level2_amended.fsf
new file mode 100644
index 0000000..0b957ba
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-30_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-30/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-30/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-30/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-30_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-30_run-01_level1_amended.fsf
new file mode 100644
index 0000000..798d367
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-30_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-30/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-30_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-30_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-30_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-30_run-02_level1_amended.fsf
new file mode 100644
index 0000000..084d893
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-30_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-30/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-30_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-30_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-30_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-31_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-31_level2_amended.fsf
new file mode 100644
index 0000000..14e8c3a
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-31_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-31/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-31/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-31/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-31_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-31_run-01_level1_amended.fsf
new file mode 100644
index 0000000..923d506
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-31_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-31/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-31_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-31_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-31_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-31_run-02_level1_amended.fsf
new file mode 100644
index 0000000..455ce96
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-31_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-31/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-31_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-31_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-31_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-32_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-32_level2_amended.fsf
new file mode 100644
index 0000000..a5b2a9a
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-32_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-32/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-32/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-32/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-32_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-32_run-01_level1_amended.fsf
new file mode 100644
index 0000000..fddc204
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-32_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-32/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-32_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-32_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-32_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-32_run-02_level1_amended.fsf
new file mode 100644
index 0000000..e791312
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-32_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-32/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-32_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-32_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-32_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-43_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-43_level2_amended.fsf
new file mode 100644
index 0000000..d8b35fa
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-43_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-43/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-43/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-43/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-43_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-43_run-01_level1_amended.fsf
new file mode 100644
index 0000000..1b3c0af
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-43_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-43/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-43_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-43_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-43_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-43_run-02_level1_amended.fsf
new file mode 100644
index 0000000..606f7dc
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-43_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-43/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-43_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-43_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-43_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-48_level2_amended.fsf b/ds109/FSL/SCRIPTS/sub-48_level2_amended.fsf
new file mode 100644
index 0000000..a84ab23
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-48_level2_amended.fsf
@@ -0,0 +1,366 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 2
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 2
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-48/combined"
+
+# TR(s)
+set fmri(tr) 3
+
+# Total volumes
+set fmri(npts) 2
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 2
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 1
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 0
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 5
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 0
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 3
+
+# Number of EVs
+set fmri(evs_orig) 1
+set fmri(evs_real) 1
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 0
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 0
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 0
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 1
+
+# Use lower-level cope 1 for higher-level analysis
+set fmri(copeinput.1) 1
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-48/run-01.feat"
+
+# 4D AVW data or FEAT directory (2)
+set feat_files(2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-48/run-02.feat"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_vs_false_photo"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 2
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 0
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 0
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "dummy"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Higher-level EV value for EV 1 and input 1
+set fmri(evg1.1) 1
+
+# Higher-level EV value for EV 1 and input 2
+set fmri(evg2.1) 1
+
+# Group membership for input 1
+set fmri(groupmem.1) 1
+
+# Group membership for input 2
+set fmri(groupmem.2) 1
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) real
+set fmri(con_mode) real
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "group mean"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-48_run-01_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-48_run-01_level1_amended.fsf
new file mode 100644
index 0000000..804d37c
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-48_run-01_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-48/run-01"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-48_task-theoryofmindwithmanualresponse_run-01_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-48_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-01_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-01_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-01_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-01_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/ds109/FSL/SCRIPTS/sub-48_run-02_level1_amended.fsf b/ds109/FSL/SCRIPTS/sub-48_run-02_level1_amended.fsf
new file mode 100644
index 0000000..defa652
--- /dev/null
+++ b/ds109/FSL/SCRIPTS/sub-48_run-02_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/LEVEL1_amended/sub-48/run-02"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "/usr/local/packages/fsl-5.0.10/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/FUNCTIONAL/sub-48_task-theoryofmindwithmanualresponse_run-02_bold.nii.gz"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/PREPROCESSING/ANATOMICAL/sub-48_T1w_brain.nii.gz"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-02_false_belief_story.txt"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-02_false_belief_question.txt"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-02_false_photo_story.txt"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109/FSL/ONSETS/sub-48_run-02_false_photo_question.txt"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/figures/ds109_notebook_amended.ipynb b/figures/ds109_notebook_amended.ipynb
new file mode 100644
index 0000000..4b028ae
--- /dev/null
+++ b/figures/ds109_notebook_amended.ipynb
@@ -0,0 +1,913 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Software Comparison Project with NIDM-Results\n",
+    "\n",
+    "This notebook demonstates how to reproduce the results presented in the Software Comparison Project publication using NIDM-Results packs available at [NeuroVault (7782)](http://neurovault.org/collections/7782/). We give visual comparisons between axial slices of the excursion set images and T-statistics for each software packages, as well as quantitative comparisons with Euler characteristics, Bland-Altman plots and Sørensen–Dice coefficients.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from subprocess import check_call\n",
+    "import zipfile\n",
+    "from nilearn import plotting\n",
+    "import shutil\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the NIDM-Results packs from NeuroVault\n",
+    "\n",
+    " - Query NeuroVault's API to retreive all NIDM packs in collection 7782\n",
+    " - Download and save the packs in sub-folder `input/ds109_amended/` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "https://neurovault.org/collections/7782/group.gfeat.nidm.zip already downloaded at ./input/ds109_amended/group.gfeat.nidm.zip\n",
+      "https://neurovault.org/collections/7782/spm_0001.nidm.zip already downloaded at ./input/ds109_amended/spm_0001.nidm.zip\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already downloaded at ./input/ds109_amended/spm_0002.nidm.zip\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_euler_chars.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_euler_chars.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_euler_chars.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_perm_euler_chars.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_perm_euler_chars.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_perm_euler_chars.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_cluster_count.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_cluster_count.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_cluster_count.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_perm_cluster_count.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_perm_cluster_count.csv\n",
+      "https://neurovault.org/collections/7782/spm_0002.nidm.zip already copied at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_perm_cluster_count.csv\n",
+      "http://neurovault.org/media/images/7782/mask.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_mask.nii.gz\n",
+      "http://neurovault.org/media/images/7782/Negative_clustered_t_stat.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_exc_set_neg.nii.gz\n",
+      "http://neurovault.org/media/images/7782/Positive_clustered_t_stat.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_exc_set_pos.nii.gz\n",
+      "http://neurovault.org/media/images/7782/3dMEMA_result_t_stat_masked.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_stat.nii.gz\n",
+      "http://neurovault.org/media/images/7782/perm_ttest++_Clustsim_result_t_stat_masked.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_perm.nii.gz\n",
+      "http://neurovault.org/media/images/7782/perm_Positive_clustered_t_stat.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_perm_exc_set_pos.nii.gz\n",
+      "http://neurovault.org/media/images/7782/mask.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_perm_mask.nii.gz\n",
+      "http://neurovault.org/media/images/7782/OneSampT_tstat1.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_perm.nii.gz\n",
+      "http://neurovault.org/media/images/7782/05FWECorrected_OneSampT_pos_exc_set.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_perm_exc_set_pos.nii.gz\n",
+      "http://neurovault.org/media/images/7782/05FWECorrected_OneSampT_neg_exc_set.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_perm_exc_set_neg.nii.gz\n",
+      "http://neurovault.org/media/images/7782/snpmT%2B.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_perm.nii.gz\n",
+      "http://neurovault.org/media/images/7782/SnPM_pos_filtered.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_perm_exc_set_pos.nii.gz\n",
+      "http://neurovault.org/media/images/7782/afni_bold.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/afni_bold.nii.gz\n",
+      "http://neurovault.org/media/images/7782/fsl_bold_amended.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/fsl_bold.nii.gz\n",
+      "http://neurovault.org/media/images/7782/spm_bold.nii.gz already downloaded at /Users/maullz/Desktop/Software_Comparison/figures/input/ds109_amended/spm_bold.nii.gz\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lib import download_data_amended\n",
+    "download_data_amended.download_data('7782', 'ds109', 'ds109_amended')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "study = 'ds109_amended'\n",
+    "num_subjects = 21\n",
+    "\n",
+    "# *** SPM group activations\n",
+    "spm_pack = open('./input/' + study + '/spm_0001.nidm.zip', 'rb')\n",
+    "z = zipfile.ZipFile(spm_pack)\n",
+    "z.extract('ExcursionSet.nii.gz', './input/' + study + '/')\n",
+    "z.extract('Mask.nii.gz', './input/' + study + '/')\n",
+    "z.extract('TStatistic.nii.gz', './input/' + study + '/')\n",
+    "\n",
+    "# Thresholded statistics\n",
+    "spm_exc_set_file = './input/' + study + '/spm_exc_set.nii.gz'\n",
+    "shutil.move('./input/' + study + '/ExcursionSet.nii.gz', spm_exc_set_file)\n",
+    "\n",
+    "# Unthresholded statistics\n",
+    "spm_stat_file = './input/' + study + '/spm_stat.nii.gz'\n",
+    "shutil.move('./input/' + study + '/TStatistic.nii.gz', spm_stat_file)\n",
+    "\n",
+    "# Analysis mask\n",
+    "spm_mask_file = './input/' + study + '/spm_mask.nii.gz'\n",
+    "shutil.move('./input/' + study + '/Mask.nii.gz', spm_mask_file)\n",
+    "\n",
+    "# *** SPM group deactivations\n",
+    "spm_deact_pack = open('./input/' + study + '/spm_0002.nidm.zip', 'rb')\n",
+    "z = zipfile.ZipFile(spm_deact_pack)\n",
+    "z.extract('ExcursionSet.nii.gz', './input/' + study + '/')\n",
+    "\n",
+    "# Thresholded statistics\n",
+    "spm_exc_set_file_neg = './input/' + study + '/spm_exc_set_neg.nii.gz'\n",
+    "shutil.move('./input/' + study + '/ExcursionSet.nii.gz', spm_exc_set_file_neg)\n",
+    "\n",
+    "# *** FSL group activations and deactivations\n",
+    "fsl_pack = open('./input/' + study + '/group.gfeat.nidm.zip', 'rb')\n",
+    "z = zipfile.ZipFile(fsl_pack)\n",
+    "z.extract('ExcursionSet_T001.nii.gz', './input/' + study + '/')\n",
+    "z.extract('ExcursionSet_T002.nii.gz', './input/' + study + '/')\n",
+    "z.extract('Mask.nii.gz', './input/' + study + '/')\n",
+    "z.extract('TStatistic_T001.nii.gz', './input/' + study + '/')\n",
+    "\n",
+    "# Thresholded statistics\n",
+    "fsl_exc_set_file = './input/' + study + '/fsl_exc_set.nii.gz'\n",
+    "shutil.move('./input/' + study + '/ExcursionSet_T001.nii.gz', fsl_exc_set_file)\n",
+    "fsl_exc_set_file_neg = './input/' + study + '/fsl_exc_set_neg.nii.gz'\n",
+    "shutil.move('./input/' + study + '/ExcursionSet_T002.nii.gz', fsl_exc_set_file_neg)\n",
+    "\n",
+    "# Unthresholded statistics\n",
+    "fsl_stat_file = './input/' + study + '/fsl_stat.nii.gz'\n",
+    "shutil.move('./input/' + study + '/TStatistic_T001.nii.gz', fsl_stat_file)\n",
+    "\n",
+    "# Analysis mask\n",
+    "fsl_mask_file = './input/' + study + '/fsl_mask.nii.gz'\n",
+    "shutil.move('./input/' + study + '/Mask.nii.gz', fsl_mask_file)\n",
+    "\n",
+    "# *** AFNI group activations and deactivations\n",
+    "afni_mask_file = './input/' + study + '/afni_mask.nii.gz'\n",
+    "afni_exc_set_file = './input/' + study + '/afni_exc_set_pos.nii.gz'\n",
+    "afni_exc_set_file_neg = './input/' + study + '/afni_exc_set_neg.nii.gz'\n",
+    "afni_stat_file = './input/' + study + '/afni_stat.nii.gz'\n",
+    "\n",
+    "exc_sets = dict()\n",
+    "exc_sets[\"spm\"] = (spm_mask_file, (spm_exc_set_file, spm_exc_set_file_neg), spm_stat_file)\n",
+    "exc_sets[\"fsl\"] = (fsl_mask_file, (fsl_exc_set_file, fsl_exc_set_file_neg), fsl_stat_file)\n",
+    "exc_sets[\"afni\"] = (afni_mask_file, (afni_exc_set_file, afni_exc_set_file_neg), afni_stat_file)\n",
+    "\n",
+    "# *** Euler Characteristics and Cluster Count\n",
+    "afni_euler_chars = pd.read_csv('./input/' + study + '/afni_euler_chars.csv', header=None, names=['Threshold','AFNI EC'])\n",
+    "fsl_euler_chars = pd.read_csv('./input/' + study + '/fsl_euler_chars.csv', usecols=[1], header=None, names=['FSL EC'])\n",
+    "spm_euler_chars = pd.read_csv('./input/' + study + '/spm_euler_chars.csv', usecols=[1], header=None, names=['SPM EC'])\n",
+    "afni_cluster_count = pd.read_csv('./input/' + study + '/afni_cluster_count.csv', usecols=[1], names=['AFNI Cluster Count'])\n",
+    "fsl_cluster_count = pd.read_csv('./input/' + study + '/fsl_cluster_count.csv', usecols=[1], names=['FSL Cluster Count'])\n",
+    "spm_cluster_count = pd.read_csv('./input/' + study + '/spm_cluster_count.csv', usecols=[1], names=['SPM Cluster Count'])\n",
+    "euler_chars = pd.concat([afni_euler_chars, fsl_euler_chars, spm_euler_chars, afni_cluster_count, fsl_cluster_count, spm_cluster_count], axis=1)\n",
+    "\n",
+    "# *** Permutation Test images\n",
+    "afni_perm = './input/' + study + '/afni_perm.nii.gz'\n",
+    "afni_perm_pos_exc = './input/' + study + '/afni_perm_exc_set_pos.nii.gz'\n",
+    "afni_perm_mask = './input/' + study + '/afni_mask_perm.nii.gz'\n",
+    "fsl_perm = './input/' + study + '/fsl_perm.nii.gz'\n",
+    "fsl_perm_pos_exc = './input/' + study + '/fsl_perm_exc_set_pos.nii.gz'\n",
+    "fsl_perm_neg_exc = './input/' + study + '/fsl_perm_exc_set_neg.nii.gz'\n",
+    "spm_perm = './input/' + study + '/spm_perm.nii.gz'\n",
+    "spm_perm_pos_exc = './input/' + study + '/spm_perm_exc_set_pos.nii.gz'\n",
+    "\n",
+    "perm_exc_sets = dict()\n",
+    "perm_exc_sets[\"spm permutation\"] = (spm_mask_file, spm_perm_pos_exc, spm_perm)\n",
+    "perm_exc_sets[\"fsl permutation\"] = (fsl_mask_file, (fsl_perm_pos_exc, fsl_perm_neg_exc), fsl_perm)\n",
+    "perm_exc_sets[\"afni permutation\"] = (afni_perm_mask, afni_perm_pos_exc, afni_perm)\n",
+    "\n",
+    "# *** Permutation Test Euler Characteristics and Cluster Count\n",
+    "afni_perm_euler_chars = pd.read_csv('./input/' + study + '/afni_perm_euler_chars.csv', header=None, names=['Threshold','AFNI EC'])\n",
+    "fsl_perm_euler_chars = pd.read_csv('./input/' + study + '/fsl_perm_euler_chars.csv', usecols=[1], header=None, names=['FSL EC'])\n",
+    "spm_perm_euler_chars = pd.read_csv('./input/' + study + '/spm_perm_euler_chars.csv', usecols=[1], header=None, names=['SPM EC'])\n",
+    "afni_perm_cluster_count = pd.read_csv('./input/' + study + '/afni_perm_cluster_count.csv', usecols=[1], names=['AFNI Cluster Count'])\n",
+    "fsl_perm_cluster_count = pd.read_csv('./input/' + study + '/fsl_perm_cluster_count.csv', usecols=[1], names=['FSL Cluster Count'])\n",
+    "spm_perm_cluster_count = pd.read_csv('./input/' + study + '/spm_perm_cluster_count.csv', usecols=[1], names=['SPM Cluster Count'])\n",
+    "perm_euler_chars = pd.concat([afni_perm_euler_chars, fsl_perm_euler_chars, spm_perm_euler_chars, afni_perm_cluster_count, fsl_perm_cluster_count, spm_perm_cluster_count], axis=1)\n",
+    "\n",
+    "# *** BOLD images\n",
+    "afni_bold = './input/' + study + '/afni_bold.nii.gz'\n",
+    "fsl_bold = './input/' + study + '/fsl_bold.nii.gz'\n",
+    "spm_bold = './input/' + study + '/spm_bold.nii.gz'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1161f7a90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1161dec90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11580d950>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11532d790>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1165a9e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1165a9d50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115418cd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d086110>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11580bd90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from lib import plot_excursion_sets\n",
+    "# Determine the scale of the activation colorbar\n",
+    "max_activation=6\n",
+    "# Determine the coordinates of the x, y, and z axial slices to be displayed\n",
+    "x_coords=[0, 32]\n",
+    "y_coords=[38]\n",
+    "z_coords=[-32, -18, 0, 12, 24, 40, 58]\n",
+    "\n",
+    "plot_excursion_sets.plot_excursion_sets(exc_sets, max_activation, x_coords, y_coords, z_coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1155c5210>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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c83Idyzr2dXV1meujghcuZdGzVTLdSC7ASpVhOFmHhaXS6jH9vD/vW2piaSyxgL8v3e3gGJv/V49NzT3XFRbmCMz/DrdfqXPFc+ofCScA/K/KiEJVCrE0lqiBW1EhZSB/LLjunJShVR1mdfkfRB/T+65BWpVCKpXymkctyq9xdejyf0PfhaFv15zSJGoGzVITS2OpZrrW7EUymcTwW0PfZWz4hmu2FyqHXtaOSFUKI2f/9+i2vMCHK9u53L3llUgsjUXPXsIaSfslshbTSqKXepMK0JOv4uqU9N2POlYsuru789TN9X30VAbXuVHXyHO16ujOSVeyqE6RKeayfr0hlsYSC0IlyKhGk9oC2WgXI7NUEiqOVJpNRS1dTRJLY9HtYR0Fc/kheuiu7mfxiXS52vM6quSjQv3pu3R3d2eig4VSYqJS8wtF2XwUUU94wagY0/rlEGStKIUm2CgFsTSWWMDBX1QJ2rWcVV9HynSfjFzWfi+MY8RywwzDk1gqi5ZpPa+VTocB8ufb0s2vQmM9fDonSaHUGL7nmj+sWNTX1+ctpOTTfPJpakaNgXEd0+P22SyT4/X198BzC02g3p/E0liqmeNnDku3ni4KD7CpxU5F+fvZqc7hfqvaAsBP05vTZp4OANj9yq5iFLemiKWxUFmiEh4L1ag+TnuU0+sKBujrewqzlopUKpU3lsS1tj0pNBa/p9SaQtfoxExXcCMqKFBqYmks1cbAm9NTKg0EgAvCg1QFKsqPaitf8xwqjGteBwYMQsVqnZ5es2bv8z84TjZcxNJYGIbUM6Do0XiuEXxRsyvKdjJrNtZ0VDAmDMq5jl3LWwPlVxZy4MCBgrPKEN15KMfK07fgMd3xKH1DPXOmTkmS4WLtW/E5/enTFSKWxlJtHHoqbXzd3d0YemcY79WhYv4+ZLg4qc7hNVqN5Hvt4XbbMRS4jFz0z3+ZFsehAN6aBbxUukonlsZy++23AwDWrFkDIF9ZXEl7emAYa8m9e9MdFFxCG8jObMn7sdOTysLFk4DsMAE9OMsVQSpHGkdPkSVdq1O15XX8PvgeYeenXGqP34cep081luqm5xsoV5oLiaWxVDP7F+7Drl270D7lpPQB2h1tWSQ8//7v3s75gU5ZeFXuzaR6DFLHNhSnvCWnjCsLxNpYurq6AGRrNr2EnKzV6VtQdXhtZ2cngFxlYS3LmlP7Na6oEmfF1ImZriRDOWN/IZqampw+RG+or693RrNYw9M/4+dnKpH0LXQKvV46XM5IyWPDhg3LfAYgP3EVyB/8VWg5v1IQa2OpZnav2ZWZ/pRNQakirh/Oa7/+HU444QRcuCCc1/g78WbYB/Oft/0h/cO8AhgzZkzePSoe1gllSN+JtbHs3p2eknTkyJE5x1mbyUFErL048zt/oNyXky7oDADdlpZRNlmrAtka25X6r6NInKuZ+9JPoOGwRqYSsrxR1NXVZRRRJ1RKf4HGyPtxy+NSPfWca1pZCqX+U2EKTfbB++lVEEqN5YYZhiexVpZa5a07V6OpqQk/n3NJ5ti7v1lbcLb8amHVHa/hiiuuKMuzq//bKwCXU3v77bcBZEO7rk5JvbiOnh5V+ghsvmjH2DU+hNfpJoqe8lTeT3eIskkonXg23/S4Gzrk0lHmPsOzbN7pzyFTTaImIGcZXIsl6bC4nlpWvmY59dBm2UzTY122b9+OchJrY6l13v3NWgD5hmP0jZowli1btgDId2ilWuiZX7T6uBYR1ekdrmRA1sA8RuXSaR/6tdxnOakw8nqtZgw7y5Qbvmb4msqiZ6lxpbDoyb61wshzohZVlc/R3yuDANyXSZJUFAZqZs2ahXJiDr5heFITysIa6ZlnngEAtLe3A8gN6zI8q5WEIWOGZuV7rF3Z6VkoEVEvY8fnyDQb1rIM07I9zxkYpbrxHIZTWZYTT0xPoSSVhYrCNBw9B7GeywvIdkKynHoOZXmuVhL2D7GMrrQivfAqtzI8zvtceeWVqARMWQzDk7qgUnLFy8ALL7yQea2XjaaS3HjjjT3e54knngAADB8+HEBuugprbT1cmTX/iBEjMueyvf7dd9/lnDNq1CgAuT7Wzp3pLnm251ledsCyLEB2KXKqz/fff59TprFj0zP5yY7XXbvSIympAFoRpdLyXPpU8+bNQxRUd6oc/RmWjelFAHDLLbdE3qccmLIYhic1rSylgGuJUGFYo9K34BbIhnipLFQankOFALLt+R07dgDI1shUKqoFkPUT2E9BX4K+EK+RtTqViypEJaHSzJkzx/MbiA+mLIbhiRmLYXhSE6HjchK1lNvzzz8PILdTkU45Ow3p9NKJl517dM51R6hrnAybVHTAGepmIIJNLDrqALB582YA2ZQhw5TFMLwxZSkTN9xwAwBgxYoVmWMMDXOMB8PZVBY5noOBAj3JOZVKdhpSOejoU1EYQKDj//XXX2euKXdqSSViymIYnljouIJgmFkvHMs0ErnwKJWFYWCmp9DvkaNAGV7W/2peQ4WZPXt2sT5KLDFlMQxPTFmqiOXLl2de6+RKdlIy0iUHZ23blp7/iPOpGX3DlMUwPDFlqVKYkKhniKQvI/tZbr755tIWLqaYshiGJ6YshuGJKYtheGLGYhiemLEYhidmLIbhiRmLYXhixmIYnpixGIYnZiyG4YkZi2F4YsZiGJ6YsRiGJ2YshuGJGYtheGLGYhiemLEYhidmLIbhiRmLYXhixmIYnpixGIYnZiyG4YkZi2F4YsZiGJ6YsRiGJ2YshuGJGYtheGLGYhiemLEYhidmLIbhiRmLYXgSS2NZsGABxo8fj5aWFrS1teG+++7LeX/t2rU4//zz0dLSgpNPPhkdHR1lKqlRVQQxZOPGjcH+/fuDIAiCrVu3BmeeeWbw8ssvB0EQBIcPHw5aWlqCpUuXBqlUKvjoo4+CRCIRrFu3rpxFNqqAilOWBx54ANdcc03OsTvuuANz5871vseECROQSCQy+/X19di0aRMAoLOzE11dXZg6dSrq6uowadIkTJw4ERs2bCjOBzDiS7mtVbNt27Zg0KBBwZ49e4IgCIJkMhm0trYGn3zySTB79uxg6NChzr+zzz475z6LFi0KEolEACA46aSTgm+//Tbz3g033BAsWbIkOHLkSPDBBx8Era2twZYtW0r6OY3qo+KMJQiC4NJLLw06OjqCIAiCVatWBRMnTuzTfVKpVPDpp58Gd955Z9DV1ZU5/tprrwUjRowIGhoagoaGhsyzDKMQFdcMA4Dp06djxYoVAIAVK1Zg6tSpfbpPXV0dzjvvPDQ3N+Ouu+4CAGzcuBHXX389li9fjsOHD+PLL7/E/fffjzfeeKNo5TfiSUUay5QpU/DFF19g/fr1eP3113HTTTcBAG677TYMHjzY+XfWWWdF3u/IkSPYvHkzAGD9+vU4/fTTMXnyZNTX12PChAm4/PLLsXr16pJ8NqOKKbe0RTFz5szg7LPPDi6++OJeXXf06NFg6dKlQWdnZ5BKpYIPP/wwGDVqVLB48eIgCIJg06ZNQSKRCNasWROkUqlg06ZNwSmnnBIsW7asPz6GESMq1ljef//9AEDw1FNP9eq6o0ePBpMnTw6GDRsWJBKJ4LTTTgsWLlwYpFKpzDkrV64MzjrrrGDw4MHB2LFjgwULFgRHjx4t9kcwYkbFLu29ZcsWnHHGGdi+fTtaWlrKXRzDqEyfJZVK4cEHH8T1119vhmJUDI3lLoDmwIEDGDlyJNra2vDWW2+VuziGkaFim2GGUWlUZDPMMCoRMxbD8MSMxTA8MWMxDE/MWAzDEzMWw/DEjMUwPDFjMQxPzFgMwxMzFsPwxIzFMDwxYzEMT8xYDMMTMxbD8MSMxTA8MWMxDE/MWAzDEzMWw/DEjMUwPDFjMQxPzFgMwxMzFsPwxIzFMDz5f2EszzN1OrcXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115d94d90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11667fd90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1161f70d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11550df10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10de51e50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116582950>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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5lxvXuovT9m1t3SqINVp5/d21SYyJbZFbPvssY3uaoWsVa/uCOgDpz1x5R5hLgDZQa4poz59LdVxFZwgR57/zOay7OM2pYufg8/w8Z3sjRRHCg6JTFPfTaLlyTqUFLdr523bgketp4RZWLUiaK9luY8vJ6/sMUtpzpPX1rRs3zd44dH8YJBq/nxwDqGmhPrkUxb03O1jL/w1tFbq33YFI+8zcl6X7/21PpChCeFB0iuL22+ldYWtk7RC39bIKwtJmbklr5XPZJq219D7rXLb5sLhO2yvtGLbSDFJMC9ux9+sTBGmPSVOdXPvy2lSatDzPdtqwDXtpLYPO8USKIoQHRaco7ufc2Ae23q603F32E3R2HKU1JcnVIrvHpNkgace4nia2pkNuCofXh3DyFSd7ReMfz038t6xntpPJ+BxudsxcgY2tKUsuu8lHhWxiCnq/3ND8NLvFXc9XSIsURQgPVFGE8KDoul5p0mxzT9FAd7te1lC2Xa5cczXStuWce4LcoSzc7mYkibuRTOTNgEbzebirH//b5ALmQ0LrZv0u43lcY9i6jnOR5rJuLcKY5HJUWIeDOweFM0jd9+Du684wbU+kKEJ4UNSKYl2JuQbhXI5moLd2TC4VSjs+Vyvutphxvi4a8VSLSEl+c9uacHVf8iUhqualT10GALhw/rcAADvu2w4g01lg54PkCq9Je46033KR6xgbYJl27VwOgPZGiiKEB0WnKK6rkS2mbTnTggpzzRvnPmzp3RaNrRyVi8F/zPeV9gkEkquVdd2i58+LZlvx83XRZKz/nBomH24y13evveFH4acomIGmJbpv5toCgIqKCgDZA4AWdzvfh83RxfW0EBbagnxG3q8NMXKf34b28DrWdmkvpChCeFB0inLzzTfHy6tXh1NmraKw1Ur7yCZhC0k7Ye/evQAys03aPLlUFH64yA35t3PurXKxBXWD/n77wzAjC1Uytq0idXPVk1DFeB07acq1AY6WK8u25u41qbB8H2n3wsFNKhffhw0wdfM5W5vEqrYURYgCpugUxYUf6bR98bQAR7bEVBseWx8lcKCiuC0aW0xrx6R5j2gr5AqyTJtKy/NS1dhq23AOmwvL3dcGF7p2U67wHLbqtMf47ECiTDyfDYN33w/fB3/jep8+fQCk50yz72fkT6PvxtSExXd/mZ/8XlIUITwoakXZsyfMqTtgwICM7WzJXHuALS+9QmzFuc6+flryCjteY/MWu9gvStlIANduYO5kbvvS2CZsid3PW/N+7RewbHZ+d9mOZ1AteC7XU8bfbD4vnj9NUXIFWVJZ0kb+j0cetD+Goq4ooggYZso8oa6XEB4UtaLwM2avvx5+nIfu27QBR/txGzugxi6H23WxRrCd3+EOHtquie26pIW9WGPeukrtnBkgMcDtYB5dsOzOuc9iuzutzTLkPdgB2jTHgJ0bw5L3aNPWAtmOBCYmX3zaIgDAdOQHKYoQHhS1opC6ujC3lTVe3ZbSZmixqmNDNdxlawzbjC7uNiqWHWhMmwvObbxPKkuurC9A9kdZWdI97SqKzShjVTQt8bYNjbfvzX2nPA+Pt++Uxr6r7HbW46++/yIAYPoV7kdg2x8pihAedApFmT497NkuWbIEAFBTUwMg031Ld6pVELqF6YJ13Z9sVTmg2doEKLa0tlW1rl/XFcs+PLMj8trch+Ejrsr16xfmLrZKwrAa1+3KZ7P5tjjAyHt0lYsqZwceeW9uyD/vkwrIZ7UfOXXfG98Dz3NFnpWESFGE8KAkyPc3v/LIs88+Gy/bzzNTQa699tqjnmfx4sUAgL59+wJI7AR3QNMOLLLF798/zP/Lvvknn3wSH8N9qqqqACSt9+efh64gDqi6A44cXOW98HPfVJ3PovzF7j0NGhTG8VNhdu8OM+unBVJSZXhN7kv7afbs2Vnvh1DRqW60XVxFZMjQ1KlTc54nH0hRhPCgUyvK8cb9lgfVha0pbQmWHEtwFYUqw32oDuy/f/rppwCSVhhIFIoqQbtg165dADJtINo+PIbnoWJRfVzFosrMnDnT4w0UD1IUITxQRRHCg07hHs4XrX067ZlnngGQGPc0vt0BQRq5NNpttHJaGlY7z4XdKBrbdGUDidPBGuZbt24FkIQACSmKEF5IUfLEhAkTAADLli0DkLh+OUcDSFzWVBQOMNIhkJZsnApFo5tqQaPenUFJZwEN/W3btgFIBmhFghRFCA/kHi4Q6Ep2W3wGcTI8hO5cKgpdve6cdto6dEfThZz2b+ZxVJYZM2a0xaMUJVIUITyQonQgli5dCiA7SNINRKRXi/bLzp3ht7HdfGfi2JGiCOGBFKUDwuBCm7URSOwXjqPccMMN7XtzRYoURQgPpChCeCBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcIDVRQhPFBFEcKDoqwoc+bMweDBg1FRUYHq6mrce++9Gb+/+eabOO+881BRUYHTTjsNixYtytOdig5DUIRs3rw5OHToUBAEQbBjx45g1KhRwfPPPx8EQRA0NDQEFRUVQW1tbdDS0hK8/fbbQVlZWbB+/fp83rIocApOUe6//35cddVVGdtuueUWzJo1y/scw4cPR1lZWbzepUsXbNmyBQBQX1+PAwcOYOLEiSgpKcEFF1yAkSNH4oMPPmibBxDFSb5rqmXnzp1Bjx49gr179wZBEASNjY1BZWVl8M477wQzZswIevXqlfo3ZsyYjPPMmzcvKCsrCwAEQ4cODbZv3x7/NmHChGDBggVBU1NTsHbt2qCysjKoq6tr1+cUHYuCqyhBEASXXHJJsGjRoiAIgmDlypXByJEjv9Z5WlpagnfffTe44447ggMHDsTbX3rppaB///5B165dg65du8bXEiIXBdf1AoDJkydj2bJlAIBly5Zh4sSJX+s8JSUlOPfcc1FaWoo777wTALB582aMHz8eS5cuRUNDA95//33cd999eOWVV9rs/kXxUZAVZdy4cdi4cSM2bdqEl19+Gddddx0A4KabbkJ5eXnq3+jRo3Oer6mpCVu3bgUAbNq0CWeeeSbGjh2LLl26YPjw4bj88suxatWqdnk20UHJt6TlYtq0acGYMWOCiy+++JiOa25uDmpra4P6+vqgpaUlWLduXVBVVRXMnz8/CIIg2LJlS1BWVhasXr06aGlpCbZs2RKcfvrpwcKFC4/HY4gioWAryltvvRUACB5//PFjOq65uTkYO3Zs0KdPn6CsrCwYNmxYcM899wQtLS3xPsuXLw9Gjx4dlJeXB4MGDQrmzJkTNDc3t/UjiCKiYD+fXVdXhxEjRmDXrl2oqKjI9+2ITk5B2igtLS144IEHMH78eFUSURB0y/cNWA4fPowBAwaguroar732Wr5vRwgAQMF2vYQoJAqy6yVEoaGKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCeKCKIoQHqihCePD/TJC9uMtl6o0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11550d5d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116595d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot t-statistic images for permutation test inference results in each software\n",
+    "plot_excursion_sets.plot_excursion_sets(perm_exc_sets, max_activation, x_coords, y_coords, z_coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10e93ccd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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3v3sWAGDJkiVwbB+0tLQMu897K5WMNyQZocFamsNrIFFmCXE0ykujungAde1mWMmhdTiFgH5o5UgyP2QXlsl6PnP0zTk9FJPuDeXsO0PZgJrQSyv/VHOLZSXo4zVOA1D7Xl1++eW1Tzamse0YoB1uAuRwOBwOh2O8gkrQWx8+AdpGOMG+sTMj2Srwc1BsQ01HyAfWiQNs/XFWFvOntFh5AwBgLh4EUA6wYEmrTi2UOzHd/pofHTE4eMdwl+MYYexm4yNPncSMxxp/Q2+MqpUNssztLcjveHBemGXMjuZxoqXaZtvLqY+4R4p9IuJc4Oedd7OVPwAADA7+uVSzY/ujDjlHqBFNU2RfEjoNsswxVeVQ4VBgGFnxZMWSLydSUMdw/P7GyuEyvW8KWqy8xkoyn/9rper0kAHiVVkbONzZObbMS+RTQc+7diywv3hNpLfiRGeVyu8xOHjokFcwvuA6QA6Hw+HYiji3IHbIH23+iKv2H3/zOfHRMHjOY/ptxV4ttqJqpToO80BOhPiFNxM6HImJD3GDleZL86idnNYo23AMU1Tw0xidom2ixE5iabNGXgr7Lk9PzVkdJz6cCNnF1lkvDgDPPfcKdttt8vCXMi7gOkA7LMq+GSxpHtwCAHg3gnZEzK0UtSH0oQiPUWdmrWgYxZTCOXjOKgCg1Z7EmWbZaGQGX2Jx7A+g+hRsQ36NoX6P/tl6oOJuu0VgzTUGsCe7W2pC6xggQzRf1g8gf1P31zySI6+hcESxbDAvoPZsnJCWVvOdY/UIWY6ZokplFwDA3lYvnwCyDPyNfKhwJEslDp7znHKOMYQ3vemX9ld4ZgcHv779GrPd4QzQFuH1Jv5zbDtUq1W0t7dj4sSJ2GmnnXDiiSfisssuw0477bS9m+ZwjGkcZpNu2tgDyCeanTYR77WJOCfLqU9faqdzUj3H5sqNLbZCQ705k61q6zZX9O9V5EyNgnm0jMH5k3E19NWnfUAKZ6kJHh5ycShpCdzP7VZaLMHztp2Xxr7IvVdonHCqb24MbzPmh517/6FAt+YCGc8YjgEaOaZsTE2AtiYqlbfbX4tkCzmd8Bg0GfOjVjXZ1ubM90ctduWEY3+KQDprfAHP2R4dwceONeV+JYjaNjfh89Eug69SCXoYg4PXYCRx4403RpmPL7roojGf+Xie5FhalWnvBB5kqv3wtJYyCvGjg97VRIIirEE+6gYK/xf8NeTIellm2SjRYsobduoLPltW/ibU2GYMEPtAg2oakP+o9GTPS120D9mkwcEX4HA4xhKGY4B8AuQYQxjfmY8djm0LiiH0YG/7ayZUibDVlltLIn5h4s1PtVVby+kpvV6yRKDdcakfTevVkyDJ5hB3WfkrK4sM0EFWctK8ysr2YtPzxtF2Vd/+qxHt32s+AzRJ1Axtk7In5fzcYKbp/Hg15gNYvVf4+w9WjmsMFwU2bcTO5BOgYZAr7NJLj6OWljd51PB40JqV5z57xnLyWEIJSmmPlQruQP6E8hUWzs2XTtXKOMagzBmoPyIf3PbsxUFLnkeE89BfhS+xZ0bID2M8ZT4my8I7OTdjfMLLMvfP4sjhW1ozDfVKSXA8dUS1Afk4KKn3yjLPpCS0SrncY2VnMhqMJdsRrpr+TavxcNTi4nk5RucK+8R9Om2sVirUhwmt8kgzh2O0w3WAHOMAnvnY4dh2qFT2sL/2tZKGzkyUp7dEHCDBaTEDL7otYkrd3jWnKafp6kZfr1laMpAN5mdcRnKZP8+TNqnuRz7PnkE3BTJB/BF9Tk4an6nfTnE7l5+Ir4VtpsnLyTmviS2kixCwOC4bzHh+n60+yUpaqh3I7eqifum4xUZgQyI/yAjDJ0CCSuVv7S8+VYxs0eGvHnThqWo3bZ2pBR1doOhnw3poFatkqtrl3K8FORsQzj0drVFL+Typl5K+nAiesT3TA2K+GdYYItqOsmshoctrOcMYIdZ7x2YyQuMp8/HHxfdnGeYCAJrsHnIcdNr6mMkByowP3+KxujOiiC3uG8Zog+zB+6+qv3zh80dstpRlXen4POVlbVu4x2QcpxrLU9xKP7bSFxLDajtmpZVsU84IhasdHHwADodjFGEjysGvWwk+AXJsdxQzH19//fXbuzkOx5hCpbKz/UUDh2lTyFTUofxhXCex6voepr89trTamCCVyNTJdillBsHZ9FsoucGUSDYNfykYY9l31+VW9iP/9n+ETXb34NSdxpSkVxIbk+4JZHDIPen0neA1cdKdBzIcUbtkUPJfIlqeYL68G9ch7zib6VeMCRrMfSfGDzYi3fkjjDE3AdqUzMdDQ310OAI57El4tsh+fOgabKkzOioH7Wv1yKN9rT4eRY3WOOpLa1TtFD7+ekVE7uNB7oh2d6hhrjA/ZGer0mK2kOG1v9sC36Cxlvn4vdYX9M+iAyV7epndlTzKSX3CCJWkU7Uefdlreu0cqvLLGlWrKsVNpsTv8i0qm6ea03Nke4fVE0cxNhaugm2ryrXoc5WrWq+Kth9t94EtYa/c6DpCDseOiUHUen1tFYy5CdDJJ58cLX/5y1/G178+nkWlRgeKmY9/8YtfbO/mOBxjCKQ8OJ38hK22iKMBAAOcoqucgppRKtvRAqDMF7EWnY7r5LmRpyGjs8j2WHR1fMA9UnL/AeQ2KVFnej67TwjlKxtrNyI0PfP9udnKhzOjRKfNKsdZtZL9yt8eY34ObowWcXAojjLmh1rXXTOAW0jKyQ9/pXKX/RUYsMHBb2HMYyPKsR1bCWNqArQpmY8Vlcr37C/VOecDr+LwsWtck5HAmmdLkUfe9ET7dZYeKvUBKtq64aiejHKtsxZ3Rkey7tmIoVLtZampOB5UlYhYH5/nqj33vfYC4svus2Z1k1L+cw1re6xnPr7JXqJ5ZvUQGkudqHLv6zLvov6E6E+NKkaTjSl+0qB/UZMd0RPtEav0lEtlW/iE5Krmyl7yiH5Zr2M7/ZbjVeiR2kv61Kh+uv6Ecf3f2hi92Jkgh2PHgjNAWx+VCiOO9FMCJwHhB2a6fMrSF6x+244lCnMkAhDQmdWgPyI6AjpQnuKEWnvMArnTPl7QQbQY41E8SuM6yq3vitbqd/ssYMOa02hNP05y43ACtIv92Lwwhn9sKpW/sL84Qv4eANCZmawMAdcPh0Qq5UW9LDNpbqfVTwd2fl7iR7fiBCieNPEzkUbm8O63RHvnYIs5AerJom1ovqZGHDG0g7tOm4pt4LnZZnXo1+dKQwp0Es8WXmtj84NjeGzmTIXdp7cY88PbtQbAcvZMKnWyft6Ml3Xqq/dJI6pohB1hvj/T+FKhdUXooIwVFcKJeDLe7MxjXpgfntRe8S02g8+ZH45nPkMEK1CTgaOK+5tFOMP245d8ygDNCGU13ju8J3k9en2l/h4HcAbI4XA4HA7HuIMzQFsHlcolKGe3iVMOMLScs3O9D8rk6OcBGiE6ga2X7WWow6ieoaHwt9rmtN6qAJAFVrdaaMUC++xCI4ufzDtLaQZiN1f1BmBP8dPHgbRW7PTs0bfZ+g/YMr+x71apjLkklmFMAcBfWcneURkFSSFdsrA1vF1HEC3A7ujo8sed4odLVSwJd5IMkLIotM61hZrjaFnGOmnUi5m8DXmW61BqWo/aT0GRmVQ9GeWOlGlVtjL1aaxq5WxZ32JMEPm6M8fAOK1UDrO/eH/OCkXVFnnx6kMDoMyp6XLMzbFKTbishEaeXiVuwpGqypyCvnCLicz4iLFSfYR4ncY68V6vzHbQYBDls/TXQEeXXRUflOXxZvbYlTwNO+MPyF+SDEHr/r39wWdnG4VF7QjwKDCHw+FwOBzjDoNwHaCRRKXyLvtrPspsR0O03CnibJoegFCX02JmZQDoMSt5lvlqKL9TrontovWh3gothWNUT1UTq8bpNOh2O8v+6hQn6rLHRG90Fv08zZbMtx125eH9ca1VKxk+X4c8PHy0hyFXKrfZX9RS2V/20JQmKY4ixSmyt2MLkE737ZJENe/tooSg1hUnRVUGSH3DlKfJHZBVGtHK2fVxk3iiJ5RvKSb4LbenHQuysz9oaxfY8zglOrL83KlNrk+IPinq/cb1N9s4JSGxZFSO12KyKSDrjRZbZIevBso9Sajfi1IwsVu6qiPHvGX5jZO9V5XFIfSFqY/JANIEDRvB7XYzH380lEzynmed0hg2HR0qSspnTRo7YI1bbds5KDWwjo/lPQDWtCBu1T1y0FC5scYYnAFyOBwOh8Mx7rAB7gQ9Eshl8WlGrETZB0hNDLImcdbj1szi1niHmEFSwbq+RBRZbjfVS6kScGK9AcitAq5TiUI1wVusLYEBmm7XlCfr0IijcI09lpahw7yKUmHSuyaMR7aiGF/CXZga4opRaVkDeeST3e8GG0991AKJownzJ1q9WvpluVYSXIDjrKdkgdMK5TgpylZqDE5oIw1Pul1oPBqhMYc5Y6T8il1bm11Lm61vqNj21MgJ+7OHeqJkvAxRCgxYcyYVHFrLKLiccQ3P5ywb28qFzpGy0Zr4+ECx1nKPsf6/sPH636NqvOq7zvrwUeXR+pEW4lTORsfpbKs53A8d3amEuyVGXIWm1RFNX7dFYkrd6HRgW9lil3hL4hQ5NaYvtNCoJvGlbC0IQQQI09ltNf/WrnJ5Ja428427H7nDEBkg1h23hQmpHxtV43Az4U7QDofD4XA4xh38E9hIgXP84iyaU0tazOqDw1l8MDVas/1pqxwo59AUBfzwvDo6S1Vqya3p1Ld11kdbaRHK/iMqF0fbRH16mHYhWMkLsgw+ygRpusxQb5sxQMoUlPL5yPpaXjDkRdjid5lFc+8osWgqlaPtry9YSaZiWigW29Uv53oyF8oBprJvqzdLQJ40NaAHe8t+GsozgDK7SRYpHLtMeI4mYU/Uaym//8oJ/cHK5YjQp3wnrVtGJ7ZGV/BwaXwD5fQZYZ/OUr8xwWqXXUN71FIlEPptg45VTUSjGkmvJ93L1kae7Z3vMI5Btp4hRrXMa74z+A7R8cn3nLJ44bjmjL2L+31+tFf5LKVUQZoZiDdASW197RZhTeu3AUu3oqVS8l2UJyDWfGjhJGR+qPaTJ3YJz8uyrEb1GBUp2j5Nf1SMqw2tnGvnIqef0p0b03AGyOFwOBwOx7iDCyG+PpBVoI3cazP1bgA9Jc8GZVrUx0fZlEWyXb0HYs3amE/KkTNAPB/3nCPri8zBbFmnrJXm6amtEdOVMT/Uc1HmKG5bj6Vx6DLrRD+59yfoSjXWaiGti7Rj4Ws2phbYcnMm82r3oM/6vtf6cLZ9729Tzi+lkqzJbzXiJCBPg1LbTyGO5eJYJYpsYqHtWYqVFivX2BniCMbcyucdpXiJ1s/xqCOFzE/QIXqbbM0ZoC6U+yuV3KJ2CpkuYyK0l/SoakNcanjZ431xG7fRe3mLMDj4JICiKrmGVg2lmhRrieXQe0iWj1FKLVaG+9Rs+9fZ2FEvJFW/4v153vp515WyAxkgFVDn+lo3RGgl9gJbSk62OfuFkGdZOE9G9HbbNalSV+5TSU4pZtDLia+5X6h/b3RmT6SW+rYghzem4QyQw+FwOByO0YxXX30Vhx9+OF577TWsX78eH/jAB/C1r31t6IOcAdoyMOdUbS8KzuQ1txJk79paOmW2het1dh+DW+nTz/u6KrM+dK6vRxZz7qS0OlKRG2xrbOF0ljwdZkqpX+b77P/AAKkqRioFJvcrcmKaYvZhYzTeaveuVuLU7Y2fViqZ59fV2Vr63LRYaePnUbufdeYTlMwix3vWJ/tp79J/i2wdc4G1R0flkVTF0Z9iCJXuIOI2dVqbO7M7yrunmtF6DbV9j7i/eprlBGJx3MWRYuWnWhmhOKqu1fqvzax29mY9T6EOHdoVxkQ09sW1EztiNE6lcoL9pawj+zKlc9NQWAfZRrRYeR0AYG/RSXswy53Yg1pQzklzvBFtNrQWGtHUyB14vzSvV1EeR1xwlG8hd9WMfe0vzWGXtcLK8Iw32RjSdNjssVxXjY15QvaM08/PNd+3qq19W6ElVBWjD1DFrmnQqhhtGcHe8IY34I477sBOO+2EgYEBvPvd78ZJJ52Ed73rXemDnAFyOBwOh8MxmlGpVLDTTjsBAAYaAKDlAAAgAElEQVQGBjAwMIBKpTL0QR4FtnmgpgztnlXm1d9Tskxno2wxanyCWsupr9hqTakFFepfbbN9tYDK/jysj6aN1teLnHXgNmUNNGKtKuvVz4n1KQulKtnB7mjD3VYG6Bdv9hBbs6ZGqdE17IeHzVqqVHYGAAwOvoQdBW3IrylnFbnmIdmbebCmyHr128qyUMl69emJecy5YmGrR0yv+dfEoy7FmqSUftRXJM4lxnx5yoo0lxTGY3aUGj3KAubpn8o66TmUlVRdGg0TYgRjZ3QFpQCpFPG6Mj6rKmXxrJXKLllFg4OPDdH+bQFtZS2/sFxDiQjq96mYP3ZI8D45yJgfdhsZCY5LrUUTtetym6zncXyq5tsOzC84TRiR4u9knb1U+GTSnWi5LOf8ir6DeWSLlWujrXxn5XpVPJ5jUT2bYpqK0bcceoyQOwQ5A1TlMBZavWJVTzVy6bP2m3fZDsRAprBhwwa84x3vwBNPPIHPfOYzOOigg4Y+YBumwpiwbU7jcDgcDodjvGHixIlYsWIFWltbcd999+Hhhx8e+gAyQKl/I4hRzQAxMkcjNKiZks/Ui6xP1f5WzxW19RSpzEhqZRHBOmgvKU6zlWoH079ffY6K1rnefbXgCT2Hsl0aJVaV/Tj9XhvtRz+UNokO0lbFMT9xzEPuyzK0b0ylMg8AMDj4DLYXLilQteWE2R2yRe+XRvdpNKHm8FLGj/XHarOMEUxlZOL2AaxCe3YntK9pe6bGD8HjeYe7o1qmRGuL56n9lmL+sj6zhPOxQIu8qEGjz0Eqfii1PiyX5GK4O81wHcQtVjbEu7On6E3Hu9Nvz0I3OrM8d9y27bWtlHmIGTv1bsx5j3b0GbPTXNJHDj1Hhkf9/AjtRs0BpvtB9kspcc+W5QPtBMW3ccpbiSVHMc/dmb2ZuKUqy9TkCf3GJzf3V0qpYsdM6Szx9dEILz6FC1GD+0yEL7J/y++jHR9Tp07FUUcdhVtvvRX77rtveseN2GY+QM4AORwOh8PhGHF0dHSgqyvM5l555RXcdtttePOb3zz0QYwCS/0bQYxqBoiz4CzCY9gjulC2C2h9qgVJaE70qpUaYaOtSGW9IR+i1poyCUQx5kDZJNWZ0FzMbIOyEJqlS+06jdGI84OvFB8CtV40+ovLweJXO7SYKQzI78P20zylT1kxNkRzQ+VQZjDOvF6+nymWRJmO2vo2tdcWNJmsnAmg0SzQ5kyptora4BjnWAxjdJYwB6n7nL+TSrq+Uf1NGfNDNSXNzV58VlJMqJ6rT8pYu7mUk0r1ZVJp5e21MM3WH7IulOolVYy3Yt9vP20rjr040lU9wDQKb6Dw9752j/SNkoraIsoRUQ1Wd2CW1PVK766k7cr8dVJMR3Espt5+yhHmbl8hmnWtlczzyPHJUak65j1ybenxHkOzOx4s7alHgVETPyb9zkD1JT6pOzqeffZZnHXWWdiwYQM2btyI008/He95z3uGPsijwBwOh8PhcIxm7LfffnjwwQc37yDXARoa54rvT8rWzlHcohEverTG1ajOD01H1VJRm0b9bJQXGZopmC5+NsUaOktf4CUrd4ldSOUKU+taI0ByTwcUWrPK9uuViBBCeyzPtVO07DW6TrPdhKultsng4P9ia4MRaLRPhhaZTWkxKYOo18lSR6+eYWgrU/kx9bUA8rs91+5Ta4m1JGJ/pr2N+aG/S1Vaosq6ufdbzMbQotb4rDyOTeMHizzBQsTg2ZSlVPu/187dGdWsviclMRe92Ty9me31dpo5Rk0cYqFFfPpbUM7Q9z17T316G/kC5feZd26hLddZ2W379UTt7Ef5LaSjVX1z0p6T8TumPdse/uLdUj8yvYv6loTsV/TgTGmh65uGkVa8ppx7D/1Bbl7je/M26LMM2TPWvlcuW7W2iwyT/rqwLS1WkhGj9xJZrh05N90WYzgGaARnLaNyAuRwOBwOh2MMYjgdoJ1H7lSjcgKkVorqz6a99WshpVGqUV0pnx61hdQ60LgItZlSPiLxUQMofl+vl311uqxfkGlPsO2UVWXMkNpO3J92hzIHtCy7rFRzOmX7zEY555nG6WhERbCJKpWvAAAGB7+OkQZVfRkzSMuwyMHQMst7OsX4KFL3WRnCodEkjKByOKxldWE5zYgq3RGytB9gzA9Vr1UqR/0RNC0TGR/VVFfmKD3meXULgbr9w58DLybaXFujSN+cJY8r7s6brI+rEm/HSvV22n2so6d255vV94dP6fU2vk7dylY6e69VfPZyOotMEFF8j3Bf9QerzcFMF92fHJs2nsn88C3AlqhHZJusVz2u4u1Tn5+qlXzbcTxrprrVssy3Hs/dWVLt13e+fk1otOOabG2s3aU8e1uhDfrmVV+oHlBDZ7Zt/yXGHLahDtConAA5HA6Hw+EYg3Al6KFBS6cq61U7tyfBXARovhy1a2tn4y7rvKRMSOWn6qWcIsux/diZlUWGSs1T9RdR/xK1jlUZmPtzuq3KPbQG6VNAC0jVaIpKP8XzqGVfZIBSvjCqraECLSMPtogWJO9IkbtTj5O0gonq/6iyuI43Xr+yenEmb70DtGrV96fon5PnB6PejjKhYW/qZrFuPZfeqVSUX5/srwxQ+Z2mcZsFZmiANv/QDE8tjXUg1wBbbb4ntKQXWzTXrpRX0kdB029ruBc73hxK5lgnzOlHOVzOzjHTHqutxQTtIkr4ZQZWOfKqLBfzxhHkHGqpeQGdJb5LGfSYnVatZYK1MELKeL+SL4yOAmV7iuDbqUGWVQ0tFW+a+y4yWlEcwkosl47J+Dlbaerss2Vv1rIW5cyCXO7M8pYtlLLBtvOIMQSPAnM4HA6HwzHu4FFgtUFL53O2TBt8vizTrmmvmSOLe9dmXsr+M2EuPkuiSvKIrJSyseabUe0dZXOKMRnFMxV9ZFIxGqrjE1tjTWYF5zoWbIvGdKhXB9cXs9YUS26nOgXtbI2UK/r96HXHui1l3xr1xRo5nGDjSf1pNAqklpWZq42rvwXvf6zFUlZSud9KXp9Gw4Uwo1kIIaRkZViqXw5R9GcoK6Gr7RvawrurlvJwnjopBSf1ASJyb5LU200ScUX78ugWWVYflZiGedhadY89vyRw9m+L925MkavKDKkjSTGsSTOWm/xSo+173C3YqsiNZmWAeWerVh4Rigbz9io+ALxuUhB9d9kfbDzvjUacxrwgnw99KrStqkzG7m2QZerka571fqR5RF2vpJ56e5Z1nFLsv55Jz8irCTU129LNxgTxGSW6UGR8GDFbtVKfTn3qhsqfN0rhDJDD4XA4HI7xhm1IAI2uCRBnx8ywTo5FjbSqlV2ZLkbxyzFn1Jylqz2gXZ9zPcWj8sxenbI3z6l+MJy5a5RTSr9aZ/ZFW0d9f1LsUThXT/YdWe0tVdAI1huzdrdnx/HLvIXEnDKtuDuw/AhpK1kd5eRmoqz8ob5X6mminl2vH28XpeeqlexFWptswVSUrcN8HIT731zyReERLVbGeYaA262kPRhTDnPNWvyArX2ftFV7iSje+c4SZ6OcTm90jGYGUxVerU3dZfplu3KY3J+5qTpLvmS1ODcd28qopfinOO8Z1cv/ILXyPi606nbV9GmQZb50SMVNml7Yyc65rjluog3hxk0TDt5sKINZVsLhiXm1RlUttsUDoS47+bD8FZ9tZWLVl43IdZOB8thSfzB9Y+nbWX3eqGle5Jp11OgzSqgfEb3M6OVUVrnWd1RKT02ZeCL2Vm21O9VqZ6TWWydmoew7qA5l+oyo3+XYwTYkgEbXBMjhcDgcDsfYhTNASQS7jXPeObJVZ405t1KMUxmQranwj9h6ardywHwydF5etgGYZ0bzHKVYG7VoNcqsqNWhZ9MohDRfEe8fq2xQIbY18xWiIsxfhuL9xvxQGyVLX2br/0DfIGVzipFdKTM4pQEbX0ulcgYAYHDwmkQ9w4M1szd4J1I2VdH7KnUHctaM1qJ6K8Sm9VwZR1ofLV8yP+zZetuhxapL+SuFO04mTn1/iHA0e57Xr95YGpenMZUaRUekONCcU9WMRkU2J8Vt8Q7Eub+oS9NZ4p9CucrG9GrbT4O6qnK2ksGtBvmkk+yPIvtp/T3DxuZMY4JEfqvFGEh6zp35OqPCNGdVOc5Io8DEI6cX5YGYDciKVTUcvxKr0TMCt87YvpSKuObd4n58t5M1myrjvRZLqW859fnRHGEc72SA2Ca+D/LcdSpKo2NRGXV9ctQPNGzvjJi6lLeU5n+PW99UUHUaK3AGyOFwOBwOx7jDNpQBGm0ToDATp4Wu3hOqGFNWni1mvVEFCvXRUY+GYJN0mn3RaV+h6c+g/A6tkNasNVyjX7lr65iU7Zf6GvukoHxCyssjVlvOofbx7LwJxcNZLf0hunYN5aMaK0c0oOzDwXOrCjXvskaLsU1bjlR+I56xPWPAAmahp2SXKQ+R3xlGwayK9lflW46CVPwIva6q3C4Gd791h1LFRR8gMnrd1pZyFOAUa3FtVV8us628zbNlPfuAfI4mWlc/D5a5L1D8DExHZ3YOdU1RnlT5xM4Sf8Vz9UTHqedftneqE3jD9+AfZH7I0XUB2T23nppiDJCIn9c9GkoyaFuaM+ziiy8GUPb4ydkw9Zsi7Ig19rzWo0xUkhYZeMT+UL2rlMJ9zDJ12ijgfaHPpPLvyvxkHpDW3Q02qNRDpvhDqR46kH10nKr2TqtF9NI3J39ehrtWQjuR0MjQWlkG9OmIo5Dp9TTX2laVMzGi9X/HQE6wjdhmQtCjbQLkcDgcDgC44POfB5AW2OgsBVhwamo/qmvsh7xtWiETBn9ALdtrFhJAt2MaKCp4qNBvhmHKyYCUbsQZwlNJfRr7aq9Xt26gfP16TCrcJd8v1JpLm6Rct1XwVSUa1HRQ87hqZdFE4L1hHaGfF4hIqZrRY88F2hmgEjRXE6F5YtRKLCvgNqLsba+2vQp8qIJvbLOrhVPO4q66Nup5Ui/b9bii1oTmNlMeovxIx/vpgxzn9ckHnbbR2rZ6WlydulvQpO22FW21cuVo5BnvXpwNp0l8B/KXktp3mw/lkLRFQ0EZAx7DsUa9KB1l7Br1HSNUlUoZEG2AknB6hxtRKwMefRpiFXLmOuos8Ul91uY40lE5S40OI5QJapAyv7Z4vBavWf059GnSET3dojBVn0v7UZ+Y7OnTl0ipUW+28n2yDORX3I6aMLNWr3+oTIUOx3iD+wA5HA6HY0hQ3mIKHo7W57wPPyRwetciNdj2gZlAt7IZmgKDZeoTGKFGmk7lO6ztrDUwQTRKyGywxc8PxK1SqdZiGguVZ0h9AFT5wnLbFeo2rc4XoTWzMsnDgPastbwjvEqd8vZCnZ33NgOQwQ9qOKlLOrGln1N3JHgUmCBFZap3SJnxoaVb1NG1v/c0NoPPZ5aO2L6L65OW9OyP8xMzR85A5p2vbI1qSEC2p+zBOpQVnzWCLJWlSV8NcR4q1qq+L6U4ieXGY6ycE1en0kbZ6O2SFUVngxYr47zHpHw1U9rqTDMjpZu06aB/DXtPtai7M6aE2ZzL9HlnxkcqRxEzgFUrSWGnFHH1hab7DRayjhfBfopHYW1vq57S/VUOIo4nIgsnLixRBqli/Qp9OWssWn6cRlwCU0tManyMcp3qqsP7oEyZPgk6dAft61BF5YGz90CLlVSkYQ2v2r/CPjxGyORp9jvYYYGC7N//tB+vs0bxj5fD8XrhDFACfNVo5/BHoCfbojJa/MlbCNTtGldWtVLNgezjqqbf09bQZuErtc3aQqSSBqbsEnXv4xu4HuXXtk54wkSFInrqTteOA6TuAWnJFCk1RYYd19cSyjV2hjWaXkQThRY/WLA/u6NSf2zVFT2/Pa9fTS6VjKMM+gTMKjjqaorFOCnFgPV96iuhtl5FEYaTP1P7W5+JYshvHm7OpI5aO8Fejsc676qK0fEeKWdQFJsAyiNf/TzyCZnu2YAOm8CkJOgUvO5Uil0VSVVnWh6XJXRVz/jMjrHPgft92lb82MpZyHpgvR2kahQcMjbn15gC7vYdmwh9YdiJUNWaFhggfYM0l9yjVeigKDKqTrd6V1usDE/LAnvD6XjOE4mq4ccejoMedPwT+ok55V4MlN/EvLc8k37mnCnLPL69JNsRh/bn7Ex87bwyfS5WGyO0qvR8KSvWBfYvWSReC/tHGRH9VeJyFaMfzgA5HA6Hw+EYd3AGSMBZMB1JU2RNvc3M8y+x6jM/s2xCxl+DChrrj8gK/ZxTZGaKUGF65RluAJCHABPlJHhqG80v1K12LsX1AvtAvkvtlz/Y9/b8UyEFuWiJp76Bp2TviZQsZK1gRmW2Qr+qszCvXm0mCg1WKjsDAAYHX0q0qYzbzLImSl/sSiheDweOfi4i4kdWk5akeKvhHnQdPamPm0podqMY1q/imqk4mYBZ5sRL/wMGfCvvSK5AgxC07ZDteX+nPkT1lbboyNTHWO9OKkWt8rWptjWq04lSEqs3hnLhPfEJivvosfLFWq9hqKemiErlBPurPjq1cizlqKUu2ZNinQ2FffWFqBIFPVHbtR/zCLTVsiVmk+fa+4+kWIrZU+ZHo7+Kzv6psBZ9BmuJKMZbaguGkvXSfLgpic58/IdrXZXVq0d2oclYvGppS0AqEYmyXMdi9MOjwBwOh8PhcIw7DMJ1gCLobFC/8RK5Zakuj4VlNYpoqHS/aH9Q/yKle5GKetAkn2pThvrebb4Nh8hRK81KeMJKzvCbM3ZmT5TtoVDn8cb8kD1RvxBa6LTWekr2VMpWL9vsASpeqPKP+g296OWgrrAD0Z7sXbUwcw+scD82h/kh7rdS7WG2RMO1434aLvFhrCFCH5aSC0mibcqB8Lq1p9WfQZmRojN1bp3rSGArWEu48unGEKqFq35IyoWqFapn1WvLoR5e4YimTG40HUukbv86otVnSJkyDafXa5pqfv/13KD0Apd5g6vIoS5waq5LZJMmgWHC1jQYoTUzalLqaS1fJVFkkvUtW1s6Q53IlYvmM0Q5iHZ7RzGthDK99CvTUAIVHdHnpnjN+kZWHno4R/j8HcM2a3BAbf5WfX/0/PNlfYMxq21WFv3TWIf+tpGjU5HRniwII34DrJRItNEIZ4AcDofD4XCMO2yAO0FHaDarmoJsGpt0d8aSzJdSY4nWAn02Y+5TW4OMD5VP+YU4JS5YmwEo2ywtAIB9hfmhXwVn/MdJK1qy5WA5rcaDaJMEmrTUD5SWaORu2dMj5ZGi/EKttKDF1qknCuUGUrJzdYU6yKyFK65KTWrVcvtRZj191vx5LtuMkGEGLmsoda1WAoGJAIAeNKBsEasPWOxl0mLHTpG9VWJSOTJlS/RsKuuvNn2R3cq3KaMX19Jk44p3ryrn5pOQSn+ZUvElNDosh/JbodwTZV8djSVSoQtlgAjl61L9qftnvmcMi9cMuhp2NoByRyhhKzRGKoJquDjHve0ZIKOgcqM6JniVsxKRW1MLTeRl9pQ42Hqruyc6l0pIqFwEmR9lW5ThS3WZMqc6DrpQjqZTJizl2aPsYDnSNH6Xd0lkYkrQUt8IKbaSWIh8vOu18BpaS79x6kcUWtVmDNBH7P141SiUVHAnaIfD4XA4HOMO/gmshDDbXWszcM68c36CX5IXSllLVLC2ds6WK56qTarWQyg1EERr0+SY6uuwELk1oAo0KcVT5XNyxiMltqg1tVgZf4EuK55SZ6YqLaM9U7SByCeEft9bLET9bq+WH1kK3uHNAe+0sgJ6b4ic8WgtpENNMX8xJ9Fu174cywDk9lrVypSPit6zWj1YLJU1KAYg5XE46o0Q7ucs8zdjn3JcaV3sH43QYz+qn5a62RHlWMJYb6rJGI05hXNV5Zza+8pAKPuh1n+KuajdosLTbxUwOixLTluUuFIaSmXJxEFpjtXFccmqUn5ihKZZIXh87hvJfg3vTY1WKj4H2j9lHiOAPm4NktQ0TwmjvikUW22P2k4Mx44M57PVhU33C9NlZRHzeF69dvr3hRraJNWJslUp1WaN7Cr2ucb8lpMss1Y+GcoZTbW23R2dczTCnaAdDofD4XCMOzgDVEKwdVptPtya2RucFdMLhl4x6gNUtCOG8/mJQzdS381zb3z9mq32VSjVOuuQvfSbr0YSVJFbKrTUCV6B+vykrYBU9JcyQNTsCH1A20O1qu83Rqi9pBVMFG2dUOd0O0atJc1epDXol+/NQeqh0lQR1DopWnQ9pTumPhK1lWWYYLQzG1/xeEpZi+qtowFFyk8SRcs4T22ijF+orWpLyigSaimrpo5a56o7pZYul1szHaqYc2L/96On1D+a6Zt3QRMi89wp5apUpJT6b6gnn17TrrydxfAl9QvSB0WEp9hmzboz3Mtf2cRU0BmTsnKkqvpT8f6UU7CE57NT3glM9sw25u/BfaWGhmgPsif9kliXSHmpKXut97X4ntCItJQqWZldUejdj9+TzXItqh+ty9pWjZrsQ/kZ4jGrMn24xVYqX0u02P5NVk8PRivcB8jhcDgcDse4g6fCKCHFaWg3cbv60hdtnbJHTO066qIlVRTiGXoyPxm1oeJ62kUfRjksvTKN9FqIgt9BNb6EfhOLIKdFS0e9lHJeJugGqaWp7MOiRJlrFwXQ16S9dJ80N1gfUv2u/ibKHmgq2S1JGJnnnuqR5fgudGbsTWhVYISUo0llL+J29nbcy22m+JqKZlIfFdZGtkChzEQsQpwaXfHaVPyi6iKpda7nRmK/MhujmbjiMVFL3VdleOqtUTP74jaqCDOJGrX+lazhtWo/qm9Mxh7YH3PWFDboYFVKzPbtteeVimMtVvIepzz0Pm6RPcrUEeqDovdF70furzOAve29RA4374/W6BLK0WJkKJQfVi6DZezHWfK1ShxNKMvVizLzwzqUoYTsp8xb3l/6BOizHlpLLf+BROJdopbfEpD7UwED6BXGJmdvlZ9V78EBWX8SAODhGjrXowXOADkcDofD4Rh3cAaoBJ3Ncn6YigFRu6GYpUX3Uc+ZuEyphuYWSrADekr2htpx4YiU/oiehxZvpRjgplml7RL2MlOmzypNcRPKUZWjRmLw1Pr1meBxedRKSgW7+NW7LzpG3CJKd4VWKvNTqXfRpqBS2cP+mm11EilPnJi56oz2UftbvbjU8yBWye606+mw62mJzlSOUEtFrGlESbeUfTWPiqF9r5F4KTUnlmQsqFbbYiWf1uYSw5Z6igLoe1XUC9fnoiJhc/XW+Oq6UNKqZ5vUP0aZBi5Plf3YjylfM/Y7I7nQi7IDkT5oRpUqm8fdqnK4Yjh2BLJ9OA0kZHpCOX/DZz2lzMzLpaZWZyJiSrncJvMpqtrahTX3KusIQfZbK8vFkT3ceNU3/0op83yMygdqq+JnfTifSx0WZTQU1Kd18KiPKZHSt2b5EEYrnAFyOBwOh8Mx7jBcFJga+K8Ho2ICNNesB0aPMCqJc99V2WxXvyjX0g3V+AW1g2MFgpT+83zZvtJ8O/Lv6prVJb6lyhNx711n2B9V2bAIZcOZ28wsWvRb1ESKAdKv82rJ6NdnVTbV3i3HcKndlvcB72V3prbM7+G1Y45y/6LaUSRDQ69cmQj1MIkjWGJOgr2jGcVqj5+UV5MyXoRyUDre1DJXH6KizlRPJFJTRLi+bvFdSEWi6fhQlSjNCp9Heamvj7IEbF+ogazAIuTXy+uqlAdbVHWjddgi0dZRb8Dh4h9Td5GnLbFgVnHDurJN3tglK/ritqSQUjenqu9fmC+Qjp0UT0EoM1QMVlN9YVXy0lcP9+/KosX0GYo9bVL169tXr0X9zDjWivdRmR3lZCHbW6yk72Jz9s5m28NIbjJ2X69dfav0moqZ6oslkb8FO+3/WUi/o/TJ15//lPfonthRcOutt2LJkiXYsGEDzjnnHHzpS18acv+NGFoHaCQnQBNGsC6Hw+FwOBwOAMCGDRvwmc98BrfccgtWrlyJq6++GitXrhzyGDJAqX8jiVHBANEy7BNvjKqVXRbV1F5S89AM7b3ydzg6LmO+pM3YJtWW0Hl2nl87fFfvkIy/KWTMD/841ko17asoO/5LKFm9GQuzxWVK265fi9VVgfupJaPf0suWZkrHtWgD8UJD7T0lnxpl8WJ7bssegKqVvEscUSl9Zg3fKcYlEepBQEZItZRiS5g6Kin1Wh25KV0ggr1TlfWhnjAGu63Mo95CP3D4cLikvJnU/4Nli5XtJQtaGbZUHBm1tgKLQP+T/ZH7m03TQUioG2B/fEY+l2xjSmlYM7oRqdzoGq1HFqFoh2fvJ2tbvdBOjdaI4aK2UrhHlmt7G5ah11TkP3WcsdRIQGWsWedqUyDWscNzqIK7+gDqfdG3tGrpFK9ZFbTVr0ufZtViU4+6ucb8VG0tr5X1qM+kcjXKFSu7pZxxJ/pQZklT+nJ6d1XHfksU0rYe7rvvPuy5555YsCBkC/jQhz6EG264AYsWLUoesy19gJwBcjgcDofDMeJYu3Yt5s2bly3PnTsXa9emRD0CGAWW+jeSGBUMEGfgnLHrzD6f8+rXYM2UVJRrVV8gndeToVhgZahrpqgE6/df9fhI+dmU5uuazqyWSItSNYqBeLNa7GptpeKWNEKmRU6r15Z/r1ULv9Y8PpXpKBVBVtuepR/Ef2+SHhDPWbWSnZzKrKxf+xtRjv5i+1rkHNr74WGfayxMKpZDR58yE3rP9Gysl3ZVX+Eq2MIeudP0VyNrqZY0oQwQWY9OHCBnVeZHPZnYkjjWiuzAgVYuBjBNXR/UfLfGDtqwqcgwqVq5MN69NMq4PqUSrKwpkVL3LSLrBQnzqhpp2Gsnqz7yCADgLe1xjilFHs0YopXoozJcxCC7TtnFIi/XIfugsK1YJ2Q/1rUosR8vnf6EytLU0vUpnlcZ0lpIPUO1vQnLozTW+8rbqkNQGVJ1S1O1co0l47WwvtiHS/UO2NgAACAASURBVF/4KSYnpWrE/TXGbfRhIyaip5CJcWtiVEyAHA6Hw+FwjC7MmTMHzzzzTLbc2tqKOXM0jYdiArbVp7xRMQHizH2hLJfpMP3qql+SB5CyMTj778z8GXiTOK8P8/gncGe0VZkgJWmoR7JCWqQKRnNSbktFGqfWB/ACBm1fKswyEoY2gbpTaKb1VP4djXdK+a2UYzd0EDcizRJRLyTOX9Rq+zXZ/eHeN26BEnQ5ToPLKfu0yHmpH0uKF4l5twXit5bKx6SjUjko9QBIeQQUlY3Lw0Sj3sLZVlnZICq9mumbxHWnsaJl21htZ+UUte/C1e1vS/QB2nU+ckohDszJHpz+vrimhoH4DOqDItJZUbRcESmPQCpP19l59TnuQDmCjFc/jZXPL+wMYKY5Xz07EM6yfv16DI1N8xbSsUVVYeY1VH5uAGWdJMiy+oMpq5SKFtP3I7FaypTPh0qf6fNSbKNqIquKuDI4bNt0UWHmOcih6KhWz0Ftay2NouJ2LZvQUyNzl8Yltsl2fYun1I62Lw488ECsXr0aTz31FObMmYOf/exn+OlPfzrMURVsq3z2o2IC5HA4HA6HY3Rh0qRJuOyyy3DCCSdgw4YN+PjHP4599tlnmKMmwhmgAqiLwVw4nBuXFWe10zTSK0/YM0vm3JxvdiYzZNWekWoUTswXlbNhkREiOK/PhoSa7TSR1iCf7Ks5dUsorrbFm63UaAgtU/EE+vU1pWTKemgZrTWfhNbMdtL70VCjloAmUXrOuyHWfBpKH0Kxm40X4Hgr1W7V5tn+feoF04cyD6ZRYLyzwfLaW3zF2Kcpv4Bc+Xq6rY8zTacUcotaLkDs/yGEA1qTXnQNdkxn1EZlUdjm1hJflbK5WVOLlaRxQp+9285XzHcHWMM19EZcwlIMWkqMOWVP6gjVZ6RBNjT21a6vC2XWKIuAsqFR4Yq2uO0bNmyIyjRiGkzHFKEs2AIbi9w/lR2+2Ca+dtSLUqOytJ+VZdZ+rK2KldZpUs87ZaqKTJBG6nH8L0QM1qFtok9crjQWruJBu9qj5JlWplQ93ZSZUs1sMkvdKGp2caserRrmygKmvAu3P04++WScfPLJm3GEM0AOh8PhcDjGHSYgHSwDAK+M2JlG1QRIrYN8DpzS4CzHENCXJK2JE7avwTIAQI9k7lF2JKUYm4rEUP1p8gfHtdgfiaQ7K7rzujRj839aeZ2VtN6qVqZinIaLHtlUhWBeCy2aPlPFzrMdF8+kPRhnrSJGQtWC97S9lBu8aiV7xs7Wl/qK34VyLJzq/4Re0PFFKPOj0Ue5EvZMW55vJTNPh97uEz8d9o+yAb0oW9Fz7djW7FrC9e0rTEwq/oL9ucDYuuZSBi211zUiLnilTceDAIBDbC0t9XqqoDciTU9aqV5F6quSGqPKCaeinEqwDq+3ITPbLqnomZF6TjLYSfttA98FdROCGsnGjRuHbAL1aVJKuMqC6XOt+mlFIlkZyZT+jkaxJtwRS2wM26RK2vqkKXumbaV/Y+b7VThnSnFZFcX0pzVX79foUL7RQm/caVR7nelWpfTNU1pl+ptTjArryLS6Uro/emf0t61W1oPRigqG1nsepxMgh8PhcDgcYxnDMUCp9N2bj1E1ATpClvN8t7RHNLN7zM80obPEetCyUPmd3PZvjWrmdvVy0JakbGHeVvJK91v5iPkJ7HN7fMAjduLbC3WoewR9f+42S4b+NEN7L+VQnQxFihlSa021QDrEjyV862bG5dg+ZSbmzqTiDSOVhtZKAYDPmu9PrrdBRopXSA4tpdxRy4ZXao5sktrO8d765V59IFpLOdBUrSQew+xTZZAURdkoHS8LpQ+rVrJXUppWxboBoDdTYOeeLVbGClLUQKLOz9ukVJc2dCBNp4gPEKERoZr3iR5aEkxWYhpV+ZjlIVYBo8Fqacxof7PM8pitidtEDnFhX+ivl19+GUOBzxevTSNJO80XkiykapRVrZwvyw3I+1PzunGZrDKhPj6E+ruoD2TqHaL3T9+fuh/rK77bUuyffjFQ7aGylvP+skyE+/RrO7Jq/czfDhXqryXlBpSjmGcWWtBTivYqapEVWx3WT7dnK2fbNSpsNMJ9gBwOh8PhcIw7uA5QTdBngDPt3CoJLE0ehTQLRRQjjFJqoFUr1b+GUP0Qle3R6KVpYo7o92Ktl8TPbIvommYT+aJFlvoenzNhoRU92X7huvXrsfIdw+UXTmXUZj0agcT1yoJ0AOiwe5RH7g2X7Cm2aDZF3YJ9qb4PnSXbW70kCOVAelHuReVeQh3Mt1Vnd0Et3zLUW0JZKSLY+7T0yARpFFmxt/R+aTSMqiGlrG59RsjckJFYYz4RA1Zy/WLZj8cTJfaKF9GCcmI6SeAUZxMrW/Wqh8uyWfKh6VW3GVuozBCbM7MvXs9nY3ahxoWFdcVT9a+L2zLzf/4HAPDss89GZQr0SFHl43xshRP1ZFF9sfp4iuGdivLT12JlSh+I0Nukvj363mSblYlTXy192lRfrcgg1YrIA/J+1og2Lj+cjQVVi+KI1V8Dtiq0/iH8GkA5r5n6o+lXB2W7GpBzTVPsvdFt7Kr+3qieWB6hGd4HjOTcsTKCbS6cAXI4HA6HwzHu4AxQTXDmTMuKPgX6bXeNWT60MorWt1q9qu7Jub9yAzqrV7+ZSjGCpbDjNGvcIrP+lMFQy8eCvjDHTthiy0W3L/3WnX8/DrU1SSb6lGaHImXl1dILKdanPg8aG5RZz8ivc5FZOh1ZGaDWkfJAz2yCArTaDlyebuxTZ8arKfehD10xQ5QqrGo8X2gp/S9S+YcUrVk9eleGVuNpM0uPfaw6LB0o+1mo1ah+bKr1ovupkq4qRLOeTNFZKSDCGt3IA6VLB9cBFXW6Y/RXgkpTnx/e4Zz5oe9Z1Uq992TwAr/1a3sS+2zMkPXi0crTzUTZn7Be3gm99g6Yc999AIBXX301nMN8gJYsWVL74gzKSpUjCGOOQe93Souq+Mxpv7Ffe8S/KMVC8R2imah0bOnY7MzuT2Bh2u3Ma41VrNrW2rxovE3fYzwnry33suHbX+O4eNU2cKfY2bt5htXWtl9HbUgxPETKH7AY9Kj+cOprNUWW1Z9IjxudcAbI4XA4HA7HuMNwUWAjh1ExAfoPi+rhF9qqlQdbyS+1ar3QN6bFyqJloN9l1VLPvvHbjoM2Xc+YHpp5bEwqWQ6ZIDv+wO74fPwezbbpNSiLAqSjc2iLlCPZAtRXR6NECNUs0u/uqmSqihVqnRS7RH2n1CeA+6qhf81m5P76s+27hyiH51ZmyhNLRwWvvAVlr4iYtpguzA/7Rq1vImcWw3HNJXs8FUMS7hojr1abhaz3tA9l/wFVLSdS2rIc4nupc4udrGpdcKAGqjBc8y+t5IPKQc7QR/N3wz2hGDSGpA1Ar1AEylKp/1AqGi5fn4rRqlqpPGc4493W2NXmG5SKZJtZqKmeNzuWkcnG+iXvfCcA4P888AAA4IUXXki0PkZas0h7JXR0yquOKO7NUc33zjLRpcpHR8wA5QwR99eYKOVF1EuLYGfxuFh/LRWJ2oXyden7isOu7HfIPVQbWs7aXVuVjL6mK+WLA8+nyjzqB1os9YuE5mtTpBhl/p5sm+nD1sIEDK0DNHIYFRMgh8PhcDgc4wHOAEX4a7PofyoW/TSbLk+zqfReNgWnvw1dDzgrXo4yQcO6SpFREpST+STQcDzWSpqE8efh3NwTwmCObZ9j9NTMgWhzKac4lU+L1rnaUs0SzdCZNSbWA1IFUvUuUVtPNUFS+j8sNQpCbbx65GxdKj9PrKuct1FZwJM3gRHSaLSy0muKCVKWZyXUK2KW+eBwOLBd6iOW8iVTJiwfMGtkWS3pWM9WfV9qKWBpBI3eF+3rUsQiL2Z+YkclqTRMbAb/eNX2a45PYJdUsYYNrCtHEGp/8g6lclalWRJCLUyNiWS/hzFDxm2pRedodFK1WJXeBLsI9v+FTz4JAHjxxRcBAN3dmybsxmvUZ0c5kCbEeQ5V/bpe1ncgf880Y285S0xt95S8TpRZo4ZOilcmL6/8TOxRtsB8r9SvULu2qGGUKjl2mP+RrGtP9t7UXwV9qlhy9HHUTbWtsc8lIzWb7B2hbxsu8ydkr/nIu1sD0AglzjRMjr8npleVjjodDXAfIIfD4XA4HOMOHgVWE+rlP00tUQMjqAasLFoPD8W7lr7PNnJ6rulgUh9XaSiqAIY2WukVq3+hpIdXfeGWQvW0WLrkO3x+kji+QD1aFOo3UjHLfVerblfrrAa7NvXP4aUPpxRdbCW7cx+5d/uYJdPfHR/baPsx+kd1UocCfYHmGXvUU/JcINjb6vVU5EzCmZlZm7eThhoNN7Wu1Q9LrfHc9teBJfRBIs957tfUGp0PKHNGqtcMWa++XaUd1LmCUOEbdkYmpCsclFJM4n7Tj7KBW8v3A8h7oypN4vE8RWuJklWuKBVDxPXU2AoXdacxggPW7zMLNc22Kna1Z5tjlzW99NJLAHLfn7POOgubA3XJYn64zkyDys4rV8RRr2O0DUUWWT0DU75oyq8oM5SKHY3RJNpRPPvbau5d9lPrRlprS5lutrDFypwnUx1sVddSHjL2qsx11bVvYpaYjM+ZrOZzVh6Ccjcq9aVVk5CmPx1/62zMqXL36IIzQBH+037AGFU7R2MBExkO41dXTB0PyLosUle9G6tWqherxhDrs5LKLijfiTREc4WVtaX6wiPbnr2sNIg//nlotx5otJc0L0GdYyspZUPr8EW/jdukkxD9kdJnufjqzH4T2b+L453r++K2EfX2Ha56OzYb+fz4bgBFocwUsR5u5t7i2FxsV0pEUNutcwadWuU/PDob0AmPlvGsv9P277Sam9BZcsDW9AGaXkBbkF03xzC/k0hIej0HBj8Hq0fnfvfGZ9ATW/2D9gx1o/YUtNgU7YVFsn+LLJcDsfnE6c8mR7f+rKrramjJQ/Zs3VaoOROOlPcQ2/Jye/jJfPzxxwEAxx9/PBwOB+EMkMPhcDgK4JQtpU1GHxdVqSaURStyXYxi7Cyp1ahVpFanTiDVi1EVap6wtgbmhwYo2RGq/XO9GhD0RyzGlqlvI1uuEbSqmUWDsjUzQhTqE5RiDdXfqQVAHgz5f6w88hT7g8wPd6gVnjxZubBqKCaZH907llkTzPp4NBSNJiTXoETmqIIzQBFUir/EdyciiOlgzIdnT5SJHFqOlbfYH3zy1HzW7wJ8AknZFGX8gTITJP61/W3RYraZn+h4WNEa1wf34eyVWJVG8hUQajWXU8w0a1WbllnyCfO6Yn1QJ5+naPinEsJqePxsALtSRoDMD9l/dfrTLIw82XWbHg5PqC3RmDkttkftjN2L448B+hkhFfJPqAN2YhgUzpL6kKgB3npG/WEKZ+pBG3rs7zqj4jXRpwb7K1mZ/WBYU+rkty7rAxsXC42da1T6pm1jKOeHdpRyuLTEu3eh/OGPbdUvze9ZEwbK+vXrAQBP7rFHtH/5M7HeKZ6VA40DT5M4qFNwqLmnkJqENU2VI3lE689+BgA444QTAAAnWOlwOIrwKDCHw+FwFCBJ5TPo1Fn5CeUtank58WvlAAKz0JNN51SZRvklnTbTqTFMIKdLRJp+sqQd9AErM80pnpafR9fEx1eL36BVcMzKtXas5gQjcnaJ0WEBPUnVrmLmt+J6Ipz4ADM0P2Frjzzc/rjUyt1Piw/beRmyCMlSxr3drHyDlbz7bwzFQtndyoV/wCiGfwKLwGehcYasoImqUaRkLmyx3h6qvbqBvezhyIQNUz5/6tTBc/Mp0bht5Vlp/tl47bdSmR5Suqr8r94pAPDlX/0KAHDZKYFLbbQHd1lGQ6k3ahxnrJZ9i5Xszr24IhV5bY1Ul6uh2lzcXgfkJBUf2HexreSDrdapt8SVlVUfNxm/M2foM8yXTF0auayuZcVUEOo4qtdctVIdTxVlB/HUBepLV7kPbWUtJ9VwRats3w7jAtXXR8HRJFHqpWGh/k8cV4vNV2hXNpWDmywfKxbFUh5/T+Ec/KHUq8xkKC2EfMOGDQDSYfFlF3798Yof6AXGmnGvVdl+yp/ma7nlzIREw/4112462MLc8T0eEwx/V9875QxrpWpQ4c42u+KHo4QNxT2nZHsGxOEbC1A7JUzKLzMj2BO+laW7Vrx9CTetOS1xHYQ+STox7LIJDO9wOxbYX1OyPeIaQo824UEA+ZjNronXwvG+u05hnys0nm9jyhFwPSdIPNbaMmmCLW6MaswEe0cl/BOYw+FwOByOcYeJcAaogCxBnLKwnCQqY6nOHMX9yfxojLjyyJxoczrPiThn8+rjl1AyXGv13R+vzowBjUZTd7rD/u3fAAAzZszA6tWro6bkbknBYllW8piIa1NvB+UYqMfGqK9G69f+QnROESlbWrmIqL3sT0YM4QwrafEYtTvJzjblnvhkrwOaTuM7xgip74a6fe2JslWtKPucIKpbI4Ty7c3RuWLvraKDZiqsWHm4okhBbBp3mnXZWWo9R6Xe0djCnWU+U+wLlZXUTy9Tjdmp50XpNwi7tWvtVhefCZJFbyusK57jHrasN7StYvfynfeHJ+2QSeHV9mlbP2j3/oADvmhHajxf3K/9EsLMcPdm4Q8HBzctjcVIoDMbC/rjED+Z6prLPlwo24vO0OxXJbJvtvH561JtHLHqnTXV6gn9p0kkUuxhtp80oF9EJLPnr1hRitK0ZYrP1q2LN/Pc7Bdltvkxj+lm2Cc9WRRpHPeqyWaznyX9fVofUqBgkjVw3cb8CwPff9mnL+JVKW2/lzbGJyU6Nt9XcseBM0AOh8PhcDjGHdwJOkJm7+iHahUhJNQiKJrvakCnBFqKoWPFZVVJl3wRdNajZAqZH/r8qKugWmtVOd2rr4YZ/2uvvYbPf/7zYSVLw3sr9HbSmKM4zaVeKg1yDTFtsXK2UD600NVfidegvgUlIm4Gcq/Hd7zd/vhnOYqtszDPmffElY0g2FupZK6ZrTsD+Y0R50xlAOutZDoWGmapdCNqEa+Wst58KXpLjppKPSpHFccNxtAHQ71IiZh3YTqIRvGPITShL69ttoXozrSSljSfDZ6VfXBsjZZpwtazfv1rALmoIDFhQvCJICO0cWOwkOkjdOONfxPtT2bolVdesfqOAwCcc8452NFAH586889JER+qbcZxzBDzLOK16BYlD2u/vaDIgK2057E1q7Vqpb6MyTL2W9lnp+iM9tLXKd+XU9fFtek7pGaKTM1ArC9Vq2RXe4EdYgNQX/nKe2oAKt/Za40Jzd1BY7aQba+yfYuknMTEscbyNK6s0ZpX430ycNkYIKWtMpdK+01YOhqZoAo8GarD4XA4HI5xBmeAItRrVMUVNrs92JZrZd0EyhIqQDmIQ6f/KtjyWzlepJr7zWKhVUsWRYPDWqwkE8RmkFD4eiJy5J//OTAk/f21LiY+Z257aHxTKPU7PME2qgIKDSi1iLidBpem1Jijy2zWyQDex7P+K2qDdu1BoZjx/VA2bKy59+vBt6zPv2dsgaZznMMVByM355QR1KjAllBMM5N2T1uv0b3qJ8BlsiLlyJR4mQL8PSUermgrp/yG+qTUuEAiHilNYunOl73Uf2mFlLxGnoXXpKHRM5F3d6NdQp019cXvh/Gw9pFHAADnnXde1MZLLrkkWl6yZAnGCjj0qlZqHKDuRyboUA6RY2UDb9hUlBzg6i2M+oM3h/Ihe899I/O+4ln4tGs8pCpnhbGjLpc6rrm3jq0SAVwc0kqzajgnYSdttP0ajT2ZY8N/sC9uizLC7Da+J1tku+5frzRWRpkKizO5+GOvvj7iG5mVdvY32dPY2xafawv00nYcuA+Qw+FwOByO8YZKBZjkn8DS4Ez/LfZVe6FZJfSq5yx5vcW7FP10lOHR6X7+0TtAnHhWiLgWoemNeBr6y3BiTuvgH8xSpY/Cv//7vwMAdt11VwBAfX0YALvvvjuA4NvwM1OSrauzzGA9wSfgAWGPKhVGuqgidJtdKn0JYigppowQr7nT1G/3NquO1pqqEGXMD50QDgawB7e+C0PDLKB1xvw8kd7z9eLT0n9XXXUVAKC1WgUAvPGNb8x8Sli+vf8dYWfRe1LBmmnWadMkMWa9RjDaQGHUYFGDqFBdKRpqVTbCNERyCnLTWL2/4qSv082nRF0VUmNaJbI0ek41k1TUu8XKObI/z/e///RPmc/O+eefH+3zSQyNscT4KPic/4eNQfWT4pjItHW4gky55psgikNF5ajs4V9sslwLbKw0Z3dV1aLU221ttFdKVlGhP386/iN2P0UWpATJCPEdrSTy4aZ0ztTnketn6wp9gN70mP1BNkf/LkLvsjHjWSSa6adNvTrebTRj2xFAo3QC5HA4HA6HY+xh2wlBj5EJ0KT/z/6gfWEW7iTzPphv5ksHcjqDBgxn/WQpHorL581ytxoyfwZNTJhSyyVOvzrM0N/whiBpzugT+vY0NoYj9mAuI1tmlMsrr7yChobQWJbr1oUP81db3bSar7vundE5PvjBoCBNWXplF4qJBYFyvqpVmf5ILOW7ymI3ukSBdo6aolHJ79+iZ5GBPXxNKCjp/pVt902beaVYkmkD8vt3f919AIBJTeEResM7wvpFDfuEHTWa0Dq5PpEJvV+sVWUM04LYap8WfYDUbg53tAkPA8iflqqVvLtkgNgGjYZRTWVtyXC+ZiWdFNk+ODiYMaPEt7/9bQDA3/3d32G846/JWNabxpGq2qvMsoaBcXmyqQi/tLFMz5FxtZdAWfOrVkpVIKdmQjnd9JM4pvSVoArfsbJOmQjg9ixv4UBhpTqh6YtM2X45KdlXlXTT8Z56FpULo4ZRPfuSvy0DNrZn9+UHTOb7sF12Zq2plBjPxY26ZDT7/hicAXI4HA6HwzHu4AzQMGAgwkn0/fkrW8Hsypw9Pxcf14+yr4+GOpmZ22KzdguCyBggWsE04Dk/V+OL9+/gX/4SQM4ikL0hw8P1ZGto+dLXZOedd86OU42TiRMnRuVOO+0EIPisAHmepCuu+AoAYMnHPx61OaW1ohoduf1GjkCzaYVrIKOgifky7R+afQCA/5WdeM8siudPd4aS9/q92GaYZCrCkydPBhDuCRWH9T6yr3n//rzXnwAAb3hrzPTt/ZE3xyeRTm6JF0v+BSnh8rSmTwPKWY9CWcwjWaxBRdAJbVvtWsvZz1Mt5PaP3nprqK8v1EhGc1JfX9a/P/7xjwHkzKgDwEKLgrUMolQByx5AvoSUdtmd+jNVK+392NuW0x1kXG8LxSOm3cT336osD1nKO42jozvai03TUapjkBj296+WI07tJpSpSh2QRuUM2P4ttprPwcp4txLUr0l/UuoZpkuxIw0Bng1gqrFCdXa2Risn8z1JBoi/cfQBah+6caMRzgA5HA6Hw+EYdxhOBmgEv/KNzgkQp+ZLrTzm3+0Psgn0sl8W79+HcugKTZTbQ9Fis/TLbTUZoIczPYbYz6jdohyYQViNjX1NyZmRW2RvyCiQ8RkYCEeuWhWiLNTfZ+LEiZlPCv2GqBLNOrleWSSW/3LllQCAX3zsY6gFjbzIr0VHI7MfB+tD/VVKPggU1tl9AvL+IzvXYqXdw1+Y5fMrW00G6KKaTd4qYD+SCZo0aVLm+0O2gveC94AsHO8jS96/p34e8gnx3kzfM7BpHJptUqrQdFoPSD0SinpAqg1UFx2h51RylOOBzI8yhASPi2PLcp6B22lJL/mf/wnXYH1JpvK1114DEPqSUZB8bngvHABW2y/AEuN+GN2lVMSbmMWc7y6yCKI23NeWD0R73sj8WGwRCSHktJIyP+pwszZqijKXOpY05zz3qwgTUFdL0krDE5WKTER35ZXGh6mMnOpacZyrD9scWeaz26gyW6pXNBNln8FMKt4OnmrlHvQVooK+VaphyaMZwwlBvzZyp5owclU5HA6Hw+FwADfccAP2228/7L///li8eDHuvvvuTTuQDFDq3whidJpVPzUL6Ex+/bbsusd8xpZtlvysTYuLbvwaXsBtZupQR/a7WeSTOrTwQE7rA+i7P9WYoC9cfz2APIprypQw3WfOIWVvPvrRj5Yuc1Pxb5Yxnv4oRFNTU1R2dISL/dgvfgEAeOD974+uKBWRsSwzD2NrTw3OTNuGVJCm9MYZxQWDfeO+1+7VD231zdsvmuHj5iu1bFlgEOvq6jJGgveLDA+ZH4IMD0veb/pvkRHqfeYZAHkS6HvmzQOQjyoOS1qbtCZVPmiBjbwuKztBq3828hsRC5HwLq4CfUJiOvRhMUOnS+4pZaU047e2mbVxnLz88ssAys8CWZ76+vqsv7V/bzW/oRNPPBHjHqTq3mv38UnTPXvTUbbBNGIyLk5UhPme7F+ZDzxzxVNxfd771lK+wdpJEufae5BN1AhTQtkUJeanMsLNBn7JNaQXZZ2dTIrZSr7zeTL65PTKeoGqU7dmfjeB2u6xEzZaVGXk4lirrUp3FS9WdZg0ki0TGaJv0Jmh/Lq18vIdL/rrmGOOwSmnnIJKpYI//elPOP300/HYY48Nf6D7ADkcDofD4RitYFAOEFw+KlnS7mEwER4FtkkgE8TMt23GBPEztTpMdKEshmvRDyss6uv2rHINoVBFFv3AHH+0pHXLm07rlsvU7PlYwh9nc/CpT30KAPAv//IvAHLGh6wDz01/CrIX/GysERqQ9b0I/itrrOQVV63UXEQZ2CXUG8FU5FYofYCMAbouFM9buMmuqTq3IcjeTZgwIWMkyLKxT+kLxL5WaKQeo8K4zPFwWltbtJ31/vX8+ShCJZb0nrXZPVqN5oJ+E+9QeBBylijWdUop93TaHR/AgwCAHquXulLKAGkybhrabzWmkoxPdk02LtmHdXV12d98TnjMYCJn3rhEFvljvj17qPdVNd5eQndeSBQRj2QAZ06yhDHQzeWKtwAAFtpJREFUWorjivXjeby+NdXHjcyPRj1yLGUkjvj+UDerbh1QSSUnVIcj0YDrXxOv7pbdNbqxrGcdeKpVtrTSmKDF2ETwImdMR3aPdrb3Yp2xeewwdhB9IgdsO/1gv7KpJ922+OUvf4m///u/x/PPP4//Md+/YbENGSD3AXI4HA6HwzHiOO200/DYY4/h+uuvx//9v/930w6iDlDq3whidDNAxFKxChkdoSITXSiHvtgMOo/24ndefoXmwer5oExQONnFN18WTtUVLCJat4xwIfvCyJeRxOc+9zkAwPctYzbPRX8UtoU41/wpuP6yY44BUNbooICsfuOeLcu91iWNcSAI8LRpXOz+EHIF6CtC8duvhtJ8D3bdgSz81tbgxzB9+vRsHRkIagQplNlhqRpOZDaUFlbW7kfGDHF/MkNsB/e/y/KWFV3bWoyhabOSUi8PJq+Ye2hWozAierCvLZMZmmJlQLf5fahG1nxTcWbUl7Jp2gdFJWhepyqnOwBcZM/Kb63/Dqc9+5DsqJnEydaYP8Za5DfLiO9qSyin2GtKmZwfI+h09eAAWxPuaRNy1fTimTTfW6+UhGqSpfSwugrL0/i+IQOkwlZ2LdThedxe4fRsVFeg2Jup7LdUTj8fWkUGiL8Q2W91KmlbBNnpTRZzZs9M9nt1nTTqth3nfQkA//qv/4r/+I//AADcfPPNmD07vEMOP/xwNDc3Y926dZgxY8ZQVTgD5HA4HA6HY3ThM5/5DFasWIEVK1agr68vM1weeOABvPbaa9hll12Gr8SjwF4nmA/lZGGCelFKTf2ITbA5a5+eRdPQGlaJTY1+IMJ8X31+yK6QETr77LM340K2DOeeey4A4BLLOM8M82Qt1I+CLMLnf/c7AMC3DjsMQB6vRR5AgxjYrfwinvmCkPl5SHZsuAeYcW/4+9mgSYTf2rYdzJIBcv+sO+64A9OmTQOQ6zex5P2lThBBxoZMBseD+rYUtYaA/N4oc0ToMvc7+umno3rJ/gE5K8Vt9E9rM3ZpzZrwUHDcVCo725EaVqNZvmJnix670Q9aIqk+YwP+2tqs7BWZIF47+2rDhg2l66AP0Kc//Wk4BIfbs/OoRMVWrcx88MzP5FGjE/h8rkE5zNBu+TR7MVZtONEzks/8w4gdcHossnC18YLKBmoJWVaePSUHU1QlJ0u1F/1jWqy04ft8EBjPfB75ZtdoRZ6Try/u35qpX2ucbPw9ptl843pt3GckhjJSGhL3Umfu+5P1xKvRNWTXZAwQWay9sOPiF7/4BX784x+jrq4OkydPxjXXXLNpjtDD6QCNIMbmBMjhcDgcDsd2wxe/+EV88Ytf3PwDh1OCHkGM7QkQLRyjMJZ+5y4cceaRAIB+m/6rJANZD37Pbc98gsKeszKPhxjtFllDK1fZlm3B/CiWLFkCAPje974HANh9990BlP2RNArnHx8MXiK3HxC+72vaNKKkAE3wezVNqJmFHRvNH+hdZrXuoNELRaxcuRJvf/vbAZTzsKlPj/at6v9wPccJ/WA0SozsiPoOpZghnp++ZQMDA6W28twvvPACgDLzQwwOvlSzHyoV0tea+W6qHXdHtP+ll14anZ8Mj6o881qKqujsR17PZz/72ZptchTwT1bSWe9gK+vtmWuwB5PyzmTDO1DW0hHnG5X44p1/OONR6IATuKH2rAznpF6VelBqBKFqkBUz2xXBV0xXYd/lVnmDRfSSRCCjo1rVtYKEgbxbmjMFbXpB6ptQqZwGq78nqrdRBYWIIoVeDdGb2Nme62c3xo21a3qRzM8O5Cs54nAdIIfD4XA4HOMOng1+hLA2zJLJgMx76SX024yaE8yXloYwsIPMWl59+OFRFTPNcqGVoZ9zLze/GfoosKSVe/zxx4/Elbwu0G/iqquuApDrBLGttMCVVXj3H0JqaFrmvYceCiA3MNUqzCbtmpWZls8xg8DX7Rvwu7bwYrYDPvvZz+Laa68FACxYEJg+ze2mzA/X0zeIDA2jmMj0sG/JCLEklGFSnyL1n2G2+tdeey1jIbkP7zeVmJX5GQ6Dgy9s1v5/8zd/AyD4AgC53xShedWK187rceZnM/ADYwWOs2esxdaTaSC1QWa8mOSNPzjiEJPp7dhqTaROLaicFycTFOdGbzYKg5pivbKX6kkr86O/h2R1iqnAdJ8UT6MMEJc7M90s6mOR+VFFbW1FzFe12G8GXZIWW/TZNF4MHagogrQQeQf3GvND9vyeuOQtnIYxDGeAHA6Hw+FwjDu4D9DIohg5csUPQ7KpxkaL2noueN/T+jz25z8HAJxmLAkteM1J9N0jglVAa5rWLSNsPvKRj4zwVbx+sE3f/e53AeR+SrTEyQxxvfpmvGb5zbpOPRVA/kVclVmynGA0oC4pfK/+yuj8dv3BD34QAHDLLUGqmpF1Wd8YA1TMZ1VcJvPDcabZzblec4lpBJfqAamfD+9hf39/5uvDfXgMFa63Fd5vOecc2wb9Jmdfzx8RUh/GRJScX4owMmOwL96VzIMeksca8qFX/7CYM283PmZAmHVlcWrr65cjtqbUaFOWhV3ayG5ozpieYjp2oCSGlDE/Vs4wffp16jBFhGteatrQPO/9rMUaeugTcpoW5DSUppokE/RQvHp/jGFMgEeBORwOh8PhGGdwBmjrgZm+NxWaaZ3W9Immtjwas1Kfd955r68CiUD4T+sTGi9ndo1OlmdTcNJJJwHIs5JPnRqsxlRkk0aHkdlRfyvVyFEGSKO/VAla65s6dWpWx7p1QQiFTN/mPgOO0YX6QdFBy/xLrFSHxjqUpJqVmaF2jrikFNTBlUXRuK5YP63T/Gk6zY+myVTE1cdIo8JUfa2WLrgqOrdmjI+yU8o3KQN0YCjI/GSsRJeU5KAW2vlCvT+3XntIGCFmuN+nGCXLuot+WeFgWw739GSMA7gPkMPhcDgcjnEHjwLbccBM6440zhrLmhQJkPm76aabAACzZgW9KPXVUQ0eZWxSOa9UKVp90BQaTbZx48aMteQ6Z37GF9YGdzXMYVATWQYlaYBSci6yFWRT6HtCJqg1Y37IlpTUwATcTrakISp7snxzgQKpMx8hDU5TDKBMFnDf9oz5YdgVWSplgLSN3G9KvDqjn5SXIl2j2eKDENMqSzh5C34dnX3qo6Gc04esg5+XqDsSQjuy4vOIwxkgh2N04D3veQ8A4OfmPL/bbiHdAB3JNaxdU15wosNlDQlXgUROdAitp1iyDoa/OxwOxw4PZ4AcDofD8Xowh8zs1OAL9KLROdNOsR2KCbXEyUaztatmTq6JQ7ZEuRqyIn2yXuO7VF9+wI6Ko8QI1R6birR6dHvpqJR2vSbp4tVaB3WY6k4WbqbRX0qpzZf1ActMC2mRaSGxuplr8n1ulxZ/fxyy684AORyjDKeffjqAPP1DtVoFAOy8c0gsqsKJDIvXVBfFhKBA/ulMhRAJZYaKjNKLL74IADjnnHNG4AodDodjG8CjwBwOh8MxEnjcmJ+Ma2mTciYyEqTfaAmSQ6kyrdGsMvD9ifWqLR1HZHVKDsYm05qmck9doVSyoEweqK8OGRyyUHoE22jUTHfqGlXHWmPSWC/VsUOmyXuMAeLeK1HOR/bceGR+CP8E5nCMTjD9g4I+QrvsEhKKkhFSQUSCTA6hAonD+QJ1d3fjtNNO2+z2OxwOx3aFfwJzOBwOx0iAmcMfsc+s00jjFFxZmCNRyaE1styeMTNqoqvXkOoz10mZUlMmq1If7ddjx9UlfIOKLWKNTZanrCepIkQ+SX14VEL7IcSYIvvpr3WseZRzPSH+a5VFp8239j2IJjBirLY89zjDRDgD5HCMJdBHiLj88ssBAFOmhJcpQ9YpqMhlMkT0EUqFyzPSq6srvHx3xFQsDofDsSkYRvVjxDBh+F1GH37+85/jkEMOQUNDA4488sjt3RzHKMezzz6LU045BbNnz0alUkFLS0u0fZ999sFOO+2U/Zs0aRLe+973bp/GOhwJ7DM4iH0GB7F2DbB2Tcj3NdgHPN8W0lG1IHAeHQiMT9v/3969xTS1pXEA/xfG0QpmEItEPQEDZ4yODzqKKF55MHhJNN4SFRTipSh46yTzIPpifCCaTpxgFIkxxxhvKGpEzdgYFQSd0YioeAnHGU8McYhmjGKoWCmy52HvtdsuKBQtUNn/X7Kz2+699l7lEPnOt9daH9Rchvem/q95BNSsRnM7LT5qm1va/PF3Xoy2Rfls7zEI7zEooCu3JUYMie8wQttGapt8T5n4zr+Hb7GqgdImri9+auJn06Rt8QDi8Ryi1Ndw/Z6KUglFqezSt+prwiH/V/fdgqlPZoCio6Nhs9lQW1uLmzdv9nZ36AcXFhaGuXPnIi8vD1OnTm1z/NmzZ/prRVGQkJCgF0/1JycnJ6B7HzhwAICneK/ICMlFWLOzswO6HhFRKAsDMKCH7hVyAZDdbsfdu3dx/vx5/bOtW7fCZDKhoKAgoGvMnj0bAHDkyJFu6SOFrpcvX2LSpEm4fv06JkyYgPr6eowbNw4lJSXfnA2MjY1Fbm5um0UN21NRUYF3796xAjqFLLE+0D+0x6oN8IxmESs//1dq0zbT0iTt5bE8MjEORtxBjAGSV/bxVwbcd9bYJ238jHdGQPRA3KFRXwlakIugxUh7eWaaOF+s3SyIASreCyl5ny/XCPu3dL56n9dI0O+nKGdAqnB4Rll1t5ALgFatWoVdu3ahoaEBUVFRaGlpQXFxMa5evYrc3FycOnWq3XZxcXGoqanp4d5SqElMTMTevXuxatUqVFVVYc2aNcjKykJqamqP/P4cO3YMS5cu1TM232vz5s1BuQ4R0Y/A0BmgYcOGYebMmSgpKYHVaoXD4YDFYsHEiRMxceJEFBYW9nYXKcRZrVZcvnwZkydPhslkwqVLlwAAhYWF3fr709TUhHPnzun3Iwpl87VM0FqTqc26cyLnIc/darNKsk6u1iVncsRxtd0gbTZXo7Tej2cvr+8Mn8+9z4rwc2Zb8orQIvMj1wrT9gNN2l77WKSW3B+k63U2ZztC2ovMkLoytKK87KS9sYgxQP587OBYV4VcAAQAWVlZOHToEKxWK06cOIHVq1f3dpfoB2O1WrFw4UIcPnwY/fv3D7hdZWUl5s2bBwCIj4/3Gd/TmQsXLiA6OhqzZs3qcn+JessvioLN2uMw8SdaflAlYoCftAUJX0uPczoPYNTASV7QcKA+rV3dv0e01F4OLtw+nw6EV3FR6Y7/06fBt1dAw7sXWsDzh6HqXvwQRDN9Vrs8xV8OBsV7r9oWPkSApRaQNfpgZ39M6DgDFMwAKCRngS1atAg1NTV4+vQprly5goyMDABqZXbv2Tbe29ixY3u51xQqnE4nbDYb1q1bh127duH9e/Uf3UB+f2bMmAGn0wmn09ml4AdQH39lZmbqU9aJiKhrfgeDzwIbMGAAli1bhvT0dCQnJyMuLg4AUFRUhKKiok7bf/36FW63Gy0tLWhtbYXL5UJ4eLi++i71bdu2bUNSUhKOHDmC7OxsbNy4EWfPng3496c9LpdLX3Pny5cvcLlc+lo9APD69WuUlZV98/WJetM/tf1IbS9yFXJ2ReQ6PBkgcUQuA+FbDDVay/z4+xfY3c4rVftlKLzLmooeiL56l5hQyd9GXmyxn++t5fHcTeKRl8jsiO/+Snovjt+Xri8yR+p9FYUzkztiAhB4zv77hGQGCFAfgz158uSbHn8dP34cZrMZOTk5qKyshNlshtVq7YZeUqgpLS2Fw+HQFxrct28fqqurcfLkye+6rtlsRmRkJABg9OjRMJvNPsePHz+OlJQUJCYmftd9iIiMrCfXATIpSmhWXaurq8Po0aPx5s0bvaI2ERF1v79pj3FFzkTkNERJjFva/i1+0l7J2Q6R+VHH4fgr7u2Zsh7t5wy5pZqmSdDGDI2DJ58j/ji+0vYiq/UaadqrSdr+T9peDH4eKV3BN3vlmcYuMjziDs+lz9XU0Z/xKwDgoTbAW1HegAI3LCkJa6qq/B6/lpSEqg6Od0VIPgJrbW3Fvn37sGLFCgY/REREBtHZLLBgCrkA6NOnT4iNjUV8fDwcDkdvd4eIyHD+qj0YKNQyQeIPUn2bM0Xmx7eAqJz5kUbbdDBlXW4h+I4xavDay9cUff2jtveMV/Kdiu8hWsZI78VdRAZIZB3UzM8g3AYA/Kx9KsYljdP21cz8fBNDrwMUEREBp9PZ290gIiKiHmbolaCJiCg05EpDRNdqGSGx0tVj/AYA+FUrO5EgZX7kOVzy6Jq2ROZH/AnUVx/0Ocv7nVyQQmRkYvQz/qPtR0j3EORxRuJKoiBI+2N/5MxPZWgOp/3hGDoDRERERMZk6DFAREQUPGVlZdi9ezeqq6sxePBgvHr1yuf4yJEj8fbtW4SHhwMApk6dimvXrrV7rV+kLMcMfdFP38yPyLXI43NEtuajdNz/qCB55aAm7W7qDKvneKvna8ScLjFyR7QUWanf9CNy2Q7fa3t6+S9tL8YAvQIAxGprGok8EjM/wdXZStDBFLLrABER0feLiIjA2rVrYbfb/Z5z+fJlfQV0f8EPUU8w/ErQRESkOnPmDNatW6e/d7vdSElJQXl5eUDtk5OTkZycjOvXrwe9byL7IWqJiRyKGOvjr2Sq2L/Xxg558jWipcgRiayNyC2JsUFq/qW9sUByNTJPDkm8EqsbyUVQ5d6LXurFwAAAb5jxCVh5eTlsNhvcbjcsFgtu3brVaRtmgIiICACwfPlyPTtTX1+PhIQErFy5Env27EFUVJTfrSsyMjIQExODtLQ0PH78uJu+CRlJQ0MDcnNzcenSJTx79gwlJSUBtROzwPxtwcQMEBHRD6C1tRXp6elITU3Fhg0bAADbt2//7uuePHkSEyZMgKIoKCgowJw5c1BbW9ulIOrAD5QVMZn+or0SGR+RMxKrHKkBoKKc6cFe9T2nTp3CkiVL9FqeQ4cODagdZ4EREZGPnTt3orGxEfv37w/qdadNm6a/zsvLw7Fjx1BZWYkFCxYE9T5kLC9evIDb7UZqaioaGxuxbds2ZGZmdtou1mLB7KQkv8ctFkvQ+sgAiIgoxBUXF+P06dO4f/8++vVTx7Lk5+cjPz/fb5tvXVDWZDIhREtEBoWi/L2TM7J6pB99XUtLCx48eIAbN27g8+fPSElJwZQpUzBq1KgO2/VkBQiOASIiCmEPHz7Eli1bcPHiRcTEeJb327Fjhz42qL1NaG1thcvlgtvthqIocLlcaG5WB/rW1dXhzp07aG5uhsvlgt1ux7t373yyQkSBOnjwIMaPH4/x48dj+PDhmDNnDiIiImCxWDBz5syQG1/GAIiIKISVlpbiw4cPmD59OiIjIxEZGYl58+YF3L6iogJmsxnz589HXV0dzGYz0tLUCumNjY3IycnB4MGDMWLECDgcDly9ehVDhgzprq9DfdimTZvw6NEjPHr0CIsXL8bt27fR0tKCpqYm3Lt3D2PGjOntLvowKX0510lERES9wm634+jRowgLC8P69eths9l6u0s+GAARERGR4fARGBERERkOAyAiIiIyHAZAREREZDgMgIiIiMhwGAARERGR4TAAIiIiIsNhAERERESGwwCIiIiIDIcBEBERERkOAyAiIiIyHAZAREREZDgMgIiIiMhwGAARERGR4TAAIiIiIsNhAERERESGwwCIiIiIDIcBEBERERkOAyAiIiIyHAZAREREZDgMgIiIiMhwGAARERGR4TAAIiIiIsNhAERERESGwwCIiIiIDIcBEBERERkOAyAiIiIyHAZAREREZDgMgIiIiMhw/g8v232omeBhEAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115bd8410>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1156ec8d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from lib import plot_stat_images\n",
+    "plot_stat_images.plot_stat_images(afni_stat_file, spm_stat_file, max_activation, [-17, 1, 15], 'T-statistic', fsl_stat_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ee92ad0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1167e8f90>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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WYBvsJz/5CRo3boy5c+fuwNPVDN98802VC+Qvv/wSd955Jy699FIccsghAELdgWztk942ADKcbMZduz0UFxdj0qRJOOecc6qsg+KVV14BAIwYMQJ//vOfq31PD4+agLqV7du3B4CAwSUb0+8yEzRs6v6+sTAVxrYsQTQlE8RNkoZ9+gAABraxA30tpa3E8LTK7E8DlUsspTrDQik1KjCHdxUeasvKaOXmW/oL2xonw/Q3zwjFoXPnzujcuTMGDhwIADjnnHNw9913V3HVFmRygjsHdWYB9L///Q+zZ8/GSy+9hNWrV6Nr1674xS9+gfLy8kCJbUdRHUVhbplUVw8oDvfddx8uvPBCvPrqq3jwwQfx1VdfoX379hg8eDDmzp0ba4X2XfHuu++isLAQ48ePx6233opWrVrhpptuChQBiU8++QQnnXQSfvSjH2Ht2rX4/PPP8c4776CsrAyTJ0/GPffcg86dO+O2227LsPL65JNPMHToUAwdOhSFhYVYuXIlvv7664y6/P73v8eCBQswa9YsPPTQQ+jcuTP+8Ic/4IUXXogoQO9qTJ48GatXr8a8efOwfv16nHDCCTjwwAPx6quvAnDPB7j+MmPGDGzcuBEfffQR5syZg6uvvhrz589HQUEBRowYkeF2IZlMYvPmzRg5ciQ2btyIiooKfPDBBxl1uP3229GsWTP897//xZo1a9CvXz8ce+yxuPHGG2tUzscff4xHH30U48aNQ4cOHTB37ly0bNkSZ511VlYP6B4eHh61jQ4dOqBLly5YunQpevbsiVdeeSUQ5uKRArBryIE6swD63e9+h6FDh2L8+PFo1aoVVq1ahbfeegvnnXdeRPl3Z4HWCd8Va9euxVFHHYU77rgD999/P/Lz8/H1119j7ty5+PDDD2vlHtlQWVmJs846CxMnTsTMmTOxdOlSXH311fjrX/8ayff73/8eBxxwAGbMmIEWLVpg5MiRmDJlCs4991zce++9mD17NpYtW4arrroKN910U+TaiRMn4vDDD8djjz2GVq1a4bbbbsNvf/vbjLosWbIEp5xyCu68804888wz2LBhA5566qmM8nY13nrrLVx++eW46qqrsM8++2D58uW49NJLA2X0zz//HDfddBOuu+46jB49GslkEt27d8fYsWPRunVr3HnnnUilUpg5cyZ+/vOfY/bs2UHZmzZtwpVXXolbb70Vw4YNQ4MGDbIqOL/77rsYM2YMLrjgAuy333744osv8Otf/xp/+tOfalQOAFx55ZVIJpO45JJLcMstt6CoqKjW9s496gYSiQQeeeSR7yy4EfTBQx0OWmdRR4fzZP+nB7gLyNBQn8Z233Ptd18jafq+ZaeNGaIaTsuO9sdgS4+z9BxL26RTQBdbOtBSMj5JS8nw7yO/yab2sdQYokYmjB3mFHhRXOBSMj9MjzZG6K3vxgQlEgkUFRWhYcOG2G+//XDyySdjwoQJQRvXVTz44IP46U9/ioqKCnTr1g2PP/54FVdsRahwtXORk6pNRZNaQHUtbzw8PGoXe9hU4FEL2NkLIBp4cMuLun3Hv2TxD7kGkQVQ8Ju7VHviAghko20B9IotgOgnl1tjLL4WFkB8V6tWrcJJJ52E0047LXADUl/Qv39vzJ//xHbOX4v58+fHnq8J6gwD5OHh4eGxezBjxgwAwPe//30AIdNDw41Thp/qMh5tF3Atwd0OWl41lpQLoINdkrfCUi6UOllKv3ksv80RciMg3Dah/khSjnPhowuevnKc4EKog1WqIFp3WpXReqy7E96n3upUMkaMGIEdRYcOHXDSSScFnt/rF7ZiV+kA1Rk/QB4eHh4eHvUBhYWF+Pe//42DDz54d1dlN2Ab3KI17l/twTNAHh4eHh5ZQT22Aw88EEBoEbrJmKBDmZFbVZWSUpAnA8RdKbIoZIh0a4zoLulhlNnJ3nRIy8yb0SiG1sFxMSQTlr5u6VBLyfyY9eQXtj/HLa9iSfksVvcRv3FbcdPNu/V5550Xc/9MnHnmmcjJycE333yDH/7wh1l1KPd+7DoGyC+APDw8AAA5OVMQfpH0i5a01D4GCPfgn3jCfThook+XANQPGT169M6orofHXodZs2ZhyJAheP3113HBBRdgzZo1e5Tn5F2DbQC+rTJXbcAvgDw8PDw8IqDOzwEHHAAAWHLkkQDC5TE3ZlrSjZimhLrY6SjnyZ7kWspvfVPJRwYIJ1lKtiZdb4c3IfPDWHnGAH22EhGwrm34VFSK7mqpxeOjnpIqPce5sbbjZ5m3/8fM8WxNHO0OHjwYI0eOxC9+8QvMmjWr2tftHfAMkIeHxy7H6whndX6p+IVaZql+BYCRI0cBAL6HssiVCUsfNBPpUaNG1WZlPaqJysrKiNPVRo0aBUFFPfZcjBkzBolEAosWLUKfPn2qvmCvQT32A9S+ffvv5NnZw8NjR/Dd4qt57Lk49dRTI79/9atf4fe//33WvAyeyyC+rw5y5lcn2nl+hnN72x+00iKbQl0fMjiiHxMwPM3FhIo/ScbwOjI/B3WzP6hPk21BkLS0p6VG3WxaGT1NRod1GmyL+oMtbWN6Rh9bsGau/Xn9CrleURm9bKsFAp1g8cuuu+66mAujaNu2LUaMGIHf/e53ePrpp6t1zd6BeuwJetWqOIW1ugP1lcGwBf+84AIAodUkCVnK3PR9wTmCY7+jHAfC7rFSfjPEnJbVVs7zdwsLA8Lggl5fY/fi/vvvBxA6mGOoEErs+++/P4Aw9MAVFtNM52KN60jQvVgZmsmZtnAMUDLtdzp4B/UCB7Q35od9S8McFl5/PQDgt5YSCYlUT5biZz/7GTxqB7vCSaxH7SDbu3rooYd2fUV2O+oxA+Th4eHhsWvBYL+09nrxhBMAhP4HScwEzA+lM5WsiFxJyehQ2mtuvncOsN8d34qeZ3nNlfmhk8OEpdk+lNxBcEJCIB2S+fk3ose5lmd85P7bovmZkvkJY0k7qCN2Yb142WzbAq4uA1R/ses8QfsFUC2AwVQZWf0W6+icPEjWJizluKNMrXODOk/NxrTymjj/YkScbmGgY3jllZHzT7ZuDQC48MILs9zVY2fhySefBAD07OnoezI/paWut9BL810//jGA8D3zOxENExye584E+0EiOF8WyQf7XQzn7I3fBPa9EmOMWlm+dB1Vls1jcbsgZKXIaC4bORJApp7sB1ddBQAYZC7z6WzP6xB5eNQH0A/QzodfAHl4eHjUU4wbNw5A6OGZQhyFNlp75emKO2GpekNmPrIkXJnrjmoQod2suRoZW7N/B8mnXpv5YUzKeSBWb4SHScUk5TfrSP1+gp4eqMyjvorYFi3kN9vg42hxvOy5554DAJx++unZ61vv4a3Adgi1HfcmDtTxadOmDYCQNh71ox8BCL22q+6OWoByTiDDynGoDCvnmHQdIOoeVkieckRBg071Q6Z1YHcrv+giAMDfzYkXFdLp4+WGG25AbWBvDfxXXXBvv1Mn9yYZPb5pU/cmt2zZAiAMOfCEKbKq3zhlDQn2h4SlatOlumDKSiqjtMyYn6T9Tu9n2sfifN4RpXI+jsXMu+QSAMAa+/0X84dCRuiaa66Bh4fH3gbPAHnUAzz33HORwH933XVXvQv85+GxO0HhjcFMJx3hYmxRhFSBKsPDs+5zcmWeK/nVe0J3K3nwUy5twthe9O+zWdJPpAAinTHikpp5rZIqWdrv1aybMUHt+ExfyS3YCDyfsJTMD6VRtsWSaG2Y8vZnnDEXADB1qivwIhM6PYiqGKAmtXYnvwDaAdAC56lhwwCEY58kLfUDNQByRzmvkm/SUqp/lUu+dGZIJXpC9YU4NtW6VCX9XPldbs9G9rf7Aw8ACNmv2lTkq0+B/x555BEAYR/ilkOLFm42PeywMy2nexPHGvOiemREaSR32Ef4vtnXVEe1QvKxr2mfFddxwRy/HJnIkzyE7hywTPZhPpt+R7RvL7Pgkrz3tFatAAAlJSUAPCPk4bF3oCoGyC+APPYiMPDfD3/4w91dFQ+PeoHHHnsMAIJgm3SzkLTzqg7T1FavPXhC/f5wBa6SFfOtlN/Uv+Fq9ziyNtTpIbMz21J6ddYN1HSwcJZh1mBcWVMasIjyeWYNxgX6RyZ5dlwfvQMfKVdX9nF7vAZ1bxLC3XHECBdzzBNAiqqswFrW2p38AqgGmDhxIgBg2bXXAgilZx0X5XJcyTz+pu4E5xSWR907pU6zlUGpWy3GAj8/lnKMqj5g5uCM3pPlvTVmDADgtKlTAQBTLV23bh2AHfMfVJ8C/82cOROAc24GhFZdtPb6wWGHAUDgnYfvjX2ko6SqL6OWVsouqo5YX7kP+xEn+XXl0XKSlrKvpvd5fl+UXeQUxj7Ha9UikfcYoIGv7QtSuT5ad6YvDR+eni3QyVu9ejUAb27s4VE34f0AedQD+MB/Hh67B1wsNm7slq1HHOG0fuh1h6NQXeWstNXtDyilqZ8frnIpQXFlzQJVb4b5jyPjQ/ZGxUZjiL7O0EoKEfgOoihpZdGL9JCCSN3y7Balb2e/IxfwfLT88uh1GVYt9iypFZHbhAxSRoVdQTk5bsGeSrXLyFE/sQ3Ymk3sr334BVAWPG7+R/YxSyhGtS415ofSszKffGVkV2jdxfHSN5o9gykiKPWr9/h0/R7+zWvJ5KglJqVjDuakpZzQVGeR5SmzRDxvehis06kWqO/Pf/4zgB3z4rs3Bv578MEHAYRbDAwt0MDiYv1yoHPoxvYlO68evNVaS2O0E3yP+t5VF1Xn7MBfnWxptLROu9xMsDTiQfrHQj8c6uGcv6lPxj7GOl+gF4piEhkgZcXYRix30nnnRX5zMe19Wnl41CFsQ/zWRC1jr1sA+cB/dRP1N/Cfh8euA5Xwu3TpAgA4+ujJdsaNuQJbhRZbZHQuOrlAT1paSSOul12q+jGr7XxjW0DnmlSY10YqxJV1hp8fMkGm+7PGCiRjpJ46gTQrE8sUxA+z2GD9rPbF5unZ6tTJ2KxlVhbJLT67WnP15b25EidsxV8e/RkICOE3XXk1V/FJk54BAFxljkDrLbYhi/nhzsFetzKoSeA/xfTp0wEAXbu6nn2FuYOnSSjThFynki6ZH0qilN5pXXmwHFe1PrU0VT2O9Dy8B5kCnReUXVImSFmotnJcaVwt770zndUS556Hc3IAAFeah+nqYm8J/Ef/Pr17u5gBrc2zdrjF4CyW2F7q40l1eNR/XBzDx5QWUnodmZ7A35OlASOkZmD2m5M2y1OmEAj7SMC8WMq+WIDDI2dK7EzCPrL/kjoU2xdIGVb9EKmFmo6X6aZdSgs7xhm84oor4OHhsYcihfhAs7WMvWoB5AP/1R34wH8eHrseDMx88skb7AjFsajrzDLjfMpsCVxoy80lWBwpj4vfo8n42G8uhlXAOkaNewKPm69bSiboKEuNh6FeARkgrvTTVUU0LkwL0/lpw6UxWaXyyLXr7GP7ltxCg1IzVFjK8uewTsIEqWsKlleIzlLRqGOiq692VrD1nQDCNmS3/NkJ2KsWQDUF9VZamT+R88+/xc64Lvw9+6XSuOphJC3loKc3m0JTKSy1+EpD5Dqyv2SGOCzIIKmUn84KckKJiwKuZaoHYB2kqvQYF+OQ85danbG/5psO0GRjgui1t7Y8SO+poM5Pr17OtJXWXn37/iSSj4yK6nepro++VzIvqluqzE/SUr5/vs+jpRz1T3eIHci1C9QKjGxLtn6nMSIXByOHd+UXgj3+bwCAZcYAsQ1YDlWAVK+NH6Kuclz1qJRR/fKMMyLP8mQT50fE6wZ5eOyB8AyQh4eHh0dt4W9/c4vOiy7idiSXtOoqk8tY9UngVsRFlu91083RbXsuMikMqoC13jIO/q/dNZAq3dYwTn7JDqTH+EKmL4XlchwIV+cax6W7lc1HI9VjkuY/7KepM2W0DMFnSFjaSaXK8uj1XNCHW7W8UhUJWGFXUE7OpwCAVKoH6iU8A7RzMXmyU/yjbsCwYY6daIVCAOEgTsRcr8wPGRuOq0Ica385WbXIZNllVv7lUh7lYpX+1btvuoG4xuFTJqAMFlzQRuXh5lGY91Bv1QplCtSnEeukukJEpekAdXv0UQBh0MUd8RdUF9AyWYudAAAgAElEQVShgzPjZSyzk/o6mz/69VEfOWyvOH9OcRaGGjldLaxUL0b1z5pKqkwQncBp+fohS68fmZqigPkZYCl7mX5Mu1t+d9fnjVc8zfooxxHvxas1/hnBZ1FfScrYJliO6QY9bUzQ119/DcD7DfLw2CPgGSAPDw8Pj+8KWn11764BeFS04RJcNx65DI2q268wBkhJEC6kyX5wIa3OXbkgH2r2Du0SdqC7LX8Pst9buBw2cA9eTayADAX+YMU8QH67JsEcs1BT3R8N/aWuH4LFv1gqpUxaoDDC8hYHwqgqU2jrRZ1Z5OQkXbmpBOoVvBXYzgEnA/oHGTbsVjvjRhW7ZVdJqZNAcOxldnQquWl87AQAoLsxQJxyWppou9FoG5XueZ8SOB2lSrMgAjL7h05nZYH07QbTCrwGIGSbKBXz2VZGcme3PAufJFOXRYcyf5dddhmAcGhPsOjme4u0/eSTTwIIfUZdeLTTe1HdKbZTkXFClcZ2qMms+tuJ8/TN95W0lH2mzFiYZqasGufPKc7SUFlH9easVoFA+FErzDD45WeOTxMXVdKNIH6IuspZPitZSY2iQPAZ2IZkjnpop2b555wDAHj5n/8EEL5Lrxvk4bEbkYL3A+Th4eHh8d3QsaMTdX4wdLAd4ZclTsTmMlOX4LzOLTu5luQCmcIgF8hc+nLxqgt9phQRz+TqdxBv/1a0OrrvuVHS9JsQKp2ZpLrRmJ9F0cMBWCc+ca4cD0ApMhmtItNFGRmZ5koa58aUnqJd66ZSJ2oN9k54Bqh28ajpoTRv3hwAMGzYL+2M62CdTRqn5EmPzZQgNf4SBze7aTK4k9pOuRHYDHMjR1vSGZgVsDz6M0AoZbs7p/cJ3kl1OkJGmKU5jocsUlNjkVSXQxkLtTZS0hYxv9VXix4vHTUKADDBftdVJujvf/87gFD3h97CVdeHKJEoXyXWq0pk0ltvARzVV5T62lFrvLBvuCPqbVn9DKmODz93kHy8XjdO0l1Vsqd1NIZzPv4KACjJMPuNer4iS9U4uD5aV62LflQ1+rxeH/RpfvsJeyhavJ168Y8BAAvtw/gvswr98ssvAeyYd3MPD48dxFZ4JWgPDw8Pjx0Dt/sPOUQ38HUzU7806pqTadJStxxVAUwFIXXdoRwHUwvDhTPVjEw9usbt4WbzDaKR6kVvaKb9JPOjrj9Yt4SlFIxVWypAcSQJWircxVFbOW0NVe9njdRpUj1hgLwS9HfDAw88AADYf//9AYQ6P+ecM0ZyulZmh2ZK6TaqRROOSdXDC9+Vkp9OZh1hvziQSMEqk6T6GOGk4u6QLqWrZYvOE4UZg62pPUPUE7FyVupZmlCtDSXIOTlk8xQMZDKaxcYEjbdo6Ndffz3qAqgnkkgkAADNmjlm5zqz+lIGKOwbOitry7s+WmAlkE3JjZwNU07e7Dvhh8j51mEf1kjtql+mfY31Vc6G+RNSTvo9WAcSLvn2DOF0vjRSd44H1T9TdpLneZ16p9b8GRaOHGhUhmVF2SmTVh8zzV70Y8cIdXvJmWQ/9thjAIBLL70UHh4eOxl+C8zDw8PDY0fBrdljjknZkTctpfjGVWHcklfBZeh7ADIdHajvG0K3YuME+5SZieWwIDU+43FZwVekfSiDaOt8FI3V9Xq0LnRYqwyQbsGqMMOFeJ/50XwaFLgy4y91KkGwleJMDtzxnBznKT+VohfvvRSeAfpuOPDAAwGEfn6OP57WXsqJOplR9Wji9FyYxnnz7WYSbwtLE3ZcHWrNsVT5Ig3HFOo2RFkbIFPq1mvDQRTtSUlL1dJGdUXUIk37IxkGtomqTioTpEOa131Kv0B7OANE31Hdujnv3uxbfzjsMABheyprEbabmhWrvVd0xo8zUi6V4zpp63FlfqhvFmdkwfuwr6kOUJRPdNAnYd0Tci9+HNl3lFVivvXyW8ef+qDK2JLQOrJx+BBUsGJF7WFbciBaBe760Y8AAGNmzwYQeo73OkEeHjsR3hGih4eHh8eOgg45AcbXU4cIKoroAl11h9wi8CwsABAaa3GhrAKTGm+xVF28qhDYV+OYKFlie/QpKyBdMAsYIFXi4UraVuila6L5C81AZL0JmlzIK7tFKNOjakvr5XilGcGEhhBuad7MDB5U64ptWhUnt9fCM0A7BlrmdOnSBQAwaNCzdoZdiqOJXd+NFOrFJOyo6uIoBaoWOOzADJbH6w+R8+r1V+linu8ox7PF5VKmRYMOtrJnKgnuEnprSYfqTdAaNWmp6jlpv+R1LF31M+J0gdQKqbnFDtuQSmFPRKdOjuegJeGTxvwk7DzbRd9HOHnqh2b7ceArTF9GGR9lmLSvaABHbnRovC7VAVKombK+9/RtAmVE1a0b66gEv7KX2kJ8xqjtWOanW5nYDNZSv8KqcLs8ep7esHl66FDn1Xz69PEAQqvSy8zHlYeHR3Zs3rwZxx13HL799lts2bIF55xzDn77299u/yLPAHl4eHh41BSPP/44AOD3prDdyo6XBH8R6iRH3cBGg6s0M8/PFPLoKkRdECQsTVqqTJAG1WXKWPB9H7U/GEdXvc0K/dI09gcy1JySa6JFLQsyumcvC9pgaaTOyl7pdrIu5FWdIBHkc+5W8izV7W01TFHDEt73ThMWb9lDhcV07LPPPnj11Vex3377obKyEsceeyxOOeUUHHXUUfEXeQaoZuDefO/evQGk078crnGOppzsWmSTQ9JYEw4QVR2Ik54Tluo7U6+6neS8SracenIln0r56feKk745eEqktnG0qtK3qtRYZm1UbG2kKpSqA6NWqvyteiKhB2PnLn7CBOchaE/xD/TMM88ACB3KzTnySPfbzuvkp2xfOMnqm1Hbw+i0x/fAVJkz1YvRjQvdSWA52pd0y6Iqq2PdDkivg1L/PK7bHerFmtB+r4yQOqTT8alR4wOWSvdp+JsVEYUotuGAIJvbqjjvvBsBAC+//AQAYPr06Xb8PHh4eGQiJycn+B5XVlaisrISObaAi4W3AvPw8PDwqCm4Rcs1HgWT9DA6QCarscLOF8jGYzMUAAh1fpTRUd/Guu2vgZ3LTA+mLLg+GgC3u1lWnap2K7IfmvUTqh4meFNzNkTjEzI3Jehmf0XtvljHxlY3FXg1WK/qBGnUNbaJBkiKc0ar59XD9C7aHao1bN26Ff369cPy5ctx7bXXYuDAgdu/wIfCqBmon9G0qRt23//+fpJDeQkO1yj5SHaDA4QgG8uraURCdkTHGzvoIjmvVGfC0ljdhe1AB4HqcoQR5ssi92BdlFaNk9LL0DNSblFQkpsYyXAoA6XSP6HOwkKGxE0bo0a9AWD3M0BTp04FEFp9BVKM5EtYygmffWIRFKrFE2fb5FqwwCbhQfb+lFbnb+3JTMvlvDJ06oRf35N6U9YP2rK0vGoBmLRU+716cta+pixhQu6tPC4k38Hym+XnafA+NY3jw1MHyF7icDvMD9jrpj/3+JAhAIBr33KfwtlmJTZ06FB4eHhE0bBhQyxcuBClpaU466yzsHjxYnzve9+Lv8AzQB4eHh4eNQXdM+hGq7IM6uaAC94WAevhUo1apU5beR91WUCoZlHTgPuJ1oeLX/rmafsPlw5g2KBETMHp+6a6v24r64WWUs/oKyii6vWtrI5xAYhpNVaYESSJjnVdiBcyQRSgqZDRzi5bJ/vJvE870WWiryO2vfqHrivIz8/H8ccfjxdeeKHqBZDXAaoaU6ZMAQD06NEDALDvvvvaGdWMUY0U1aRww7/Q9FDybM+f4Bhjd2dHTshxSv3vSenq70T1NpTlVZPSuPhSQKaOjQbyE1cnsQp2ZL2o/xTSwwSvKI7cV6nwOB8tGl2cTAnbPJwu3CyWk/NrAEAq9XvsDnTu7OJY0dPz3d//PoBMY+E4N3Id5Xycrk8IcnZsOffJWGmTadz71b6jk3t2rjOTsdL6ZnhTNpBESa/9V5KqVVactZd+THWrgX1F7eW0TvzukanJMBtm52YTs5OrAyFur5ijok7WyJ2s4n2ssQKXgEe7T9uR77kR/49/uK/2GWecoTXw8KiXKC4uRuPGjZGfn49NmzZhzpw5uPnmm7d/kbcC8/Dw8PCoLh56yPn7GXfNNQDCBWzc4lEXqYRukerilIvcOEeZ6gRTGaA4dwoqIP2L9zPLrb6WJvhA+oBZKr3a9Ilm22EyQIWBRZx7KjI+KppwvZzJGMU9RXHa/2F5DA2T1x8RtFSfFoaNwvgwzbRe2/Px9ddf4+KLL8bWrVuxbds2DBs2DKeddtr2L/JWYNUDY32Fuj8f2hn2rKSlcX6NVQ/Dddmldj7fdIJ4FXUMiD7ym4Ocd1XHVhpdXqX5uAje2XQflHLmb2UolPpWHRE+G+tcEEwOqvXhxF/6F4pTclRP9qwX5yZOJuECn60Yz53sSjz7rPMdRd2fOy3GlzJtfM4gvpukzFcWtGccjxcn6rj2UD9MLIVmyEk5r16UCfXMrbHFCI0iwLfCSVcn5fR76rOzzTpKGhcQk/kLjH0sNz0ztZbUOiYs5TcxJy5QnVJFkHzqI1CcJ3WycjrZy29rD75kgLMXa/lfF0xsxowZAIBhw4bBw6M+47DDDsOCBQtqdpFngDw8PDw8qovcXLfEVRcEhC6k1QNAHBPE413lN8vRkDwq9MW55wj0XCxNGcPzbztMSytGbn/Z0sG2Sj7a9A06pUeItpustoejD+z/s5TWXZ1NiFMrLQ0IreA3ub0xRkUxSgoZwqd6m01aKgvz1dYGFASWSMo2yeaKYq9CVQxQLa5a6uQCiM6+6Pdnn332sTNR511hl2UXUuJX+RGi1K4uiVylkbl5FUtnx1X9CTWL5HVRb0SZrAzkeDpYF0rfcU5uVVlRJz5ez2fILMnVsqdMGqrBQkT5oswJmeoYCwLdn4SUGGXjcnKcd+9U6tyYO9YOHnzwQQBAv379AAB/M+ZHSYEK+a0fFmXCMp9LfUTrpyFqU7jY2qlU3OarngvfGktVgyfd0lBGiR+BhJSvTB9rz8k4G+J8FGmfpJ8dtsCUoISKyPVsU/Xvox/tHP3QqAMvtS+Oqp2F5/UVsTH5VbfOnbDjja1TF5u+GP2czJzpPt/nnHMOPDw8qomqrMCa196t6uQCyMPDw8MjxJYtWyK/ueZLWsrvydJA8Ig632hlC2wVDfU3U92uJ0OkfoF0Oz+XejCySM0x868hbhcxWHNyoc1tUjJBQ1hMmklU0/XRvNT9KcPh9pdbxh9thgXUzeHin/fkOvi9yFXhsxcak9TM3CKUWY721oaqjP+pFdCDSkiysl9dHj1MYZTZaRlHI5sicKHtDDNSqf9hr4L3A7R9tG/vBnGTJk0AAL17X2FnqophrnoYGqc9yn6oVgoFRZbOjrlCjqvlVRwdrWlc7Skhp/eJuAjYG+U8U84TSalz6KCMVl9RsZl0sTo44wTH8pQZSFjKNmBLh20RZzelJav/7J2DDh06AAitvuLsCONcyKh/nPBdKaGtnxa2Q3Yf2fwwaT2U1FDWMGqzl+l7h/n5XhIx5aqHa31b6WUpW6Qfz7hRx7JoLlwu8eoItinzKwO0zm7QUh+O0P0Z3QNpfkQ0f7+kS78wJ4LaeNyGsS9Vp2Pch/bj+e5dN2jQAEDoU2rEiBHw8PCoAt4PkIeHh4dHdUE1AHVMGkYgp7GBqrdXWr7iSMoFe09bgFOAUnFEmSENZ0Lkqv8FlQ5tsZprkhldDlCgMoOugM1Zakv6zmkuS/jNLAmct5In4k1coDGud7ng5jqZx8kyqUJFuOCO+jJqa3VQ4UB1id62glQXX12KULCmyES1gaLgufgO48TgOg5vBZYd48aNAwAMMKsL+v1phbkAMiVOgh2rJKB/2UXVmwq7rOuSKnXHKRKqhYwKmqrbow6tklIuU/XVkq7vF7dAVmsfZQA0YF9ZxqByd2fwQ054qscRbSlgHjpH6rHYznQzBilpx0Mv25yQlR9QPs1NAw8+uAEAMGrUKOwMkE38s0V5Zy2Skk/ZDbVMUiu879nzN7ZUfeEo21dkfbSZTap8K+rJmVDlU77XOEXOpKUamZ3gR4H9RT9sej8gnMjVj1Wcz2sdD0zZI1h2EB5BfisLxuv5TC3Z6bmHwQrqQCaCj/MHLm3EVujlkgNs5B3AXrHZJYOdlRqo4mOMUO+73Q0XjHHlMTyFh4dHNeCtwDw8PDw8qsLDDz8MADj4YOdkowj0sKtbzFwFHizHo1uuoYiSAAAsNU2UxuaSgCIj05WSqko/F71HW4Z2vL0uQpOWbky/e6inQ/2XpcEdHLtTGBEF+Sx8Vi6plwBZcuk3VrdUlZmJU74nKKBqsN+nLGXNGVdNN8FVQGatCzKE1DDQ0V4JzwBlR6tWzqcK6d5evdzw4JBXCTOkgcn8cEBoB1LVPodlxixprC9CpW21wFLPtmpmqX5TlMFS6TxdB0j1gwheS1lVByvvGU6U2f3rRm2xMg1qOMjnBW1LzzTRSGkF9hQFGVwJ76s6Mivlt6vJ9de7KbC2GaDJkycDCPXK+G7YfjoOs9sLZvaghOTX0AS6A8ByVgjzw3LY/roFoZOtTta8r7rPV/aE4POrIVVcCmS2SZyNpX5Qop+lzD6nLFvCUrVAVGKnwh4219Ku9k3POcUy8OH5nSRTdKKlzfnBYcrW62CpeZxv9IlLD7DP9AEv2QM4rurwh51OUeGpzkM0rVcvueQSeHh4ZMcuJIDq1gLIw8PDwyMEF+7hNpsKdfpbtXR0ucr8US+Qi215WWr6LxromYtQLtw1fBAXwSdyLal76KJfQDaFrhK4yzjehNKyjKAuQKbphXI97hkpNJDrUkeefDY6bS0yZ6bcxqYQwoU520BDtywO9K+i/rALTKOpm7VlnFPZMByRmpCo+9u9C7uQAKpbCyBG5KZ1BfUk1PU4O3Lo8jxhaXdJVSEw6lWnyLrmsyardjbrFH05qrMTZznEgaWsgcbLimNt0hGn/sbBrRR0JpTjcHel7o96r1ZNnZA54BRF+1al3AltDU4/vFNU9bAVlkZKU/btu2L8+PEAQo/Pr5x+OoDMyZC/OSGrzybte0pK62+1imP76uSnfJk6M46LG8dUP3O8Xp2+8X4LgsnavZ9lYv3HNJuenbaNvmltU6X69d0mLFVfRNGAAyGKJZ++oyTLNS97CQbYpKpPukkbAJzNK3Trgf7G8uX8SZYaM3SUqbEmXQ1Of8TFBvvXz/4JAHj0UaeMe9lll8HDwyMKzwB5eHh4eFSJvDy3lA2dwarJRKbI4tAV2aHiHFeHTnOlUMwpisSL5NLg/g7dTJDh3U5Myu1slZqqjN5dw51QvKJd17OBXRg1atKvVkMKJ3oyjA+ZHXXLw6qpUMDrVKuKC231kxnGDmshaTQ6WEFG+GBFnF9vFXP3LngGKAYc5EccQX8ajuEpC5qLLsrJ/LBLchh1l+N95Te1EigasgMmAACFGR6lozJtpTFEcf5PVEepLHCoVRapRa7k19/pYA3UKkytjRIZdXD3rLAJivdgS6lnYGWnMuui2i0kijn4eSUnpzjbOJfGDe2cnP8PAJBKvRGTo3ro2tU9aceOrr5qFaXtR6iDN4JTkk7g+vQJS9UreK7dKF9Gftz71dYk1Gu5+vPhCOB9Q4/OgboqAKDQJudKLIiUp06U08vS7RBC2Si1pmTd9INCxKl6xsVJ47vgdRy1bKtlFnJgiFPNQQ4ZIT5I99dcehjvpJzsSZImLF1lqekK5ZuVmA2JU8f+GADwzP97GgDwwAMPAADGjBkDDw8Ph13oBqhuLYA8PDw8PID7778fANDXQrZs27YNANDKrLVCkZBbm7rPp9DNVl3Kq/GCqhEQCUvd0r3AyptpC+mj12evTVwQZ4KLWTJBSXvOBZGc3FaPCqatTDBlTdUNCZG0tCjQvXG1pG+k/iYs6lavxtINBYByyaF6VhqgCJKvXFJ1jOLA7fzrr78eewO2YddpN9WpBRD9/nh47Cg05lfTpnsnjeyx56JFC8cLtmvXbjfXxMNjz4NngKqEOt93KMqQK+JCPPK3yQQtcuy3bYmtVwNclXq4PqXEURmpjXoEVfN3llpmV5TZir9Ygq9y+4RbCNk8XsTt8Otnncq2uhut2wfcuOL1fGLdbgg3BbiPHRdmU1VXVYU1ul/fzNogfqutdhcsH1hAXa196EGXdy2L5GO9VLFX5WWVbCUIdrhlZQdy7SVvlH0kzU/wPfItxJmgx5UTvkfVN3A9och6XalJv9kC9sYFZo3bDoScV3lX66qjOs45ox5XT75MaQ3PntfYtsQ60UqB+4J5thXGB2zOrXUKYnzSeZYmLd0crbB4ET3hRqfJ8reb/oodBUO2NG7snpoMEGe80F0F1QLUtaV6tdFtfZ1Z9G3pJnCwmSvHzSmkzZUvW33okCQhuXV8ENo3wi3YpcFfcYr/WkN9AvaDIjEEUCcVuslflR/mSpvLSjJqEBdhbYX81pDVbu5sJp6ofz16NIC9hwHyOkCCCRMmAACOOOKIKnJ6eGwfnTq5Dzs/IB4euwv7778/AG8V5uGRDm8FVgW62Z4u18eFQRgGZW4I3XOVKCzltjIP6AZ1bcgT3EVWV4tO/k7YL0o3VKUm1PldpZnxM/xBSZC68rnSz6YEyicsCRS+o+xUM9tzj+G8MoJjxnkDIeKk91BqoTc5NVpWJWdabyQtpdjturxKV5nPWbvxb5TBCXtOlH9bb+9C1WGVvVB+i6XEuUgIpFSJm5JnGZqujJarmho8nrB0ieSryl4kPM6cGvY26juF90vvD2xDVZTW0UNEvaJksozqXyUapCUznIsqYfP+agelYLk5veUCdmkGZWKBh5gsv/9syUgLrG9dsslaSwcTHzTOlXANoNZfk0w4VFcgbJtW1n9LgtYjZ6hcL6E9WbUy4lTe1fKJPcdZa70F5yxSGcqqGJ841yLptVZuSxlBfYJM1xqujZaKi9tuNk9zXucIUYMJDeky1dKSYFRqFF7VSmJNmZ/Mj6t5KzGWIfRrV9fhGSABB3tublyoPQ+P6oF6F4z95eGxu0DnhS1bttzNNfHw2HPgGaAqwBV4qJfiGKGiDH38RZLzaDlv8kOlNXelhvqk9KLGyKrx0db+d/VIRI5mSsC6Yl9uEkbIdrjtmbLAzJ9Il5OoraMSe7ld45453+qkDvgI1q1Yzqt8l5TreL6z7XAX4m25Iiq9qI+OZngeQKZOkgbI4FO1CvbTa2dobOvePVLLTKW7KMunTA/bKY5ZCUNbZAefM0d9JQhNmGOPm2cVDX2MRKH6Z9rHVIdLzeVDsO9H9RL4nlRDBMiMA6W6a03lt44H5Vf1uDJKyikqM6SzQJyOUgAtgA/0X0sp0vPGh5CmeytaUUIrpgR0nAueGqBhw4aR3xoMF/I7fF/qmEFdYmrP0ZERF3BIr8sebmi+MUCctb6SXCyVfYWl6gycKykQDh3Vt9MhxjLUTQKx0eZMtowy+kMkv3JqHPO8/m82dy0I2lg9VWsbRgPrtDeLN9UrjNOdq+vwDJCANK8Oeg+P6mLmzJkAgB49euzmmnh4OKRSKQAhs01dx+uuu2631cnDY3fDW4FVAY1nHPIxziJApZ6SDE2PpJRImUF1f3StrTIpy3HiOSWAOI6CpSXkN2UlygdfGedDSSIM5sorgVCM1Jg41LFZH7lCmRWV/JUJ4ZOydA1RSl6HdU9YnZfBWc4UZWhy8OmWR2rL67WFlVlhKSWB5d13g9qk6fPSdiOuXir0q8cOQiWZOC21AKr8ZBdsXB+tr/JrvC97sD6X6k5ovRiXqBiLAWQySirPp+tPaBwo1UuqCiw7GrEp0yJRw4Wo3VIyJh/Pq/NJ9ulObDwSxnQ2o0SyRszRoacUg6ocEvZu+7/iwsg8f+hzqClo/fXtt9+mFxlA2bYyqNJ/TEfLYCUq5Lyy4HGfKh5nr1hv9XBz2UpjvbVpCJ1D4/TJ0n9ruBjluAg+EfsH3eEmLdW2I/NDJijH3nuFdaA8u3EPS7uviV7POXNB0JHUAo9QFi7KH7Pe6pR2b3PkkYL3AxSBZ348vis6dHDeean7U7a9zB4euxA0YeeixsOjPsMzQFVAtfqVCWoqv2F7uiUZWvYJS7kyj7Mv0aCp6hvDHV9ve7UsXY0+WKoG6FBpnFI8w4MuMmmpJKsFFKUGJy0wiCj3qVUqD/2DRO9FoZZ116CZqs8Ux14lLC0KpBe2edTvqvri0CdTX0ihRPDdwqK2sC2w/0hpcTGI9N0ogxLHKqgEo2Ea2CfWWUEt1fWHIbUiep1GOeL70sCjZCNVD4f1rJCUz0t2VQOZxJWTfm99h8roxOkhrZd8Om7yZF3QtjJ6nsZayyWNI2YI9vGkNS6ZgDySqAfLBdpohKp0KG2oCm7qEKwGePzxxwEAPXv2jBzXZ8wsWhdXfAi2UjLmjnGcpb5VSD59+65m7W0u03fD2qgWTFUfwvTxqXnVwkxbgE+mmp76pBol/hB13Gw35pjJsUFTvj56/1BfUq3Colpt9Fytz0HonLSr9GV2FbbCK0FHQAlpy5Ytu7kmHnUNf/nLXwAAhx9++G6uiYdHdmzevLnqTB4e9QReCboKqF0RV4sqi4Q6QNShoUjHNXUc/0Co7EAZQ22XHArseLFZOgTuQyR3fsxvfS7xVoRiY5jctQWRGqr3ad3RV+l7iRxXeVCtM6JxjDN1UMhMkLUKwRIHRXIyqnSh1eB74mmZ9VfdpM7fcfOKwjdZAm0HZXSivmDjLZLUAor1VklGdY94/2M0RuzG6M+kXEdnxawvL1dWBZKPKesZZzGlLCWPZwsGqyovbBMKyhqhSfOrzk/AxqliVttoHRJ2o6+WR8tLSj2UxSTYFxJS36H2R64qk0B+K2WlClmanxXkS/+3pTVYm7dp4yK30oS+USM3hXO8KruWyaDEcZqq06PshGoD6szBHsdGY7mk01w5OokkAboAACAASURBVOdFbVczdROVayeyWSOqhmacjyHt89lYTSB8Ij6Bzp3q1WejPYSOcWWXmxlLXxZ4v4+yYurnRwM1qw8w3n/69OkAgPPOOw91GX4LTFBZ6boQFf48PKoL+v2hJaGHx54GeiXnYsZbg3nUZ3gl6CoQ50+E4OqxLBCtKKIl5Mo4nxfqbUX5AMjvqL1KmXE3U/EAAGCEndX4NyoN8a6qU5HNUqKmPi/YoZTxUM/BKj11lVT9d5B5UEYofFqm6ic4ak+02N5Fe7NCUkYizu9sTaF+qPVN87h6pGbt4yyMVGqL00uLi8j0lb2ojiui171uKZk1vi8tJ07aVYYwznJE5X6+NY6cdjxhL7hiTXgt24BMjxpVqW2L3iuOCc0QidVJlTXCMZa+LBL4YrN8am+sYZzHGtb7DEsDH0mZZqbRDDx/ECtKLSLTSvqiPHp90lJrmApLPzr1VABAt6efRlVQD9DjTKdNmcBMq0Wnf1KUEZsvjitSnk7tHPmWlDdJSLnZz+q4jtM00r6jaTrTqVpHcZakysjomFbGRc+zrpwTE/JbmSL1xs58ecb8sCVVzy7qOS09UkCUhy4hu9Y2W8yAugfPAAnI/FRU7Kp1ocfeAkbe9vDY08HYYGS8PTzqI7wOUBVQR6q6K12GbvZXQlLqAMVJQQRX0uoxQjUgVBmAMgW9MTsJtNQkUF4dp1oQFydK9TCA8Ik03pFKRmotRGk3aWlRjJSsWk95dqM8q3QPa/T1dkGmHhafLiG/VXKkeL/E6nOs/V5p9S+IXL2jPi/eMkk5ab/V+q0sIxJ0SaR2qjWmYDspv0UmhWwIn179KqmPWNaP0t8iyU9pkFY1ajWnjJRqralVH/uJkiztVG2OUevTRO/SymgZZNX0GVRnQbkF1ZkLKscDrJwqKCUt29vR8ninInv6crOuUSMtFsd6sv494tx+60vGSZYySrxVrONTLuVLo+LWe9HDNTEG4zYZDUNYNfanOL9T7HeNbTwVxvLnelw1LVX7RuO3q0mcQze7L7vTAEs1dlkcW/iVnFe2BsicWdTTUZxnMtVmItTjeJy1o/YnnSM0HqAySzqn8Z3pWM+MNskSXOvsLeF9fCgMQXm5GwbffPPNbq6JR13BI488AsBbf3nUHfADRgVnD4/6CM8AVYFer7wCAMg/4QQAmb5tMtfclErUn63unKtGh67V42Q1zefW7j2NVVENojivvHpcWY/0OF6HSF71/6I79jxPaabI4ow1E6Yjm6dfAKiwAnJlc1YZhFA60jZTeV+fklIN4d5mib3LEvs9ceJY7AjidJZC5ifKmVBXonGa5V36dbpvr/GsOkpKqNSp0mfoDdxB/TWV2HtjS6sUyd9KUijrqJ641cqNxwPwAqvIp2kzFPWTXo9myYgWJWHOMvTMMjgJVv4QSWOG5+C3o9kKROYvQ2cAQDPxs0Iozxvr0kYnnH7z7I8O0QtJ25H5Ybi83PRa1Sw4PJkfqgPo+2RbKsOpz1aYwYfE1YJvTz1RqRc25XSYvzSSOyG5WN/XI7kzWUNlY5SXSofOXdrX1ReXWt+ybnFx95St6ii/9eOtns51vo+bA5YGc1PCUs6ROtrdHegwePz48QCA66+/PuYJ9mx4BkgwevRoAMC0adMAeIue+oyrr766Wvlat24NwHvX9ag7IAPUtOneFtzAw6P68AxQNUFJjyt6rtyXBkcolXCtr8yQ2l2p5ktV8ah1nepsjL6HeZG7U8rRaU2lGOWr1LdMulTOZ2U08TzRxeH+cZyk1ExiXakkSSmEXnaDe1dG65S0VKM3Z3oIUrlHxWq1Y1Ju5bsNiTiNhVbG0pXEWPgVGuNSbu0V5zuET6P+d4g4b+CqsxUXY0yPa4Ro9V+k71vJC5Xf1YIl8MFjQ2mjKY89ZIdJagBhH1gQsFONrY5FkXtQftW+xjfNNgl0gBKWplOf6b9F1G93lUtHTOJp987m2VM1s3etjKo6bGaxXe2Zc5TUUKUQBoWii2C+NNP1yQigZ9NOJ6tI/xqYvFANgLpAytyS+WHVqPPHfhbquehTq0809drkHpr9LvSxprZU6k/ZtUnUTjbUBVKWUPWwtMn1LumzgnqTVkZdQ7ip3hxbgnVKWMr+SEJPy1XH0MqlxbmH4rvSZysLxpGOCGXrom2dk5MDIOwbdRVVWYGpr6jvgga1WNZOxzfffINvvvkGmzZtwqZNm3Z3dTz2YLRu3RqtW7dG06ZNvUTtUSfQsGHDyD8Pj70BL7zwAnr27ImDDz4Yd999d5X5t8EtGuP+1Sbq9FKR62KupCnRzY2N4a08hcb+Uvld15rqZ8GJdq0wF0Bm1GDlndQyq1jOJ+QuGtcn62YOrXJMfGhbHi1D/YIo78LA1/RgEuf7WjV5KFnGeT0Nn1YDIFXlhVvfWSKj5B2B6kRpD8mU66JWaiXWovTiGhf1PXsEpEynwWoRFccGqq1cqbEahPYV1cmKU2NRyyeCkvpqq9AiS/9lx6mrsSCwtEyvBUtNAgCK7Km6W53VZjLOM3rgh4cZKXqL1VfQiGJGdq7EYmprzI/2OK01QeKGk+0A093JbWMHlOSIc6XDQcJGXpk9X+9VqwAAZWVVezknA0Q1gNCnjAPbks+g/Gk4w8X5NtPIWO739+wdKhlWkqHBEvW01dP0rXSeidrLZnIaKyWN0wmpif4UEafJqXpUnJfVGFDZdZ0zdazp2FZWq8R00zLfomrwqSAXvQPdJ+xJIaO2bt2Ka6+9FnPmzEHnzp0xYMAAnHHGGTjkkGze7RyqYoD2q8X61akFEF+stwbzqApkfbwOkMeeDsYCo2Kz9wPksbfg3XffxcEHH4xu3ZzAdP7552P27NnbXQB5HaBqQvVXQpd36uuTOavaWY7TR1G7AMoQTjQl40MpJyGlqdXAV5KPkdsZe4hsDi2u1AMHkOafR6razh7taMug0kfck3FPnpxXXLy1uH31TAZIo0WrVw7eST3LElV53qkZ1I8I71oUSF9x0lXCUif3lZlPjlam38KzavWljIy6Y4xjfrR9mY99i2SCMkSqnUYGQK1oeD3rydhuXSUfr5tpKS29CtBTrgAyY8dHe8cyYw/Ucy7BZ2ApAdNCaBcgsxIXqM0e6pR/uFTHnUr0Oo/oeCV3eagqqrDzUyFKXf6SKqDnZ1pS2g2/XmEX1MDBK12C7LdfVA7mLeN00JQRjI+3zqd3D8kYfeofKmSC3LstM+anlTGkfNdDLeUcGcwz9o6bpnkUTy83k6G16+R3+vk4hlHL0JlFdYG0P+r1yp4qsRdn70rwbYf+fVhTvWN1+S2Xr6TEvYs9yfrrq6++QpcuXYLfnTt3xrx587ZzhbcC8/Dw8PDw8KiH2IaGKMsQGXcO6tQCiGENWrZsCSBzxR2yDyoP61o9Tt7ObgmUqUOUXTpKWKrSdNJStcjg3UkGtpPbql+IbPuiPfgoErypnd10wMfpNQ7vybryCSl7qMVCYBFjaZw1E1so9KujbattHierQs5nvt3qgJYQNyUSAMI3T3WS0Fu4PlmcNYtDZ2F++O7V/48yMyoNqg+QOMsVlq96Yho7SVkLpvre2QdVj0GtwVRbriB4r9peQCjX850lkI4iK32+WUfquNU2zBhQBCsd96pEoaSdNdoQq1a65Vo6tDj+zuBGrBy+m0BHSd0Si7vv1Xac7yL/o08BAA2MzaFvnz1Rh8PD47ugU6dO+PLLL4PfhYWF6NQpw8uYoAEydUN3DurUAsjDw8OjPoOLI26FffYvp55ebgFVVVhSkS90pMklL5e4bqOvm7gu0G1LFQm5NQvb+lL1XV5HIa8lV/K2wo4zgVDl/jiki6waYiVOGOH6VNer/K3CCuvOZ9OtVG1jbaNsdY3WSDf24kxRVGWccDUvLlYXkLsfAwYMwLJly/D555+jU6dOmDZtGv72t79VcVUO4kOd1y7q1AIoP9+9eDJAKviF/hjUb4LG7lbbKN1zjbOQ4FBJRkpXD7cq9auUr9xC0J1l1K+3EakRzIGQsTnD8vSgdCyOfPQJVcdDvZgSGvYozku1+p/pbKxYYRbPIw7q8Sauo7MG2ZmY6iI6vQNlGGh/DbJUp0fyBK5FqNOgbAXbjdyHyjSqk6F9QH2IaCwn9mB9T2qxFOcBWq3M4qZGtUxhftY7Xg5Lr4n6bNJPhyt1qdW2Eq8ByIzpF0Dnd331i+S3KnHwZdjL6jg/WmM+qxI42e3/QujTrbNGWm6psqeqC9JuofOqpb58aPK+devWSOrhUdfRqFEjTJgwASeddBK2bt2KSy+9FIceemgVVzWEZ4A8PDw8PCKgdSMXT99++y2AcHdQt0IR81vDjNJppS60ueDXLVsNgKHH1blgYFxhi8XS9dHrNJAp88d58MrmHlVdPORLqpvwus2r6+yklEuogoUujOPEad6PwgUVyIsywpIQarqiNYyKpdxK3dNw6qmn4lRjKKsHzwBlRYMGzm8jBz95HFq8hHv8GilLORkOtzh/uTokeJzTjCuHHTxuktDuqndT25lAj8ceLGmiKi1w0nUYKD3zXqfYNeo9WnVA1J5NNXRYpzhf2DpEOUEJs41MrRaiQtI4v55q51SzAUEpmvUuCnR+yPzQaxOnS9IK9HTjGl91cPrK7zg1FNXFYXsrw6LnlZZnqiqB6k1ZJ2WeZz9Rel4/WF0l5XPwvu2N2SvKeLL03HHvLGo7FDeN5+jDiC5NgKRUgV1ITesaR2ujUba1j+dJGsfkMj/HFtlFVlM5z+EFznsynbeyb9JzLz9cnN/21A+Zh8euQQPEL30BoPacINepBZCHh4dHfcT9998PAOje3S3JyQRxMcVFHRdvupAOoS5BosKcbn3rok+1U1Qw0sAaTJOSqhuNkBVxUPFJzVCIdLFI86jLD92W1m19dYur5hpx/Iwu83n/hNxXdZT4jEWxHFScqwIVd9yTNmhwEPYO5GD7AS/q6QKIVhKUkCh5MchyUeCjRLuqar7o9MAOpZoYUWKW+iBU/COHoGtV1etg943TlMjY7ayMXs/JrQSHB1lKglKXRi6Ns2miJK8slMbFUY/PWuc46ATazCh1fdYwhlAzuaIqC7yahbNgXxn70UcAgBmHnmRnyHHxybM/IS381Motjt6O0/Mi4jw2q9Kqep8lyzBfjqsXZUK9jauHkThfs6pmqdsIYR9dkXEkU1NJPz387Xp0nOPklB3I0Vtp55QvUwX9ZimdZsc5fpSp0Q+eqgUTGq1e36FuO/H3r7/+2lXTlPnYJ5mmUikAIRNER4je0atH/UZVDFBVFsTVR51aAHl4eHjUR2ikeDpCHD/QKfUHTiQt1d3EcHewKHJcxQwVBVUpoI8cp4hIpQNVnteFvIqcmqpDVU3VUWu6oKF1heTVzfiqdO1VqFE2TIUQDUzLNuW7UX2n7sFx906WZoithHJN2QOENGp0PPYOeB2grKDpJyUo6v4kgxzK/CgXQxnQdWmyFIR2bA5qZUPOsDQh1/Eu7NjqcZTSPX2xsN7UOjnRlH0qREEwXAunr3zdUU4sjNrOeyUiuTLVIzRemQrZ6kWV+XLMg2tqTfbru0t+ZYDC2GJlVn+XlsVq/WdTd6waNBcOIyPHRblW1cTGUs9oLfSdaJ9R3SCNOgc5r5ZGunWgWwK8P1lI1T9Lyv2UMdJ663UE9c34HkdYusgUNyst/h2Qbka8NFJ3Is7vlW635LSQC1QIlL0DMj9B29jvfOubbDuODTLF2pZqG6rWabT0S1iqPUnHVvARt3lq48aNkZSKy2r1RR2hr4058vCon/B+gDw8PDw8DGoyz2Cocdt4agEV59xRhTtdECuDROGNSEg+LbcqIwHmU05DF6fE9lzXqq68LkjjXILEmWmoLhDFZ36auVCmYYQaTKjtVtx6PmSitBVy5berSTcTQlR94bYRTkxZt24dgD0rJEbN4BmgrOCLbdPG0RCZehDsatz1Z9d3HasVCiL51Q6JHZKxuSipsmPzlSQs7UQaxQo61G63ziRQSpQcEHGKihxY+XZAdQ5CqrQARJHp0LDzs+z7TeflTvO1wMHJOqt9nOqAVBU7jAdyhGJguXFUOaLZgwmW72KZPVtRxlTuGvWBBy5GTUDpm/oVn3wyGwDQq9f/sxzq7SW6GVAQxOmJsoRK7RO6JaAWReojJinXqfIop0K+5wpjytTUV9kHZZ4IVYbVST06UsL3omOAZs/p0I+Z+iRSYy7mZx8J+pZ2Fu18HKhi06387grJpqG6CjJiMEV5znmBp/eCSK7AmZ9lT1RGq8k26ml9b8OGDQDCYKdcvJCdpC4jz5PhHj16NDw86i88A+Th4eHhYeBCnorT6iwx6tUnfgta9V50zan2SMqO6FawGj9Q3yXuerXqUsFLmR8Npay/0xf2UVX7zHuoqw5V9Nc6UkANw8BwKVxh5RdG6sDyuRDWrVZVvud5XkfDi2JL1bKOaZyaAe9DfbG6C88AZcXPfvYzAMCzzz4LIGQ3iI1YDABYIMOP8Zu0wyidmrBUB2WcT5wAvNB6Zku7oKWNjL7W8080/piSatLSboucDxpOal9Y2tesQjqvcZTSGRbtFwC6dXN+bdjZGzd2tSPzcfOHHwIIpc6kMUKUYjno+CwtGYHbRv1Ge3hOqBxcjDafsvMq7TO6uHqCUbs6Tj6sz3BLV9i7WmYpma3Roz9ATVBa6vrA/vvvDyD8gISaY6yR2s1pL7HyLI3zsKwTvTqtV+YoV85rOUUy6RbadN5JlFgJlqM6Q3o+zp92Us6rlyRl9NK10XSbgu80p7f9QR9oLOQpS5V+ivN+pwMuYdm5HyO6QElLF0lahmPtL928oJYP4WzuFpt23iLTdyKz1NLSPKtnnnzVK22Rkpfn3jrHIHV8mNLqi9tba9euhYeHR1VWYLWHOrUA8vDw8KjP0MCpaqmkRgkqoECOK7PCBXqhLcCbGRuRIfTJdcqaELo4JuLke93CLcpwl6Gbz9uLGp7dgw9VIXS7muAzhcyPhlZyT7XAyh9gbZSQXMpm6QJdn0QZHi7TB8jvuFAtLG9unWeAGmD7foBqD3VyAUS/GiOt7x9s/Zwd6G2jJlVqV6lZfaLEWUwtlN9MT2XPJu2hi1YJtJVn4nlfq+/m4c4uhdYhpLd1kqOOAFMg1Beg91j1KULwPHkPTjyU8HMolUfZXeSZ2Ey9pEDPwh5elRmVP9GmiFPW5OCmVRMt4pR5qClWrVoFADjoIOccjObD77/vdIH69bvAcuqnI+ogTnV04uK+qQJmnHcjgtO2higIPxjqYztaO+2LSpNrn1f2UrcolOWMM/Xdnk0e2ybnOPuDaluX8sx9LrlovEsnmXbOf+00B1oQNl1uquZXBrW0u8t0bzgWujbjh0x5Rw5cVVvl5Ove6lvGAPEqPnxHWp3Z4XVLXZ9raPMT78+ULC2h453zmodH/YZngDw8PDw8BGoNFmdlpQ5BlOWICwoULsjdB6jMVp8bA/cHUahiuy649f661FSo68xmgZsM3aYk0pkC9WqU3flEiT1tvm0nq8pDATrbXwlLyb1Qwym6An/PgvuyLTrZOrvxymi52kZqBcZ3RmGQC+5DqZ6gegUixWy09fO+++6Lug2vA5QVEydOBAC0bt0aADDj4ekAgGFPnAcA6GECZcI6AlUMqAOgkqIGj1azQvUjosHzTtVgQOdYquK4mMf85eWpAIDUp58CyIz9Q90B6vdQMqRVSfrfqlfACZKDgFLnUS+8AABInnwygJBZyVMNPjFRq7TG07ZQAx1lDtTtvPrTgeTLsRN9xLKGjMejjz4KALjssstQHVx99dUAgNdec5OTtkdn81lTaWmch2dC7YWUgSHU5FUZHo0zpVsY4ZSdvaXUcrB/1lyZJrnKwPG+7MKEMl1qCqysanpNc3lzqtbQeRCetvQoS22CPufMaCWSltpDpqzvrZj1OYBMJoXpYbYY6GPnVUF4uenOcZy1afM8otDlQrQVltoHcbYxy6ymblX0tPuQdeUihVAFZjK61P25+OKaWTp6eOyd8FZgHh4eHh4GDZiam+vEMS5wuTBWR5yysx0saPmbwmERWtlfXFJT3HNL7UIrMU90guKiWOnnK25LlfVVTwf8zYV4GPBH90XTN5+V/9JgJVFnp7r5Her8qKcfdW1LuNotMLcKU4xRKl2ZvRaqs8PfCUu5IduJt1VLBD4Glf9lPzwgiBrvGvZk58EzQFnRvHlzAEDLls4OI9B3Ed51cPRn0BE52Cm9sp+RakxYqr5YqD9DQZXlvWn3PYYZk4jWR5WKDGvMqov+QCgp0r8R3dyTzWG+dPAYdVto9cSyKW3SYRrvoa7p26n2oziA6WTPtGh9NBuvVx0d9ausDIL6jAkmBWujljYjDrCMm552WkGVRVF/PNXFp8ayKav2T7O8+1Mf9wSqu8TJUSfkOB0Y/QAoQ6RTcZxn6MxhH520y+xDVWxbEmSCEpay7yvdzvotl/NxjubUWo33yVb/oJuL9SMa8cxRiMJi9gml//Lv5gAAmpnODt/ZFuvb7PM8znxcDBDs+0wVX3/t4sJx/ujQgT6iOVDnWMqR7+7H+SNp6Zsr3ChQT89crHD8apR3Wn+R2WUf/dGPfpS1vh4e9QueAfLw8PDwMKgzRS6uEnZet1oJLkHVsSkFmCJ0s7+oY6P791E3rqVYACDTkklVVuNifhFckFMAohoCF/AkN14OrqAgEJXY6BU5HU3NyisuopbKepmWaioGKM+ldXGtSgMOdauifnsoNNIAJJekG1UoBkl1+NK4AleprDSaTw1h6h48A5QVpPaU1dC20pjuajaoxGaCqVhUzlkfzacIBth7ciKGWnrgs/tddRu7Zteo0B06dAAQ+q7hpJdIJCK/AaBVK8cE5OfnR9KlSx1ZTEaIui/Bve53dVj2858DAFqY1+oean5lWCdxyVSHRCcy1c+Ls8DjhBfI7mqNZhccd6ubEp8d8wx2BPQd9eSTTwII/SeRRbjhk08AAJ/16gUgU31Spz6VSzhRxxlt6lSqHqH1QxEyK/yLk23XSE4l9VWHSGOEce5kfZOWLgi2PpjffVCUseL7ysYcaZy3E2faHwPsyKUH2YE7Lf2zS+zrNqHdgwCAtqYLw3FOXRkyJrr9o7o06mFZFwv8zXKIoqL+kfts2nSulXs6gDB2l5ZbYjo/GsuLqYL1Kytz20jnned0F0866aSs+T086ie8FZiHh4eHh2HUqFEAgLlznUk+F2MqeCjUwukrSeNjpXOpuz5yvsgUwous5Pa2YGYpCUs1LAqhW8uqtUOZkcv9UIByrI4uyNNtw3hvFVq47Rso61vaQtJ8029amqFfpG5cdQO7IpJL46WxNLJbh/IAjQQut/QosnFDLaXAa+JLx7eiBbKRSefZq8qmMlG34LfAsoKSFik+6r+oOaD6OOEgUiU0DrZ2HEVh0C0AQB/nEDYjyjuv+0FjOUAWhbDqzXzo7wCAVOpLAKGuACXTG2+8EQAwefJkAKGPH0qkZHGaBf5MQiswSqdkkVg224jMzzfffAMg1IHZMmUKAOBlszxZYWO6q41ebTtNlVnglMA2Vp0YpZ0Dc1fOPmxL0r+cpYwlUEu5muLCCy+M/J4zx+l5sD3y33wTANDxmGMAxE91qgMU5/RM20lpdlWQVAZpaUa8+KinnjirrJWRXOFxfvA4V4bPxZq4T85iy7HYSuopSq/ZdKA0nlxTYxWPudUOvGcx7A45P1KJF4b8GwDQvPlqACEzwn7Ofs++TOaH44ZMC/s45wcNGKphJHSLgB8MZXA4tphyPPJ+7JO8nmOSad0NRunhsTvht8A8PDw8PATc2uY2oeqXJGOu01hdoXqAM+1vbKlu/Zbge3LE5WgmfoE0mG+cO4k4x5wEd8LVEEV1jfgc6W4gVFRgGB2NocsnIctEjiv0y1Ni188DAJQFfoGUO4qKLRqDTNm5oC1YaVJCR51ifzAILluhg6Vumx77O7cvGHobKxq5wbtjXH2//ewz1G00hGeAsoB7/ZS8jvh1P3fCenzKerIyhOpcK2B+GKuIymfsUDZy2lk6yMrnoMu7TK77t6XGGL1n+Tf8w9EXzUxCpN7OCrMeufnmmyPPd8UVVyAb/vxnpzNBK7H0Mm644YZI3jh9gttvv909Qyf3FPSl1NGYkDknngggU/VRmSCCE1TUb3I4CegUwd+cdALDOHX5zBO80BghSt21hRPteWfMmAEgZA+Sf/0rAGDIT38KIPzA5GjFky7paw/+VXnkcIafHR4n1O39EihIx0dtFtvbVoB64lYmSJkoDbsVMnT6qYp6t1ma8QlyYDiB9DLJmgXxwuzSQZMil2LBK68AAPYxJob9usgs/fiRb9vWtRJZOjI6ZGKYcj4gs0MGSHVz1PMywXzsY2R8yKayPuvWrQMQbkd5eHjsDHgGyMPDw8NDQKeJXBRyXc4FrSq/q3NXyG9ep1uyJsthiQWYfj7wkUN3DE5fpZkthLlM5nKa8ou6mFSrMApQ6uqGy3Fe30eOa5ys9DII9bDM7XrdjqZgqwYcvOdKY8e40C/DQCmpkx13WG/59T6BjU3C0iC2Si9LWa7yYjxvwm1z07U42Dl5ZcXUKKDuwitBZwV1BAI2QMIl5VhKT9AEBwalcXbkTvRYS4dT7LEUl60f9uBo5YgghflyNP86E5K/feMNAEAj65CUPOnH6De/+U2Wp4sHOzQlUyCT+akK1JdiHdSS7rT/uoBM9x/nAjlpPDRK93E6QQSbUC3nNO5agic4+3Ay4IXiupjSeG1j2LBhAIBZs2YBCN9VO9VJYh/hzGt6Lnz3nYzC6WS/k9YH1WJVLRLZvlFj47BdDw+2GkoixwlO1lQ/0w+RMnSZHrmTckeCNcweXawkzXqssdWNw+4E6//sW0uNmQlMuUUHh32Rlnk6zqkLxD7A49S9UX8/tPIiI0TGhx8IVRLl9WSKqMNz+eWX0kx73QAAIABJREFUw8PDY1cjBz4YqoeHh4dHBFwccpHHBXTCUooJ6kQ4h7vnqhBjK+FUefSwbql2M36jwLwe0/6qTNTiGWerlF6R7azK83EOQtVogHKRXp9gmn6Cwok94wCTJtoK/aXbxMq3kFWidxP1pl0WiCmsXdTzT7ExQCyXMh6dvGbE5A3aVGuioENRUx7q7XR+lqRcTbeaW4adJSzuOngGKCtGj3ZKYv/4xz8AAHPHOEnz2Ov+P5fB+o8JkjhUenh3o4ACfQv6K1H9DqGKKmwA5ZIZuj16/oN33gcAbP2tkzDJ/GwVBoiS6L333gsA+MUvfrGdpw2h/lB2BLQ0mz17duS4xi26+Z13AAAXH+UGmzJBSflNcFLReGka8I9jP4+Wd0MspT5VoyPsDxdZ+9XyvwAALvvhD7M8Ve2BrAAZssC+ll8S6inqHoNZpgYPZg+asL7SwvoSGZhO1iD6wWFxuZLqB4nvg11xhZyPs75T661EUN2oh+3Q1Zvb2igLJudoCa+99nRwDRkcWmcRam3FPsZxoMwNz5OJKS52T0/miP2fDA2tJceMGRMpZ9y4cZHfnDc8PDzqArwOkIeHh4eH4LrrrgMQunHgAlm3loN4UnQpExfLxdIcozvamaLMIbafSV2gUB5XUxK1z3LnS0zMLLEatrJtUnWnwIU85QdqFajOj6rqB/o+6d9JVfqxNGH7w8X2rCq8qb4Rn4hPqKGTVhgbtsysxEJLOVcyd80PkTRDhaIJn4LWXtTdqSqaO83HHBGg7hjYR+oscnKARn4LLBa0xmDMrPcmvwsAGHDLkS6DKlzYKFtp4jI79L9sJJxKJigRzb/OzlOvopjxsCw9yLwI55pOguomsGNqB6XX5vHjx0fOU0+nXbt2rlzze3LAAQcAiHqwnTZtGoDQHJbU+MiRI5ENDz30EIDQEo33UimcUvu0Bc7l/QmHHw4gqvPhEI333tgmBY0Knyu/A5KXTMoZljaiqZDjIN5993gAwDffrMr6PLUNsgz0xj1/qvsi9F9jhDj1xZpbO2wxF/zky9Usy9jHltZMLYXdzjGqpe3HLuXkStJSnJIHSMrvMICjQ3t7D+z6qlSq4ek4J7N6GjV+hTFEvO/djz8OIPSCDGT67dEYWOo3Rxkg1dEh07N6tfMPVNMo6Z7x8fCow9h1BFDdXAB5eHh41GdQ4Ilb0AZ/aGRxddGsXmJNuKukyZRkD0Fu6BA5rhYjTuosMamyozFBGnGM0HhdeZIS3Kptl+6dQRWLpHHarowe1raLC+ujLiuUFVtplnJNJR+vC0IsURkoEIZ4R8a6pylDnDUYkbDUtTGNY7glXOex6xxB180FECXC559/HkDIlHz4f27UHjbTBp/YWrK7kdFhB11n3o9b2mTA+FfUFVJfLiO+dB6d1ZpErVJUsuVx+uAhG0OJl1YwBx10UOQ3dR7SYwyRwWHKKPBPPfVU5J40l6VPFTIdcfpJZIAoxb9nTrUOOuhUu3N238ZKbStVzWmyE5UxyeL2PiRyYOHCzfas7iVQD2Rng+9O4zUFOkqNukUv4MipMCZEdICCzsXfak5nYN/iVgAnV/ZV9edDFAZBLKMem0KNnqh2UXv58EiYuqAUvje1JvvJH/4AIOxn6ZMt+2+LFq6UjFh9BmV+mLIvqldz9ddzzz33AABuuumm/7+9cw+uqjzX+BMEShqEQIMp1Iac0NoLbclBTAlWwHoBqyAXAUEoF02hyO2PM6Npxzkep+PoMOMBixSnHCyKylW5tIIMYITDqVQKAoKIikExFEUEAxiSSM4f3/esnfXu7NzYwA77+c0wi7XX7Vtrr+z1fc963+eFEOIyRQqQEEKIWLAjmv3qqwCA64Z7jxibbUAFiEH9Gew4+3iTj3xX1wTjpBsFKPI8sokY1gs6VhUwKkAOqiT21TinNpjfPg+D/JZqh7mKG3EnbKrZGdtgLSrYJl4Km0vFwQHHRNxPsTlsLzONCgbiyXzpBxFt3vAfcBhiX/t3NvMu9mjHDhfrc/as+y7pEdXkkQJUP/gjQJWDCsnOwTsAREaS3V90jtE22I03bFC8zis/9E6hAsTR8INe+bEjU1td2o50bQ0hqjbW9ySWGsPMpKD2GaK9Tjja5pTxUYzLOHnyZKgNvFZUz+w5UQHitKTEmW516vQDAJFYE0ss5SfwBfK/Ch+NdNpH1aGwBwsVKl6Te++l7faFhefJe4nXY0ORCzalSsHv4IYfOb+kKAtsm7bFm4u/bX790yYejfeaVXxslfdIocVOZkrse4CwMvR9U8IgnMAbOR6be8NzLguP9zBVmurO3IzJ4/3LmCDGp/GetDW5rFMzv3t+F7zWzz77LICIMiqEuIyRAiSEECIWv/nNbwAAy5f7DI7f+gUcgVjfH/agjx0Mr0ezG76L9R15dsA55IqOLrEvSbkmpSYe2OkpOb7jzTffPzZrWdsMG4wP8zk76NVfDV9lm0RahLehMMRLQGsKGtlmm2MV+6ktJ2MHD1SGurY1C9LMBjZDoY2R24ITYDYYY4SYJeYUojNnrvdT19IxY8bgsqAuG6CqWpY1kCbdAWLGE118OdLk6JQjzU0LNoa263bTTQCi3YwZgsZUTI7K3y5xf1kcobKyOlUCW3vIZrtwO46IORKmYRXXY7vffdfd8DbOp3pMBWNVODLnMbjvWHWSOKUiRIUoloplK2N//LG7Khyl39KxI4Bo5Z2KDzM+92/fHtquhb+GPK49zpEjR3Ax4fWyyhfjWfgjw+u+5R3nnB0ob13dtEWu+46793Sq46deKGnhf5z5G83pLjPPe8+mN0dM2OjLQx2Tv6Z88NiSjOEqY7vMUuvvxAdSP1+vy8a51eSmzPuY3x0VIV47GxPE75zr817kPNejOsm/G+szJIS4DKnLCPpsLcsaiH5RhBCiicLO54ab3Kvam//NFfkNesx8kFihhgWc2ePm6M+/Y7V2QZEBeaSOfPhAtspX2E2W3jh0jegRWhrBGoNShWmQBWwMo5+0k6FZtORozSs2TIJJ92VuGArB9V/xUw4iuDlVrSCjzYZDURGyttdBbBItJXxtr45+MPqlD0uwkpMf43DAUN2SIpFYtWoVHnroITRr1gzNmzfHrFmz8Itf/KLuDetSgNQBClNcXAwAyMlxAX4ccdpsE44g3ykqAhAZiTb3qsjtXg2528cwMB7EVotmfIpVDazvCVUNzjNLxsY8UJWJh4Q5b57z06GyQxiXwSmzq9gWXjOeq1U87DlSIXpxzx4AwLSf/hQAMOENBvQ59tMDye+P++HonrAdjOsqKCio7ynHhQkTJgAAtm1zAYZUHdhOfkf8LmNlOHG6c5uLQ2OcF1W8Q9/9LoDIj2uxn9qYH/ouHfe/5u2jYq74RLN6Oz/nHvkIcedR6o+8PtDowkUPVq+eCwA4cOBA6Hytq3N1GG/GKR/K/GHmtbRKZqzYH15zTu21XrduHQCgf//+MdskhLi03HTTTRg4cCBSUlKwe/duDB8+HPu9d16tKAZICCFEXdASZKN/ZYkuLCXDjjGVGJP1RRWCb0x9P5mvbK3oQBUk04fThwuoANHFW9wBcryqYZMjbNV4K1gRW7fLGqvWmCxkH55ekWnJS2IPQsXG+/Sk+NHJ971JKV8icyjxLq720zTfRheyQNPtq6yMxZPltebYg9WD7fqHwgkp9iL879WuBNS5c+GSMYkGB0SAGxTVNogKcQWUBdYQWAto1qxZAIDMTBcnQQXHjuJtrA5HnrGqRnO5/QKtw61VCTjCtd46th3jx49v1HnXxKRJkwBEXKap+ARqF2Nw/KicbbUuvJRXeW5UlKimWdfrx7xyYrPJrL+OvcbkxAmnRAwdOrShpxxX6LnE68V7x7oc83xs7BSxWXm8brmfuF/Xf/f3wB1+/Rmd3KOBv5V8wNhsu/Z+yfHgnQWttPkzbcNIbZpyzfl569e7z99/370D+e1vGVXbcJ7xbtG8RlR4OM+/S8J7kdfSzvNe4X6se7kQIjF5+eWXUVhYiE8//RR/+9vf6reRFCAhhBD1hYHkQD+zhK/CvQJEO2TC/rD/uIMXjKxTs3VsZhf9jJ+WenniSl8Jnd1wOthw3prREluyJZa7UK1ex9aj1eYJcLmtM2NlKJapMatF0gnCNqJv+7mFXgn6pv8qrrFl0vhQ5zU/YT63AU9W7vLt58CAr5qnTp2KRGXw4MEYPHgwNm/ejIceeggbNmyoeyP5ADUOKkFz5swBAHT0GUo2+4QKTKx5q1LYiumMl7GZVjbmiFOOaK2HSuRHK/5MmzYNAPD000+Hjsm2sk3E1isjNiOH83Y960dE6qr3xD/iQYMGNeT0LhiHD7sfcLp0E56HVS+I9bqJdY/x/K2a+N+ffBKat+tbl3G7X36enW3VRH7PlMltGKr7Xpl5GI9CilQ0+XdoszKtqmavhb1n7N8T7x0hRGLx1FNP4c9//jMA4JVXXkEnr2z37t0bBw8exLFjx5CRkVHbLqQACSGEqD9DhgwBAGza5F6i/vKXf/VLIi9TAQCpXo7oZExvfPZXin82MRPKxu7wucQu6Mlg+eHQerYCDKcs9cLtbLaZdYa2yUBcbp2iAaCt30k7mwVGKccejGOCNDP/WXiWbY0UHmZRL+pbrvTSLq8AMeqqA9sTfOCnlL9s5WiLdbb2yhZfBbNYcCJx//334/777wfgXqdXVVUhJSUFO3bswNmzZ4MyULVSVxZYHLksO0AcxS5ZsgRAZHRuM5ns57H8Rjj65kjVZodR2bGje7tfxrnEqth+IZg4cSIAYPbs2QAileZ5DjaewsYKcbTODB7rWWTPNVZ8hlV+qH59YpSPSw3Vi02bNgEA2rVrByCiVHHK75RqIIkVX2bjWazfEK+bVY6InbeqCY934MDToc+tRxXj0rp1c8Gp8+Y5FWbixPNXfiz8u2DbrYrFeytWDJ7NvuQP/+TJk+PeViFEfFmxYgWeffZZtGjRAqmpqViyZEn9AqHr8gGKI5dlB0gIIZKRo0ep+GzzU6oUJm/LBtdQuvHjkc5eAbLlq6jg2MLmtsyQVWo43WXmbcV16wxtQ0FiOUNX30c7q+yEvUCjMYFJXxwKtzWyOY/AICJqQ2dCbbHla9rZ41mFinCDGIrQ6z8rcvv3Nb/oBp6oPPDAA3jggQcavqEUoPjAHwN6sdi4F45M7ejajlRjjc6t1w5H1zbbhdtfTOXHMn36dADA3LnO5yUrKwtAdFySzcDhKJ2jcSog9hoRG5Ni/XF4HHrEsF2Jxr597meve3eXVmxjnGyMjr2OvH5UzmzWHO8dmyXG621jfGIpQzy+rfVm28vPWTAxovxMrN8FaQQ2boz3gHV4trF3bKt1io5HfJIQIsFRDJAQQoiGMnLkSADA6tVu0Ddw4H/5Jd6A7iuTR8UHjUmzonhChSfbT6mGWB8eaiI2c6rYzMey4LEJU3b9WM/DGj/3wkzbXTV+HJVkle13UuyVGb6Up9EEy6VFVTQ2Rb0ookW5Z/M/VkAiNrXNpsB5aMCbaGEDcUdZYPGBmVBUPb7rXXg5KrcxO3ZqR6h2autxVa+QDURGurfeemv8Tuo8YfzEokWLAER8ghhfEUsVi5UhZ885ltcR1bF//csV8kt0+ZZqw7JlywBEXMZtPTer/PBz66zNe8QqY9YnidTlWWXjZvjjyPZQfeR6to7dhVR+CNNzV6xYASCiHhLrNm6vAc9Nyo8QSYQUICGEEI0lUAk2upIsgapgA1R8rE+U9bOZtRW/iFVsrCcxFSQettxMCbvGsbK8YljihMQUW62d+zxjPreZav9XEV6fSVp2f9FlZtwaV3u3a7vfQMRIM1OumG0aWmLW8+1anbEKAPC5d3xOZN+fuKAYoPhis0YWLFgAIBIbxNE2R6Ac5VMdsdWsiY3/4AiXasfo0aPjeBbxhW2jezYVA47KrzT10KwCZOMzqDDYCuBcj5b9TY1hw4YBANauddUjmUVnr4PNmuM8lR/rKk6sQzanNnvL+gHZGB9+XzweY33svU2n64vJpXb3FkI0IZpBWWBCCCEaB0uZrFzpzGAH7RjsFth4k3At3CjphQNxW9aK6oiNBeJzyzo/wyy3GVNc35bFiq5G74h2pra6TGSeNjzWIDrbzJeb9YtNW21VsBxfpoZ5dtZeiNtfc9qsQNWtg/ncnNTmTi4Kqdz7/cSzZFJCIwXowsLK3/XFVli38R+JHs9SG3TPFrVz2223AYhUIk9Pd4+CWBlNVh2kslNXxqFVgGz2l3WCtvtju7ifY8fcry2VvYbe+0IIcVFRDJAQQojz5dAhr2fQwCfdrGDtjs+EP7bYGByqJRywp8dYj9jsMvuc+8wsp3hiE6dq8gmynkP7zPxh7+ScYwoM2/ijYj9lSE4pXbT92Vztt2ebrIhGBSpQz7zi03WV2YA7YEyQCVZqnhVOYEgalAWWWLDCuhD9+/cHAPz1r67UQGam+3G0sTrWf8cqNrHqXFmnaBt3ZrHZZJynWsnPpfwIIZoEUoCEEEKcLzQaXehfjY7dNM4toOxh0rKqvApB5YYqCNWNfebzg8jx/2MAi9M/2sMZnVLcCDvmRFvjcN46QVslycYiVTdWsG3m/GGwsLHbW7lXcLjcFmGnqnU8OLfv+akrmHbSn5vNTLMeSWwbr12JP9DNLp8CKWxAPkLsGbYbAPCVj/1J5GSaC4IUICESmzvuuAMAsHTpUgDAt7/9bQCRrDnraWNrflHp4bz1wrEO0VR2iN1PrGyxpJPPhRBNGylAQggh4gVtKJb4V6MjVt3tFhgZhLNULw6ZKdWMSGV05kBxyO6eXMeraUTV17LZzVaIsplUNgvNUtPn9tl5pVdsiM1Us15Hke0ZpBM+SqlXlFr4/do2cJ7qlwmvCtSr62g1TWNn37DTv3JXpaSE1zDJUBaYEE2D4cOHAwD++Mc/AgCys7MBAG3atAEQ7RxNnx5b6yuWw7Z1giZWGbKK0hdffAEAuO+++873FIUQ4uKhV2BCCCHizYgRIwAAa3w5oAFPDnQLjIzBWaoi1kOnvY+jiWgr1E2sW7KjpfmU++NzzvoF2XYQKwxUf05yn9l+aourW8+hNDMfvU9rmhQ29DluYosi08Ohtfkp9aS3uDd/ch3ecdMvtmwBAHzqlZ8xY8YgKdErMCGaFrHs6Rkj9K1vfQtARBGyjtCESg6xDtF1xQLRmXvw4MENOwEhhEgEroAUICEuBwYMGIA1a9Zc6mYIEeLgQRebE5WWZWap51D5sbE5xwNdpSZv5uhsrVi+Pzb2yOpJXG4zr6o322aM2QyzWPvks9aqU9EF0pwiRB8gxvZEfILSQvPfxNHQcQgje6gvdd+8GQDwuTctHTJkCJKdOtw/4oY6QEJcQFJTU4M4IQD405/+BABo29YJ4vTroaM056kQMUYoll8Qs7xOnHAPoqRLmRVCXFZcgWi/zgvFZdkBWrp0KWbNmoW33noLeXl5KCoqutRNEk2YI0eOYOLEidi+fTuOHDmCDz/8MAh2BoCuXbtGHHfhOiW33XablB+RsNAfaI4fak+Z7V7htvSBKmle3qCKYp2bI9lcZ8w0XOf9oI+TyfHRQrZ2mFWUqCdZjx5mkdlMrerqSoVZxm0O1bBu9fWK/dRma1m9ispPmlnvSq/0lAZXyU2P+jU7+Uw47p/t6Pf3vwOIDF4GDRoE4WKgW12kY12WHaD27dtjxowZ2L9/PzZt2nSpmyOaOM2aNUP//v1RWFiIXr16RS3fu3dv8P+qqirk5OQEVeQtLFJZF3PmzAEApKW5H1EqQrYKfVOuQyeEEJYrEF365EKRcB2gmTNn4o033sCKFSuCz6ZNm4aUlBTMnj27Xvu4+eabAQDz58+/IG0UicsHH3yA6667Dhs2bED37t1RUlKCbt26YdmyZejbt2+j9pmZmYnJkydHmRvWxObNm3Hs2DEMHTq0UccS4mIyZcoUAMAcznsl6DtepjjtZQ6qKNZDJxLRYgnrLcZwOtBK+KCzjtOmLFaUIlRTxBHbxH3azDXug7Y7tuU8lo3pYattnBKPZ52mI3lobs2dwRZOKZq3bZs7TqlTlN57z7lns8xOspPUCtDo0aPx8MMP48SJE0hPT0dlZSUWL16MtWvXYvLkyXjhhRdq3C4rKwu7d+++yK0ViUaXLl3w+OOPY/To0di+fTvGjx+PsWPHom/fvhfl/lm4cCGGDh0aKDeNhQ8mIYRIJuqKATpZy7KGknAdoI4dO6J3795YtmwZCgoKsG7dOmRkZODaa6/Ftddei7lz517qJooEp6CgAGvWrMHPf/5zpKSkYPXq1QCAuXPnXtD758yZM1i+fHlwPCGaCuxwz/bB9dP/cwaA6Kwrqimdgi2divEZ3gUQUWjolhzLhdnG8HC7o4HDdAe/n3K/POytw1ih6g9Kto374hDEehpZdSni52MdgsJ5Yke9MtTeKzmxsZXO3BHnbH0JQMSk9MCBAwBiW2gkKymoXQGKZweoWRz3FTfGjh2LRYsWAQAWLVqUvIZQotEUFBTg7bffxtSpU/GNb3yj3ttt2bIFrVu3RuvWrdG1a9cGHfOll15C+/bt0adPn4Y2VwghBJwqk17Lv3iSUsW82gSirKwMHTt2xJYtW9CzZ0/s27cPWVlZmDRpUtAxsnTu3DkUjAq4GKBFixYpCyzJOHXqFLp164Ybb7wRa9euxZ49e9C+ffsG3z+WyspKtGjRIioLjNxyyy3Iz8/HI488Eo/TEOKS84W3Ydjg59/zU5tZRUWm2E9tLI/14LHuy4zHORqoMd1iHMGtyawyZnoBwPf91FYnY1ve9NP1RmWK7VJErHOQ22OmqQV2HD8w+3XTrVv/AwDwqa/urmyv2sno0QMDt2+PuXx3jx7YXsvyhpCQClCrVq1w1113YdSoUcjLy0NWVhYAYN68eTh16lSN/6o/vL7++muUlZWhsrIS586dQ1lZWVBbSVz+TJ8+HT169MD8+fNx++23Y9KkSQDqf//URFlZWZB5dfbs2agq64cPH8Zrr70WFJ0UQgjRcBgDdDEUoISLASJjx47F/PnzsWDBggZv+9xzz2H8+PHBfGpqKsaOHYu//OUvcWyhSERWrVqFdevWYc+ePQCAJ554Arm5uXj++edxzz33NHq/qampwf9/+MMfAoiYEgLunsvPz0eXLl0afQwhEo12/h6v8MkDLf3f0I/98tNmfSo7VHSoFNlq7yVm/jiu9v9radagmhL2fi6voRK7jbyxaQgVUWt2MkuKAURifPhpqalhz7im68z+9/k4qJd3ufI3X375JQA3OAIQMkQVsUnqLDCSlZWF1NTURqUTjxs3DuPGjYt/o0TCc+edd+LOO+8M5lu3bo3333//vPdb15viwsJCFBYWnvdxhBAimUl6J+hz587hiSeewN133402bdpc6uYIIURSM2rUKADA09675mv/Wvk7fjm9d1qaaThqJgL1nEjWV4WZ2giemkMYOlX7/3fMlMewalO0v3W4ddEeQwdDcz389Baz1o//8Q8AzjkeAIqLiwEAEydOrKHlIhZJrQCdPn0amZmZ6Ny5M9atW3epmyOEEEKIi0RSO0GnpaXh1KlTl7oZQgghDFQzZvskgI4/+hEAoGW/fgCAfL+ercTOWCBbcT3T+wjRWedKP18aZINRpSn3yw+H9l/9VUmsHK7oz2uuW8aaXtwnj8F4J6pNzDZL37rVtdWrYvv27QMAJUKcJ0mtAAkhhBAiOUn6GCAhhBDx4bXXXsMjjzyCHTt2oF27dkFsCsnOzsbRo0dxha8M36tXL6xfv77WfbKaPFmyeDEABJYl+b5oMFUUKj+sw7XLT+krlO6Vn4gjj8u0oo/0lTGqyf9q5cqgDZ07Owegtm3DL1D25eSE2hLxFAq7GXErTqn8fM9Pu2zcCACBBcbBgy42aPTo0RDxoy4n6HiSkD5AQggh4kNaWhomTJiAmTNnxlxnzZo1gSdWXZ0fIS4kF9MJWgqQEEIkMEuWLMG9994bzFdUVCA/P7/eDvd5eXnIy8vDhg0b6l65kYwYMSI0v3rVKgBAZqarn5XfsycAYJ9fTgUn20+pDFEpYr2n94zfD2OKtnhbiuXLlwfHpO8OS9+keBfrPjt3AgB+6RWukp/9DACwLdjym6FjMINtjPcS437p5Pzxxx8DUA2v+lBUVIQZM2agoqICGRkZeP311+vcRgqQEEIIAK5zQXWmpKQEOTk5GDlyJB577DGkp6fH/NcQ7rnnHnTo0AG33nordu3aVfcGQtTBiRMnMHnyZKxevRp79+7FsmXL6rUds8Bi/YsnUoCEEKIJcO7cOYwaNQp9+/YNsrEefPDB897v888/j+7du6OqqgqzZ89Gv379sH///gZ3oqozcODA0Pyypc4dOSMjAwDQq3VrAMBneXkAIjXEqMKwbhczr16KYUR61113Bf9f6o/RvLl7rLVq5XQElkGim/ui91zkUbNmbvz/9ddfA0BQ6oZZXR9++CEAoKTEOQnJz6dhvPDCCxgyZEgQF3bVVVfVaztlgQkhhAjx+9//HqWlpXjyySfjut/rr78++H9hYSEWLlyILVu2YMCAAXE9jkguDhw4gIqKCvTt2xelpaWYPn06fv3rX9e5XWZGBm7u0SPmcnai44E6QEIIkeAsXrwYL774It588020aOFyoR599FE8+uijMbdprJ9aSkpKnaVfGsqwYcNq/Px/5s8HALT250QVZlFBQYOPwVpb8+bNAxB5UFIJau1VJ16XysrK0Pznn38OABgzZkyDjy2iqaysxD//+U9s3LgRX331FfLz89GzZ09cc801tW53MQ2QFQMkhBAJzM6dOzF16lSsXLkSHTpEkrl/97vfBbFBNf0j586dQ1lZGSoqKlBVVYWysjKUlzsTwI8++ghbt25FeXk5ysrKMHPmTBw7diykCglRX5566ink5uYiNzcXnTp1Qr/yYZurAAABlklEQVR+/ZCWloaMjAz07t074eLLUqri3dUXQggRNx5++GH84Q9/CJQMALjhhhuwdu3aem1fVFSEG2+8MfRZnz59UFRUhL1792LkyJH44IMP0KpVK+Tm5uLxxx9Hj1peQQhRH9555x1MmTIFr776KsrLy5GXl4fFixfjJz/5yaVuWoA6QEIIIYSIOzNnzsQzzzyDZs2a4b777sOMGTMudZNCqAMkhBBCiKRDMUBCCCGESDrUARJCCCFE0qEOkBBCCCGSDnWAhBBCCJF0qAMkhBBCiKRDHSAhhBBCJB3qAAkhhBAi6VAHSAghhBBJhzpAQgghhEg61AESQgghRNKhDpAQQgghkg51gIQQQgiRdKgDJIQQQoikQx0gIYQQQiQd6gAJIYQQIulQB0gIIYQQSYc6QEIIIYRIOtQBEkIIIUTSoQ6QEEIIIZIOdYCEEEIIkXSoAySEEEKIpEMdICGEEEIkHeoACSGEECLpUAdICCGEEEmHOkBCCCGESDrUARJCCCFE0qEOkBBCCCGSDnWAhBBCCJF0/D+ozCQokLUqXwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116018810>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_stat_images.plot_stat_images(afni_perm, spm_perm, max_activation, [-17, 1, 15], 'Permutation T-statistic', fsl_perm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115751a50>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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4Vqv13EWLFvV5Hz/11FOHc3Nz3TfddNP88vJyp1JKEhMT3a+88sr7SUlJHpfLdU5vb+/eSM5BC61GcwZTWtsB4CurGAqf0Ka46B4wulhqi1ZzqhMbG+spLy8v89/3zW9+MzszM3Po6aefPgawb9++2JiYmKgVldCuY43mDKa01mvRhiG05hravCQHyabrWGcea05H6urq7Hl5eb4/3pUrVw44nc6oCa22aDWaMxSlFKU1Xos2PNdxQbIDm9Xii9HqtbSacLnlzccKDrTVR7ZNXkp27wMXfjJks4KBgQGLtx1eQUHBwEsvvXTktttua964cWPxU089lXLxxRd33nrrrS1nn332xP8EU0QLrUZzhnKirY+OfjcLUl0ca+2le8BNfGzwjwTvGlqAFJdOhtKcHgRyHZ9//vl9x44d2//kk08mvvTSS4nnn3/+0tdee6189erVE7t2poAWWo3mDMVrzV69NJP/fus4dZ39LMqIDzhWKUVlcw8bl2YBjLiOtdBqwmQiy3OmSUpK8mzZsqV9y5Yt7TfffDNPPfVUUrSEVsdoNZozBI9H8dM3jlFvxmNLazuxCFy5OAMInRBV0dhNU/cgFyxIBcBptxJrs+gYrea05MUXX4xramqyAvT390tlZaWjsLBwMFr300Kr0Zwh/GFfLXc+eYC7/nIIMCza4ox4itLiAKjtCB6i2n6kBYANRWm+fSlOu7ZoNacllZWVjgsuuGBxcXFxyfLly0tWrVrVu2XLljaA/v5+S1ZW1grvdvfdd2dN937adazRnAEMexT3vlQJwG/3VPOtyxdRWtvJefNTyE1yAFDXFdyi3X6khfwkBwvTRnJZUlx2HaPVnPIEWhN7xx13tNxxxx0tgcZ7PJ49kZ6Dtmg1mjlAW29or9cf9tVS1tDNT69dTqzNwteeLuNEWx+r8pJIcthw2CzUdgQWWqUU24+0sOGsNETEtz/ZoS1ajSYctNBqNKc5D+2qIvXbL7C3uiPgca81uyw7gS+dX8iXzi9kW1kDAKtyExERcpMcQZf4lDd209A1wIai9FH7U1xaaDWacNBCq9Gcxhys7+Lv/vSe8XtDV8Axf9hXy6GGbr57RTEWi/D1D56F027866/KSwIgN9ERtGhFoPgsGDFa7TrWaCYmakIrIg+ISKOIHPDblyoiL4nI++bPFHO/iMhPROSwiLwnIqujNS+NZq7QM+DmEw/v9q19PdnWF3DcY6W1FKY6ue7sHACyEmL550sXsaYgiawEo05xTmJs0Kzj7YfHx2fBWOKjs441momJpkX7IPDhMfv+GXhZKbUIeNl8DHAVsMjcbgP+N4rz0mjmBN945hDljd38/qbVpMfFUNUeWGgrmro5Jy8Ji2UkvvqdK4rZ9Y8X+x4bFu1417ERn20eF58FfD1pPZ6oVa7TaOYEURNapdTrwNjeWx8FHjJ/fwjY7Lf/YWXwDpAsIjnRmptGMxd441gLVy/J5LLiDAqSHZwMILTDHsXh5h6K0wMXovCSk+iga8BNV7971P7yxm4auwfHxWfBiNEqBZ0D7nHHNBrNCDMdo81SSnk7TNcD3vVJeYB/1ZBqc59GowlCR7+b9LgYAOYlOwO6jo+39jI0rCjOiAt5rdxEw4U8donP60eN+OwlY+KzgO5JqzltuOuuu7LPOuusZcXFxSVLliwpeeWVV+LWrVu3uLCwcPnixYtLVq9evWTfvn2xAOvWrVuck5Nztsfj8Z1/+eWXF7lcrnOmev9ZS4ZSSilg0j4nEblNRHaLyO6mpqYozEyjOT3o6BsiyRS7gmRnQNdxZVM3AIszQ1u0uYnmWtoxcdp3qztIcdopShtfC1538NGcDvz1r3+Ne+GFF5L3799fVllZWfbqq69WLly4cBDg4YcfPlpRUVF24403Nn/lK18p8J6TkJAw/NJLL8UDNDc3WxsbG+3TmcNMC22D1yVs/mw099cABX7j8s1941BK/VIptUYptSYjIyOqk9VoTlU8HkXngJtEMxFqXoqTjn43nf2jRa+iqQdgQos2xxTasdWhSms7WZWXOC4+C+gOPprTgpqaGntqaqrb2wYvJyfHXVhYOOqP9rLLLus+ceJErPfxxz72sdbf/e53qQC//e1vkzdt2tQ+nTnMdGWop4EtwH3mz6f89t8hIo8C64EOPxezRqMZQ/egG6UgyTFi0QJUtfezLHvky3dlUzcpTrvPxRwMb3Uo/8xj97CH92o7+bvzCwOeozv4aCZD7f23FPRXH4homzxH/vLe3C88ELJZwebNmzu///3v5xYWFi6/8MILO2+44YbWa665ptt/zBNPPJG0ZMkSn0voiiuu6Lr99tvnu91u/vCHP6Q+8MADJ370ox9NOW8oakIrIr8HNgDpIlINfBdDYB8Xkc8DJ4DrzeHPAlcDh4Fe4HPRmpdGMxfoNJOWkpymRWsK7cm2XpZlJ/jGVTT2UJwRF9Ai9SfJYcNpt4xyHb/f3EO/28OqvMSA5yQ7tEWrOfVJSkryHDhwoOz5559PePnllxO2bNlS9J3vfKca4Oabb17ocDg8+fn5Az//+c9Pes+x2Wxq3bp13b/61a9S+/v7LYsXL55Ww4GoCa1S6oYghy4LMFYBX47WXDSauUaHV2hNsZuXMmLR+lPZ1M2li8ZnDI9FRMhJHF0dqrSmE4BVuUkBz/FatDpGqwmHiSzPaGKz2di4cWPXxo0bu1asWNG3devWNDBitBdffHFvoHNuuumm1htuuOGsr3/967XTvb+uDKXRnIZ0mFZkksP4rpyT6MBqkVFLfHoG3FR39E8Yn/UytjpUaW0HMVYLS4IkUiXE2rBahLa+qHUX02imzb59+2L379/vi7/u3bvXmZ+fP+Ef7ZVXXtl955131t1yyy1jl6lOGt29R6M5Dekwk54STaG1WoS8JMeozOP3m41EqMVBmrmPJScxln21nb7HpTWdLMuOJ8YW+Pu4iJDssNHep9fRak5dOjs7rXfeeee8zs5Oq9VqVYWFhQMPPfTQiY9+9KNFoc6zWCzce++9DZGYgxZajeY0ZKzrGKAgyTFqLW2lL+M4PKHNTXTw7KFGjEgO7K3tYFNJ6FacybonreYU56KLLurdu3dv+dj9O3furAg0Ptj+QO32wkW7jjWa05CxyVAA81Jco1zHFeYa2rPSw0v0XJ2fRM/gMM8eaqS+a4Cm7sGg8VkvRWlx7K0J3DVIo9EYaKHVaE5DvK7jURZtsoPq9n5f7eHKpm7mpThxxYTnuLrhnDwKU53c/WKFTzyDZRx7uaYkk/LGbg6bbmqNRjMeLbQazWlIR78bi0BcjNW3b16yk8FhD43dRuZwRWMPxenhJUIB2K0WvnV5MburOrjvlcMArMgJLbSbSrIBeKYsIqEszdzD4/F4Qq8tmwOYz9ET7LgWWo3mNKSjb4hEh33U+lj/ohVKKSqbuicsvTiWm9fkU5jq5I2jrSxMc/lKPAZjQZqLZdkJbDuohVYTkANNTU1Jc1lsPR6PNDU1JQEHgo3RyVAazQxT0dHIruYqPl107pSv0Tng9i3t8eJdS3uyvZeCZAcd/e6wl/Z48Vq1X3h8H6tyQ1uzXjaVZPGf24+Mqr2s0QC43e4v1NfX319fX7+cuWvYeYADbrf7C8EGaKHVaGaYn5a9yc8r3uGGBedgtUzts6ejb2hUfBZGF6341+fKEYELF6RO+to3r8nn0b01fOzs8CrObSzJ4r5XDvN8eSOfPEc33dKMcO655zYCH5ntecw2Wmg1mhmmvq+LYeWhob+LXFforN5gdPS7R2Ucg1Hk3xVj5X//dpzKph6+dfkiVucnT/radquFl24/L+zxH5ifQnpcDNvKGrTQajQBmKumvEZzylLf1wVAdc/Ul8V09A/5Ovd4ERHmJTupbOrhkqI0vntF8bTmGS5Wi3D10kyeK2/EPRw0H0SjOWPRQqvRzDBeoa3pnY7Quse5jgEWpceRER/DIzetxmaduX/vTSVZtPYOsePktLqJaTRzEu061mhmmEhYtJ0BXMcAv7p+JYNuj6/t3Uxx6aJ0RGD7kWYumEJcWKOZy2iLVqOZQbqHBuhxG/XMp2rRKqXo6B+fDAWQlRBLgZkUNZOkumJYkZPI9sMtM35vjeZURwutRjODeK1ZmLpF2+/2MDSsfA0FIo1Sitr7b6Fr37OTOm9DURpvHW9l0K3jtBqNP1poNZoZxCu0gkzZoh1pkRedNat9h9+m/Y3f0LXnyUmdt6Eojb4hDztPtkVlXhrN6YoWWo1mBqnvM9rQLU3OnLJFO9K5JzoWbfsbDwLgbp9cv+uLi9LMOK12H2s0/mih1WhmkPpew6Jdk5ZPTW+HryXdZBjp3BN5i9Yz0EvnzscAcLfXTepcX5xWC61GMwottBrNDFLf14VVLKxIzaFveIi2wb6JTxrDSOeeyFu0Xe8+iaevk5isRZO2aMFwH//teCsD7uGIz02jOV3RQqvRzCD1fV1kOeOZF5cCQM0U3MdeoY1GMlT7G7/Bnl5I4rrrcXc2oIbdkzrfG6fdpdfTajQ+tNBqNDNIfV8X2c4E8szSi9W9kxekjj5vjDayruOhlpP0lL1M0oVbsKXkgVK4OxsndQ0dp9VoxqOFVqOZQXxCG2d0xqnp7Zz0NToHopMM1fH2I6AUyRduwZ6SC0w+Icobp31Vr6fVaHxoodVoZhBDaBPJcSYiCNU9U7Fova7jyFq0Xe8+hWPBGmIyFmBL9grt5BKiwHAfv31Cx2k1Gi9aaDWaGcKjPDSYFm2M1UamM35KFm1Hv5u4GCtWS+R6abs7G+k7uoOEVZsAsCUbLfKmmhCl47QazQhaaDWaGaJ1oA+38pDtTAAg35U05WSoSMdnu0v/AkoRf44ptIlZIMJQ2+SFVsdpNZrRzIrQishXROSgiBwQkd+LiENEFojIDhE5LCKPiUjMbMxNo4kW3mIVXqHNcyVRPYXqUIF60U6Xrn3PYEvNxzFvFQBis2NNzJyS61ivp9VoRjPjQisiecCdwBql1HLACnwK+AHwI6XUWUAb8PmZnptGE03qzGIVPos2LmlKZRg7I2zReoYG6DnwIgkrNyIy4o62J+VMyXUMej2tRuPPbLmObYBTRGyAC6gDLgX+aB5/CNg8S3PTaKKCt86xv0XbOtBLn3toUtcxetFGzqLtLd+Op7+b+FUbR+23peROaNEOtVQFXAKk47QazQgzLrRKqRrgP4GTGALbAewB2pVS3tXx1UBeoPNF5DYR2S0iu5uammZiyhpNRPC5jl0jFi1Mvl1eR99QRItVdO3dhsQ4iSu5dNR+WxgW7Yn/vJKaX3xm3H4dp9VoRpgN13EK8FFgAZALxAEfDvd8pdQvlVJrlFJrMjIyojRLjSby1Pd14bLZibfFAowUrZjkEh/Doo2M61gpRfe+Z4hb9iEsMaP72NpSckNWhxpqrWaw9hA9h15heMyXBR2n1WhGmA3X8eXAMaVUk1JqCHgCuABINl3JAPlAzSzMTaOJGt41tN446IhFO7klPkbWcWQs2uGeVoaaTxC35JJxx2zJuSGrQ/WWv2ZexE33/hfGHddxWo3GYEKhFZGXw9k3CU4CHxARlxifOJcBZcCrwMfNMVuAp6ZxD43mlMNbFcpLrtOoDvVExdEJz/3co6V876/vMzTsoW/IE7HOPW5z+Y49tWDcMfsEa2l7yrdjcSVhjU+ju3TbuOPeOO2bR1sjMleN5nQlqNCaS25SgXQRSRGRVHMrJEj8NByUUjswkp7eBfabc/glcBfwVRE5DKQBv57qPTSaU5GxQjvktkBPEk/W7KW9vz/kuc8eauDelyopb+wGIld+0Zvs5C1Q4c9E1aF6y7fjKr6Y+BVX073v2XEu5g8VZ5ARH8P/ffVI4PMH3TR2DUxn+hrNaUEoi/aLGElKS8yf3u0p4GfTualS6rtKqSVKqeVKqc8opQaUUkeVUuuUUmcppT6hlNL/gZo5xVihbewagMb5eGwD3PrX8a5XL0op2vqGGHB7+JdnywFIjI2QRWtaq15R9WdEaMdbtEOtNQw2HCZu6QYSztnEcE8rvYffHjUmLtbG1zcU8WJlE387Nt6q/dZzFZz30zcj8TQ0mlOaoEKrlPqxUmoB8E9KqYVKqQXmtlIpNS2h1WjDsyN5AAAgAElEQVTONAaH3bQO9I4S2obuAehJxtqXzBN1O4NatX1DwwwNKxw2C8+UNQBErGBFSIs2KXh1qN4KIz7rWrKBuLOvBKs9oPv4S+cXkh4Xwz0vVo47VlrbwdGWXrr6J9eKT6M53ZgwRquU+qmInC8iN4rIzd5tJian0cwV/lJ9CICS5CzfvsbuQUC4vejikFZtm9lE4M6LFmAz6xtHKut4qL0Wiyt5XMYxgFhtQatD9Rwy4rOOeSuxOhOJW3IJXXvHC21crI1vfDCwVXukpReAo609EXkuGs2pSjjJUFsx1r1eCKw1tzVRnpdGM2fwKA/3lL5EcWIGH523zLe/wYxPfvu8dSQOp/FE3U6a+8aLTluvIbSr85LYssZIWopcjLYWewC3sRd7cm5A17E3PisWKwDxqzYxWFfOYOP4xC6vVfu9l9/37RtwD1PV3gfAkebe6T4NjeaUJpz/1jVAiVJKRXsyGs1c5KmTB9nXWsvWi2/AZgoTGEJrEUiPj+XrS67g25WPcv3Lv+flaz4/qhRiu2nRprjs/NtVi8mIj2FFbmJE5uZurwvoNvZiS87xWbTD3a0Mtdcx3NPKYMP7pFx6u2+ca9H5APRXvUdM5sJR14iLtXH9ylx++241SilEhOOtfXg/UY60aItWM7cJZx3tASA72hPRaOYi/tbspxasGnWssXuAjPhYrBbhCyuWQf0CXm0q52eH3ho1zus6TnHGkJ3o4PvXLMVujcwSeHd7bcBEKC+25FyG2mtoffl/qPzqPI7+63JOfO9iAOKWjlSSisksAmCoMXCG8bLsBDr73dR0GHFof3H1upA1mrlKOBZtOlAmIjsBXyawUuojUZuVRjNHCGbNgmHRZsYbTaqyEx0sti6hTfXxtV3bOD+zkHPT8wE/oXVFtjWeUsqwaFNCC+1wRwP1D3+ZuOVXkHyxYW1b4lJxzB/54mCNS8ESl8JgEKEtyYoHoKyhi/xkp89dXJDs4Eiztmg1c5twhPbuaE9Co5mr/KLiHQrjU8ZZswAN3YNkxcf6Hn+wKJ3fli4idnEL91fuGBFaM0abHMH6xmBUhVLuwZCuY9ei87El55Jx7T0kXzLapT2WmIyFQYV2WbaRbX2wvosrFmdypKUHV4yV8wtT2VWlGw9o5jYT/ucqpV6biYloNHONrqF+Xq07zB1LLxhnzYLhOi6an+J7vKEojZ+/fYKi2CTq+kbKMnpjtMljqkH9x/5XcVjt/H3JhVOan68qVAjXcfzZV1L84/CqocZkFtF3fE/AYxnxsWTEx3Cw3ii4caSll4WpLs5Kj+OP79UxNOyJmDtcoznVCCfruEtEOs2tX0SGRWRyxVk1mjOQl2reZ9AzzKaCkoDHDdfxiEV7SVEaAJbhGF9LPTBcxwmxNmxjhOjHZW/y9d3PUNMz+Z624L+GNrjQTgZ7ZhFDLSeCNiFYlpXAwQbjeR1t6aEozUVRmothj+JkW19E5qDRnIqEs442QSmVqJRKBJzAdcD/RH1mGs1pzraqgyTHOLkga8G4Yz0DbnoGh8lKGBHa7EQHSzLj6em1jhPa5DEFKtoH+qjp7WBg2M0P9r86pfmNVIUK7jqeDDGZRTDsZqjlZMDjJVkJlDV04fEojrb0UpQeR1FaHKAzjzVzm0n5apTBk8CVUZqPRjMn8CgPf6k+xFX5S7AHdBsPAviSobxsKEqjqQPqe7vwrqhr7xsixTl63KEOo0LUwoQ0fln5zpSs2qFoCC2EjNN29rvZXd1Ov9tDUVocRekuQGcea+Y24biOP+a3fVxE7gNCV0DXaM5wdjZV0dTfE9xt3G0k8PtbtGAI7VC/jQGPm45B49+srW9oXMbxwXZDaH91/scZ9nimZNW62+uCVoWaChMLrZF5/PRBY+5FaS5yEhw4bBadeayZ04Rj0W7y264EujAat2s0miBsqyrDKhY+nLc44HFv1xr/rGMw47Ruw3r1uo/beodIGZMIdbCtHqfVzoacIj67aC2/rHyHukn2tZ2oKtRksaXkIfbY4Gtps4zM46cP1gNQlB6HxSIsTHPNukXrHvbw90/sp8LsjqTRRJJwYrSf89tuVUr9u1IqcCdojUYDGEJ7YVYhKbGugMe9Fm3mGKHNTnSQHmtYfvVm5nF739C4jOOy9gaWJmdiEQu3LFrLwLCb3c1Vk5rjRGtoJ4tYLNjTFzDYFLi/brqZeby/rgurRZifYljSRWlxsx6j3VvTyc/eOs7v94aXYa3RTIZwXMf5IvJnEWk0tz+JSP5MTE6jOR2p7e1gf1sdG/MDu41hpM5xZkLMuGM5LsPy81m0fQEs2vYGliUbBdsK4pIBqPNLoAoHd3sttqTIxGe9xGQWBXUdw4hVOz/F6VvOU5Tu4mhLL7NZ5XXHyTbAWOer0USacFzHvwGeBnLNbZu5T6PRBOB4l/GhvTwleOXSxu5Bkhw2Ym3jE6XmxycBhtAODXvoGRweFaP1ZhwvMzsBZTkTEITa3vATosKpCjUVYjKLGGo8ElQ0vYUritJGLP2itDh6Bod9Xz5mA5/QNmih1USecIQ2Qyn1G6WU29weBDKiPC+N5rSlod/4sM50xAcf0zUwLhHKy4LkJFBCfV+XryqUv0XrzTj2ttyzW6xkOOKonUSMdqQqVGSF1p5ZhKe/m+GupoDHR4Q2zrfPK7qzGafdccKoTvV+Uw+Dbs+szUMzNwlHaFtE5NMiYjW3TwMt0Z6YRnO60thnJNRk+TV5Hzeme2BcfNZLfpIThmKo6u6gvX98VShvxvEyP4s5x5U4qprURIxUhYq86xiCZx57ax6PEtr02V1L29o7yPvNPSzPTsDtUbyvM6A1ESYcob0FuB6oB+qAjwOfi+akNJrTGa9Fm+GICz6mO7hFm5/sAHcMJ7s6Alq03ozjwviR8o25zsRJWbQjxSoi7zoGGGwILLRr8pO5akkmVy3N9O1bkOoi1mZhf93suG13njSs2c+tM3r96jitJtKEk3V8Qin1EaVUhlIqUym1WSkVuPSLRqOhsa+blBgnMdbgpcQbugbGLe3xkpdkCG1tX5dfi7wRofXPOPaS65qs0HrLL0bWorWnF4JI0CU+cbE2nr11vc+FDGC3WjgnL8kXJ51pdpxoQwQ+vTofi2ih1USeoEIrIv8hIl8MsP+LZtEKjUYTgIb+7pBu46FhD629Q+OqQnnJT3KCO4bmge5RTd+9+Gcce8lxJdLY343bMxzWHCNdFcqLJcaBLSUvZOZxINbPS2ZPdQfu4ZmPj+6saqckK4HMhFgWpsVRphOiNBEmlEV7KfDLAPt/BWyMznQ0mtOfhr6ukIlQTWb5xWCu49wkB7jtdLn7aOk1MnG9MdqxGce+c1yJeJSisT+8ggvuthoscSkRqwrlT0zWIgbqyid1zvp5KfQODnNghq1JpRQ7TrSxfp6xRGpZVrzOPNZEnFBCG6sC5OgrpTxA8KaUGs0ZTmN/N1nO0BnHML5YhRen3UqcxYlCUd1tfOh7Xcdl7aMzjr3kOhMBqOsNTySGmk8QkzY/rLGTxVl4LgNV+/AMhV6u03diLx07HgNgnSl0M+0+PtrSS0vvEOvnGfHuZdkJOvNYE3FCCW2fiCwau9Pcp3taaTRBaOjrDmnRNgapc+xPeqzheq7t7cRhs+CwG+ttHzn6LjaxsDa9YNT4HFeiOT68tbRDzceNeGoUcBatR7kHGTi5L+S4pj99i7oHbkUpxcI0F+lxMb7EpJnCK+zr5xtCX5JlZB5XNulSjJrIEUpovwM8JyKfFZGzze1zwF/MYxrNGcu/7HmWb7/7/Lj9g8Nu2gf7QsZoR8ovBo7RAuSa1aEa+rp8buOq7nZ+VbmDWxatI9sU1pHxXqGdOCFKKcVgNIV24XoA+o7uCD6HYTe9FW/g6e/C09uBiLBuXjI7ZlhoX6psxhVj9VWs8iZplTVoodVEjqBCq5R6DtgMfBB40Nw2ANcppZ6dzk1FJFlE/igi5SJySETOE5FUEXlJRN43f6ZMfCWNZuYZ9nj4n/K/8eOyNxgc0+TcGyMNadF2hY7RAsxLMKpDNQ92+xKh7tv/Ch6l+JeVl44b760OFc5a2uHuFtRAD/aMwgnHTgVbaj625Bz6jgQX2v4Te/GYy6CGWo0azevnpVDW0EWnuXY42mw7WM+Du6r4wvp52MxykEsy43XmsSbihFzeo5Q6oJTaopQ619y2KKX2R+C+PwaeV0otAVYCh4B/Bl5WSi0CXjYfazSnHPtaa+kY7KdraIDX6kcX0G8Io1jF8bZeXDFWEmKDL/8pSjRcmS0D3aQ47VR1t3O/ac3Oj08dN34y1aGGmo8DEBMli1ZEcC5cH9Ki7SnfPjIfs1H8+nnJKAW7qybfW3eynGjtZcvvS1mdn8QPrlnq2++wWylKi9MJUZqIMqnG75FARJKAi4FfAyilBpVS7Rit9x4yhz2EYU1rNKcc2+uNpSt2i5VnqspGHWs0rbRgyVBKKZ4rb+SDRWmIBM8pXJiaCMNW6vu6SHHaQ1qzXsJdS+sV2mi5jsGI0w42HMbdHbiIXG/5dqzxacZ8TIt27QwlRLmHPXzqt+8yrBSPf+ZcX/zbS0lWvLZoNRFlxoUWWAA0Ab8Rkb0icr+IxAFZSqk6c0w9kBXoZBG5TUR2i8jupqbA9VQ1mmiyvf4IixLTuTKvmG1VZaMK6Hst2mCu40MN3Rxt6WXTsoB/3j68RSv6VT92x2BIa9ZLjjO8MoyDTccBsEcp6xgMoQXoP7pr3DFvfDbh3GvBavNZtKmuGBalx7HjRHSFdldVO++caOOHm0p85R/9OScvicqmbpq7Z6/JgWZuEVJozdrGX4nwPW3AauB/lVLnAD2McROby4oCtv9QSv1SKbVGKbUmI0P3NtDMLMMeD683HGVDdhGbCpZxrLvVt+QGjOQlCO463lZmjN1YElpo802hxT7IESlHQUhrFiZn0VpcyVjN9nrRwFG4BkQCxmm98dm4ksuwp+Tjbh3po7t+fjLPVzRx9n9sH7Vd/asdDEWomEVprfEaXbE48OfH1Uuz8Ch4rly33dZEholitMPADRG+ZzVQrZTy/gf+EUN4G0QkB8D8qf/KNacc3vjshuwirsk3Ynvb/NzHjf3dOK124myBM4q3HaznnLxE8pJCF4rwWrTE9lI2+D63LFob0poFQ2gb+iauDjXUfDxq8VkvVmcCsbklAeO03visa8kl2NPm+SxagDsuWMCmkiyKM+J8W3pcDM+VN/LG0daIzK20poMUp52C5MDvwbn5SWQnxPq+FGk00yUc1/FbIvIzEblIRFZ7t6neUClVD1SJyGJz12VAGUbP2y3mvi3AU1O9h0YTLbzx2Uuyi8iLS2J1Wt4ooW3oM4pVBIq/NncP8PaJNjaVBO9T6yXZacfmiQWbkYH7zRWhrVkw1tIqJq4ONdR0LKrxWS/OovX0Hd05rjdtb/l2YnIWY0/OwZ5aMEpo189P4Q9b1vCnz671bds+v44Yq4VnIiR8pbWdrMpLDBojt1iEjSVZPF/epAtXaCJC8LTHEVaZP+/126cwSjROlb8HficiMcBRjG5AFuBxEfk8cAKjY5BGMyv0uYd4vqaca+efPWq/Nz6bF2csv9lUUMK9pX+lub+HdEecWRUqsNv42fJGPIoJ47NgZO4m2Vy0ABellExozcJIdaja3k5yXUm+/RUdjfyl6hAAdrFwefNx4pZfMeH1potz4XraX3+ApifvweocWffbW/EGSefdCIAtbR5DOx9HeYYRizXgdeJjbVy6KI1tZQ388CMlIZPIJsI97OG92k7+7vzCkOM2lWRx/46TvH60hcuLdYhKMz0mFFql1AcjfVOlVCmwJsChyyJ9L41mKvzs0Jt8Y/dfqPzYXSxKMj5ovfHZ6wtX+sZdk7+Ue0pf4sWaCm4sWk1DXxfz4wMvAd92sIGcxFhW5yUFPD6WvNh0WtzHuCH/vLDGe4tW1I2J035j1194uuogACmDvVw22Bu1NbT+xJVcithiaH7yntEHRIg/5yMA2FMLYHgId0cD9pTgLfs2lWTz5Sf2U9HYzZKs4EunJuL95h763R5W5SWGHHd5cToOm4VtZQ1aaDXTZkLXsYhkicivReQ583GJaXVqNHOWp013cLVfSUP/+KyX1Wn5JNodvvW0wTr3DLo9vFDRxMaSLCyW8CyyFQmFUH4ei1PSwhqf40rk2rr9qNcfGLV/b2sNnyhcQcdN/8aHHEZcMtoxWoCYrLNY/L8dLP756G3JL7pJWHk1APa0ecDIEp9gbCwx+teGipseaujiU1v30NEXvOBFaY3xJWRVbugvO64YG5ctSmfbwYZxrm+NZrKEE6N9EHgB8H7drAT+MVoT0mhmm5b+Hv7WeBwYXdKwrMP4kF+dlu/bZ7VYuDh7Advrj+BRHpr6A9c5fv1oC10DbjYundht7CU/yQkIKc7gpRr9yYyN485jb5HzztZRz6Wqp521aXkkxjj4sNMFQEd8etjzmA6WGAdWZ+KozRLr8h23pxo1m90toVtcz0txsTI3MajQ9gy4ue6h3TxWWhsyW3hvTQcxVgtLs4JX7vKysSSLY629HNLlGDXTJByhTVdKPQ54AJRSbiC8ppcazWnIs9XleEwrxt8NW9NjWLf5caOtoQ3ZRVR2NnGgrR6PUgGLVWwra8Bhs3B5cfgCNz/FsD7T48IT2uGqfWQO9hDX1cSwuZ52X2stXzvyGh/6/Z0ozzBrzP/4v/afGn1BwrVowYibvnWsldbewXHHvvzEfsobu4m1Wdh+JHCRDIDS2g6W5yRgt0780eddgvVYae2EYzWaUIQjtD0ikoa5rlVEPgBEv0aaRjNLbKs6SLYzAZfNTq1fAYjq3g4S7Q7i7aNrFG/IPguAx48Z3WqyHKNdx0opth1s4LJF6bhiwsk/NLh5TT7PfmGd0Z82DLr2bvP9PlBrJD/ta6vjvLYTxNSW0fnOo6T3tNBlc/Bk04iwba87zG/e38lv3t/Jq3WHw55fJLC4krA44kdlHgdjY4m5vvXQaIv1wZ1VPLS7mu98qJgPFWew/XBzwPOVUkbGcW7o+KyX/GQnm0qy+Ombx0K6ozWaiQhHaL+KsfSmSETeAh4G7ozqrDSaWWJw2M3zNRVsLCgh15k0ynVc09MxzpoFWJWaS1KMg8dMoc0cY9GWNXRzrHXialBjccXYuGoSrubu0m20u4xErIHqAwC813iCs3qM9adNT93LYNNR+pNyeKm2kn73EH86/h4ffP7n3PLm49zy5uNc/sIvaOybufKDIoI9dV5YFu3agmRyEx38eueIKLf3DfGPTx3gkqI0vv2hYjYUpVHR1ENdZ/+48+s6B2jqHpwwPuvPd68opq1viJ++eSzsczSasYQjtAeBS4DzgS8Cy4DyaE5Ko5ktXm84StfQAJsKSsh1JY52Hfd2kOca/yFttVi4OGshh7sMS2qs63jbwXpg4mpQ02GotZr+E3tpXfMJ+i026o7tBqD5ZCl2NUzC2o8zWF9Jz4GXiMtcSK97yCjr+ObjrE0v4OjHv8nzV9yKRymerZ7Zf29bWkFYFq3FIvzThoW8eriF10338I9fP0pHv5sffWQZVouwochIHHstgPu4tNZwxE2UcezPuQXJbCrJ4v+9dlRbtZopE47Qvq2UciulDprdfIaAt6M9MY1mNnim6hAOq43LcxeRM6akYXVvYIsWGJWJnDnGdbytrIHV+UkTVoOaDt37/gLAsou2cMyVQuOxdxkYdmM3XciZH7uX2IIVoDxk5y8jzhbD3+94EosIj234NAsS0rgit5g8V9KoAhwzgT11XlhCC/DF8+aTlRDLPS9W0t43xI9eP8rm5dmck2+8L6vykkh02ALGab0Zxytywhda0FatZvoEFVoRyRaRcwGniJzjVxVqA+AKdp5GczrzbPUhLstZhMsWY9QO7utAKYXbM0x9X1dAixZGhNYqFlJjRwR1pBpU9KxZMOKz9owFFC46j5aUAmyN71PW3kBxdwMeu4OY7GIyNn8XAGdmEVfkFgPwmws/yYIEwwoUETYWLOXFmkoGxvTZjSb2tHkMdzbiGeyn6an/w5F/XYHyBK7I5IqxcdcHi3jlcDM3/vZdOvrdfPeKYt9xq0W4eGFawDjtrqp2FqS6SHLaJzU/r1X77ecrkK9tQ762jR+9dmRyT1JzRhPKor0S+E8gH/ih3/ZV4F+iPzWNZmbpcw9xuLOF9RlGJmyuM5Fe9xBdQwPU93XhUYr8IEK70ozTZjjisMjIv9Wz5Y0oFV23sWegl56yl0lYtQkRISH/bFJ723jtWClLupuw5i1DLFYSVm8m99aHSD7/09y35hr++MGb2Tx/+ahrbSooods94Cs1GS5KKfY0V09p/vY0Y4lP+2v30/TEdxio3s9AdfC217efX0h2QizPlTdy7dnZrBpTACRQnLatd5Dnyhu5emnmlOb402uXc8+Vi/nuFcUsyYznwV1Te66aM5OgQquUesisCvVZpdSlSqkPmttHlFJPzOAcNZoZ4XBnMwpFcaJRCSjHNVLSsMYsXJEXxHVstVi4Km8Ji5NGVxH603t15CY6wq4GNRV6K99EDfUTv/IaAIoXXwjAtl1PsqS7iaQFRhE2sVhIvvBmrPGpFCdlcF3hinHXujRnEU6rfVyf3Yl48uQB1mz7L3Y2hecC9se7xKf+d/9ATJaRwe3fGH4sTruVf718EVaL8J0PFY87HihO+1hpLQNuD59bWzDp+QHMT3XxnSuKufvKxXx+3Tzeq+vkRGvvlK6lOfMIJ0abLyKJYnC/iLwrItEvlKrRzDAVncayEa9Y5vqEtoOaHiO+F8yiBfj1hdfzzOUjRdMauwZ49lAjN63OC7sa1FToP7kXAOfCtQAsW3oxAEvrDpDo7sc5/5ywr+W02flQ7vg+uxPx5xNmlnNb3QQjx+MtWmGJdVHwlWewZxbRe2h7yHO+fEEhVd++fJw1CyNx2pffH3EfP7iriuXZCazOn/4XHm/2eKSaHGjmPuEI7S1KqU7gCiAN+AxwX1RnpdHMApUdxgfzWYlGUYkcsxB+XV8X1b3tQHCLFsBlixm1xvZ371bj9ig+O0UrKlz6T5RiT5+PNc5Y2hObWYTbamdjg5E9HDtvVajTx7FpXgknuts40FYf1vhhj4dnq42kq8qOpkndC4wG9K6SS8m99SFicxYTt2QDPRWvBY3TghFPzkkMvL7YahGuXZ7Ng7uqeOdEG4cauthxsp3PrSuYVkMCL4sz41mUHqfb6GnCJhyh9f5lXg08rJQ66LdPo5kzVHQ0kudK8onlaIu2gxiLlfTYuLCupZTiN7uqWDcvmZLsqRfBD4f+k6U4/MRULFbcGUXM629HiQVH/tkhzh6Pt8/uB/7yE1J/921WPvlDX1WsQLzTdIKWAcONWtk5eaEVm53Cu14mcc3HAHAt2YCnpy1knHYi/mvzcvKTHXxy6x5+9PpRrBbhptX5E58YJpuWZfHq4Ra6B2YuaUxz+hKO0O4RkRcxhPYFEUnALMeo0cwlKjubR8VYE+yxxNliTIvWWEMbrkVUWtPJ/rquqFuznoEeBusrxlmtGYVGy2h71qJRtYXDIceVyP+e9zE+v2g9NxWt5khXCze89tugDeW3VZVhEwuX5pxFxRQs2rHELbkECB2nnYhkp53HP7OGus5+fvXOSa5ekklWQuzEJ4bJppIsBoc9vFQ5+vkOexSHm3sidh/N3CAcof088M/AWqVULxCD0T9Wo5lTVHY2+RKhwHRPOhN9yVCh3MZjeXB3FTFWC59aFbz1WyTorz4ASo2yaAEc+csAcE0iPuvP7UvO5ycf2MxPP3Atvzj/47zRcIzv7H0h4NhtVWVckl3E2vQCjnS1BBXkcLGnFZhx2lendZ2185L5z00lANz6gXnTutZYLliQSrLTzraDI+7jisZuLvrZWyz6/is88q7OStaMEE7h1QvNnysiEd/QaE5Fmvt7aB3oHZc1nOtKpLa3g9reTtamh2edDro9/G5PNZuXZ5PiCq8hwFQZOFkKMF5o8wyhdcyfXHw2EDcVrea1+iN8/71XuCS7iCvzFvuOHe1qoay9gVuL15MU42DIM8zx7jZfnHuqxC3ZQOeeJ1AeD2IJxx4wrPu27b8i9bIvITbjdb/zooVsXp7NvJTILv23Wy1ctSSTpw7W87WnD9IzOMxDu6pw2q0sz07gi398j3Pzk1mcOXGXIM3cJ5y/4K/7bd8GtgF3R3FOGs2M403i8bdowSu0nUHLLwbi4d1VtPQO8fn10XUbg5EIZXElYU+fP2q/86zziM1bRvzZV0XkPj9ev5n58Sn8+OAbo/ZvO2ksA9pUUOJ77aaSEDUWX5y26r2wz+l69ykaHvkKHTseH7U/0iLr5bNr81EKfvnOCX73bjXXlGRR9o0NPHfremKtFq5/eA99Q7rRmSYMi1Yptcn/sYgUAP8VtRlpNLPA2KU9XnJciRzrbjWKVYThOh50e/j3l99n3bxkPlScMeH46eJNhBrrbbIlZlL0vQMRu4/TZufq/CVsPfwubs8wNosVMNzGS5MyKUpMJzHGyAKu7GziapZO637eOG1X6bawrfLBBqPzUMebD5J8waendf9wuGJxJq3/9uGAx7beeA5X37+TT23dw6+vX0l6fOTiw5rTj/B8MqOphmn+F2k0pxiVHc3YLVbmx6eM2p/rTPT1pg3Hon14dxXHW/u4+4riiCwlCYXyDNNf9R6OeVOLw06WDdlFdLsHeLelBoCOwT5eqz/CxgIjDpoeG0dKjDMiCVH2tALiV15D87Z/p+/E3rDOGWw0qln1HHqFweYT057DdLhqaRY/3ryM58obWfYf23nivcmvL9bMHSYUWhH5qYj8xNx+BrwBvBv9qWk0M0dFRyNFCWk+S82LtzoUjG/4PhZ/a/bDS6ZW6m8yDDYcRg32RiQOGw6XmPWcveUZX6ipwK08bDKFVkQoTsqY0nFlegYAACAASURBVBKfQOTe+iDW+HRq/vt6XyP7UAw2HsGeWQRK0fHW1ojMYTrcedFCdv/jxeQlObjuod3srdZtvM9UwrFodwN7zO1t4C6lVPT9MhrNDFLZ2TTObQwja2lhYot2Jq1ZMNzGMD4RKlpkORNYmpTJ9jpDaLdVlZEa6+K8zJH48OLEjIhYtAC2hHTyv/Qog03HqHvg1gnHDzUeIW7JJbiWbKD9zQcnVdkqWqzITeSPW4wSmO/WaKE9U5lQaM2ax97td0qpt2ZiYhrNTDHs8XC4q2VcIhSMCK0go6zbQDyyt4bl2QkzYs2CkQiF1U5s7sxFcjbkFPFGwzEGht08W13O1flLRnkBipMyqOntoHtoICL3cxVfSPpHvkXnzscZqA3eJ9cz0IO7ox57ZhHJF32OocYj9L1/anxUzU9xEWO1UNHYPdtT0cwSodrk7ReR9wJs+0Uk/FRAjeYU52RPGwPD7oAWrbcMY5YzHvsYt7I/SilKazq5YEHqjFizw92t9Bx4AUfeMt9SlpnAG6f970Nv0TrQ63Mbe/G+hoc7x7epmyopF98CQFfpM0HHDDYeBSAms4jEtddhccTT/uZDEZvDdLBahLPSXVQ2aaE9UwmVdbxxxmah0cwCPUMDtA/2847ZcSaQReutDjVRfLaqvY+2viFW5U6uqfhU6Hr3aeoe/CLu7mZyP/vLqN/PH2+c9t/2/RWbWEatqYWR17Cio4lVaXmjMpSnij1tHrEFK+gu3Ub61f8UcIw3ESomswhLbBxxy6+kp+yVad03khRnxFOuLdozllBCaweyxrqKReQCILxq4xrNKcrgsJuiP91HQ1+Xb18gi1ZEmB+fQmF8asjr7a0xknUCdZOJJJ27n6D6p9cRO28l87723IwlQnnxxmkPdTRyWc4ikmKco457C1WUttbydtMJ/vvQW7xx9Zf5QOb8QJcLm4RVm2j+y30Md7diDfBeDPkJLYCzaD1du/+Eu7MJW4AvUDPN4ox4/nKoAfewB5t1Kos9NKczod7x/wICpfp1EoF1tCJiFZG9IvKM+XiBiOwQkcMi8piIzJw/THPG8Vr9URr6uvin5Zfwy/M/zrMf+jyZzsDF/x/f8Bl+uHZTwGNeSms6EIGzo9hAQHmGafrTt4nNLWHhd3fOuMh62ZBjiNlYtzEYHYzmxSVz3/5X+HHZG7iVhxdrK6Z9z/hzNoFnmO73ngt4fLDxCBZXsq+DkXPhegD6ju6c9r0jQXFGHEPDiuNtfbM9Fc0sEEpos5RS49pnmPsKI3DvfwAO+T3+AfAjpdRZQBtGjWWNJipsqyrDYbVxzzlXcuviD3BVfvCEomUp2cwbs752LKW1nRSnxxEXG05V06nRueuPDNSWkb75uzMalx3L5nnLibPFsHn+soDHL85eSFFCGq98+HaWJWexYwrN4MfiXLAWa1JW0DjtYOMRnzVrjD8XLFb6ju6Y9r0jQXGG0fVJx2nPTEIJbXKIY84QxyZERPKBa4D7zccCXAr80RzyELB5OvfQaIKhlGJb1UEuz12EK0KCVVrbEVW3sfIM0/TkPcTmlpC49uNRu084XJG3mI6b/o35QdzpD190A4c//k0+mHMW6zPms6Pp5LSX2ojFQsLKa+je/xzKPTTu+FihtcTGEZu/nL4jp4bQemseVzbpzj5nIqGEdreIjFu8JiJfwFhTOx3+C/gGI+320oB2pZS3uWM1kBfoRBG5TUR2i8jupqbIrNfTnFkcbK///+3dd1iUx9rA4d9soy0dRLqKFRsa1CRq1JhqienRNE3vvZ7k5Es5JyemN1NOKqknaqommkRjSTQGsaDGhoBKld7LLrs73x+7IAgo1aXMfV1cwr5tXgSefd6ZeYZD5UXMDm86I2ut4qoaDhVWdepAqNLNSzFn7bVnsy0sst+ZtMdpQ/1R1xMCIygwVZJaVtDuaxpHz8JWWULlgQ0NXpdWCzUFh+3FKupxGzCBqtTNx11AvsF5OnHebYCHAR83vZri00sd7zf2XuA6IcQ6IcTLjo/12B/p3tPWCwohZgG5Uso2BWsp5XtSylgpZWxgoPMHOSjdz/J0eyH8mcd5XNwaO7LshQg6M6PN/2lhl8hmW2tCoH15uo54fGwccTZC70LZ9uUNXq8pSAOrpUFGC+A2YDy2ymLMOQdOeG5T1l6S7goi8735WCuK2t3WYwkhGBLooTLaXqrZQCulzJFSng48DRxyfDwtpTxNStmeUccTgQuEEIeAr7A/Mn4d8BFC1HZwhQGZ7biGojRredoexvqHtmp92eNJrB1x3EkZrTk3FVPaDnym3tQlstnWGO4ThLtO3yGBVuNqxH3oNMoTlzfIPs3HjDiu5RblGBB1gsfHNlMlGYsuQ1rNlGz6gpTHhlO+8+d2t/dYgwON7Fd9tL1SSypDrZVSvun4aPfENCnlP6SUYVLKfsBcYI2U8ipgLVD7dn0+8EN7r6Uox8qtKuOvvLQmR8y2VWJWKUGeLvT1cu2wc9ZXlmjP4Dxjjj/yuSvSabSc4h9GfH77Ay2A55jZmHOSMWcfHcncXKB1CRmGxtV4wgFRRz6/C1PWHsLuWEr/JzejNfqT/saFWMsLO6TNtYb08SCzpJpyk+XEOys9Sld6e/wIcL8QIhl7n+2HTm6P0gOtyNiHRHZY/yzYp/Z0Zv9seeKPGEKGYQiKOvHOXdCEwAi2F2Rish4NMFJKPk/Zymu7f2/VuTxj7HV0at98ANTkpSL0Luh8Gw7rEBotrv3HNcpozXkHyfroZjLenkvaq7Mp/v0jAi74J8YRZ+PWbywhN3+KrDFREr+4tbeKOf8wWR/eQMbbc8l4ey6lCd/UbRscaB8QlZxfQXFVDf9cuY+88o4pVal0bZ03F6EFpJTrgHWOz1OB8c5sj9LzrT+SQoCLB2P9mxxr12pmi43dOWWdVt/YWlVKxb71+J97b6ec/2SYEBiB2WZlR2EW4wMjyKwo4eY/l7Iiw167eHLQAE4JCGvRufT+EbhEjKY88UcCZjwEOFbtCejf5GN1twETKPj5JWzmKoTOhaI175Cz5BH7ufzs1/SeNJ/AC5+sO8Y1IgaX8FEUb4jDb/ptLb5PW42JjDcuxpS9F71/BJbSXCr3/4HnKRciNFqGOALtvtxynlmVxHe77D1w/z5/aIuvoXRPXSmjVZROF5+XxoTAiA6rR7wvt5waq2R0J2W0Fbt+AWsNxm742LjWhAB7Vaj4vDTiDiQw/PsXWZudwguxM/E1uPF04q+tOp9nzGwqD2zEWl6IrcZE9eHtjR4b13KLmgBWC/tv82HfTW4c+exO3AdPYuBzexm4cB8DF+4j9KY4RL0ykUIIfCYtoDp1M6bMPS1uV87/HqD68DbCbl/MwIX7CLn+AyzFWVT8vQqAgQHuADz1y36+23UEXzc9nySkY7XZ+5uzS6sZsnANq/ar2RQ9jQq0Sq9RYq5iX0le3UjYjnCosBKAgQEeHXbO+sq2L0fr4Yf7wNM65fwnQ5iHN8FuXvxj6wqu27CY0b4h7LzwAR4aOY0HRkxhefoetuZntPh8njFHq0TlLn6ImryD+E67pcl9PUaeh5jxCH7n3IPfOfcSeuuXRDywEr1/+HGv4X36VaDV1S1MYDNVYM5Jbnb/0s1LKfrtLfzOewDPMbMd7ZyF1uhP8YY4ANwNOiJ83difV8Hs6CDeuWQkGSXVrE22L8Dw/JpkkvIqeO2P1BZ/L5TuQQVapddIyE9HIjs00GaUVAMQ6t3xA6GkzUr5zhUYR89AaJ3ay9MuQgjODB6IRPLGhAtZe/6tdTWR7xo2qdVZrWv/WLTeQeR+9ySFq97E79z76oLbsd44sJnoSgsfDj2XoCuex/u0eS16mqHz6oNx1AxK/vyM8r9XkfLYCJL/MQzTkcZThaSlhuzP7sR1wHiCLnvu6H3rDHifdiVl276vmzIUE+JFpK8bcfNimDOiLz5ueuIS0skurea/mw5jdNHy875cskurW/z9ULo+FWiVXqN2isn4gNYF2u93ZbPpUNMjUDNLqtBpBH2MLu1uX62itf8l+5PbyfzvNVjLC7r1Y+Na755+CemXP8Fd0ZPQiKN/drwMrnVZ7Zb89Badq7ZKVE1uij24Xb6wyf3i8w7zcMKPeOpdeGL7z/x+JKVVbfaZtABLcTZpL56D0OoRGh35y59ttF/5zpVYS3MJnPMEQqdvsM170oIGA6s+nTeG7fefgZ+7AVe9lnljQvhmZzaPrdhHjU2y9NpYbBI+39ryDF/p+lSgVXqN+Lw0hngH4uPS8gqiUkpu+XonT/+a1OT2jJJqgr1c0Go6ps+3+I84suNupST+Kyp2r8YlbATGUed1yLmdyah3wc/Fvcltdw2bhJtWzyfJW1p8Pp/J1+MaOZaw2xc3Wfe50FTJFes+J8zDm90XPkSUpz/z1n9Bbr3Vmk7EM2YmHtHT8TvvAQb8KxHfM2+j5M/PGz1CLt4Qh9Y7COOIcxudwzVyDC5hIyn+42MAvN30+Lofbe+CceFUW2zEJaQzPzaM84b24fR+vsQlpHdqpSrl5FKBVulRKmpMTFv5Ds/t/A2LzVr3upTSPhCqldnskTITueVmUgoqm9yeWVJNmHe7Sn/Xqc7YTfant+M+bBpDFuUxZFEuUc/uQuvW+WvcOpOXwZWzQgaxPH1Pi4OL++CJDHhmK4bAfk1uv33Tt2RVlrJ46jWEG31YOu1aCkyV3LhxaYvbJXQGIh9ZTd95L6FxcSdgxsMIrZ68Zf+u28dSmkdZ4nK8T7u6UTYLjoFVk+0Dq4rWfdDo/saF+zAsyIhWI3j8rEGAPfjuySlnS3pJi9uqdG0q0Co9SmJhFuuOpPDY1pWc/tMi9hTbp1CkVRSRW13e6v7ZxEz7H7tDhZVYrI1r5mYUV3VI/6zNVEHGW5ehcfMi7NYvG4yC7Q1mh0dzuLyIv4vav9T19oJMFh9M5B+jzmS84/97tF8IT8Wcw/L0PWzKPdSm8+p8+jbKakv++hKsFnwmL2j2OJ8zbsB9yBlkf3wT6a/MpKbwaNE7IQTvXDKSuLkxDPC3D6i7fHQIrjoNcQkte5SudH0q0Co9SlKpfWrEC7EzOVhWyOk/LSK1rOBo/2xrA22WvbyixSZJL248QCWztLpDAm3hqjcxZ+0l9JYv0Pn0bff5uptZjkpdtXWo2+OZxFV4G1y5L/qMBq/fOWwiAS4ePJ24qs3nDpjxMEJn4PCL51Kxbz0lf8Th2j8W17ARzR6jdfcm8tG19L36DSr2rSf99TkNtk+JCuDqU47OI/Z203PxyGD+tz2T6hrrsadTuiEVaJUeZX9JHnqNlvuGn8Hm2XcjEFy+9jN+P5KKi1bHKN/gVp2vto4xQEpBw4LwpdU1lJusHfLouGL3alwiRmMcPr3d5+qOgt29iA0Ia3eg3V6Qyfdpf3Nf9BmN+uKNehceGjmVXzL3tyurjXzIPkL68HNTqU5LxGfSghMeJzQa/M6+i6ArXqD60FaqDyced/8F48Ipqqph+Z6cNrVT6VpUoFV6lKSSPKI8/dFptPT39Cdu0hVsLcjg7X2bGOsXiqGV02QSs0oYF25fmvnYQJtR3DFTe2w1JiqT/8Rj6NR2nae7mx0eTXxeWqsGLB2rNpu9J3pyk9tvH3p6u7Na98GTiHp2J35n34UhZBjep85r8bHep85D6Ax1c2ubc+agAMK8XdXj4x5CBVqlR9lfmscQ76PLJ86JHMF9w89o0/zZcpOFA/kVzBzWBxedhpT8hgOiMh1zaMN82hdoqw8mIM1VuPfyQDsrLBqJrCvN2FL3b17GiO9eZPh3Lzabzdaqn9UmtGNFIY2LB32vfoOBz+1Ba/Rr8XFaox+eY+ZQsukLpMXc/H4awbWxYfy8L5esEjWntrtTgVbpMaw2G8ml+Qz2arhO8cJTZvDwiKncNGRCq863M6sUKWFsmDf9/dwbZ7QlVUD7M9qKvetACDyGnHHCfXuyMf6hhLp7t+rxcY3Nytv7/kQCw7z7sGBgLPcNP/738dYhp2HQaFl8cEc7W9w23pMXYC3Lp2zHCgCkxWxfkSgnGXP+4bqRyfPHhWOT8MU2Nae2u+u+5WYU5RiHK4ow26wNMloAg1bH8+Nmtfp8tQOhYkK8ifJ3bzTFpzajDWnn8niV+9bhEj6qVZlRTySEYFb4MD5P2UZFjQkP/YmLgOwszMZktfBkzNlc3j+mRdfxMrgytW8Uy9P38NL4k18MxDjiHHTefSnZEIfW6E/WB9dRk3u0mEbIjR/jM3kBgwONdXNqH5wa1WH1uZWTT2W0So+RVGIfcXxsRttWiVkl+LnrCfNxZYC/BykFFQ3mQWaUVBPgYa/wU5+UkpL4JdhMFceeEoCawgzKdqwEVP/ssa4cMIYKi5nv0v5u0f7xeYeB1lf7mh0eTVJpXt3PTHNWZSaRWlbQqnOfiNDq8J54DWXbl3P4uSkgbQQv+C8hN3+GIWgQRever9tXzantGVSgVXqM/Y4/msdmtG2VmFlKTIg3Qgii/N0pN1nJKz/ar5ZZ0vTUHlP6TjLfvoK8759p8rwFK14g/ZUZlG1bpvpnjzEpqD8DPP2JO9CyKlHxeWn0cTUSafRt1XVmR9ROJ9rd7D751RWc++v7jPz+Jd7Y8wc22XgedVv5TLkRjbs3vtPvIOrfO/GddjM+E6/GZ+pNVCX/iemIvRLZ5aNDcNNr+Dih7f3JivOpQKv0GEmleXgbXAl0NbbquMTMEm5euqNBQQqL1cau7FJiQu1VmaIcq/PU76fNKK4irKlAm20fzFO4ehGW0tzG2x2F6TM/WGBfHUb1z9bRCA3zB57CmuxkDpc3ri/9SMKPLEs7Ghzj89u27GGk0Y+RvsHH7Q+OzzuMRDLQM4B74n/gnF/eb1BtrD1c+g5myFsFBF/zJpp6P6/ep18NQkOJY9Wg2jm1X23PomjnKrI/uV2VZuyGVKBVeoz9JXkM8Qps9R/d7/8+wvt/pbGx3sIBSXkVVFtsxDjWmY3yt9fprd9P21yxCnP2fgBkTTUFK19qtL22GD42K8XrP1D9s8e4NioWieTT5K0NXt9TfIQX/l7HY1tXIKWkyFTJ/nYsezg7PJoNOYcoMjVdXjM+Lw2NEGyceSevjr+A37IP8GXq9gb7WG1tz3Kb+jnV+wRjHHUexRs/RTqC+oJx4ZRUVnPoo9soWvPOcZfrU7omFWiVHiOpNI/BbXhsXLsk2fLdR4sDxKc5ljUL9Qagv587QkBKvj2jNVnsj5HDfBpPIzEfSUIfEIn3qfMoXP1Wg6xW2qyY8w/hMWwaIdd/AIDH0GmtbnNP1s/Tj2l9o4hL3tIge/vE8Th5d3EOWwsySHCs9tOeQGuVNlY2M50oPi+NET59MepduCd6MjF+IfwrcXVdVvva7t8JXfKvJjPv9vCZtABLYQYVe9YAMG1gAFfp4nEpsg+Yqty3rkOvp3Q+FWiVHqGixkR6RTFDvPq0+tisUhNAgyo8X27LpJ+fG8ODPAFw1WsJ9XKty2izSuzHhDYx4th0ZD+GoMEEXPDPRlltTUE6WGsw9InCa/xlRDywkoDZ/2h1m3u66waNJ7WsgA05BwGw2Kx8lrKNKX0H4KrVEXcggfi8NASCcQHHX8S9OeMDw+njauSHtMb9tFJKNuen1wVxIQRPxpxDclk+X6ZuZ2POQR5M+JGcqjKe27mm7TfaBGPMbDQevhT//iEAGmzcVrmYJG0kwqsvFSrQdjsq0Co9QrJjZOhg74BWH1ub0SblVZCUV05aUSW/JeczPzYcTb3l76ICPOr6aDMdc2iPLVYhpcR8JAlD8BBcQobiFXsJxb9/VJeZmR3TOAx9ogAwjjoPXRveHPR0F0eOwMfgxoMJyzFbLazKSiK7qpS7h03mosiRfJm6nd9zUhnqHYi3oW0lMDVCw2X9RrHk0A6u++Mrik1VddsOlOZTbK5qkC3PiRhOjF8ITyeuYu66z+ln9OWaqFP46EBCh2a1GoMrPpMWUBq/mIx3rqTot7fxLjvI2+7zyPA7hcq961Q/bTejAq3SIyTVjThuS0ZbzVmD7AF6+e4cPtuagZQwP7ZhplR/Lm1GSW35xYZ/5K0lOdiqSnHpOxgA96FTsJYXYCmyr9hSO19S7wi0StM89C68P/FSNuen84+tK4g7sAV/F3dmhQ9jwcBYisxVrM460ObHxrVeHn8Bj42azmcp2xj+/YvsKswGqFuEov75a7Pa1LICcqvLWTL1Gp4dez4C2pXV/nD4b+as/pg5qz/m0jWfkFSSR9DlzxN48TOUJnzNkc/vxiVsJFWDz2d59SAsxVmqn7abUYFW6RFqp/YM9PRv1XFWmySnzMSpkb6MDPZk2e4jxCVkMDXKn/7+DRcqj/L3IKfMRLnJUles4tjBULXTMgx9hwDgGmEvolCdZi8ib85NAa0evV8YyvFd2m80dww9nVd2/843h3dx5YCxGLQ6pgcPItTd3nfe3kDrotXx7Cnn89esuzBbrTyYsBywjzg26lwY5h3UYP85EcO5ZcipfDJ5LmMDwgg3+nDj4Al8dCCBQ2WFSClbnW3+3/Zf+CMnlbSKIn5I281/929C6PQEznmCAU9twTjmAvpe8yb3TBnI8mr7GzjVT9u9qECr9Aj7SnIJc/duUTWh+nLLTdgkhHi7Mjs6iN9TC0nOr2DBuMb9foMC7VN8Xl6XwuGiKjwMWrxdGxZXMx+xjzg2ODJal/BRIETdai3m3BQMgf173XqzbfXSuNmM9Q/FKm0sGBgLgFaj4dqBpwAwITCyQ64TGxDOIyOn8WtWEn/mHCI+P43YgDC0moZ/IoUQvHv6pcwdMKbutUdHnokA+n/9HzRxD+HzxRMcOEEhjFqHywvZWZTN46Ons33O/UwPGdRgypFrxCgi7v0Bj6FTuHR0CGeedip5Gl9SNv/cIfetnBwq0Co9wpaCdMb4h7b6uNqC7cGeLsyKtmcvHgYtl4xqvJze7OggLhrZl6d+TeKtjQcJ9XZtNEXDfCQJoXdB728P1Fo3Twx9BtZltDW5KXX9s8qJuer0/HTWDXx75vwG/7+PjJxG3KQriPEL6bBr3Tb0NAJdPXhs2wp2FGa3OFsON/rw9bRreSrmHJ6MOZsam5V/7VjdomN/TN8LwOzw4Y5/ozlQms/+ksbzrwHevHgkSZ5jqNi3nozipqclKV2PCrRKt9ee+ZTZZfbRwyHeroyP8CXC142rxoZidGlcBtxVr+Wb+bF8edVYfNz0jAz2arSPKXs/hqBBDTJW14gYqtMS7QOlclNU/2wr9XX34qLIkQ3e1Hgb3Jg/aFyH1v/10Lvw8IhprD+SSo3N2qqfp9kRw3lyzDk8NeZcbh96Ol+kbjtheUewL3Q/2CuwblrarPBh9tfTmi6k4W7QMXH6BQRaC1j0/W8tbp/iXCc90AohwoUQa4UQe4QQu4UQ9zhe9xNCrBJCHHD827qaakqvtSXfvrpJWwJtVr2FAbQaQeL9Z/DGRSOa3V8IwbyxoaQ/cRafzmtcxN6ck1T32LiWS2QMNbkp1OQdxFZdpjLaLqw2q4W29/8+NHIqLhod/3ZktftLcnlw83KSS/Mb7FdWU83a7GRmh0fXvRZp9GNUMxWrdhZm8dKudUSNPw+A+8Oz2tQ+5eRzRkZrAR6QUkYDpwJ3CCGigUeB36SUg4DfHF8rygnVjhBty3zKrNJqhIAgT3vfrq+7ARfdiftP3Q063A0Ns15ptdj7YB0DoWrVDogq3fItgAq0XZiH3oWXxs3msn6jCHEMuGqtIDdP7hhmz2of3fITMT+8wsu71zPq+5d5bffvdTWTV2UewGyz1tVdrjU7PJqNuYcorFexSkrJzX9+zUNbfiTN3Q9D0EA0VUVtv1HlpDrpgVZKmS2l3Ob4vAzYC4QCc4BPHLt9Alx4stumdE/x+YcZ6t2nTfMps0urCfQwoNe2/1fBnHcQrJa6qT21agNt2ZZvABVou7prB8ayZNq17TrHQyPsWe3zu9ZybugQts6+l+khA7lv8zLOWPE2B0ryWJ6+Gx+DG6f36dfg2FmOilU/16tY9Uvm/ro3lD9m7CXq+SQCZqlcpLtw6nq0Qoh+wBggHgiSUmY7Nh0Bgpo55mbgZoCIiPYN7Ve6Pykl8XlpzAgb1qbjs0pN7V5Ptpa5dmpPcMOMVucTjNYzkKqUvwDQB/bvkOspXVcfN0+WTLsGs9XKRZEjEEKwbPr1fJaylXvif2D0D6+gFRpmR0SjP2YEem3FquXpe7gyaixSSp5K/JVIoy9uWj3L03afcHF7pWtx2mAoIYQR+Aa4V0pZWn+btE9Ea3IympTyPSllrJQyNjCwY5ZDU7qvQ+WF5FVXtLk/Lbu0muAOC7QNp/bUEkLUZbU631A0baxkpHQvs8Kjubjf0UFcQgiuHRjL7oseZHrIQMotJi6JHNnoOI3QcGHECJYe2skT235mWdpu4vPSeHzUdC6MGMEfOQcbVLFSuj6nZLRCCD32IPuFlPJbx8s5QohgKWW2ECIYaHp8u6LUU1fBp5ULf9fKKq0mJqRtfXHHMh9JQuvhh87YuGiGa2QMFbtXqcfGCiHu3iybfj0HSvMZ5NV0ydAXxs3EZLPw7x2rEQgijb7MHxhLQn46C3et4ZfM/VwxoPFgPKVrcsaoYwF8COyVUr5Sb9MyYL7j8/nADye7bUr3E5+XhqtWx0i/xvNeT6S2KlSwV+uKXDRFSknFnt9wjRzT5PbajFYFWgXs2e1g7+aXdPQ2uBE3eS4/nnU90T59eDF2FgatjlMDIwlw8TjuOrpK1+OMjHYicA2wSwiR6HjtMWAhsEQIcQNwGLjcCW1Tupn4vDRO8Q9r1M/VEvWrQrVX1YE/MeckE3DBP5vcXhto1RxapTVmhkczs970H61Gw4ywoSxP34PFZkWnKox1C84YdbxBSimklKOklDGOjxVSygIp5XQp5SAp5VlSyo5d5FHpccxWC9sKxSL95QAAE2lJREFUM1vUP5tfbmpUg7b+HNr2Kt4Qh3DxwCv2kia3G4KHEnjps/hMvKbd11J6t9kR0RSZq/gz95Czm6K0kKoMpXRbXx/aiclq4dQT1LstqjTT79nfeHb1gQav11aFau+jY5upktL4xXiNuwyNq7HJfYRGQ+Dsx9D7q5HySvucEzIEvUarHh93IyrQKt1Scmk+t276hgmBEcyJGH7cfX9PLaTCbOWldSkUV9XUvd5RGW3Z1u+wVZfhM3lBu86jKC3hZXDlyylXctvQ053dFKWFVKBVup1qSw2Xr/0MndCweOrVGLTHH2qwLiUfnUZQUm3htd9T614/tipUWxVviEMf2B/3wZPbdR5FaalL+41mQCuXhFScx6kFKxSlLZ7Y/jPbCzNZNv06Io1+J9x/XXIBkwf44eum57XfU7n3jAH4uOnbVRUqf8WLlMYvBqD68DYC5zyJ0Kj3rYqiNKb+MijditVm4+MDCVzebzSzT/DIGKCw0syO7FKmRgXwf+cMbpDVtrUqlJSSwl9exVpegM67L56xl+A77ZZWn0dRlN5BZbRKt7Ip7zAFpkou6de4ok5T/kgtREqYNtCf0SHeXDyyLy+sTSbQw0BmSVWbqkJZijKxFGfT9+o38Dv7rlYfryhK76IyWqVb+TF9Dzqh4dzQIc3us+ZAPhUmC2Dvn3XVaRgf4QPA25eM4owB/tz53d9szyxtU0ZblRIPgNuACW24A0VRehsVaJVuZXn6Hs7oO6DZlXr+SC1g+rubuOqLbUgpWZdcwOn9/OqWvgvydGHlTRP44PLR+LrpGRfR+vKLVanxCJ0Bl4jR7boXRVF6B/XoWOk2UssK2FOcw02Dm88k4xLSEQJ+2J3Dk7/sZ0d2KU+f2zD7FUJww4QIbpgQ0aiIRUtUpcTjGjkGjb79pRsVRen5VKBVuo3lafYJ+rPDo5vcXmGysGRHFteNC6ewsoZ/rbIXqJga1fw0iOZqzTZHWi1UHdyC75QbWnWcoii9l3p0rHRZUkoOLTyTrA9vBOyPjYd59yGqmRVPvtmVTbnJynXjwvnoitH083PDTX+0f7Y5Res/ZP/dfbFWFJ2wTabM3UhzpeqfVRSlxVSgVboM05EkLGX5dV+X71xJ5d61lPz5GYWFGaw/ktJsNgv2x8ZR/u5M7O+Hr7uBVbecxg/Xja/rn22KzVxF3rdPYC3JocQxL/ZY5pwULCU5QL2BUFEq0CqK0jIq0CpdgqW8gINPxnLo2cnYqsuRUpL33VNoPfyQFjObf3kTi7RxQTNzZw8VVrI2uYAF48LrHgcPDPDg7CGBx71u0br3sRRno/Xwo2RDXKPt1qoyDj4zgYPPnIq1opiq1Hi0Rn+1Co+iKC2mAq3SJRT+/Co2UznmI0lkf3Ib5TtWUH0wgT5XvIBrRAxy82IGePpzWp/GCwjYbJKX1qUgBFwbG3bc61QmbyJn8SNYywuxmaso+Gkh7kOnEHDB41SlxGPK2ttg/6LVi7CWF1BTmE7Wh9dTlfwXbgPGt7pvV1GU3ksNhlKczlJeQOGq1/EadxkuocPJ++5JyneuRB/YH5+J11JQmkvk149xl48/GtHwvWFqQQU3LN7BupQC5seGEeHr3ux1aoqySH9tDtayPEo2forH8LOxFGcTeuuXuIQMI2fxwxRv+ISgyxcC9my2YOVLGEfPxCP6THL+9wAAXuPVUsmKorScymiV47LaJC+tTSH06VU8vyYZi9XWquNLzdXE/PAKN21cSom5qsl9Cn9+BZupgoA5TxBwweN4RE/HWl5AwAX/ROj0fOs/iBqhYUb23w2O259bzuiX17Mts4QPLx/Nx3Njmm2HtFrIfPdKbKYKwu5YgtY7iJI/P8N96BQ8hk1F5x2EcdQMSjZ+hrRZAUc2W1FI4IVP4nfufRhjZgOqf1ZRlNZRGa3SrKS8cub/L5G/DhcxONCDR3/ay7e7snls+iBcdBoMWg2TB/gdtyj/or0b2VGYxa6ibH7O2Mf7Ey/jvLChddvt2ewbeI27DNewEQCE3PYVe9d9hffp1yCl5L2sA4SFjGR0/BJs815Go9MD8MyvSUgJOx6cQpiuAkthBnr/8Cbbkff901TuW0/ITZ/gNf4yPMfOoXjDJ3gMO7NuH5/JC8hIXE7hL69hCBlWl826DRgHQOjNn1K88VM8hp/V7u+toii9h8polSbllJmY8tafJOWV88VVY9j3yDS+unosKfkVXPhxAue/H8/0dzcx4fU/2JlV2uQ5Ss3VvLx7PTPDhrFp5l146l04f9UH3LhhCSXmKqSU5Hx5f102W2vR9hJGr49kdtw2vk/dT0pZAcuMZyPKcvjz8UmYc1PZl1PG/xIzufP0fvjsXkLyI4NI+ecozHmHGrWjfNev5C9/Fp/J1+Ez6VoAhM6A79SbMAQdHdTkGTMLrXcQOV89SPorM7FWFhF44ZN127UePvifczfiBMvyKYqi1CfaUhmnq4iNjZVbtmxxdjN6HKtNcu57f7HxYCGb753MyGCvum3FVTXsyy0HIDm/ggeW7aaoqobnZgzjgakNR+L+Z8dvPL5tJZtn3c24wAiqLTU8nbiKF/5eS4ibF/9z0+L3zeMEXPgkfS56CrAXnYh48QdsPtmU1ZjBtQyrvhKfQ5O5UbORS9PewKiDHYFnk1Jcw9ywCmqS1uM2aCKmjF24hAyj32O/I3QGwN4vm/pEDDrvIPr/Xzwal+b7cAFqCjOpKUwHQOvug0vI0OPuryjdkRBiq5Qy1tnt6C3UW3OlkWdXH+C3A/l8cPnoBkEWwMdNz6mRvgCcGunL+UP7cNPSHTy4fA8D/N25aGQw0DCbHRcYAYCrTs9zsTO4KHIE/7fiddx+foO0kBGEnP8gABablctWfkthcAIGnRYfnQtlJguDNUNZ8/BZuOrP5aznR3FLzpsMzV7HEIMOme9O36vfwHf6HZRt/Y6MRZeSs+RR+l75ir1f9p152MyVhN2x9IRBFkDvF4reL7Qjv52KovRyKqNVGlibnM/0dzdx9dgwPpkX06JpLCaLlUmLNrKn4jBhQ7IptVRhslooMlexZdh4fFe+gEf0mQRe9DQagxtVqQmkvz2XsopCzh89jwKDJ94GVyRWimuqCbKGsvOqG+nj5tnoWglpxUxctAGDVsOhx6cTYGxYb/jI53dTuOpNtN5BYLVgLS8g5ObP8Jl4dYd9jxSlu1MZ7cnVKzPaQlMlyaX5J96xBxDFWUijP+hOXAC/oMLM1Uu2ExGs4/opPiTkp7foGjYpiRyRzpb0HaQVe3LV0GG4WMxcsOM73N55mRqvPhSseJGy7cth0DTMf/wXnU8wxuu/pnxFHtKYRRESf3c95Hjyzfw5TQZZgHERPiy7fjxWm2wUZAH6XPEiGg8/LMXZALj1O0UFWUVRnKpXZrRLD+7g8nWfdUKLug6D1cIdh/7kuvQtHHL35fGh57HLK7jTrqcVGi7qO46vVxu4xvswjxa+CkWH8Tv7bvpc+iwVSRv5e9F8vKuPkNTvYibf/S7T4/aQUVxF/D2T+XBzGs+vSeasQYH8csupndZORVFURnuy9cpAm3FgE2m/f9wJLWpaqclCTpmJ/n7u6DQno6KQxH3vGgx5qZSPmonr4a1oS3MpHzMHizGAQ4WVlJstDY4oq7aSUVLFuHAf+vuduC/zWL4u7vi5uJOSsh+fvd+Spg0m7ayXuP6Kuei0Gt7bdJj7lvzF+X2q+CbfHw+DlgqzlRU3juf8YUGAvfiEv7sBbzd9h3wXFEVpmgq0J1eXe3QshDgPeB3QAh9IKRd29DW8ijPx2RjX0adtkk2Ch01Sm0vqNIKTUb1P7xdO8IM/Yxx5LtaqUnK+egix4VMsVgsjmnlvpRGg3du2xkmgAPDV6nCZeiefyCv4amsR7+du4MEpUdz9/d9MGRrJkpsmsCY5n7u++5srx4bWBVmAAf4ebbq2oihKV9alMlohhBZIAs4GMoAEYJ6Uck9T+7c1o/1lXy73L9vdnqa2SGm1hYySai4ZFcyVY0J5YPluDhdVMbSPEWdUyk0pqMRo0LLo4pFcERPS6fV6l+7I4vZvdpFfYSbEy5Xt959BH0+1WLqiOJvKaE+urpbRjgeSpZSpAEKIr4A5QJOBtq28XHVEBzU92KYjCQGXjQ7h0lHBCCE4Z0gg//ntAAfyKjr92k2ZNjCAJ84eTNBJCnaXjQ5hapQ/C9ckMzcmVAVZRVF6pa4WaEOB+kNdM4AOLyx7Wj8/lvbz6+jTnpDRRcd/Zgw76dd1pkCjCy9f0PTSdoqiKL1BtyvBKIS4WQixRQixJS8vz9nNURRFUZTj6mqBNhOoXxU+zPFaHSnle1LKWCllbGDg8Rf1VhRFURRn62qBNgEYJIToL4QwAHOBZU5uk6IoiqK0WZfqo5VSWoQQdwK/YJ/e85GUsvOHByuKoihKJ+lSgRZASrkCWOHsdiiKoihKR+hqj44VRVEUpUdRgVZRFEVROpEKtIqiKIrSibpUCcbWEkLkAYfbeHgA0FPWylP30jX1lHvpKfcB6l5qRUop1fzIk6RbB9r2EEJs6Sm1PtW9dE095V56yn2AuhfFOdSjY0VRFEXpRCrQKoqiKEon6s2B9j1nN6ADqXvpmnrKvfSU+wB1L4oT9No+WkVRFEU5GXpzRqsoiqIonU4FWkVRFEXpRL0+0Aoh7hJC7BNC7BZCvODs9rSXEOIBIYQUQgQ4uy1tJYR40fF/slMI8Z0QwsfZbWoNIcR5Qoj9QohkIcSjzm5PWwkhwoUQa4UQexy/H/c4u03tIYTQCiG2CyF+dHZb2kMI4SOE+NrxO7JXCHGas9ukHF+vDrRCiGnAHGC0lHI48JKTm9QuQohw4BwgzdltaadVwAgp5SggCfiHk9vTYkIILfAWcD4QDcwTQkQ7t1VtZgEekFJGA6cCd3TjewG4B9jr7EZ0gNeBn6WUQ4HR9Ix76tF6daAFbgMWSilNAFLKXCe3p71eBR4GuvUINynlr1JKi+PLv4AwZ7anlcYDyVLKVCmlGfgK+5u5bkdKmS2l3Ob4vAz7H/RQ57aqbYQQYcBM4ANnt6U9hBDewBnAhwBSSrOUsti5rVJOpLcH2sHAZCFEvBBivRBinLMb1FZCiDlAppRyh7Pb0sGuB1Y6uxGtEAqk1/s6g24anOoTQvQDxgDxzm1Jm72G/U2ozdkNaaf+QB7wseMx+AdCCA9nN0o5vi63Hm1HE0KsBvo2selx7Pfvh/2x2DhgiRBigOyic55OcC+PYX9s3C0c716klD849nkc++PLL05m25SGhBBG4BvgXillqbPb01pCiFlArpRyqxBiqrPb0046YCxwl5QyXgjxOvAo8IRzm6UcT48PtFLKs5rbJoS4DfjWEVg3CyFs2At1552s9rVGc/cihBiJ/Z3uDiEE2B+1bhNCjJdSHjmJTWyx4/2/AAghFgCzgOld9Y1PMzKB8Hpfhzle65aEEHrsQfYLKeW3zm5PG00ELhBCzABcAS8hxOdSyqud3K62yAAypJS1Txa+xh5olS6stz86/h6YBiCEGAwY6IYre0gpd0kp+0gp+0kp+2H/ZRzbVYPsiQghzsP+mO8CKWWls9vTSgnAICFEfyGEAZgLLHNym9pE2N+1fQjslVK+4uz2tJWU8h9SyjDH78ZcYE03DbI4fqfThRBDHC9NB/Y4sUlKC/T4jPYEPgI+EkL8DZiB+d0se+qpFgEuwCpHhv6XlPJW5zapZaSUFiHEncAvgBb4SEq528nNaquJwDXALiFEouO1x6SUK5zYJgXuAr5wvJFLBa5zcnuUE1AlGBVFURSlE/X2R8eKoiiK0qlUoFUURVGUTqQCraIoiqJ0IhVoFUVRFKUTqUCrKIqiKJ1IBVqlVxJC+AshEh0fR4QQmY7Pi4UQHT4vUQgxtbWrxggh1gkhYpt4fYEQYlHHtU5RlM6kAq3SK0kpC6SUMVLKGOBd4FXH5zG0oB6uEKK3z0FXFKWFVKBVlMa0Qoj3HWuw/iqEcIO6DPM1IcQW4B4hRKAQ4hshRILjY6Jjvyn1suXtQghPx3mN9dYR/cJReQkhxHTHfruEEB8JIVyObZAQ4johRJIQYjP2QhKKonQTKtAqSmODgLccaxQXA5fU22aQUsZKKV/Gvi7oq1LKcY59apdgexC4w5EhTwaqHK+PAe7Fvk7tAGCiEMIViAOukFKOxF6t7bb6jRFCBANPYw+wkxzHK4rSTahAqyiNHZRS1pYc3Ar0q7dtcb3PzwIWOcoTLsNerN4IbAReEULcDfjUW1t3s5QyQ0ppAxId5x3iuF6SY59PsK83Wt8EYJ2UMs+xxu1iFEXpNlQ/k6I0Zqr3uRVwq/d1Rb3PNcCpUsrqY45fKIT4CZgBbBRCnNvMedXvn6L0AiqjVZS2+xV7gXcAhBAxjn+jHCsqPY99NZ+hxznHfqCfEGKg4+trgPXH7BMPTHGMlNYDl3XUDSiK0vlUoFWUtrsbiBVC7HRMCapdYeheIcTfQoidQA2wsrkTOLLh64ClQohd2Ec8v3vMPtnAU8Am7I+l93b0jSiK0nnU6j2KoiiK0olURqsoiqIonUgFWkVRFEXpRCrQKoqiKEonUoFWURRFUTqRCrSKoiiK0olUoFUURVGUTqQCraIoiqJ0ov8HJKMPWqY6Qo0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x115bbd990>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1159b1310>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11540ed90>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from lib import euler_characteristics\n",
+    "reload(euler_characteristics)\n",
+    "euler_characteristics.euler_characteristics(euler_chars, 'Parametric Analyses')\n",
+    "euler_characteristics.euler_characteristics(perm_euler_chars, 'Permutation Tests')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.0470668\n",
+      "0.962265\n",
+      "0.857786\n",
+      "-0.0580588\n",
+      "0.93592\n",
+      "0.865696\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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U0kMlPT5z7nlk1jMUwCW4Qfw7zOzZeR+a0V+epZy9EjKoK8m9fdZjEFznvy9IH5T0aAZbPLwa+Af//buSGnuB+AHgn+Fk5Zszee4Adklqe0GW9FBv4c+iPjN1PJV2QtK5kvq9Z4riNs93Xz9IaPDiQvLuHKDM6/z3BemDfmHw6weqZR/w7iyfAp4o6ZK8NJJ+yNenjtKfCW8dfyduJurPss+lpHnl7JWQSTODW+Rcw0WO6vT8X45zy3nKEFW+zn9fkKnDQLLRzL6Di1D2ONx6mUO4/miB3N4QedMn9etz3JfX13WVtEvSD+Ukm8P1VRW3vikgIKAEhJmGgDWHzAK4Odwiy8f6379dwEf1ClzYzt/wCuWLuBeFx+Hikg/90mBmJulZOP/wyyWl92l4JC62+GOKlOWVaf3l+k1dOK/xPsAX4Prj/V2K/S/gN4G/94sDjwKHzGwUO8u+ARdr/d3ej/hm4Dxc+/8F53O8pmFmn5X0Jzi3mq/5dhzD9fN5uIhSf5rJ9l+4hbAfkvRJnD//l83sCpyV9HRJn8G9qC3j4u8/AreAPP1idTouOtj3ce4UZbctkfTXuH0avirpP3AzJA/H7R3x3/S/c/T/Ap/Bvdx9Ftc/u3H99S2aC5tHiafiZnDeLOn5uH0MDuFmhe6Ju24PxA2aAP4H93L6Qkk7aK5d+JuiEY464Hme65dwEbw+jLveZ+PWKV2Em0HohCfhZoKuMLNu/fYmnMvNc2lGdOsXo5CNb8Dt17Ab15dtEcNw0aAWJH0O9zwIN5vwI7jF7+m9FPq5rqcDX5T0VeArOLfVzb49p+Ki4RWKuBcQENAbYdAQsBaR9qut4RbqXQG8rsgCUjM7JukRuIhLF+CU03dxccj/nJJeYs3sM5IeggvnWR/UXOk5H03BQQNO4Z4NfNHMvtAj7d/78p9Ll0GDmX1YUj3iygtxL4nfx4WDLRVm9hVJDwdejYvuVMG5KjwRp+zX/KABwMx+S9IXcS+BT8ctvL0W+F3gtT6EbBqvxr3sXYhzG4txeyRcgYsm81O4SEU/hnNhud4f/0szOzjyBrXi93DP0bNxriSHcQPe36W3q1sbfISwi3B98BO4xa034V5sX80YXK/M7EZJ9wV+DRck4Odx12Cf5/8b4Kup9Acl/TROvlyMM0gAvJ0+1qrk1OOg3EaPL8Td68/Fya0bcDNYvfqivgN7R4OB5/mEpG8D58ttNPmlbuk7lDEK2fheXFjjnXR2TXoJTib+MO5+WcLJo9/CRVpqhGLt87peh7ueF+AGvjtxe318y3O2zXoEBAQMDuWHaw4ICAgICAgI6A7v2nQNLoRqWHQcELCBEdY0BAQEBAQEBAyKF+HcjUbh+hgQELCGEGYaAgICAgICAgrDb+b4VOCuuPVMX8GF823blTkgIGDjIKxpCAgICAgICOgHdwb+CLew/KPAL4cBQ0DAxkeYaQgICAgICAgICAgI6IqwpiEgICAgICAgICAgoCvCoCEgICAgICAgICAgoCvCoCEgICAgICAgICAgoCvCoCEgICAgICAgICAgoCvCoCEgICAgICAgICAgoCvCoCEgICAgICAgICAgoCvCoCEgICAgICAgICAgoCvCoCEgIKAjJP2tpN9b7XoEBARsbEi6WNKnU78XJN15Neu0XiHpMkmvHlHZ10n6sdXKH7C6CIOGgFIg6eOSDkqayhy/TNKyVwD1z5P8uesk3SZpLpX+2ZI+nvptks4ZsE4XS6pluF/nz50h6XJJ+yUdlvQ1SRf7c2d53p47pmfad0DSRyWdO0h9x4msgu4EM/slM/v9cdQpICCgXHgZu5iRgaf5c8+S9H+Sjkq6VdIHJG3y50b20lkUZjZvZt9dzTr0gqR536cfzDmX2/cp/fKBTPq3S3qF//8CSTd24U2XfVDS+yWdWXoDB0S3fsmke4Wkt4+rXgHDIwwaAoaGpLOAhwAGXJST5E+8Aqh/3pU6FwMvGGH1/ifD/Tx//G3ADcCdgB3ALwC3DsjxJ2Y2D5wB3AZc1m8BRQYo44akeLXrEBAQMDQuzMjAmyU9DPhD4Clmtgn4QeBd3YvpjbUox0aMnwZOAI+SdGrO+ba+T527v6QHDcF9odc7e3C662+GKKts9OqXgHWKMGgIKANPBz6He1l+Rp95/xR4kaStZVeqB34EuMzMjplZ1cy+aGZdrSK9YGbHgX8CzgOQdD9J/yPpkKRbJL1O0mQ9vbc2/aqk7wDf8cf+StINko5IulrSQ1LpXyHp3d4idVTSVyXdTdJL/YzNDZJ+PJV+i6Q3e+6bJL1aUizpB4G/BR7orUGHfPrLJL3RWxyPAQ/PWhwlPV7Sl3z9rpX0mGH6LCAgYFXwIziDyhcBzOyAmb3VzI72U0jKav4sSdcDH/PHHyDps172fVnSBak8F0v6rpdh35P08x3KbswyS5qR9FpJ35ebGf60pJleXDll/qDcrPghSV+XdFHq3GWSXu+t9kclXSnpLj264Bk4WfoV4GmFOq2JPwH+oM88bTCzJeA9wN3zzkvaJul9km73sxLvk3RG6vzHJf2+pM/4dn9E0s7U+V/w/X6HpN8pWK2e/eJ1x28DT/J66Mup+rzaX9MFSVdI2iHpHV7v/K83VNbL6aUz/0XSP/q2fV3S+QXbEJCDMGgIKANPB97hP4+WtLuPvFcBHwdeNIJ6dcPngNdLerKkvWUUKGke+Hngi/5QDfh1YCfwQOCRwK9ksj0BuD9Ngf+/wL2B7bgByLslTafSX4ibJdnmeT6Me45PB14F/F0q7WVAFTgHuA/w48CzzeybwC/RnIVJD9ieilNkm4AW9yVJ9wP+EfhNYCvwUOC6Xv0SEBCw5nAlTla/UtKPKuNWOgAehputeLSk04H3A6/GybEXAZdL2iXnivrXwGP9DMeDgC8VKP/PgPv69NuBFwNJN65sAZImgCuAjwCnAL8GvEPSD6SSPRl4JU6+XkOXl3pJdwIuoKn7nl6gHWm8AbibhvTvlzQLPAmn0/IQAW/BzarvBRaB12XSPBV4Jq5fJvH6WNLdgTfiZuJPw83Kn0EXFO0XM/sQbrbrXV4P3St1+sme83TgLsD/+DZsB74JvDyVtpfOvAh4J05nvTen7QF9IAwaAoaCpAfjhNG/mNnVwLU4AZTGi7xl55Ck/TnFvAz4tTxBXwIekOI+JOkB/vjPAp8Cfg/4nree/8iAHC/y1vprgHngYgAzu9rMPudnMq7DvdA/LJP3j7yVb9HnebuZ3eHzvBaYAtJK7VNm9mEzqwLvBnYBrzGzFZxgPEvSVj9w+wnghX425TbgL3DCuBv+w8w+Y2aJt2Cl8SzgH8zso/78TWb2f330U0BAwPjx7yn59+8AZvYp4InAD+Neuu+Q9Oca3CXxFV7OLOIsyx8wsw94OfFRnHHoJ3zaBDhP0oyZ3WJmX+9WsKQIuAR4gZc5NTP7rJmdKMCVxgNw8vk1ZrZsZh8D3gc8JZXm38zs816+vgP3MtoJvwB8xcy+gZO995B0n0yatr5PYRE3KBl07ci/e71zGHgUbta+DV6fXG5mx/1M0h/QrofeYmbf9tfvX2i2+2eA95nZJ31//x7u+nVDkX7phbeY2bVmdhj4IHCtmf1nSu81yiugMz/t748azuCWHpwE9IkwaAgYFs8APmJm9cHAP9HuovRnZrbVf3ZmzmFmX8MJ75cUJZW0V6kFZl2Sfi7FvdXMPuc5D5rZS8zsHsBunLXr3yWpaB1SqLfvVDO7yMyu9XW8m58K3ifpCM6qkm3/DZl2vUjSN/0U/CFgSyZPet3FIrDfC8P6b3CK8U7ABHBLXWnhBi2n9GjLDV3OnYkbFAYEBKwfPCEl/55QP2hmHzSzC3EW2sfjjB3PHpAjLTfuBPxs2lgDPBjYY2bHcFbxX8LJpverd+CIncA0+bKnI1dO2tOAG8ws/dL7fZw1u459qf+P42RpJ9Rn2DGzm4BP0K77cvs+hTcBuyVd2IWnE57gZ4mngecBn1DO+gFJs5L+zrsYHQE+CWzNDBA7tfs0UtfWX787UmWnF3nXZ+yL9EsvZPVc9nfjuhTQmdm2TevkW3tTGsKgIWBgyPmU/hzwMP9ivA/njnMvSf2O5l8OPIdWAd4RZnZ9eoFZn1zZsvbjpr9PwynQsvBG4P+Au5rZZpz/ZnZQYvV/vC/mi3F9us0rhMM5eYrgBtxCtJ0ppbXZD5JaeDvVp0OZvXx8AwIC1hG8hf6/cOsRzhu0mNT/NwBvyxhr5szsNZ7vw2b2KNyL/f8Bf9+j7P3AEvmypytXBjcDZ/qZizr2AjcVa2ITcguY7wq8NKX77g88tZ8XUjNbxrlD/T6DyXn8zMu/4txhH5yT5P/hLO/393roofVmFCj+FpyxyGVwrlA7UtzpRd7XD9Av3fRNT5SsMwMKIAwaAobBE3CC6u646cx74/xaP0Wf/p1mdg0uesfzS65jLiT9saTzJFXkwgz+MnCNmd2RSjYlaTr16fd52QQcARa8Ne2XC6SvArcDFUkvAzb3yQmAmd2C8919raTNkiJJd5GLmgLOcnOGUguzC+DNwDMlPdKXd3oBK2FAQMAag1xAgyfLLZKV3Hqlh9HqFx9n5F9RWfF24EJJj5YLvDAtF0L0DEm7PfcczqixQA93Fz8z8A/An8uFLI0lPVBuHUZHrpyirsRZml8saUJuwfSFOBeafvEM4KO06r7zgBngsX2W9TbcbMFAQSX89Xs8bh3GN3OSbMJZ5w9J2k7reoBeeA/wOEkP9tf/VXR/b+y3X27FudQO+i5ams4MKIYwaAgYBs/A+R5eb2b76h/cQqOfH2AK8FXAXM9U5WAW+DfgEPBd3DR3NlzsAk7Y1j+P6JPjRbj1HUdx1rReIQ0/DHwI+DZu2nyJ7u5CvfB03KK2bwAHcQqgPm3/MeDrwD7lrzNpg5l9HrdY7i9w1pxP4PotICBgfeEgbmb3OzjDxtuBPzWzd6TSvIRW+fexIgWb2Q04d6ffxr3M3YALnhD5z2/grP4HcAOVXsYUcLL0q7hFrweAPwaiHlzZei3jBgmPxc1evAF4er/rsuQW2f4c8DdpvWdm38MNAPpyxfHupS+j/1nuK7xr7hHcOoVndFgf8pe4l/b9uEHhh/qo29eBX8W5Hd+Cu29y948YsF/e7b/vkPSFovVKoWydGdADMhtqdiggICAgICAgICAgYIMjzDQEBAQEBAQEBAQEBHTFqg4aJP263GYbX5P0z2qNrbshcTK2OSAgIGBYnIyy82Rsc0BAwNrFqg0a5DZleT5wvpmdB8T0jiG/rnEytjkgICBgWJyMsvNkbHNAQMDaxmq7J1WAGb9gdha3OGqj42Rsc0BAgIeP8PJ5SV/2VuRX5qS5WNLtcpsOfknSoPHzNxJORtl5MrY5ICDAY63pi1Xb4MLMbpL0Z8D1uMgMHzGzj6xWfcaBk7HNAQEBbTgBPMLMFiRNAJ+W9MH6xoMpvMvMnrcK9VtzOBll58nY5oCAgDasKX2xaoMGSdtwodLOxoW9fLekp5nZ2zPpngs8F2Bubu6+5547+rDwV1999X4z21V2uYO0OSa+72wIOxwQMBCOcrC0Z/nRD5+zOw7Ueqa7+isnPmxmHWOumwtZV9/FfMJ/Qhi7Lgj6Ir/NQVcEBJSHsvRFUV0B609frOZW2j8GfM/MbgeQ9K/Ag3Dxohsws0uBSwHOP/98u+qqq0ZeMUnfH1HRfbd5s7bb/fVIt7+hgQRFo+TW0za+I2GJdfweJ0fjuwhHvYxRckiYjZgj044oEsmYODpxjYSj3pd1jkbGLhyZcwNzZPCf9p7SnuX9B2pc+eG8PaNaMbHn2p290kiKgauBc4DXm9mVOcl+WtJDcTHIf93HpD9ZEfRFTpuzuuIB0SPXtxxeBVk/TjkcZH37ubJkfS5H/Z2mIEdZ+qKorgCY2HPtuZLSgupS/1w3m7OG9MVqrmm4HniApFlJAh5J/m6GGwmDt7nxrHTZHT1zqp6lPgBo25Mj+/yNkUN9cNQL78yRz9kXB2PgqLfD/0zGyGGZ86PksA7aoitHG0W2zOx3Bw7l/18OjJolPT/ATklXpT7PbSvJrGZm9wbOAO4n6bxMkiuAs8zsnrgdVt9admvWGYK+KNDmVZG6jwRaAAAgAElEQVTD2YdzFByZ80NxkC+/SuXYoLK+sBzuzNCGMmR958IbJPmHe3AMjmK6wuuL/WZ2fupzaVtpa0hfrNqgwY+U3gN8AbfTY4S3mGxUDNvmKI5ahWjbA+7SpH8rUstvoDFabzysqdH7qnBkkeFwMxndOFp/j4xDJXFkJNZG46AEjqxFrFH/FIekHI5UESXrAQMSrOeHAkqgWUc7BPw38JjM8TvM7IT/+SbgvuW2Zn0h6IvebW57fhiTHLYxcIxC1m9wOTwKjkHkcC+OMmR9kXaQ8qDI45jdPNOlwP5QVFckfQ5W1oK+WE33JMzs5cDLV7MO48Ywbe5pPcpJ0++O36vCUSBNv5aAkXBku2YQjl55AkfHMoauUwlISIYuQ9IuYMXMDkmaAR4F/HEmzR4zu8X/vIiNb1XviaAveqbtt+y+06wJOVwGxwaRkWuBYxA5PA5Z3y9Hdbnas8x+UIaugLWnL1Z10BBQHGl/PkkQgdWscQ7cqNnqfnpm7iExOvobZtcutHGkrBSKnLXBzJp+khuFI23R6MihFiGU5ZBEkjghEfl6m9HOQXtZxTkg8b8dR4d2DMoRuYnrFg6a91VPjg7HV5ujLBhGrZyByR7grd5PNQL+xczeJ+lVwFVm9l7g+ZIuAqrAAeDiMogDNi4sMRSvYzlciKO7jCws6xmcY+SyfoPK4TXFkV1XIbFyorxBQ4m6AtaYvgiDhnWC9I1vZlDLPwftL0tti4fq78jZw1kOy5xTftpSObqUubocndM5pdH8nfTqqw4WkN4czXO9rnFhjtRvS1ptfH1zdDje0o5V4CgLBqyUYD0ys68A98k5/rLU/y8FXjo0WcBJhY0v67vLyHUr68chhzeKrC+jHdnLUvIsdVm6Ataevljtzd0CCqBlzQAZf8B6mszag7w0w3P0TjM0R4E0pXNkfvfP0Z6/ra+G5mhvR5Z3II6UrMzliKLM7yHbkZM/y5vN0wvZMvvN3w/K9lENCBg31q6sH4EcXi+yvqccLpkjTw6vF1mfniEYAUdZGMWahrWAMNOwDtA2k1BrH8FmB8p5aYbmKJBmXXL06cbSztGev3yO9nZkeUfCkSSZ30Ny5OTP8rZZAHug58xaSTAoc8o5IGBVsGblcI4c7Y9jHHJ4lWR92Rx5cniDyPphOcrARtYVYaZhHaBhyZD71H033f+pYzRH1VnrR+HfuRxaRxz1f9Z7O/rgUODI4xiFhSkp8AkIWE2sDflV/2e9c2xsGRk4MmWWiCK6Yj3qizDTsA6Q9cNLe/EZrcfqaXtFG+j627IcVi5HbjvK4mi2YXQc42hHHxwMyeF5NhpH2SsbDKO2DqeTA04O1N991ob8ahS4zjnGKIeDrF9VjiiKSnuL38i6Isw0rCO0+TrmjIyzfon9+wyOgaOAf2wbh9YAR+b0huHIXvM1wtHL8tPuS5zJX7LMNoOVAp+AgNWACjxjaaxbOTwOjiDrC3P0lMMFOIaW9SVwTM9N9SyzKIrqivWoL8JMwzpCm69jzg2X9Uvs22dwNTjy0mQ5+vQPHAlH5nTgGIJDOWmS3vdetzpkORSpJcrY8BC1suewAwJKQlJL+nKxWLdyeBwca0FGrhOOXnJ4HLK+cd+nkvXLsbiw1D1BX9i4uiLMNKwHpO69Fqu+2n/n/l//3eG8MufGxqFmur44OvwutR1FOVIWEI2hHbkclvp/TXGoMwej76uyYUBivT8BAauKccnhk03W95KR4+BYh7K+7Xemr1pmEYaQ9R3LESjzO4+jzAXTRXXFetQXYaZhPSDtxpcYkvfntHYfvyJltJWZOTcujrR1YKQc2XaI8jisyeH6awQcqesxSo7y29F6cjQctCiBFo4RSOSNaj0K2EAYRLb4dEHWF+NYX3J4HBydZX2v69H1figo69McjXcXMnkLcJSJjaorwkzDOkD7qL3APg19+rcW4miLwzxk7O7V4sj6R/bpQ1skHnXZHHnXoy1299DtyOFQJnb3sBw5+Yf2ae4RHaMsGE4R9PoEBKxlrF1Zv/bl8Hg48uRwyRx5cniDyPrs/TwqfdANRXXFetQXYaZhHcBthw7mXfQavqv1EXJ2ZK2Mz18HC0J6RN4yu9CJI225HZQjta37yDjU3Ha+F4eUif3cydqSOt7eDuvdjoE4ul/zdMxwSYNx9Ljmab/QNo5O6MaRtPdV+hor23cFOJLMNbcsR0kwYMWCnSVgDWMtyfo+5HBWno1CDq9rWd/SjhHJ4Y0g69s41HZ9el2PMrCRdUXPVkl6oKTXS/qKpNslXS/pA5J+VdKWcVQyAETn0bMyo9Velo+GRSATn1gagqOTD2OWo0s55bWjOEd2iqZTZKhsLOesfaC/virK0U9ftdanwVEvqxNHF7/QnhxxCRydrmnmd7bs9nytdWu7ziXAEDWinp+TFUFXrD7GIutHIIeLcgwjI9tkfWGObDGdOdpmBIrK4WFkfRlyuK2vMj+zcng1ZH3mNsure0+9oHyOfmfSeqGorliP+qJrjSV9EHg28GHgMcAe4O7A7wLTwH9IumjUlQxwVoD0i3naTzTtExhFylhX2i0G9fONdI1RfA5H/f80Rxy1W3BqY+Doox0auB35kaHaOWwgjjarXUkcWetWg8Na87Zx1ApyRO0zGaVwJOl2qM3amC2zzcJEkyOKUxx9RmApisTU83MyIuiK1UfbM8rqy+G1KuvbZgS6coxBDq8xWZ/tqzY5nP5WK0eLHC5T1qf6qpOsb+GIinMktaTUkKtQTFesR33Ryz3pF8xsf+bYAvAF/3mtpJ0jqVlAO5R6coqmKZBl+Px9khRpx5Ackuh/c68eHNmuGYCjZytWhaPIJW7NNBKOfq9xW+pB7qviMDbu4rYSEHTFamNYWT8KkjUq60WPPKXI4XFwrL6sH0wOj0HW93nvlTnbsJF1RddeylECDUj6TK80AeVBUnsc7pznoSWNH1k3p4/9V1SfkmufqhuIw6x9ajHugyMHXTn88bq1IW9asyiHenGky7YyOFiDHLZmONLXPF1GlHPPZuvQb5z6/iFqFvX8nIwIumL1kbYkZ2XkKOVwkPWryTFeWV+WHHYcqfeSNcBx/Ohi9wL7QjFdsR71xTALofeWVouA3vA3fXrBT89BdI/z9dNt4dD6MXZk0jbKav1qdxcZhqNRZuZ4dlFVAQ5r+yfDkU0/AEejYzukXfccfvVbGRwN61zf16N7+8uAASvEoyPYuAi6YszIysiecngAfZKVnetV1nc6vO7k8Bg4RiKHO17z1hPj4CgLG1lXDDPMGaF6DmiBWh/8ugWhI1IjaqDNtzHX5zGPI6ceQ3PYCDkYkCP1ezCOdoo0h5nlctiQ7UjzjowjVWhHjg7fuRx5lyNq5aBPDmU4RjXbYBZmGgZE0BVjwsByuM0QkC50XLK+j3aQx9FOUUTW9ycju3OMTg6XzJF3OdaarM+0p15mL1k/LEcZKKor1qO+6DrTIOmJnU4BM8OSS9oKvAk4DyceLjGz/xm23LWMgdo8DpU7CEe/edrSFyhg6LaPvvOKMIykFh2sJ4OX17uEUbSj/7Un2fzjQ7JB/VSHxah1hecI+qKMNo9D1g+CdTC0HIusXyU5PA6O8cj6tXEjbVRd0cs96cIu595XAv9fAR8ys5+RNAnMllDmWsdQbU7HjHYHaD4jqf8tsbZoGvW8jTjHKT+/7CxDLodlOGyVOHzZ5XG0Rx4pm4MRcNgoOepFpvLUORpp62UV5gBL2iggaW9HW95BOIq4XPQBg3UZIm9MGLWugKAvCrd5fcr60cvh4Tgy8muVZf3gMnIAWT9sX9UxKlmfbscQHGVhI+uKroMGM3vmqIh93O6HAhd7rmVgeVR8awGltLmPGzvp4BPYtu5gCB/UVeGwjc3R7sA7Zg7lHMspM9snpXIkw7cjG7N7eGhdTiePA6PUFRD0BfTX5iDrh+doly3F3yzHIevbfP5HKetHydFJ1jNmjlIHDhtXV/Tap+FCSXdK/X6ZpC9Leq+ks4fkPhu4HXiLpC9KepOkuSHLXOsYqs1RHLX786Vvcktt9OJ/5/qSZpEqY1U4MudzOYq8AI6aI9MXo+Kw1eawUXFk2tCNw5fRlULqylG6nyqwYnHPz8mIEesKCPqiZ5vbnp88bAQ5PA6OtSCHx8FRgqzvJYcHlvXp15AxcMxuKsWLslFsEV2xHvVFLynzBzihhaTHAU8DLgHeC/ztkNwV4IeBN5rZfYBjwEuyiSQ9V9JVkq66/fbbh6RcdfTd5hVONI6nt3kHckfFbSPqPl+cxsFhWY7cNPmWgVXlyMrzwDFSjl5WnzYrWMmDhDY+wo7QXTBKXQFBX+S2Od3eE7X+QkYWeiY7WJpL5Qiyfm1x9HnNB5HD45D1/XIsL630LLMoTtodoQEzs+P+/ycCbzazq83sTcCuIblvBG40syv97/fgBGS2Apea2flmdv6uXcNSrjr6bvMEUyBa9ilRpBbrfvq34fz0WuITdzMUqPk9FEcReA5bcxzNvIGj2PXQ5AS54TGyFH1zMGA71FAsjTLL9k4CEot6fnpWV5qW9Hlvif+6pFfmpJmS9C5J10i6UtJZ5bemVIxSV0DQF5DT5jxd0REZWV/ombQg608ajjhyieIYRVHfHJJQFGGVGHx5iiOII1dmHI1O1tfP5egTCnIk1VrBShRDEV2xHvVFr4XQkjQPHAceCbwhdW56GGIz2yfpBkk/YGbf8uV/Y5gy1zoGbrNlZt66WfoNWiIU9BqQW/N7ZBwt+QLHuuWIIzQ9RbxlM6pUSI4dJ7njQHeKNktXr3a01q8QMu0f1YyDUdrithPAI8xsQdIE8GlJHzSzz6XSPAs4aGbnSHoy8MfAk8ogHxFGpisg6IvCbe526wdZHzg6cVQqRFNTRFs2g0Ry6DB2dKE/jqkpmJ0hnpsFM5LDR1AcEW3aBEBt/x2wuJRbn1JkvdHmetUPR1Ki3ihRV8Aa0xe9Bg1/CXwJOAJ808yuApB0H+CWEvh/DXiHXFSI7wLPLKHMtY6+25xd+R/FkdsRMZ0mx3cxm2ZojgJp1iVHJtJF/xx+p82RcrS3I8s7Eo4oarisqVKhsnN701o1PdV+4/XiyKljlrdTdI9O6BTVpWwYombDT1+Y02xeIzPhP9kKPx54hf//PcDrJMn69d0YH0atKyDoi1LavGblcI4c7Y9jHHJ4lWR9psyhObIyc3KSyo6t1L2XNDONFhf7sr5XdmzFYv9KKVHZsa2Vc3oalpdHLuuH1SdloCxdAWtPX/SKnvQPkj4MnAJ8OXVqHyUILzP7EnD+sOWsJwzS5uwNnye8s7dGPwK+MEeBNOuSo+81GVmO9vzlc7S3I8vbIkynptD8LMnho1CtDs6R9oHdNEcyPQVLy8jfcNH2rSRHjsJKQY6cfsjy9r8Op4tlrkSYwYr1srMUg6QYuBo4B3h9ygWljtOBGxyvVSUdBnYA+0upQMkYta7wHEFflFHmWpXDfQwYiubfMLK+bI6szFxeoXrgENrsZhpscQnrNmCYqBBt3oQtHMdOuHWX1QOH0Owsmp1xLkQpDgPYNAczU7Dvduqjk1HI+pYBw+wMmp7GDh+GPu/hYVCmroC1pS96be72PDN7HXCTpHsAX/eVKstyFFAE9QFr3cJb99nzD0zLb4Hcn8bvQlN/3gXc1hoHfsqxXw7cn7FzeJ/LVg6XaVQcaixGcRzxrh1oehokotlZarfvx5ZOtFP4+6onhyLiO++F6SlntZmowMKiWy4zN1cOR0s7/D1SsK/q+do4zIpPexeCim7Ys1PSVanfl5rZpekEZlYD7i23ede/STrPzL5WYmXHiqArVh+F7ZpB1m8YWV9cRrrvjhzVFexYleToMVSpQK3WcQ+DeNsWND/vCp2dpXboEHb0GCydwE4sw/Iy2rbVcRgkkWBuxkUzMmDTPHbNdZC4QUkpsj7bV4jKKTthcgIQmp0hufU2LG3cynAqEpS2rKGwroB1pi96DYUuAV7n/38bOQvPAsaA+o3tb/JuPo+qCxPL5C3AYWuRI5W3Hw45eTVSjrpM6OZX2eSwkXFAXTkbTFQaA4Y6Xd7LfDNPbw5tmUXTU1g97J0ZpHbVKYOjLX294AJo1FPCsNGuaSgWe3u/mRWyDpvZIUn/DTwGSCuBm4AzgRslVYAtwB391XisCLpitVH0HSUtI4OsH5xjtWV9HxzF5LA/Vq127Sv5NQruh7DjS57DwIx4drqVozIBUSpE6uISYOXL+rTOmpluDBgAqNXaZ04ynHEclTZo6ENXwDrTF/2s1Cg8bAoYDaKod9zh7J4JhfZQWG2OHLRx9LlRl0bBkTk9knaUwNE7w/AcY2lHLwoNdx8OgjJC6Ena5S1GSJoBHgX8XybZe4Fn+P9/BvjYGl7PkEXQFauAIjKv3/TDyuGNIutPZn3SiyN7WirC0f+ePK35e98jUTZPj3ZMzkz2LLMflBVyda3pi14zDVsl/RRucLFZ0hPTJ83sX0dRqYB8JLWkJdyYn6VsmYlo8YcUhayu6TJHxZEuo40jcz6Xo8j9P2qOTF/0zcHoOVRETPTiaMxlN++BJLHG8ay/am69enKk0uW0o+Vcp2Zkps6zHOVON4MhknIWt+0B3ur9VCPgX8zsfZJeBVxlZu8F3gy8TdI1wAHgyWUQjxBBV6wyklrSe7i2EeTwsBxsDFlfCkcBOZytTz0Mb5NCLRy1apV4cjLrINFEHJHUulj9c1yVesn6tnaQ0VmR6LVHxfGj/e1z0g0l6gpYY/qi16DhE8BF/v9PAhemzhkQFMG4kfNAdUIktWxnX3/IGgFv6mVly8z+7sJXlKOrMByYo9XnshdHt67L5h2UoxuyURxGwUFSo3b7HcQ7tkEUoVoNKhVUqxXjkIg2u+nn5MhRACIMji/CrN8x06xx2wBEExXs7L3YvluxpWXHu2UzWlok8VPXbe1IFzBIX2WO53OUa2gxylncZmZfAe6Tc/xlqf+XgJ8dmmx8CLpiDWE0cjgiSbklDiy/unCUJYe7yvoxyOF1w9GPHJbQ9CQ2OUFkhlWrbkFzHKEkZXi846Czn8/MuJf25WV3H045S340PYVt3YIdOgwG0cwUTM+QHD4ClqCpSdi5HfYfwBabbq+F2yERVaLm4MbMLYKOosYC7Fx9UqK6KEtXwNrTF72iJz1zHJUIKA5LrPVB7iCMpfaQbPWHw6w1fZ4vaG6YsqxgHpqDtGv8ABytfZBdqJflaPHrbOHIqV8fHERAvR2dzCtDcuRd87Zop3WOpSWqN9+CJqcakS0aybpwxNu2wMyMWwgHaG7WyeFKBVZq2JEFVIlhpdbMOjeNVSpOOM+fjW7d7yI3STA/h92yr7H4rG0hZf073X9F+6p+PO96+N/lT86KWvC8yUXQFWsLo5H1+RbofuVwZ45hZeQGkfVpud5L1pfK0f5S3uCIIqK9p8OmOXcvmJFccx3m912wdD+YUd1/gMquHW7gYMDSMpxYcZu9VWvE8/PYzIxbcD3p3YLm5yB2ekOSG1h87VuNinaT9Y1KzM5Q2bIZJibcoaUTJEeOtK2566RPysPG1RXlxYQKGB36sTQPmmdVOAoUMEi9yi2gFIaR1KKbdc1oDBgKo1Jxn3p5lUqbclEtaXJILn3KbYnpKZQWllm/Utr7QbhFbYNi9FfYwaDQDp4BAWsa45D1gxQwrgd5CIxF1reNEMrnaBsg5BxrmeGXYG6m6ZYkuUhJXVCX6Q0NkCQtkl5xDJW4ZdDC/FyrPskg63XQ1q4oQpMTTc9aM1hZ6VrPUWAj64owaFgPsNYHumfou4wVom5JakzJ1X9nrNc9OTKWp3wOJ+86cqR+D8qRnXbt2Y6c/mzhsC4chfqqnaIvjkyfFW1HmrcrB6KybQs2N4eWlqjuP+AI0xzbt1A7ew8yiA4soPrsQL06kdDcrAuft7QMyytobhqT3P1WS+C2/SS33UE8P4tt3gyLS9jySms78i5HzhR7th0SGHKzIXNz6MQJqrffAeaicIxqmjmLjWo9CtgYGKes709GjoijRZ/kdMhGkfUlcmhmGm3fRsXPCABu084oQnccwGoJ8Z1Ow6am0C23YXccRJaQfP3bRKfsgF07iKpV4tP3tMjhOkdiBju3snzuXmRQufUwOrJAcuAgtVpCtH0bmpkmitTUL1JDn0h+XcVt+yF1D0eb5tGWzVSqVcdZq7XeT5OTxFs2N8tcqWJHjjjdlOmrNre9EWwAt1F1RRg0rBfUBW5jCrB30izSI/90UVmhU4SjaR5oTVf/t1GUMhzZSg7A0SiiLhyzHNl2dEUPjszhju0ogpwp/7RfZdv1KIEjktCpu9FExQnH2RmYnETLy03Fs3cP7NnpwuIBydwU8aFmPGurRDA3Cz7uNjNTMD3Z+C2g9q1riepKb3GJZOF439FcurVDEtGpu936CcOH1Mu0Y8Qw04a1HgVsLIxD1tfRUdaPgyOrcLqiIAfly+FxyPpmH3Ve9xVt2ezWrHkrfmX3rnoiBES7d8H8rHMjMtDuHdQOHGqSHDiEKhNEUxMkUq4cTu58Gpy5u6FPqhUj8gMAgOToUSoz080yI7mN3+r6RCL51neJUiO0aNcO4pkZ176pSTQ7gy0sNC/p7Azxju2NdtnyMrVbb29OdHe4Hr1mLgbFRtYVfbdK0qW9UwWUDUusuTlKHTkD2SiOWq0SOb7w9TUCScNC0/zum6NuwejEUSvAkYO1xlFXgg0O64ejWWbap9Yy1yHJ5dBQHEliEMep6VpQrRmH28yINs+5RWIeysazVpR/X6X7aKVK4vPVo7jUo1U02tED2XaQaYcqle7tiEcrpA1YsbjnJ8Ah6IrxIoqHlPW5Za4tOdzG0ZccLshRmqzXWGW9ZdrR0FkpGRnPNvfwAdz/6d9xTFRJ6Ytq0vIOkVRrqFJpXoccORxvnW/RJyyeIK08FMWtswy4OrTqk5WGPrHEiFLRmMwgSjKzDCk3WVfvquur9DXO6sVMX81sSg1khkRRXbEe9cUgMw2lbmkf0AeKjIizSfodRGc5CuXvk2Sgkf0a4Gjr2/45euYY9vrlZGnzDR3oEremGva2Gg1GTaJ+NuwJCLpivBjLQ7kG5HAJHD3XUJUi6/vl6Jui/0teKNFwsr5IhvYk/q1+FZHrejYwNq6uGKRVt5Vei4CeiKLWKEL1Ldqh1VCQJEnLxidmRtRhM7ZGOnXgSBXcwlHLcjA4RzQAh1rz9sNBSRzq2lfN/7McDNOOATgUR9jKSiN9FAmmvGuR/20HDqdC0YFNxGDWvB61xFn605c4scbmORJuAbS39Ef1e7PenLrFR5l2Zv5PakmjjLZ2RMKWlxuXL4oEk1ONdihSw/qV7Z+yYEBi6vkJaCDoijEiScYs64eQw6VxxIPJ+n7k8HqR9UTOYt+Qw5XYEfjfikRt4XhjUJB7zZOExK8jAKASddcnwkUqSsn65I4jkFijXPOuR/UyrVZr5bCkuz6JI5JU9CNFIqlMNPWJBMsrLTpLExMktaR19tlo/G5ch5ReXDqWCg8+JIrqivWoL/oeNJjZY0ZRkYDuaDd8WMdzWUtB1mevOWXXOj3bbvgYA8cg7ah/paZ2W45nLendrEQDcrRb6y33/7y0A7UjjmBqknjPqcR7druoE104DNx+CZs3Q6XS9Dc1I9qxnWj79ibHzbfD1691C5ePHMO+9T2qt9zaCJWqJIEjx2DFx+U+tgiHF1LRM0R017PB+8e2tC+K0PQ08el7iE/ZCRMVt2Btz27XjslJNFEh3r3LLaybbN2Vs16WJUZt323UDh4CM9+Oba4dymv/aGYdytjh82RB0BXjx1hlfUZ+rYqsz+4HuRFkfb8cAHGM9uxG97gbnLoLKjHJ7h1w/j3gLmdSXwxiRxeo3no7JEnr9YiEVWJq2+epzU5iZtjKCsmN+1xaaNMnVqtRO7rgwq6mZP1ETUzcfBDVEqgZ0aY5uOcPYPOzbj3c3fZSO3U7NuXCosqAI8dI6uHBjaY+iSIXgjW9BsKMeMtmp0+iCMOwpSWqqfDempggPu1UZyBL91XdZTZzHUaxrqGsHaHXGsJC6HWA7GKpKI5ad230Rty0v3dLmg7PQ8e1D5040pEq1hAHw3KkrTwDcbRauds5bGiOyo5tMD3TPD8/h44cbbQty2GIyul7Wk169bpJbkH01CRJ/cV/4TjRV7/T0le1I0eZ2LmNJAGZwcIicZpj8YQbBEiuzJ3bqe27vTFrATCx5xQs9mImjpk4fU9Lu+JTdrX2zaZNRCl/1jYFeXSBaMtmMDXbsTBJstQM/zeKSBiuKuvTMhRwEmFdy/qUHB2JHF4fsj6vr1pke5YjiojPPae5jmDHdmpn7m7K/h1b0c23YQt+x+PlZTixBDOzDYpkegq2zTXqVVtZJrrm+85QRL4+qd28r+Wip2W9TqwwcXSJ2rR/aZ+Zgnuc01Lv6tw0E8tVrJY4u8/isp+1aOoTTUy09FU6v6amiOdnqR1ZcCerNa+ztruZ8TiG+U1Ey531Sd71KAMbWVesv2HOSYjsC1DedujZgXJ2I55yODKWmiE58vIXSdMXR46jYls7+rQytNexPX/ZHCjzqE5MYHMzLYdaODKRiwxIZiax+hQxYFs3tVhiqjs2kWxpKhLiOP3+j0UiqcRNA1wkalNxi7zVls0QN2dBLOOnanGMpaaMbXmZ2mJzWthqte7XPE8OT061zLyMKpqSWVgIHbD+sRqyPpejgBzth2MccngcHHl9lS2zVdbLvdynFxtXk5bftnm+RdYns9Mkk02bsVUiahkDk83PNX/EETY50Tr5snNrQ+4abhBg6Rnw5ar71PMIammKWutsB2YNN6N6PyQLx7FaMzBHWz+sVFt+W61G7Xgf+gSc4WmYSH85KKor1qO+CDMN6wEp94vWbd1pidBQD0eXO2quP6yW+l0vJpOvI4e15hsJRybf2muH355mXHl8PUwAACAASURBVByVmMrOHY0dLokEM1Noyzwyw44vYt+/sRGLWhLMzhBv29qwCiVTEyTb51EUOaW3sIjmprBoO5hhtx+getpmaluckqjcdAfTNx+Bqekm5+QETE26ONrTCVUZte3zDX/f+MgSUc2IzjwVkt0k+24jnpxqDCDcPg8zbsBgwPIyyc23kiwcd/WennR+qEePAc3QhOm+0fQU8c7trZGeBNHWLURbNlM7fBgWjjWfh0iQCQQ1LDaq9ShgA2HVZWSQ9SPjoDVvQ9YvnYATy9jsDFRiopUaLNeoTcZoMsb27oEzT8VuOwDb5lnZNAsS8fETCFGdnQSJyIx4YRlVJuGMPbB7J8nBw0S7dji5a0aysuKMS9ruXJf23Y7t2Ul1egqAysEFKkcWYWGRysIiNj9Ndecmkqi+1sKI9x8lOrpI2ktC4HeOXiap1Uj23YZqScMVNZ6bbY6DalWSg4caOz1L7o8tLlFbOlFcn+zY7lx/EyM5dAiOdriGA2Cj6oqugwZJf0OXiRsze37pNQpoR3ownrZKGK2+jzmCJa+MtjIz59o4rPV3x7jbJwVH68lRc8TbtkHKz98mJ5oDCAlqtRb/WKvEVLZvawwYDEh2uLjcBm4AsHm2SSexctdTSaaaFo9ochpmVpp1imPMKwSApBJT2zrdLFOgxpS9IBLx/HxjMRwAM9NYfQZCkBw+6gYMdavS4gmnABpNyfQjEO3a2eZu1egqCaq1kc0yuGps3NjbwyLoijWEVZeRPTiyz/Z6lfWltGMAjvq5tKyvt7k+i9uQ3T7UrA+tamee0jLTW5vLhBldqaEVb2mRYHLS7d1Qh0SytblrM3FMcvbpjYAUAFrxctjXIanEjQEDAIsniBaWmrI+3QU+X3LDzS3N0MRkS7rawcPNtRT1dCn/7L71SSQ3gChp0LCRdUWvmYarxlKLgEJo83XMsW5EUWuafv27V4UjL02WI7NhzapwZPpiLByVqPObGE5RRHFMklSb+ftEFEe0tCIl8PHVafGprVvAMtVuRWtDshzOetbasmxf5W3I08Kg1iIiQYu78QjWNWzUXT5LQNAVq4wojqAPL6JVk8O99Ml6kfXDtmNYjgKiSFGrnI7iqPvkq+W1IyOqs3I4y9GYBsptAiTt0aJ6bWqnzD4/WVmfl7eXPsliJjuAGhIbVVd0HTSY2VtHXQFJMU7h3GRmjxs131rAoG1u883LeQDa/SP7e3EqwpFNMzRHgTT9WpBHwpE5PR6O7KqALJTrA9sPnPDuzpL15TWzFuXRXkL2Pkxa/UZz6Nr8hbM/zVoGDl2tjXWOEscNxsadch4W49AVcPLpi37a268cWLdyeBwcqyLri3D01zdZOZ0k1jIrkFel9nb0y5H0XCzbS9a3cSTWsqyvyDtHvxxLiye6J+gDG1lX9HJPegudu9rM7Fkl1OEFwDeBzSWUtV7QV5uz/njp30r9tbpjoKXSZH83ymwVBv1w1M/15ujyuxOHvIDIchQpM48DNaYm6++QQvntSJcBaGYaW1xy+Xq0A4CJCWRg1RXXV73a0Y0jkp/iTIjjyK1fMEPLVZicwKLI+exv24QtHYM7jjjuWuKiF8VxczagVoNKpXHNa1MR0bIT7JYYkwcWWd7hF7EJkq2zcGjJTVUbWLWKqlWsEiNFKDGiakJSiVEEZiKZrhAvVRscNjuNji353wbVVB2AaOd2kuPHseM+zUQF5qbh4ILvC9CmOezIQqNvkoOHiLdtQXK7otrSEpqaauwDoelpWFry10NocgIWKRGiug4Xro0DY9IVcPLpi8LtrbunliKHKVnWZ+RwV44iZRbgKCzrB+Zw//fP0Vv3Ak6fALayAgiLRDQxQbJ0wpWxUsWOHUNzc419m6yWQCVucEQ1L6dV7w9z69LwHVSPXBRHTvdOxNhkjJZrjfUUzd9eLi9XsYnY6SiDaCUhmYgaHgfJ5lmiE1Wvs6CyuIzVkibH7CRMT8LisiszMZiswHK12Ye7d2K33+H0T13279jelPUzXtbnXY8owhDRZKqvksz1AOzECTQ93ThfO7xAedi4uqKXe9L7co6dCfw6MHSPSDoD+EngD4DfGLa89YBB2twe07n5AFj6BM2Ho1se93scHB1+d+Oon8ty9CqzEwdZjuaxXI5IMDdHvHkTimOsWnWh5bpwMDuDZqbRjItmlOw/AIuLnTkkmE9z1KjdfEsrxynbYcdWbPM8VTMq+4+4SBSWYEeOUds6RzI/TXVuAvbclfjGO5i4cT/R/LyLXR2J6uYZaltmXYQLMywyatMxVnELv2ZuOEJ87ARarhLvO8zy3u1uEd2JhOqOzUQHjxItrjgFs7CITU+SbJrBJiqoZkRJlSSOiKoJxBG1mQniRTdgolLBNs+5xXlx7EKzAkksLI6wmQm4xzkkh49gJNTOdDG5432HqOw7RLRjG4pjkoVj2Hevd32ycIzq4iLR/DzJ8eMuYohEtHkeW1rGfJxvZqfR9IyLinE9pcEMahvUelQCRqor4OTTF4O2txQ5XP9dlqxvk8NdOAZtR7+yfmgOa/0uzEH++YY+SckvwI4fR2ZEc3PuhffIEezwEQBqBw7B8UXiM05zL+J+NrcWyxli/KxCdSIimYic7DejcqxKtFQlWnbOStW5SWzapV+Zn0IrNaJqjdq0D4G6UmPiiJOv0UqCVROSGX8uMaITNTAjXq4RVRO3J8PyMqYIJirEh5aozVScHqglVLfNo6kTxIvL2JRbhM1UDVtegalJomgTtn0ryTevcX2wuET1pluINs1jJ5qyvu16TE2h2Wmi+Xk3+DpylOTw4WZnC6d7N21CPtJT7fARkmPHG+Fly8BG1hW93JMur/8v6c7AbwMPBV4DvLkE/r8EXgxs6pRA0nOB5wLs3bu3BMpVR19tnma2dxxncvy7C/h5tuQvwlEgzbrkSPtXSsRbtzSmW1WptHVuNn88NwvTTX/IaGoSlk+08HbniNs59uxqLj6WGpYY8Fa0yEg2TTaV2dZ54uNVrJo08tS2zzenjSWqc5WmW1Ak4hPLLWVOHluhNt1Mb5MTRMu1ZjuSBE1WmovTDOJqqm+z094SzEy3tmuiQpLe/fWUbdjcRGPRtG3bRKwJzDusanoKS/dNLSHxCtNlMDi60Ho9ZmZgNhU6tkRs1MVtw2IMugJOPn3Rt64ogjUrh7N7IPTN0Z6/q6xfoxzx7CzMNMNpx3NzrfsTTE46g0p974FqtWXAAGCTlabsB2y6QuMdViKuJY0BA7gZCZOam2lOxNhUpSmX41a5p0zzBFSWay36RzPTzUh2uMGGKtZYdmNTE6CoGTAjjmGmaV/QxIRrU91dygwWWmV9tu80UaGyZXNLX0XpvsroXoDkyNHWoB0lYaPqip6tknSupLcDVwCfBu5uZm80s+UeWXuV+zjgNjO7uls6M7vUzM43s/N37drVLemaxyBtnmCqPVZ0jvDOWi/6EfBAMY4CadYlR/r3zm1Ud21pxLC2Skx0lzuhrc47INkyy+KPnsvKD5zmXmZTVnTALRLesRXOOM1Z2yWSs/awcs9zML/Qqm5BauTJcNj8DNW5KZKKn0YFVrbMUt21yXEClWPLTFx/sBHpwqZiFk/fSjLlXupP7JxhaecEtUk161U1t3rMnGUoqUxi05PO/3Ii5vgp0yxvccrFBCs7Zzi+d6uzUAE2O9VcZ2nmrFGLK+D7OKmIpZ0zVGcqTbeoo8fQiRNupkOZON3glNfCihPaSUL0/Vuofuc6FxkDIIqI7rIX0jHDM8gq6NqRoyQHDg691iOL+oY9vT4nK0alK3zZJ5W+GFRXFMGalcN9DBiK5i99L4g0h0S8fStRPWxnAQ5NTxHt3oVSRo0Wjjh24a0zaG7a6Qwryd3OgtlpJ7tP2cbK1hmSCVeHlfkKx06bZnne7aFTmxSLWyJWZr27auKcqhK/x04SicXdUxzfHpPEONlercFS1cllM1RLqE1XGvv8JBMRtcmYpL4IeqWKHVpoyHqSBDt6HB1fcrIfsErU0o7aTIWlbVMkfvaDE8tw9FiLxT+68160bYv7MTtNcu7ZcEZz47rWvouw03axsm2+OciZiNH27c2Ig4lRvfU2Eq9fkqWlkQwYiuqK9agveq1peDdwX+C1uGnmGrC57nNnZgeG4P5R4CJJPwFM+3LfbmZPG6LMtY6B25weUUtCagqSyD+46fj0ZtawQrdFN2r4pbYONrIc9fUFhTiykSKKcESeozYgR73MXhwUaIciOO8cNDuFKSLZsQktVV0bAJ15GifO28vyjhmnIHZsQts2M33TUd8OkSiC+Rmi2Pncs2Welc1TRBOxEyL3OZfJ6/e7tQT+w9wMTFQaHNW7nEEyP+UsLIKk4vsxAmYmWKnETN5yiHil5mJxf/8AJ+68EyYnSCYqLJ69k+p8BJMR/5+9N4vVbdnuu36jajZft/q1m7PPOfsc3+vr5trEjjEkECFFSCAhAoGIBxIpDwElUQiSg5wIEiGQUYQQD3mAEMAiDxEJEkSJiIisCDs4JBEiwnYMxteX+Ma+vveee7rdruZr5pxVg4eq2a5vr2bvb+3urCHtvVatb84aVTXnN/5Vo/VGKPaE9NghLmiHjAf7pCA5KRBjkHHO6mDK6s4YMaES5zK3weQcfVbnk9uMHhdNG9UQv6DBtG5WFcX+CJ8HcVJlFlkdkTyJNRi8x4mgu9H3lo4PtRes95gHS/jaNxDn0dLh//FvYb//y5gsQScT7Ifv4X/jW+h8sfZd6L3jZQXO4eYbDWgAwL+lGTFelK4ZK+CLhxfPNd/nkvUvKoffNFn/ojwE1KYktw9DdjsFkgT3yWdtDNcaHvbWQYi7EoE8wz0EPZ23/Y5H2P39Bk9681DFG4O7tdPw1H/iK3gbtOtqDGViKXctbhTcU4vcsjrweCvBCpyDq0rGn5dhz50mFNOE+b0RYsNaLaxl6zsrDIG/LRwx1BC1Bj82+MQ08QlYMI9OsE9OwzvgPSyKZh1FBLdQ/OE2YgLyeaCcpcFqo7A6MIx/8yGmcmHO80UoXJokyGSMGY9w999BR8HyIDuz4Hr1mx+1e6HJGH7wS63L0aEh+ewIk6aoTbC3b+E//TSkKC9K/IOHIQ2s79Q3gtZFbAP0tmLFRTEN/xRhGf8k8JPxb/VKKPCl52Wsqn8a+NMAIvK7gT/5FgMA8GJzDtkDYmBSDDCqqavZWAcaZzINaD2e83lwFR5nOrsEj3O0QOvSuK4T8BvjMc5hHA4M4cOwQW6+9iKU9YGBoJ3JTsrmOlRhFNyMah4+S4I7Tt2F92fn1a2gKYLORo2bj9ZAHH9XwEaB3GhsRkkwXTdmZUFz25iiVUA6OfZUIZmXjXlZFdxeDjXAaszWJNIoYIxTxLZaojCYzjuoNAeG2MQetxt29YpMR2i9Vl0/1LqTkzlSebQ2I6dJm2McQpxG5xBwxk94+P24otbyMqRA5d/O4LYN0LVhBXzx8OJ55ityVmZuXA6/DbJ+AzzMbATGtJ4zRRmuGVhUejzqA0MYUVOYrOaRbM36eDKYh2ZJkMP1R9ZA3rqLIlBNWvmkQtDi1/0Ak1PfJJoApZymaDwwAKQrbQ4MbUf9vbR2+lRoirQ1axUPSgEmNLohtYcnTSRiVhx25ZD6wFB3arvzkBADVysgEOTJUYNXqsDuFiS2dZ0tXUjSUeOAd+HZNJilwRLeNJVstLnEGW8zVlwU0/DhSxrHDV2CmuqRzR84czIeXnPVnNevhMclrnkpPDhf0RCyM/Tvv3AcZ0D3Ai5nwgIunveZLgd96OBPw1Gby8zjzHqePw8ZcLkEiyarxWVpfUaqzR8WGnpDzckvg26w4tWTGLlSnYbnk5FX+469trL+Ih4D8XZ5HoP2OTzOiO1LyL8zMm/QR6NpOofHRXQmTmBgeVk3pv79oOcWg1jT6QXDWru2F7K42jNPsmRz2fbeYqw4N6ZBRP7dzu8/dF2DUNW/o1+AnNtduuycu6+dd74xwYZOzl58pijL0FRNa8aVQbvhIS+Zx5DW8VgjYF6Ih5FePmrBD4RS/bPTR+V7m9/KSv95dMYG4cvVm0die372dd9dHur6PLzX/obbml7AsVFw3SA8hhvp/k8IVpLeWq2qvsweLpsVXK+YnznLYzBON1gbX1bnHxzSBN/R/Ijq2edRp5mNpBosIA2PwTO/DEBehZRgcr7o3xeRXhZWwBcPLy473zOFrHgD5PAVeZxpXxcPPZ+Het/b9Iq1ZwueDtwn0Y6MFELa7G515qI8Ow/p/+p1UDyOgRzWvvgenCGojPbxw2kvqFkT05P1DPf20UzRwxORXvE173SwT+lvIkRayzgA1oY9xDPeJ6hxscPDJr21w7k+HyP4aoBZ3p/l0Rnn/Ghz7qyXxYrL4IWIvC8iPy8iXxORXxWRn1hzze8Wkaci8svx33+0sckM6KJA6H+r8/t/f12DuKFLUP1unXe8PrPB73/5zmiU68+HPM6j15hH235WR9QdhGC0D94LbjAisLMVNr4AqpiiQo7nITMFQGpIjx12peHzykOWoXkWAoetsLg7ZnGYhzZ90zAEgVy+uw/RlccIcHQaeKgi3pN+9Bh7vAxjN1BMhWocA5EF5ve3OP3STjApi+DGg6A5A2blWyCwhuWhxefR/SgVnnz/jNVhCIDWRNpxqoJX7FGJPSobnyg1hnKSNC5M1TRh/u6kCaQjs5hVqOnQrM1kEupJAJpaylmOy59hrlXFjMfIB++3QeVVhf/GN6EOrKsc5p07wScYQorbL32AqYPkmieunW43b3XYRGDb6wYCG6IbrHgN6LWSw8/s6Pl5DC2Yr4qHHp/gHj2GuImXcY45PECiW42ZTTG3b4VaMYTldh9/2qQKFWsxX76P7G2DgKQJbM/QUdaM0e1McNsT1AhqhNWtMYvDrHEnqqaW5W6CxoQZJIbk1GE8zeFBSm3bPrgvuQ6emJWSHFfN1Kqp4fRe3vSpmaWcJmBr/DC4NIynxpPjH7pFcWsacC+1LN/fozycBbeiGhcPUtQEnuWWpZiZZvepI8viK4f4UcSLcUaxnaOZbeblM4sm8bmoIu/cQW4dhodoDNgUVlWTUEM/e4T77EF7mKjXPT5bGY0wd25hxhFPNqxggsthxSWtERXwk6r6VeB3An9cRL665rq/p6o/Gv/9J5ucS5cuimno0hdThfaKSQe/qOpZl5c11L2m3js1vpGDn74+5V/Eo3PN68hDz/AgmEq7Jvv9Xew7t8CG4GO/M4PEoFG4q3PYB8chewTA8Zzi/iEkBkSwp478ZIWsQpF4zTNW721RzNIm3mGRJqSrs5pyVSBPWd3ZIf/Ww7ba8/E8bMi9Bjn66VNWh3eoYoxEBTC1OAMYwU23cLMx2dKHYC7ACzAyeIlLUHjc1ARTBMJi14asGTYM5uj+mNFWODjU6JmfOMxp1Rw4RCt0kobNd2pYZgK5aVKmnt4z5I8rsCbMY+XJny6QKszLj3KKezu4zAbND5B5haLzQEpHUvngC7s1xb/3Dnz7o5C6b7nC/fo3se/fC2coa+HwIKQXzLMg6GcT5GTe+gfr2XdoU6RsrMpnDQK/JCJbwC+KyM+q6tcG1/29N1SjfoMVr4heXzl8CR7CxTz0PB4DWX/NPPR0jjMGu7cb5NN4hN67i1GPGhtk4OEB+t1PIg9H9cnn2K9+BY3FN7n3DvbWLdSaWJQM2BrjrDQpVBe3Jri9FBfj307HFuuDNRdguQvJIlgQBJC5C8HQEvflpZKXilahEF05SbCZDf7+IiQr8NbhtsMmvZpZjpOM8ZMWw4pp1IRF2e+Acsc2eHL6Pbvku1MUASMUW+DfnVHmJmAncDJLMAg+HhYKE2IoVIFJxvx7D0iflo3iqMgSGPvAUwQHpKdlKDoKcPsAe7CDIuGw5hT30efIg4ehbg9QffRxCBSPGZkUxb5zJ6RzBchzmH9n4wqmDWIFqvox8HH8/VhEfg14FxjixUuhiw4NuyLyrxPevW0R+X3dD1X1r1/byG6opWvaBH0xqL94Ygxi2oApjOmluAsWArCdOi8+M4ieo39KbRD6ddtIEKYxCEuJZtA6KEskVFeuOo6fg8AENdDV3AXLQsfglBg0bftUAaycmz5ObX8GPjUQN/jhD/QBNloe2qBqCQejGlRF8JnBnOO/6nPbViCNa4PQWkKE6Mf1rEFrqCXRSWu7th5Et2lC1dBzHXGfgxSh8ubiCy/q5zUDgQ3RDVbc0IvRG4Bzkli07LgmifTHLRLSe3bqFWieIas267BPbQ9PdJJB6TufmyAz68NKYvCpbQ8vIvi0lfUqgk9CZrya1HCmlkKX1Ajaeuu0Y+/K9jRYKuq22gGPgRjW1ITTRD2PaB3oWbfq1IH1Pb0eJBQO7f7pHPdfCBabIWaJ67saa9UBc5FQe+maaVNYMSQR+RD47cA/WPPxPyMi/zfwXULihF/d+AC4+NDwvwP/avz97wL/SuczBW6A4GVQV3va0ZSce0tHS1LLglrb86yfF/LQy/Ooi9FcCw8rqLs6D5mMQ+GvpyfIOMePs1DAhtB3lQrzd6dU+YzRgyXjbzxh8ZU9VnsZSQnZ5yXkltXuBFMp+acL3CxhuZcCgpQ+mF/3LSuF7MhjV55y26IG0qVijx0msRT3D0gWBfLwGD3cphqlmLLCfHZEdXcWtPNxGj4XXG21rkAtFIeWpSr5E49ZesrdBDWQlAIrj2QGW4W1qkQxEtZMTKiVYAyUY0FUsHNHtlDwYZNvCgcKtlS0LJCRpRwb/DSJ2quw7m7LUuwZsgWkD0oktyxmU2yppA8XrO6OmB8mGC+kjyuMCotxgmhC8jRUji53Qm75/NGK5KMn8NEnaOWboDURcB9/gplNYDaDkxP8t+aY6Rj/zh3k6DjUdIjP3GzPkO3t4A7w6DGcXvhVuRJdMmbhUER+odP+aVX96XUXvg4gsCG6wYrXgF6qHN44D15Q1q9ZkCGPbt9XnIe5tY/cuYVVxX37Y8QI5t13giJkWeBFcXd2UGtIjhfI0znuzgH+K+9gHj1Ff+Mj/A/fp7q1RVIqyYM5xe0xi/0UWyrj3zpFJwmL2yNEIXtQoBaWdzNUBFMFTXm5HawI6Uowc4+bWgqjpIVgjyuKWwmrsZJUQvrII4lhPtUgh48qjBcqC6SCFB6XCas9G5Q5PhxGZGw5HSvZXLEnjmo3wdnAI3lSYRHSE48kQpErqaM5MChQbBtW+wIIydxjVwFPQDFJiMVIVto+a69YjUooAZaOxGmII7RCaQ3kluXBCPFK+mhBcrKCxSpgxShUlbY2gTu34egIf3TSvk9GUGNJbh2Eg4MRfFnB06ft55fYV12FrhDfdim8EJEZ8NeAP6GqR4OPfwn4QFVPYprm/xn4ynMM+0K6KHvSH3rWZyJyZ/PDuaFnUq3Y4OoZYpqrn5Eqb5M8GtPudfLwV+chW7NeJUhVjx+1adxcYjj6nrzRbKwOx6z2R42vo8ug2ElINCCNT4WTD6dIN6VdbmiKQApU2wZXdQLdcrDHRG2JUE1yGGcxSA40T5n/4K1en24kaCa9PjSNbRHKXYOUnTFkAKYJVtI6QNnUaqrAXlRQCVYEu1TsoqMZsybEJdRDRalmttHyqBHKab10QjVWzNQGnir4THjyAzMkFhvyFqqxIS3j/QLlXtZ7ZBUF5je/hTSCu+MTAOhigZ7M21zs8yX6jW+2RZUUzN4uZmvajNoe7m/20KCXNjk/UNUfv+ii1wUENkE3WPF60FDWX6scfk1l/RkaWDB0+PMS85D33glFxiIe2A/ejX1HebQ9ptrKWzzZnqBb4wY//MEu5f39Rn65TJh/ZRtJ2vbpl2aYRuLC4m6OJjRy1+VQjUzbHgXckjjJKof5no0QJrgUmJiIWUEOuzQcPuo5ltuWctr2SbRwa93nVKhyaWS9y8OmMZSHA3FKdho33bGL5YGh2mrxAqkPDJFF6XtpwEWBMv4iAR+SlQuHCQjW7pEES7oSrO5lGYrBhQeILNr0tRiDjMZwdNK+H8Zg7t5GOmm83ceftkbqTce/XR4r4BJ4ISIpASv+yjqrbRc/VPVnROQviMihqj64yrAvQ1eyn4jIroj82yLyt4F/uOnB3ND51BQNq2nNO2m6GQXiCXqw/1rvu7khHjwPjzV0hoe8GA+xtufGoiIY09lsG+0FxCl0hGdoG+hlfbCJ6ZtBh+ZaDYV5mrYj+MR2bxJpn4sC1vQ8dUwiV+Lhowuodj4f/hTpzyNV6DPpPxtvBNtdK425uOs2xEJv8XbOziMROYPFPVqW2E7NCq21h3Wfrv+MtR5DJ9OHzTP6L+ylhfalSNlMIDRcDgRU9ST+/jNAKiKHG5zOtdINVrx8Mta8mKx/Vp81bUAOvxIe+hw8BtYRO857+BHckjpyV6THQ+lvpAOeDFha6clIawZ4YuiJMBWwXVkfZXlXDkPb5zrMSsT0xLsf8mDII1innynr6zF0LfqZ7ePUwAJ0BrOiRXkdVjU/B8849Xq+K64PFv12CBKt1/XnGgKmI36owmR7/Mz+rkqXxYpLJs4Q4C8Cv6aqf+4Z19yN1yEi/zTh0T/c2IQ6dKFzl4iMgd8L/AGCGX0L+NcIJugbeon0rKI6511z1QCfy/AYbipfmMf6iwbD2DyPYZ/1RvWcG86/fs39l1rPYafDw8slbjmvfWZM61heyGP4PK5Il3rkV1urF33Xn4c2Edx2WRAAPlVVvW4Q2BTdYMUrpjdUDr8UWX9VHhfJ1MvwuKI4usy8L+QxFE9n5qFXl/UXyuEhy4uAdE2fV8RFr5fQeF/xAVTlRcUlrkYbrNPwu4A/CPyKiPxy/NufAe4DqOp/A/wbwB8TkYpQbeLf1GsCxHMPDSLyPwD/HPC/Av8l8L8B31DVv3Mdg7mhZ1OvlH0MvmratXuLas83E6C2BJyppRC1LcNc0i0PEDFNLYPGLcQr/o3iEfwXMXchYwAAIABJREFUdbUEnbW5nUsXBGgMmjVV0FyIlVbLHVkQ5ZXLwBTRimoFpx410RUoBjKrRA8nDeZg0ZDST73ik2Aeto4QwCy0AWueJpBZbQiCEyNo6dHcxAQSIb91fakS7vdOMXVWD0MIWFMaH19RGs1PVzMFYWyriTBa0szDJwZRFywvHkzhg4apXmcFqXnFflwmwQStwZpiCsWNAlNDyA+exM+EMGYAE1P5+d0pKgZJCFWhBbA21GtwvnUx6Pqo+v4zd8cn2Cxr3yO3zsn5+UkR3GaC214rENgE3WDFqydfy6/XUg6/JFkvbTakLg8lWBuaWgpdHkJwaYn42biwqIIqklj80yPMZBxdPds6A7WMFOdRVYwxbTYmQ1td2cRJ1HLMEDAglWYsLrqS1nJYVPEdPMFHTX9XKx+vr9vigvyv19ylYKMXjxEoU0jL2KcItlJcnIcSMhl5DZ/3ll9a5VJX1mNC5j7xNJhljx1lnsRnHoO20ZZHtHSboQyv5yXB1deWYcICmGWFG4W030ag3MrJTpbBncvEdz9aE0Ahz/Gxro+WLpjgvUeSJGKYInmGFkWz/uWyDVZ/UdogVqCqf58LTmGq+ueBP78RhhfQRZaGrwKPgV8jaMWcyHkx+Td0XdT9ctUbt3WfQSs0n/V5a8J+9nXhe+X7n3Ve2+fmoS+ZR5IgeYrZ3Q0b4tTg04Ti7jaa2uC7r8r8ThbyQGsw2bo8ZFEyJSRL8Dm4bYMpID/yFFswv20wFUw+A02V09thTJPPw32rbUENjJ4qdgmrHcPpu4bxA8foc8/ilqWaWdJjR/7QsbxlWBxa7ApGjzzVBJZ7BvGQnYBaZbUV5pXNg6CuRgKiJEuQCoot0ATSYxg9jcsZl9ilcR55uDeZgycENFdjw+hRhabC/HYOCrOPCkypnNzL8KmQLhWpwmGIupZOTLlXbAtmpaRzj8tD5g18KB5kV0p6VEt/xRSeyedLAJZ3RmhiSOcCP/oD6Hc+gScnmHt3YDxCHzyCjz9r3/f6ma97N+YLqtUn2IM9cB73+Ambpk0Ub3vdQGBDdIMVrwF9oWW9al+5bC0kSUiJagz+8RM4nffGRJpgdneQ0QhdLNGnT5GtGWY6haLAnZyg+9tw7zYOQcVTTSwn749BYfs35zHZRY5aCRttK6x2QuagZO6wS1juW6qJxa48dulZ7QmrHcGUQbZXE2W1A8bB+POIJzESaPI5SAnFLBxEbAW4IMcxhCDjZcAriRnwfAg8oJqE69KTsLnXicGPlPTU4xNY7oZ6CclSEUdQntEqmYwL/SiEM48LKVl94ZHCU+wZVrsGu1Rm33GIUYwTRg8cxW6It7ArcLlAFTpyedAcJUsfDhuufR4+gXJmcHdGJCeO2TdP4XRJdrrCj1KK+/shi994RPF+SvrgCLWG6iAAY/LoBLynuHuIfvUeyT/6Dvbjx5i93VhEzqFlhX96hHYyWm06CBo2gxWvI10UCP2jIvIDwO8Hfk5EHgBbInJHVT99KSO8oTNal57GpL6mo3141jUvzqOv5XkTeCR7OxALggFU0xHV3e1mD1rMEsqDpKkHrQLlVBpA8xmUnZhdn8PRl22Ta9pnML8vuM4pbnEQtDn1NJY7QrLdaqcWhwnlrO2z3LLMb7eBY24E8/dt65tqoNzv8yjHkPhWNVNOQNJ27aqpoke0NReAcrudl1qCVr+OFUiFxfsZriPnjt/PQ0aLus8M0mgNqQ8j2rqN4nOhSNs/CDB+4qEOglPIHxWY07K5Jv98hYzTsGmwBt67S3Lbtc9wbwf95PP+yz2gnpbSOfyDh9cCAkEL93YCwYvSDVa8OfT6ynppLJDPx2Nw/2hEsr/bauEnE2S1wtepro2Q3L1D7fYikzFm0vFrz3PMB3fwtpVpi/enLLfa9vzemNGxNuNwqeB2kmZe5TRhcdjKDJcbTu+aHr4s7sU6OwSF1fy+4Drn7cV+yJRU9+lSMKMW8GslULMuUVvfpMpOQDLQenNuheLQhgxK1HyVvOzEWHhI6FRuVnqptX1mmL/bwayx4LZAimiB8TA6AVcn4BAJQdgdE4ZLDPnK96wmy4Ok6bOaWezDIyQ+U7ssSZcVrk6/nVqqe3s9aCjubCO5bZ6Hv3+X3Oah7g+gHtynHSXUNdHbjBWXcAvTr6vqf6yqPwD8BPCXgP9LRP6Pax/dDQFrLAlrhPdwT3UVAX9pHpe45nXjwSA4zY0TfDewq66n0MpGpJNdQqF3vYpS5Yq3cbONUiW+1/apUoy08VF1mbKa+qbtTas1gvDT5R0hD1RJ53Ogshoqasa2WqiS1gtWDVTStr2B+UGcX2y7pM+jGAWXqYaH6cw12qnV+d7L5ejzdGlH/mp/7VAoU+nxKMehwmg9D58Yqm6Q9PEp7rMHaHRjUGvw9++gNVBYi9w6gKw9yQ21lDKZIOMR10GqcuG/LyrdYMWbQa+vrL/aTu6MtXF3B7ZabYxuT3HjvJWJ05zyvVshRSqgk5zicNbUr/GpYfHeFm4U010bYbGbUuVBENcysqmPAFQjWG63Iq8aw2rSykiX9mW9t0o1UdS0eFFmAzxJHd4G/FCCDC+zgay32pO7fYwKrrRdWb+ahLHU7WIUr4ltl8Ny2hl3Ftudw0wx6fQp0Kvro1CO+jzKpD8uGMhqH9yvuvOSUlu8UZi/t4UbJ83zWE0trlMZmtJBxKiAJ4Lr8jw6xT0+avGkqvoDuka6DFa8iXhxpSoXqvqLhOqlf4rgv3pDL4G6vn5ASC8XHQybfPbUee37P9s+Lmq/DB5tnw2P+A3eNA/JM+zhPmLjBjW3rD7YDwIoblKrscGlMX6giulNLSQKWkCZg8asrF4Jh4F9wMa1qjRYHGpAiBU2fdQSVVOwTqlCCmnKWTQTJ23bLsCPQrvKg1uT7/A0nn4Wjeg7qrWwzYIGyJnow6pAAj4Vql1Y3YL8c8VnEmo0xD69DWtVjSEpQr/1ocS44O6Ei+9FnTKDMHdvg/bKZcGHuEohPVGSIl5f0hRs05GhjP6p4sBPMthJqZYumMhjUTw1ivn6b6EPngSt1GcP4bd9H357ihxO8R/cxXz7c5LpLMSh3DlEv/sp/tGT9plnKfbwAJJY8KcsYwm1TdHlsyN90emLghU/9VM/9aqHcIZeJzl8Po8gR19Y1k9GmPvvImmsXl+s8KMMHeeoMfjtMT4zVJMsyMgv3cU8Osbf2kWsobo1g9WK1d0pYgzL93dIH88p9seQGBQlXfhQDDMVbBXkX7kNlbGUwPIwuPaUk+Bjv9oKcraYgRgot8CUSrkLGHCTEPflR6HtlVDRONM2SMFJqMuThhIIfhTiE3yU9ZoEF1NxIDYqkiy4mA67UjDVAG+KYN2oLcTGBSuFjwepcju44/Yw67Tto5qCXQQ8MdDGzXmopoZyAskqtOuDkvGKKWhiIlDFFhoOCAl4K8G1KxUSD7oElwYsXX24w/L9bfIHC9wsg9SiCtnckT0uQxwKiiaGYjdtFFSIZ/QPv4l8/Cjov54+xYzH+Pni/Hd1YweKtxcrLgqE/g+Bv6Cqj7p/jwF5f1dE/nlgoqp/8xrH+IWn5v1uDuDtSbx++eu/NT/P8f1c334ZPNo+ezzYPA+zuwPxwABQ7E+oJm3lZ5cKLtY/UKUJIK7TfCpBWDdp8gSK/VbYIkFz3+NtO5MDSNprlMDDdzQxCPjxoJ0N2h0TcvxTP51d55oeD2nbbiStPBR6mn8EhvFaEn1mm7UhHlQ697hMWh5oE2zXPI423rB1iarHLYJi+uN+eIQ+fBqsGyhMcvzWqDngYS12a7szT4M/Po08O8+8rvYp9KwRmyCFjQW3vW10gxWvnurkMq+THD6fR/+z55b1d29DlkVZI/jtGZq2xTHdKMVPkpZnluLv7jdtNYbi3qwja4TV7WmrqEEoJ31BXOWKixYKBTQDGXVSrFooYsmYwEMp9mj7FHDjQTvXjozsLFA7jAZ/lCCTm3iAeq4dnshZvHHjPn4MU646C5J37lmDWS6X3vBkIOt1YF3QOv6tHrcDqWMntB6nNNjbpfp5VHsdtzEBO68iVgSqcunhsf3sKfLJI+hUh/YxnmW4rM17ZAzD9LDPS28zVlxkafgV4H8RkSWh2NDnwIhQZOhHgZ8D/tNrHeENNXTGL3SNYDGmf81VKx2+Eh7rrhnyGGgHLuQxyHndy7FdXyOCG1pbzut00IWRjs/ns8bR5aEdi845LLofD3no2WFceR5reXT/suZmE7N7PIvHRTqVs+/IWSb990Kag8qzeJzJBDV85psmPf/ZfcHpBiteMckVNz0vRQ6/BB5iL/7en8m2dpEcPiNbpO9as05GXlUOD3lIP25tHa2Vw1fEky6PjjGouf4iHmvlcOfzM2u1bh4DXLsU9nZ5SDRln8fDGnx1/l6mS6NpDk/P7fLy9BZjxUWB0H8D+Bsi8hVCmsB3gCPgLwN/RFUX1z/EG6rpjPBd81LWKe2aS64YEPpKeKy7Zsjjit9A7xyStpaFdd9gd0Zzxfm730EXFx0YzvC4QMCvYXGGh6wRfFedx0U81t3rhv7DAx4XLZ33g/zgw4mI9N8L1bOF7AY8zszDuT4AXoN1+G3NiPGidIMVr56a1MSXpJcjh18Cj8qdO23hGfLrHBrKt4s2wfAccnjIY7iDXyNVu/NQnhNPOl2uxZML1uqMHB7i4nCt1qzBEHMus1bdjx3+IjeZS+1lurQ4WZ5/wRXpbcWKS8U0qOqvA79+zWO5oWdR54stppMrXwgm1fpLOlQZDNW0vT1aR5tT3/cm8jChroLMprBcNrn53TghyVMoQuBTUjic75hjk2hSLmhMqj6JGSIEMFCNFbsKeaE9itgqpPHzcexjj1QGraKaJPPBd7cIplashn8rQRCw4CYeuwhtFSgzR7q0zbSqzJOUbcSzZop4Ce5CCpqHn1Jni0uCe5JZtOOuaz+IBFN0sePJjk2o3aDx+k4Qm09CGr/aP1WzKKRrHrWV1bc8vFWMj2ZqEaqxki5p/JSHPDQRTKUNDxIJ/6pwmDC7M+RwH/3sYUgSLkBRwihv+xwn2GXVtM39d9FvfxfKEvWKe/yExFqIh0VdrtgkBZB+O4FgU3SDFa+OGrH4tsr6Z/Dw3/0E+8F7kOdgYl0ea0KALGALh6CU45DnH40yspZnAkqd57+zgZV2Gm4E1oHExG9Gpa9hj3FeUsb+UnBTj1lIqCUggs8cpjKtPEs9xpumwrLGxEF1liNyQmBCjSdZiKszC2l4ljNITuNQBbwopjMPlyrWSYsn9Y6v5iH0LLpSx+jVpXFMCHBOls3SUY3pyfoqh6QM66lKU7OhzghFTP9KFdcmFyor2HlUJIn0MUtjLEba8iA+H2qeWym2KpHSB3xfKX6kIUhaQO/s4O/fwXzzk4C1RpDxGD05jX22L1b9Xm0y497bjBVXCoT+ItDrGNjW8xyJBWAaLUP3xH7eOz/UFpxzXzeH9uvOg50d7GSMWIuqUo0tfm8LJiOcKqYocYkJAbiVUm4J5Y6J9Q0EHYeiM+VIG/BwoxC4hoHSKyatcDsuCj+HFBKipZOYF3xpg4BM4+BKQVTQ1DfC3hRJyJAhAYDM0uCz0F6NHWYpIZ7BgFOPXcVYBRvWxvgACBqNJ1KAURMDxkBysGXMVCRh7YUIEgKLiSd7aGJdhRB4l6xo4jmqNOb/jkFxxQSMVWwlaJTmXhTSABJBkxOC5IwPh4JiGupRaI+HNgDlTASXMuTndlawyyocBFYOuXMLd7CL1wq9vR9RSqHyGKdgDG6cYucBuWUyhu/7Evr//eNwwKgc1SefIZNRSLFXbK5YT1z1tza47YbeInpLZf0zeaxK3D/6Tfjh78VvtfEMpnLYVciuk8xBC0d1MIqKo+CSWY0MbizUoNoE8MYDSzkN8i7U8AnJHry0sWfeEhQ3Uc76iUfHUG5F2V9BshTcWGPAs4NS0LRtGweaaJT1YMq63gPh4JCHQ4pPWlmfzG2jACvHsRZCE1QdTgGatm1TaQdPIFkYKDtL6eNZQcPBQWOwdc3D5W0QNRJiI5J5nLcJvKefKWYZ8QAgE7wNGnefAkkoPuisgljM2JPN641+AAxbhTGJhkBx0VA/IhxsNOivYrbD1XZCeuqI1elIThykIKlFHVTf9x5mb5v8Ww9gNEJE8NMp/pN+BuirWrYuR28vVryySA0ReV9Efl5EviYivyoiP/GqxvKy6HnnXFe2bNtnH9vQFcOsueYqPNbdf5lrXjYPO5siMY+2iMCdfZjEdJsi+O1RODBEUiv4sWkWTK3gJtIuoAkZJJpvhgG379rjtYCMwoGhaefaHhgAUkWyFiixQAxOrts+73wuNFk06rbmwTJRt8loDgwQfte87VMTIAYnN310eRrCHDqfNxr9eh7ddlwbsd21EfyoXTskAERzi0hzSGk+b/RD4XMDvZziAEnVGnJllCN3D8LY6qE77fXRJRFBkoHuY7m6hgNDIO/lwn83tHm6wYvNzfn1lfVX++4M70d8c2Bo+tRWdohX6MozEdy4L89qhUfd9iMJB4Z4vR8ZfNbha+jJdk3Abfdlv5vqQLb32+T0ZX3SaRPbAx6k0ucxoc8j67fJ+ngifVEf/9yV1QRLcAc/fAdvqA9OHR7WSbAu08JLl9QKmrbrr4ngc9tb//54gsWhW2vI+LaNCKR2gBVgOsvPZITZmoa9AYS9wksS0ZfBijcRL16lpaECflJVf0lEtgjp+X5WVb/2Csd03fRcc240NfHL4p0/E3zUOywLFwYzD/9+KR69uvKX81e9iMdwbFficeeA6u4eZlEgR3NILPZohc0TynGKyw2rPYsoJEcu9JcbzEJD2rlE8GNwiWJKQJVqS4NQ9qGdbpXY6QpfWYrCkiSe8aTAVZbFIiFJPDuzE7wzPD6aADCdrTCinJzkeG+YbBWYZMHi6YhqZRlvr0jHJcsnOcVpihl5zKRClwnuNIHUw7TClBZ/YlGjuIkjVL+JUnqkeBysbDAl50plHLIwSGWCybXrAiDBTSlZCbIMmqTllpJUYObhomKkGG1dnaodKFCyJ0EYuzE4q8H87MH6mCY2ZlvSLKQaTFeCPQ3XVSZYcmo3pCIDkxjsPDz71e2c1a2M8Xfm2MJRHOaUY0sy98GdKTeUU0syrzBLjx8ZVntT0pOS5EkBxyf4xbJ9j9IUc7AHqrgHj8I3bkOk+vaanN8AusGLy8z5TZH1so7HJTS+sQ8Zj0KV39UK9+gJpAn2qMCWjylubUNZIV//Jq6okHffgckIP8ug0OCaagSfGUwZDgrehM24T8B6gVLxKagPe1Iv4bNyooiCPQU1Srkf0m7beUzdfVAhE4esLOqEbFJickc1T3ArSzauyGYF5SJleZohVpFRhfEGVxjEKsm0BBWKk3BSSMZlWJ9FAl5IZg7dLuBxhi4NTB1+7JETC0uBsaJThyksujCQKNXIY5ygy3AQKLYrxAnJ0+AK6yehzo+dh02521KqBJK5IFVI1+0NIVtT3JhrPFOohntODpV0JeSPQv2fxWH4e/I0/NQ0WMubdyAVlqmSzCOGZMJqHKpTm+g27HIJLsNVOGgVmWC9IEuPKT3mtEQQnDVoIripRY1gVuE56Shh/uEB2SdHmOMlenJy4fdjE/Q2Y8WlDg0i8p8DfxZYAH8L+G3Av6eqf/l5Gavqx8Qs6qp6LCK/BrwLvLUg8CJzNmZQ7bIj5UViDuz48TAzwZlMBTGTxDA/sRhpKgRfmYddn1HpIh5dP9UzPAZzbjJgiMAPfy8yHYeS8ttjNLGNL6UUjmrbsrwdkk0LsDhIIAGJvqymgsVerd1SXK4Uu4qxoS1Wye/MsVkIyLWZJ8tLbFRjJJljlK9IbQBcSZTDg2NKZzFxHls7C1QFG03hs/158Nc04fPx/gomwR6uAnbqYOQaaWysxycuSutoPo6HB6nTA476bZ145EQwmJhbQpvANTUhxataRaJA8za4HdmOqbqcteZpBZYHwTRtoruQmmAONwjehAOYS0AsgOAmICtIF/Edqt8FT/jdQrFtwdDEpJx8zxRbaOQB1cxgKjAmDMptpVRTDdcD1W6O/42PSL77CFRD5qTpGLu3G565gNy7C99a/316XnpbTc6bouvACrjBi8vO+VmZ7V47WU90e7xI1q/hIfu7mMkkfM/TBBmPG2Ely5L069/GP3wUhuEV98knVL/zq5gkbJDFQTkzGBs3+j746VOnULVCOaJx5TQqlLk26UwFYbXv8ePWVbjadZjdEmMC3thRRZpVjazPZyVme4U1oKKMZiWaeJwLmm9jFEkLbHRhFSDbWaHeNM8jmZXBfanOdne4whdJUMoAuu1gK6wzAox9cJGtd/cGNPi5NhaNYs9hlraZR7kVXGBNjJCupkpyCoZQr4IEpIqPWCKvKm7QE6FKgmtrragSoNiFdE4TdK0xbkJqWT8FW0aDgRIs/6pYja9kGnCqsbRYwZxUZE/LJg4CqxTbSajho4rLhWSpSGrx1lC8s03yjW9hneu9i8+qEbIJelux4rI2x39RVY+A3wN8E/he4E9tahAi8iHw24F/sKk+n4deZjzDVefsnfbMsX0/0YHw9X1BPcxmUAvjRijXP5z2TMRdod3nYQY8zmqizuNxZh7reFjTB5GhZqo+MBD9MAdaMr8Tq9PUrDs1GCAEWVnTphpVEw4UTRuQzAdgI2y9bcfu6VGyjmVVUbQOjqMGRpoDQ/0HY9rPiRWcax6+3t53eBhMr133o70+Ozw0PvPOPJBWieJpA8rqdtIRbmE8/XskHnSajUNchrpdr2/LIxaMq9uq2I5bURtoKB3tpoRn3n1VBXqvj5E+j4dHEDOveO9JZpP2mSvtw9kgqV787wtO14oVcIMXz7ombLyvQQ6/gKy/FJ5cJOsHPMx41PueS9IKYvWKrlYhh399z2wc5VfsK25Ym3bcyDayRTXIM2nbkvflssb4gmaUifbksEQ8aWpQiCJGe3jitS/bk45rVI0nPVnPQNbHteriAuYiPJEeD/HSn4fpHErCRWfwpA7cbtam85w8UfHW8KCtJdF5pD08ada9c4/v8mjXpeaZrXyD+aqh2BzdQ6nv74V0VSLeN+9uUyuk/uk1pFzdIF0GK95EvLjsoaG2SPzLwF9V1U1ls0VEZsBfA/5EBJvh539ERH5BRH7h888/3xTbV0pXmXPJqvv3y3Q+aF95cBffP/zbFTdnl5rHhUzfDLrqVM9cfplHftEFmxBMLzqPl8DzZbwjqnLhvy84XRtWwA1eDOfcnW+hV8sW9lLk8LXwkHNaz7j/wmFccMFzyOGrz/ziO64M58/D4wVl/XNJwKsu/zUohK4at3MRXQYr3kS8uOwq/U0R+TrwTwJ/W0RuAS+c1FZEUoIw/Cuq+tfXXaOqP62qP66qP37r1q0XZfnK6apzTsnr+6LvaeclW7MZHMYyqG81Sk08a/xyNH9vPpfGv/XKPKTP40zfAx7nHjxEzvLQzjysgcq1PI3gJQbt1l0tqj6PwTyM7+fVjl4wzS02pp2rTb91ZolubFk1qD0geIR+n07bIN/AQ9sY6+hGY2Xweb2Gg/vXkgx4RFeCbrv3M/Loxp5Vg8VZd3BZV+ynEx9NbTaveVSD+LZhDvMzLKRfy0GiFaLl0Z+HAG4yCu9CPcblqq+62bAaRwkZMS769wWna8EKuMGLdXPuzjfR7Mqy/qLN2lo5/KKyfh2P5os/kPU1D9v27YuyhYtaNnWjbpOk15aiolsKwlC7pMR2dLs0nXmp78svXw5kyaApGgq7PUvW1zLedmSkiG/+vk7Wi2gfT4ay3vR5SBzYUNZ38cQPqgfoYAc4lNWmdt/ptjmLiz1ZP0xdOuQhfQuFmCGeCAi9d6D7vADKbsA6IE77zyS+F02fWTLYQ8Txd97Z+fHmSslcFiveRLy41KFBVf8D4J8FflxVS2AO/N4XYSxB8vxF4NdU9c+9SF9vCm1izmfyVK9lVF88+Nk0B6ZfHbYH/VyGxwXv/hl/weg+IlmK+fIHmA/fi8LeYA72MHdut8XZOi4sANTpOONM3Nhw8uUZy/081iVIOP4gZbUVTMw+gcVtWByA2iAol3tQjWoztOL2HOwWSBaKC0x2l+xMF2S2BJRpVvDe9hN28gWCMk5K7m895nB0jKAk4nlndsy92RGJeIx4bk2PeW/rKZkNecL3RnPe23rCOA19jpOSe7uPmeYrQMlSx8HOCZPRElCs9cy2lowmoW1EGc1WjKYrRBRQ0nFBNlshJpqkjaIzF9LBRjO6HzvUhutDXIOGlH+RfAZV1nEdGEgFl0af32jCd1lYP5+FtsZaEXWQmy1o0+7VfdqYnjA+Rh+DD5vnW3XeSoVhsc/GzE+w6SZLh33/XeTOYTuPJ09xDx+h3oNzuM8esFHSG0vDRXQdWAE3eHHpOV9R1l9Kfm9C1l+Whte2/p8N+c8+xz05CmJikjH//kOW726jRtDMUv7Qfcof+wo6ytAsYfWDdyl2LZqE2jjLfWGxJ/hMojzT6J4UVstnwc/eJbTticaU2Rpq7+yVsFUgRhHjmezNmU4XWBsUR/tbJ7y7+4RxFmT9zmjJh7uPmGVBlk+yku+/8xm3to4QlDyteHf3KfvTE0QUazyH2yccbJ9gjUdE2Zos2N+ak9iAUYl1TGZLbBraaeoYTVckSQUoJvHkW0tsHsYgBmRcwSh8jijMSnS3iCmHFD/ykLs2/gDFRZwMwj7EsHkbN+kqIVatrvET8baR9QF2cJ3oWW9CWIXGz2WQrEJFKWbg0yDrTQV27jGr4LMkBIxf7qeogFqhGtvee6oEF+TAUEmeLLB37yDjUe99ujbvoEtixZuIF5cNhP7jBE1HXRIkA34f8BdegPfvAv4g8Csi8svxb39GVX/mBfp8bnpJ/qnPN2fpC+JhkNgZ0v41rf9e/HidD+pleERBspaHvzoP++G76Gxl1mHFAAAgAElEQVTWaK7M99zHFEXDw+1sI48fNwXbNLHwIz+IxhSbqnD6bh7NArA8zHn4QyNcRgM21Q4406oxjt/VUCQntsuxg4MqCEGA7YJ39p9iotAb24J3tuY1C1K75M70GCPBIjPCMU2e4hqWjvvbj1AstdPlvdkTQuRXaB9OjsmLvMHD3dkpaVY06zAZF9hEcXHYSeJI0xVVo/1SRltLRE3rIzsqYJ4H2W2gGjuaYmyAyxxCe73LYg2GeI2mQeBr1d6jgHQkRDkK2FHv55db9PxwPTD5JGRggnD4EBcfp4QAaLUafVTrm0IGp6awnIaicQzf2c7P8bdPmtR+HB4gj4/QRVBm63yBXy43WqinR9eGMm8HXRNWwA1eXGrOr6usvxyPjhwY8hr89E+PKH7sA/wsxDdU4wzdmYTAaxGYZiz+hR9BpwYfrS2LfaXaNo2yYjkDGdPIIg+sdmlUqd5CdVjjRxzXdokfhUx8AqS3TplO2ryg4+yEvfEKiXmlb20d8a5xjbb/cHrC/qQ1hNzePmFrsmq09XnqmKSOqoUsbm0foZo0sntrsmBV5k2cgRmvSHJpteqjEpN12rYCEZxGvlbBuAZfsIrfLTG+PhmAJg67suEZG6hSjy1NYxHwNtRpkO7uO4vYUPMtgCI2TVA+1QXiANSFuj31H+rYBQUwQjGCySPX4AmFghUcwdpVTRP8yNJOJHRikSamTVcrJr/1GEoP1qK7e3C6eOZ7tVF6S7HisilX/7Cq/ld1Q1Ufi8gf5gWAQFX/PhfqLN4ueu45v4yX73l4XPWe4fVFBZWDNL6GlQv/6roLxsAoR0+D2VBF4i61gzfDZNCDtppwX6OEqAvc1JtOq0gSM0wQhGBqHY62ws/IVpSdBN5jW1Kp4CO65LakVEOlYWypCeWbi9i24kmNY+nrr1uwMixd0vJISlauTcadWoc6Q23EtSZAhI9mgNp03Sj/RBHrQwW1erGtBpVOvVrWd9qACRWam0qborEyZ2ybiDJdrb+2t/d+h0YL1LvEAq572eB5dQIimz4cZ6wdXfYut8jCtT1NR2hRIIMgt+ugN1Ez9JJp41gR+7nBi411/JLu2TSPLOvVXxFHT9hIpeG73/X56SxfnTijy04TCcXDACWkCpWOTPSJDxtlarcWHwKaoxyw1mONx2krpzNTUdZyGmViC5a+lu3KNClZurSR7RNbsHBJgycjW1KoUEa8CHgCq6i+N6JkScmyavEkTxyFsw2PxHpcXZkNMMbHmgC1y0/NvV4rpV8aOuJH1VmzDl4EKzZQddbKaG+tQnYlOn1GmG1ODSEQuxapAc/DLxI/VxsUWfUofI3dncNm75nK2dfIZwmm7GTmGLxH10VvK1Zc9tBgRUQ0orGIWIIG6a2g17IK9DMoaGM6X4vul7BDdSzDs9LWnUmNt+a6s50OmvpiPNxHn8DHn8KdW4h69NOHeMBuz+BgH7lzALqPPZ1THR/jv/8+pCHFXOUd5UHeaokMVHvBf8ZWwRzqpkG7ASBO8WNPtRXGYAvB2Ir0zgoEUm9IpOT+4VOscXi1FB7emx6RmxKvhqMy597omGmyQBE+X03ZTkp20lMAPl5u4zRhPzsFlKflhBOXcpjNEZSFG/GwzNlOCwyewqd8upgyTSusOJwaPptPY70ahyI8XeZYhCSWyjwtspDW1Sjg8M6yqgSTCGZakDrLcmlIstZeXC0NNgGNu3+/DGlhq1lomxODVUMVCxVJFXxBg3lXMU5gqSRlWOskantstEoIQKGMn8b3UUJSI8kFF+WmXQaLQl15FFHEhfoYAfcVKZRkHrNipIYyozkESjy7aG6YfzDFOGX00RyTpri9L4H3yNd/C/PwSZuG4xnfjRehNzHbxUumtxor4M3Ai9dL1j9Dk3uGxzPGZxOSWwdBmeQ91ZMn2Lu3sE9K9Mix2stIS485Dnmjq+0MN81ICtDChdou24LPDVIGvVORxfTTBF2KlkpxGFNHK5iVxx963Cj6WS4M450CMwubzbIwTPOSndkcAUqXkNuC92dPMaIUPmXlhPcnT7HiKH3Kg2LC3fyYzJQ4tXy02GYnKxiZFYrw0XyHrbRkLztFge+e7rDwKbt5UJo9XU14WmRMxyUCLMqcx4uM3ckKI57KJTyYj8kTHy0dwukyRcSgpiQFVqtQwllFg+7MBYEtCaAV4g3qgBxcXmEqg56YgNdjxaigS0UslHGt7KmiE6GKWGuX4bpyAkyCRcIW7SMPsj6mYdVQJbpMFVJpssOyVNICqmmos2QXDredUNY8Th2jJRBjTdSEDEre2rCHqBQzd5BlLN/fJ10U2G8/xmBI7twCV1F99gDx/kxNkU3R24oVlz00/C3gfxSR/za2/2j82w29bNJz3u7BR2diFQbuSt0Tf//Cc/rdNA+v8MlnQesSr/EIZhpzb4ugu1v4D271zMjLO+2BAWB5IL23uZoQqmJGcjOPn9QqDJCtimR71bSztOT+/qPGUGGl4svTpw0LK54vTz8naTTgynujJ9FtKYz7VnZK1SnluZ0umXRyj47sir1ObtjMlOzkS+pBJOKZpGWjcRI0tDtq99T65vNwkdKU+hTAemwvc5xi0o6fEoDV/uOI2pzGBJ9Cn4Viy879TrEd64OqMnnQV4n6PGwWusPsKbLKWNCnaSvpaadT53sdqIIb1Ygi+ERws7zVOpmQa7z/qm5W06MK6p9hArmhmm6w4jWg10vWDzq6Ag9UsXdvt7LeWuyH7yPWhg2oV0YPV029FwD8oHsDbtRNbxoKVTaum1ZZHbQsVKB4p2pdMwXy23OS1Dda8d2tBbOsbPrYzRfcGp9gajclW3BvdNr0mduSD8aPOsG+jnfGR7SCVrk/fdzURwDYH81Z+BbEpumqcasFGKcFxrqmndgqxlC0pmAxGk9j4a/GaEhDWuNgxyUMIVhRunLTeKQeoxBcpGx9cVircocYYxe5WtB2WGi/SDdSQXba+VwV36lwrcQaP/XnAst928OTpIyWpYaH4JMOXqCYegxGqLKEpIkHFUjSgDEM3t0N0duMFZc9NPz7BOH/x2L7Z4H/7lpGdEPPpGFRnTOnYx1cs0Zzs456+ZNfBQ9t/mt49vJqA00xM57x/e7k8A88pJcVSGvTaNNWjBh81L5LIxyfXfk0HCjaPq0xgHvW5YDGImt1mbXLKDT6G922SFvscQ3mW5Em40Ut8odr1b1N6K+VHfBo/Eo7PHs5r7XNNrV+1DRF2Foeg+I5ayZibFvgafjMQ6fDten3adSfzQW/YXpbtUcbpBuseEVkrDlPfAEXyGG4WNaLXPi92iSPtayM6cv6QW2iMx6QoljTlZFBlJwrI02/nRiDSqegHVELUrfFY6V1GBWpA9CejVq1m09NVkxjDV5HF8n6dSQD2W4wPcQ6e0ZTrJiGh488u+dCM1hgGeCFFdOrJXFmgEM8WdPHmXkMsMAO3sMz9w7whDqLZLegofbTBEy2x7DBBNFvK1Zc6tCgqh74r+O/t4beBDNzl7w/R/jWf3qGNuh14nGZ6wOPVjhd9ft3mQ3jMLPIVbkM08Ctu/9M9pIrcajvP19jPiySsw6eep8P1tZfgseZZ37u1VFgdg9pqhdwWAdg5w9r2Oe1BT/3mF4/i+chETHAjwD3CNWY/19V/exlj+NtxQp4/fFCh7L7wusvK4ef3X4lPNbIlovojIy8SLasGeOF8mutv9VF18ugfQGPlyDr167VuWPqdzm8f/09V3seQ/JesRdcc+bdu+DrUSzLyw/gMvSaYgW8GF6caz8Rkf8p/vwVEfl/hv9edOA3dHnqKi3ESK+ap+m06zzaTT2H6GP+ojxkLY/6w6vxaKo2GkFSi45SNEuQxCKJRVfRDGtqfxMN/vXxbVWhzZ5D+Lvp5tE2wf0FQhfGEPzy67aAVkIdyixA5WxPxhtihee4IAbF99pQ12WQaKPITUXShFALCY5UyjZXN56RlNR6MkGZmrLpU4BZsurxSE2FEHJxGyC3FVZ8bAvWhPSubcgbWGkBTgSM7UsvSRSilkoAH1P21WvTLKMEHj6hCWpb/5glpGTt/qWTN1toY0uMRCtEKk1lVpHobmRoB+DBdIW+gi06fXoNmr94jXiPP9gJv9fv6ma9k4DNpNATkfdF5OdF5Gsi8qsi8hNrrhER+S9E5BtR3v7YM/r6soj8NPAN4D8Dfj/w7wA/JyL/p4j8oQgQ10o3WPHq6SobrzNyeNOyXl6AR82nptPTducqQNXxazSCsx1ZYgVTuh4Ps6q1/3H4DmSwgezhiYCs2q+MEaiie6YhxFk7bxu5bIHSB3ttkz5Dg7updGR9LlUPT2ZmhYl1EwyQUDV9GGBiClJxWIIczo0jMxU2ym6DkpuqxRNRJknRw5NxWkaMCu3EdvBEQiC3iDayvnFbqh+BAdJ+rY1uum6IBhdt76mzPIU+Ce6u8X0wElKyuoR29+l7Rpvwp2Fx5qo/LpfXeB7ww0S8qTHMD2o/aGLQtCMGVZFRh4mAq87zGrgqXQ4r3kS8uMjSUA/u91w4szeIXneN0TrqeXYMTtB+sLnqaSyucNo9j4deA4/qYAt/e5vyK++AQvq1j7Clo/ryPUgsUlaoUY4/HONzQ3rkGD10FLsWnxnwIdvFal+ophI2mpVSTZRqVo87bIr9VhAI1jiyxLG9P0dEcS4I/Xe2j8isQ1XJxHFv/JSJLSm9oVLhdnrMTrKkUsOxy9myS3btHI/wsNoiEceeDQFsn5Y7VJpwKz3CoHxWbnHkxuylcxLxPK1GfFZsM0tWpMazdAkfF9uMbUmeOwpv+O58FyueUVbhER4tJygwyxw7LHm0mLByCbn15EnFskgoqoQs8UgGVWUoSou1HmNCwFuxCn6hJg3r4uYxe1OquMTBPBycGh9UJ2FT7qGcKGZFiG0QQnYjTxObUI0ElyjZcRDqxoG6kP0iOBGHg4MnZNkIQl/IjhRbKT4zuLEhPa5IT0Mfdu4RCz4VbKGIj4BDRf7pKemTJZoYykkCD58iHz9c++5ulDbTdQX8pKr+kohsAb8oIj+rql/rXPMvAV+J/34HQXP/O9b09WfjZ39UB+o7EbkN/AFC2s6/tJGRP5veSqyANwwvLvl+Xrus1/6lV+Ix4OMePYGTU+x79yDPmmx71cRSzlKqrRS8Mnq0wluh2AubQf3/2XvzGN+W7b7rs6pq7/0bejrDvffd++6bX/JsAyaGJ2IUKXIwluJAsORICKPIIUJE2EASkGJHYOVJESg4f1hGxhKyIARHiVDASIlDHAhSnEAkRzHgxBgT28/P9hvueKbu/g17qFr8UbXH7tP963P6nNPn+Syp77m1d+1atYff+lat0UDIhfVdGxeoPv5Tz9L4QWlm0OxpXNgGQBSdKWRR7mVFQ1bUFLO4ai2MZ55V3JmvARANzGzDp5b3sBJlecDw8dmHFMaz9jkrn/OR/BELU7MJGR/Ue7yWnXBgt2yD4zfLuxzYLXfcKR7DV6rbGAK3ihUgfGl7l03IOcrWGJR3t/s8bJYcZhucCZzUBR+WSwrb4PItpbd8uIntfBZovOGD9TJuKoqAasnpZoYCWRZxcbPOaLyLMRDWoz4uaMUqHAW0FHTl0FwjYDaQPYiFLKSNWWkz5TWDV5hqONRzMCkExC+F+kDJH0G2gqYgYkq76RWo94RmpmQnpFTdcRxThZhUYxM/kIB2rsemTIk9glIcx1zf6j22bsjeP0Fqj4aAbreERydQDywLenUr2qV0fcPdKLy4cNOgqu+k//1+Vf3BCYMfJvqvvqJnTNOYgfP8RNsqvRf1eWoeO/S5Co/w2Y9Qv3mrazff8DY0PdpUhznlW1nnH1nvma4mQ5yAsHp7oFE2UL02KBoG6EEgZL0GIV/U3DrYdiC1V9S8vljT2i4zE/j04l4X0JaZwCezPoDNSeDt/BFG0iYE5fXstLtegNezYwIZ7Y3czlbMbOh47tuSOlvTTnxmG46yTdfOTeAoL2naNH4oB3lDGXqtxDyrUWxncc0zD2K7OBBrA9lAPWSsYlyvihTT+pW2LwcoiAHR7bNDcZr8UwXUgQlj/2E7lLtuYKpKQ9pm8F0KhEL6rLBAMwNbmc5i4AtLvvUx3zpx82EGfqimgdmX7iOthaEJuF/7CmE7SMW4Q4zNE5GCDt7BEw8T5eo76f9PROSXgY8CQxD4LuAnk2D/ORE5EpE3BzK5Het7LuDzPvCjTz3hHegVVrw89CJk/W48Bj7oU6pqWMyi2bg99Noc32qPrVC9sRjJpnpPqG8NgmMz8HkPlArURyPzMrocatWFvb0KcT1+3J5vWeZ9gfM7xZq3Zie08v8w23An2yApcmBpS17Pjrsh56bm07MPuvbMNHxmdq/DBkfgzeyEXgwrH8kfcRKWXZ87xZrMSoeLS1exCX267sJ69vOyazsb2Msb6jatuMBi1lAPZJnL2pTe6YCN9Q66dq5jmepaC0MaE8FW4zWIDYzwJeTxHbe7g3qZrBvDcYfY7YQwybs2W2ms19Ax6WMdhLhxKLbaDWkqT/H+Mdr0MTPh3v1n7zp0TVgBNw8vdg2E/g7OCv3vPOfYjaaXSmM0oDOWhXOE93STfBUBvzOPHfrszMMajDrcqqFZWFDI3z/BNEp9Zy9W9zRgTj1+EW2OwcVKxPlJXDz6HLylLWaJGsW7NKdWRriU5DmxzlxDE5KbjCgH2Yal3bD2OYpwYDfsmQ1bzQkYFqbkNXfMSZhTaobDc9ces1XHWnMMyh17Sq3Cw7AA4LZdY4H3/R6KsJQth3bDe/4AjyWThjezh9xr9qg0w+J5PTvm2M/ZhhxBOcpOWfuctY9Sc+E2uGBYNVGLltsGIxtOqgIl5gefZ1vWVbwPZzyzrGZT5Slft1LkNXXjCKltihoaQ6gNqERf1PZZpdx33oaYi1tjOrzaBUwVQUKlXfQnc7+CL6JGyaTSF3UW3QHaTEmhy3YEoLhtHB8rqKR3fGDIHnlMSJWjnWBr7VwKmrv72OMNZlvH3Ouv3UWOT9BHJ+d+p9dLOwHBXRH5+UH7J1T1J84dTeSTwLcA/2By6qPAlwftr6RjIxAQkd970URU9e/tMuFrpFdYccPpucv6nXlc8Lst8nG9HQF70sDcpsxIY2WGklxgGmJ2OImyKRSK2Ua8CDNFswBeonwz2gsuBGMCMkjxZkQ5Kk6xxrP1OQK8lh2zNFtWaXV7x57wuj3lvl/iMezJltftMQ/8khpHITWv2xMehRkbzbF47tpTTrXgJMwQ4LZ9RKWWh2EJwMKU5KbhXhPxZG5KFnnJ+9UBDZZMPG8Wj7hX7VGpwxC4U6w4rQvKkCEoB/matc/YpLyo86zEBWFTx3kXWU1mA6tNHsWzUYzxhDrWfhBR3KzGVzZmBVKo9zx2azB1TOFaLwJSC6aKb6maAR5sCSQ312AVqXt3pCbTiCcaX5rUyaqQNg8+j9gRq04rjYCxycVMY5pukVZhFfs1Jv4rqsim6jcMcRqYW7cIxydjN7dnQjtvGl4qvLhw0yAi30f0d/rMxC91H/j7lw3+iq6Jht+eRg1NZ04TuviFNt+wAIhcLf/wFXhIG/xwRR5dhoTDffgdn8CJxZ16socV7t4xxsfUoOZky+qffh1xhqwEUyrrt4WmiDfXLKKQqedAShiieaBZDgKTjcKswZiYocJaz+H+FmsU1CI0fO7wA+auwYgyMyveyI85dBuMKIU23HWnvOFi3u19UxIQ9kyJCOxpSdANc1N3VS5v6yZq15NEvGVWPArzrn1oVrwfjqKPqSgHdsO9Zj+Gpkm0QDyoF5SaISIsbc06VGx8hhFhgTC3DQ/rBZmJdt+Zqzmu5pBiK3K3xgeHtBWrs4bVtojFhwQyV1HVDh8M4hTNA2FraU5zjMSNWsxvGq0RghAk1lPARJN/Y4k50InWh5DHjZytQTPBu7g5aOMgFCJ4N9EHVYnCPj8JmLYaj1f8XFBraHKoZ0Jx2n9UTQZuE2K9jf05YW+GO9liqhDHPDpA7z8kfPXd3ndadbdv/yq023gfqurnL+skInvATwF/UlWPn3BGf+qcYwp8M/AxuDRe8FroFVa8RPQ8Zf018DBvvY7cvtU5qAdn0NySVQFXBZraUh1k3TjBwPaWBQs2xCw71evRtRGBJld0EQgzTUEKiloPrrU6KPOiZLm/7WT7QbblM0cf4gQQ5Xa24dOzD8hTted9s+aj2TFzU2IEDswaAyxNiST88GpZmirKflOyVUcmHhE41JKNrmlUukxBB2HLfb/Xt82G47DAEHHuyK55tz7qnveBK/mw3sNrjA9ZupqTqqBSF60L2rD1NVtvkxyGuWvY1Hl6doE8q1lvZ2iKi4tF4Wz3zdg8UK8yQmPQudLMPKbSPn6gUEIDqKT0seBnaaMQFyZ4o+Tr9JpzaBzkp/QpUkOsEaQmWYcyyE4D2SmIFdQI1Ipdx2/HANokyzMCTgh45r/1ACmbkReGGIPsLTHLBf7De+imtxrF7EqXf9s70+7Y81LhxWWWhr8C/Azw54A/PTh+oqr3d5zkC6WvC43R0P4GZ/1BB2YGIaUim+bOvkYekIa/Io9uiLffAGeTz6NiTzZI5WmNxv7WLAYupbFDETUO3dyEGHg7AL8m1WDobqPwaUMRjxR5jbGxpcCtfMPCNakITjQTtxsGiFqlN93DbhMioiyk7OJ0RaCQGiu9W1I+USw0GFyrggdUDOactH2t/syI0mATzwgMXm0C4DjzKlZc60zVXmNAnqZjraAfkh8EuEVrsPTPSiCUUQx0WS/CwOBAv/AevubpK7fDAptCV9G569cagBIPW6bKz+2zScVM+89wsnIQYnB1146VXGlT8YmgJ6dxrJsf04CIZEQA+Muq+j+d0+WrRCHe0tvp2Hg6qn9wMu7vAX4IeBf4D65ntjvRK6x4wTTJSPx4ep6y/ml5iMCd26Ob09zG33vq52c2jREP1HPp4rJUFT+nS+YAgAM/14FM1MGGIdJiWY42Pm/tHeMGcvVOdsJs4Ju5Z0sWpuzku5XAQqoBfgTm0ozwpJBe0y2isahcUrkLUKtBBu2ATQqoOKuAwUhfV0FQAjLi4Ts8Se20oRjiB4N2UNOZAVpl1miTKRAaM2rr5N92QxgGwGG6Z6nJSjD4DDVaDoY0jQ/O2k1Gusi1Rd3OZJNK+LIqkbKB8JicVCKjDQOAteZFbRoupZuEFxdGSavqI1X9jXZQVf1N4FPAHxaRo10YvKLrI2Mmr+sckBhmqDivfTN4TK4/5/LpkJcB4vS02QFBzxb/uviaaX95Ah67XHHR9efR9F7PXqGT8+MeuzyrMzyu+D52elbTPpfxmJzf5T6eihS6qLuL/i4hiTf63wC/rKo/8phufx343pQV41uBR1P/1MmY3y4iP0sMdPsRVf1WVf3pq97ik9IrrHjxJFPZfQmdkfXnjnl1mffseVwdfy6XkRey2EkOn0k6c2V5tMN9XIpZF69UzXTJd073qz6rJ5HDV5b1k/ZOmHVmEXFx/3x+jYXrd8WKlxAvdo1p+Cng8yLyWeAngL9G1Cz9gR2vf670smuMHkfBh2iqHGpkhgpZOes7uovWdWi+ey486mY0pJJSxLbp+dBYXCYJBqOXplju0qW2Y4agkwI94x+npqJrfT3OmIK1P9/fen9sUhdApwV8zt606tCy0NoEHi8o4hyGFkKP4DoOXUq/QTvo+C4vfVbpbrtnRUDE9ibcyS0ZEXwY8zivyJxM2kMKGtP6jTSQAz5CX7SoPT9Nc36Gh47nGmAUXHnt5mZAr+ba/Tj6PcQMFb8oIr+Qjv3HwMcBVPW/Av4mUb7+GrAG/uh5A4nIvwL8J8SyRD+kqv/HtczwyekVVrwgCj5cJFqAS2Q9XC7rd8gwc7089JwffkCkL/CmTYiputshJ1ggSh+vxVjL/Tia3mZ0i+9HVWQiRQXU95aV8wTYpeSJsr/FwYmsF8Z4IpfjyfThKR7BjsYc85AzeHL2WU3HHD8JryEW3hyMMSQjk4yPXP6oAmMNt1fFytn31DMxZ3hc9tLXJ5uLO1yRrgkr4Ibhxa6bhqCqjYh8N/BjqvpjIvJ/X5XZK7oGumCNeqbrJDtFK7w7od6ONU299Kx4kP5dbZGDPTRpoXR/HtmdbkAEP7P4XGKKNoljupXGtKpCTMPpFBMkjt0GsJm2ImrM9NC6uLQxBLGVXHhE8doKTWVmPFY8MWeEMpMY69AKP4NyKJ4SS2uU3pcogts8Fcsk9lZJlB8Q6zB8GByKUIjnI+YRH4QDPAYnytvuPu/7Iyq1CPCR/CH360M2yV/n0G5YhRnr5JC7cBWKsPIxW8bMNggx7Z4iZEYpTMmmiQHRVuCg2LKuZjEbk4IxHlWXXAJAFhWyLvApgI1cIQhUCbasEjLF1Ca9c/BOsY10r7Tag2JDt0gPJsFf8s6afk7NIgbX2VXqn0UXAtPE/jZIdEey7XcTs2flG41VPRX8PMOVTUR0VeT2LSQE/OmK81wgroV20AxdOkQU1BcOlLJg/Hs7DPfTxKC3e8APiMgPTMb51550nk9Ir7DiBtALkfX9ievhYQSZFwTjsSYDH8B7+NV34KOvwXIe/dsHcRAKMQg2EH3ihRiLNVznGoFaYlpVae+jhaikbAqCcYqqYkSj0gMlEP+16d8WTwrxZNLQYBCEucCh8ZxqXKA7DPsmsFHBp2uORFmroUoP57bUrFRYp4neMWvyoNzXiI8LKgpT8SDEgGgngdtuxcNmmdyShFtuxXGzjC6pwFG+5rRZUKWUggtXUvqCbdeusBJYNQlPXIOl4aScocS4BGM8GuJ9iAp23qBl1mVG0jxgGosm2a9W44NvFTY2Ple8dPsN72JmPYh43swh28ZuIsS0rVnaTGhMiGFD/1k1c0EqQbYhXtP2a7Vqi5z6zh7uw1MkBKhr/PEp9vZhBLDgwTnE+7GC9DrpGrACbh5e7LppqEXkey1fADcAACAASURBVIDvBVqfqGzHa58LfT1pjC4iTeXQO8E6Ucq0JCJnLADtj6OT5+0Q5+TQPpfHRPhflYf5+Edhb4E4B5sGM88IJmbN8QcLtm8tKO9kNPvRTKhzRZzgs7gnsGtl8wZdsRivis4btNCkmfaIVdRo57e5vygpigqbCpzdzle8tlixcBUgzGTD28Ux+y5qGZyWvJU95JZdd9h3IMo8BbyperwYcjStZxWvBoeSEjiw0Cgvs+SoOfcN90KGTe2FfMgHuk9GDIJbmvf4zfpuF+D2Vn6Pr9W3YjE5A7ldEaolW80QUfayLUYa6pCDKJmN2ZS2TdHFMzjZsmpihg/rILNr3jvej/EMyR6hPqVoFYFljXE2BjC3Kh0RxDs8ChaCCbjSRIy20WfVbhMQWCiXkJ3Sqci8xoBphgmsbPpXhGrPIjPFhhg8DSA+MDsOSJn8eL0SZkkdZg2lU2bvV0iI531ucasNuq0xqnDrFsYHwnpz/SAAZ4oQ3QD6fS96AhO68VgBX/948UJk/WDMszwGmtfH8hhcmzn4zMfQo/2oSFKFX/oinK5BFb3/kOpf+mdpDorOYd4vDI0DtQIN1PvQHKZ4OCCIorOocAKJ6b3nIcq7pLU/OlxTFH3w7GcPP+SoWKfEE/DR7Jgjt6JIWZWOzCmvuRVLUwLgNLBnDIt0fqENHsM8bU4WqlRAnp7pXJW1Eou6GVhS8tBbPAFrYGlrZr7kVGe4hB/Oez7wRyjKzNTcdY94tzkClMzA7eyUe/USiPELh27Fo2Ye4xkMZHaLa3KapDTLnceZhipEfMkdGBs42SyjRt9AwFPVqS6DBeY1bGJsiQgE66G0nUnAE60N+LR5MyBBcbWJ34uLGOmqtOfLoLKxbkO3x62hOAnYcoIfCoihclDUAbMO/X6Q6JakQQkHC6qmwf3ql9F1jF9oNmukKNBtOfoUnwXdQKyAa8CLXTcNfxT4d4H/TFW/JCKfAv7S0zJ/RTvSFTQ1T3zNc+Ahixm4wSfXKx4A0MLh9/r1hRpB856HQNRayOD6tGFo2zKpfhyLm/XHnPHMBwFsIspiEMUrAod2MzKMLIwftWcyNMZCLgYzcApy0vqexj5WYn2H9gojkNN0NyICTjyaDLCS+tcDNyUjOlKXWQl40Y6rkYAzoWuLtEF1PY8I7n17qrron106E32f+gWHjM5273/YVjNun3EFmL5zM9iktPfSjLqPJyuChEDnYywStY+hdRVjXMvjOilZZm4SqerffdFzmNArrLjp9ELwZIcBhl1E4GDJUPDq6RoZ7FCCYxz8NpElbfadri2KjlY8kvJ19wCS581I1u+5qtswABSmIR8EMDvxzGWYlhVmg/5GYoKMXnwJw2LHIgwq+kTKJqvNTDxugC9WwplHFbc8PX4YBgHSklyCGONH6BygYspuq0M8IVnn+zGY4sdQzKb+ESPaB6gpc3dsn1G8n4MnOnxW0G0YhpeM9KTnxV+MrFwC9aj4ULdheKZ0A7ECrgcvdto0pMpzf3zQ/hLww0/L/Gnp611b1JEy8gu9LPXdSGMDvSapXeSlBeRUw3Qpjws0T+1Y3djn8PD/5Ncx+0v0rY/g78zZvDlHglJ8WEXzoljmH3rKA0MzM2iR/NSJANAcCtIoooK3AVmkCpBEgSgm/lKjX2aIBWuMUnuHsw17WcVr8zWtv+nM1LxVnCSPJiGj4fXslJqoTc9oOLSQiRAwBDwFljkWj6OkwmHZk5jbeqNRGM0lavhXWtEQKIzhTQk8DLBWJRfDW3LKic44UYdB+Li7zyrMuefn1Oo4MGsaHMe+QDEcujX7arnfzGnUsrQNC9tw0hQ0ajhwHsm2nNQz1j6jMEKRbyl9xqqJJTVf219RNRkPN7EEp3Eeq0KVsmGYvMGqUFcG9QaMxDzmjcRc5gbqearTUAtGDH6eUuVVYBxUt8DVYE7je6vnyXNsE9+hn8XjpowZlGYPPBKgPDT4THANVEvBbok5uGeDXUhQZg9L7KpGMot3Ah/cp/nS1zBFDrduoXVNOF3v9LO6Osm1mZyvi0Tkp4mxA39LVevJuU8D/xbwG6r6F57HfG4qVsBvD7zYRQ4/D1l/dR4DSwREt5F/+EvI228Q3noNZhnh934L5oP76K98mfCxu1DkmKB4IWpmGsgC1FmsOB/mICXRzSXXWNnYaXS7DCAz7dNCa2CxaKiCxSlY03BrtqFUR2gMM7vlyG2Ymy0ei1XPwlTcsZvohaOQi3LbCjma3IOUuRTkQIVS0zCTghnRJWmjWzIcRyajQjkOm2jtMBAQHgWlAebimckpx6FIaVqVj7oPeeD3OAkFYHnDHbMKs9QWbmVras04aVK9IbslqGWVTLoLtyVgWDcZQYW9LEBWc1wVlN5hDRwu1qzLjG2T4YxgZjXBW8pasEYwixrxlqqMPkGah1jvogKCEATEgXpFgoAV6rliSjBNtFA0RarDUMeNSrOIbktSKsUmbnva9YypFLtVyIRqETHLZ0I4jKnb25St3Z7BB3Jx6Oc+A++8j957cM5aKLnYXXtB0JuHFXA9eHFZnYa/qqr/uoj8IucsUVX1m59k4q/oCagVuO2/F37fF2t1uh9K0k9Mf0i78TifOu2HkSgsJjz0dM12XtO8dSdaEqywvZvHysICEsBtA9tb0mVYUAubO20xUEGCorcapPV3RxDnMYPA59mspihiYFoA9rMtnzp40KVUnZuazy3e7RRVBvhM8T5WovbFI3zMBZxEm4EFlmRYiTEPFuGAGZY2JSosyDtljABzMlZada/kwLQBYHEOe1Rs1RBSec09W3Lfz2lwiCgZnoKaLQVGwIhn32458fPuLe/Zklr7vNv7WUmjFiEGCs5sw6qO8RAiyixryCpH413SDkVrzFA7ZIziU/xCvHmNG4fU1ozoT9rKRQta0K0eQi7gFNfE8+qgWfYsFDB1YP5h6Ey4xaOAL0waUwgzEJdyfatijDD/jRNsslwYH/C/8lvIeo2qolVFeOfdfgXzrOj6gtuui/4d4D8CflRE7gMfADPgk8AXgf9SVf/as57EK6y4WdRroV+crH8qzEppw/WdDwgfew1jU2DCG3eoP3E3bhIkFWVzoHmSVynH//YuvUiTQH3H95rxIoBV2voHgrJ3tCGzcbEeVPjY3iP2sxoEGrXctivezI+71KF7ZsPHsx5PnAhv2hj0LcQYhiUOxCIEcixLslE7Zw4So+ZyDAtxlGm7YYCFwLHS8ViYmrXPuoX0od1wHGLucRFlaSpWIUexIIFcPEYCQS0iipPATGt8smBbAnNb0QzML3NXJ7fWOOa8aKi8S21wLtAEgyGm7DAuIDUQbKqJp0mLF0Gww8Mwxg/q/v2oi5uGWDBU0VyYvxewmr4nBLP2OJ8+p0ZxKx/fecILXxjMehAk3gTco21cQxiDvHEX/+Bhh9XGmD5xwMU62Cenm4cVcA14cZml4U+kf//Vp5vnK3pa0qAYa8aZJs750qd9Rlqi9E+bVSCkSKb2/LXy8I/jEeDufgxga8dr+SQKTrBiaCs3hFjws/sNBmVUgyGggzzQoCh5Nh5z4epO+ALMTE1MkZf8RKVJM+krzMSldscVI47WLUlQjGSD9ti02s4rbjfaMVtgbWs7tCbinkelQ2O1EiQfPX8/9eURsCkTVHw20oF3+yyGxmxFCcGOnpUVw7AEgobhXcTpWemzG6m2WTa606MsTgElC4O70OhnOszKYSsd+Xwq48wdSnrn7ZhBMU2fczsERcoS9f33debbfRb0DPcjT0Kq+i7wA8Sgtk8CbwIb4FdU9VmZXM6jV1jxgsnYK8p6Y7rjjx/zaWT90/NQHyDPBrIFcKZT4qoqJrOjJGlhUNMH4iLVGunwJPKY4IfRgRTWVMOnl5kHqQhoSzMzbmeQNgRxJhHTTIcfbVa8Ubv3+UmoYLrrIYZcDMmrEDMftbKeEZ5wDp54tSMsEHGTvdlY1ns1CQ96WR/zCvZjMGgHFHSIJ2DVjJPWTWVmiO+j/TZba1X3jnVcwyeokg+CoFXBZGMeMv3EfMBa6b5NbTyIdN9Z8ONvNQRlvj+DJy2Zdh7dMKyA68GLCzcNgzyv36+qPzg8JyI/DPzg2at2JxH5/cB/QVxZ/deq+p/vct3LbGZ+0nsGnq0G9XE8dmJ5tXndwN9Soktm1mrNnivtwvSyJ/r0k55yeC7v8KZ9KMqNNDkDiMgbwG2gBN55zhuGZ44VaZxXeHHRPV/59/IkP7CrXnP9PM6asXgBcnkXeh4CbHrz1/AgrrzOGM/hGSwZnoiufBfXOacbjBXwdHixa8Tgd5xz7Dt3ZXIeiYgFfjyN803A94jINz3NmDednuaejZVR3mEZqtaHWnofRsVuVBUzSFvZXTv4tz1uzITHMMjoQh70rkITHsb2wVltW955RKumFomWzNG91orXPiOChF673Y3l+0I3RgQdpCg3QFnTPR8BVnU2EgrbkKHquwk32ubHTvNFiEOYbtRA3x8MQZuh7mpwB2lepImmMfrg6JRqFoOlDzYDw0zKwUgmJXjtX/SwwnQ8okm/1D6LkEaW7j6shP5ZITjbDDgKffh0nLmxSrSK9A80aF/TIta/0O59mPaupH8ffhCwPtQitTyaQjprAkSrdkh+0sOnOeThMzP6dnUx74KexchEW8kzoTa130V/z5NE5HeJyM8BPwv8+fT3d0Xk50Tkn3u+swGeAVbAK7xgh3sOQR8vh1+ErH8KHi1miRHYlr1cMIIM6lGIEUI9/t2bis7lBEDqaCUdLnaGeBLrBkgnQ61EvGjJIDys8wF+CJvQ4km8plIh5hyNXELLpJeaSWr3eDJ8EIoZVOOJxzPDqE+U/T0GtdWhe/wQHH4g+w1OmsHZFo/6UU2KA2wpE5/SyrbPJp7v5XC0O/RuvQJmnL0oyBhPpv+vJmY+7D5JmeK7ELLxt+tt/8hEiO98gO9qGeEJ1qRaTWnM3IEIxkXXrOkayBhhezquEP20tAtWvIx4ceGmQUS+L/mofk5E/vHg70vAP37K+f8LwK+p6q+ragX898B3XXbRy6w14gnvGThTKGSUg/6MGnh8YGpO7lPlTY9Phrlor74jj85krcQF3sE+8zBj+eVVzMOvoM7QFEJI6VU3d2Iqt+ivGHM4mxrwMS92yBUqQ6gTQxP6VJ4oxoYYtOtjVqPCNswyz71qARoF4+1sxWmYoypYPK+5Yyq1yQSs7EtNFQJVmn+GwXbL4ygs7aQ+p0kiNbnjAsoc18OEwC0Ts2eoRrfOuZRk1LGthiO34rY96fjMbM2ejcIsJDPu0lYxPSuQmcDC1hhCB2KH2ZrMxDG9wjIryW2NEsFsOStZzmJSbBElzzx51nTvSY1iZk0n0VRAC+0yk6iAXwZC8kFqP4Wg6X0GpZlBPesVLj6LublRkCYGwlWHhmAlBUgbtE0WpYpUGu3zIf7Z4wbNLN7FWA11Bj73CfjMx87dITwzWaw7/D1f+ovAn1DVb1TVfzn9fQPwJ4H/9nlN4hljBbzCi93u+XFy+HnLes7+FK7CIyBgDfLGa7iVx6zqiAULQ3VgaXJBJWZQ8jMhmJjLv57D+s2YcCFYUKfUtzxJNAGKZCEm/U8L4qxoUI0J2IwEbs9WzLMKr4IhsO82LFzNKhQYAnOpuGNXVDgEsCgHtmKL79x68iiRu3s23dJ+mne2c8KhMI6l5N3RXOCWaV0/46bjyGxISbBRgTez+ywl4oOIctedsGfWoPGd79mSpU2yHmVuaha27LYwmQTmto5OTSrUatnLyi49uAgcLjbkNqnQTMPevCR3dZq1YnOPyWOBHRVizMi86W5TlJilqiULzVJjTQUFSWF/YbAuWb0pbA8T/uewesuyvtu7QdkGbJnwBwi5oT6wnduzOkN9NI84AWAs5hs+C0eH8fzAha7lee20C1a8hHhxWUzDXwF+BvhzwJ8eHD9R1ftPMOEhfRT48qD9FeB3P+WYN52e6J6nkf3n+W4P4z+jpqbvc+b3oJN/d+Gh4z5THucgxJnj7nd+Gs2iBsetPfnGU+6lT9AI233iDz/19xZ0vw2Gij9wf9hqmQQawSxjEYB22TgbLnZVuD0rWRZRqHq1WDyfmD8g5VsCAp/MH2AkpkANKG+YsvNXbQjcphhpyTLJMaMbjvmb4qwExXf6HBEhU6FMfYwIcwMPvOs0I4U0PPBtQBssbMXDIFRpE5AlS8E2BaxZUWa2ptGsm0VmPN4X8VkJFLbmNBV4E4HCNZymAj4iUGQeH0KqdBoBxwpUvi/gpi4gtYuwJhBswKrtqqs2WSDfDKtXQ8Ka+D2m4jydS7NAXoJZp/djhWopOJWREM+qwSa5DizuVd2gmlk0G/C8c4T52gejjEnXnwljcH/PX8hfRktV/QfTg6r6cyKyfI7zeJZYAa/wAna455si65+MR++DjjHYz32mzX4Bm5qTTy26FKtqoJn1vvUAm7cjhrRUH3p0Lww8/APZTDvJL+LZ36s6PAkK33T7/cQiLqs/vbhHYeIC2WO5YzfcccfpFqJsvmPrbhEeCCxl1qXgVgKZZOc4Denk/9r6OganGRVVaseUrWt1RPxQZqZiG/aI1l44cCtWdS/rl7bkoV/0sl88TQgdJjmUwih1mkJr69g2sy4+MLcN3hcxvkBgllcgvfWlyBs2VY8n4hQC+Pad26gj1LK3SqkoBhPxIxUIna2jl0BnhG9JhPKAQUpVick0CkXLhLVp7+cTHKgV6kIotimWxwj1zJH7EAP1jUFfvwv3HpzdCD8DuX4DsQKuAS8ui2l4RCw3/T0AIvI6MdJ6T0T2VPW3rj7nq5GI/DHgjwF8/OMf5wtf+MLLrj26lIb3/M8D/6v/H8YdmrPXnBHk5/W5iPykfd71u/S5iP7fSfsfXfH6l4Tyc47tTdpvPI+J/HalwXd67V5KN89P9WdE5H8GfpJ+gfkxYnG1v/W8JnETsCLxHeHF1ztNseJv6/94+UXPQ9Y/LY8K+MXJ+a9TvJi6exxO2q89r4m8ouvFi5uHFXANeLFTnQYR+YPAjwBvAe8DnwB+GfinrjjhIX2VONmW3k7HRqSqP0HMK8vnP//5m7l3252ufM8Hclu/Q769OzfS4AijTBNnsk0Od++dQ+HZczH1nPbty3i045w35oU80nhvf4Tms2/i86giCDPD5nVL3Wa9MNDMwBepLUpT6ChtJ6JgEwtRmDW4vM8TkWcNh8sSaz2gLG3Nx/YesnTbpK3xvJU/YG7KVICn4e3sHkupol8scCglMatbtB8scRgxyaCsFDhELJrQT9TS4GkS+okaGpSyRUM1lAoPtXUuUmo1rLVIxmNlHTKO/T5NcnPahozTsKBO7SY4alz3YH2wrEJG3fn2CKvaUYaoeVIVNo1J7fh9bBtL2cxixiSN/rx1k3VtDYa6cgSf/D6DMEyAIiroBowm9ywFs+l9glXBbGF+QlwYaEyD2BlkFLJSydvzJL3eIFzDKNiTBrfV/tOqPSQrgyrIw1Psr/4Wutn2MTKDb1eMwHTD/TT0YszJF5Kq/nER+U6i28pH0+GvAj+uqn/zec/nGWEFvMILOOeez8WKy+TwML7oWcn6J+ExITEGnCX8zk/QvHUUKz0D20Nh+4aLxduIBcCag+gOiYDaWPm51UDjlDDz3erciEJeY1xvtF4UWw4WFUaiJH5tfswbszVWPIJyZNe8mZ+QmWhVODBbPuoekkmNJGvAgXhsKohmMcwHWnIBchlWJk3yu3NhUkRj/QZPjJULCicBytTDB8taLXWKhAsYHvqCtc4g1RY68UV0u014sQ4OnyzSqlAFR9W205ibkFNr7O+D5aTOezxRYdNYvPbW3W1l2VQFmlymggcJNsVxgPoeBwCktFCaLnbRBMEdC24t/adUxvo+7QFVJT/tPMjwro9dRJV8FfALQ13EMbKtUjwKtGkArVfMpo97MEGRL70LX303YYWe/13vsunehW4gVsD14MWuFaH/U+Bbgf9NVb9FRH4f8IevOuEJ/UPgd6SKoV8F/g3g39zlwi984QvAS+mv+sT33NLI7UJh6CfaFQw+x1z8WJMynPHn24nHJWM+lodC9fo+mvdCqFwKzaBMZnAQ2g0DMeXqaMOAdgAQAUcwbjhLYTmr0oYhtg/zDUtXJpAScqmZmaoHLZRFAgCIzkt9Jc9WrMcEpi15lGGyv5p6lNavxlMN2g3Kg95xH1VYad61BXjklylTdwTUtebUg7Z2laHbMSUFcac5BdiOcg4q29CblUVg2+Sd6zMCtbejtvfSCf7+7gdtVUzied6nhkBxzFjjP0jBihC1ikNN40TIqk+FfIbjD92SBOSf/Aa6GVf3fCa+qSMGz3b4JyFV/Rmia9BNoGeBFfCEsvMlt0w/GV5cRdZPYuOuVdZflcd0yBDQWqnfOmKYU3t71xIGoqC8nZQS7XVusGFomZpRC2v7/0eVg0XZYYEi3MnXnV+/Ihy5NVkqVa8IM6nIzCDIWBVrerwI0dt/jB/aJ3oAaCamlpKGgScnG40bhrbthW4xLyhBSRsGocWPk1S3AUgJKHp3XxHwA8daIcbR1SqD82M8CeiZNN+rcgDORCXTSLa3a5DURUVH71gqxW3M4HrIhmXGFPLj8WchgV5xL0J5ZEfvVNr4t3aICZ6EssJ+5Z1Rn5ZXN4aRs9avp6EbiBXw9Hixa/akWlXvAUZEjKr+HeDzT8oUQFUb4N8H/heiJuqvquovPc2YN52e9p7b7BQdTf0AiUVLRl3MYyTyi+SRTfaqZsxCGWT76LtcSNNYWDs5YES7vNsQha5IP2q0H1zCZTJmTG4ynPdUvaCjmbeFai6m6RzGPM4KIkVGPORSHtPzZhKh0QcN9iynz3P6vM+OOabp+5zex3nvXC57HZPFSSwANRjzGcQ13LRsGBeRiPzEC2B77VgBr/CCHe75jOy+hKa/l8h00mcq66+YluxaeExZTrHBjtvnsbPDRa7opfdhz8WTAX4ki8SwPZR6bdm4i2k67z7RBpyjAFEmsv6s3D1Lk2c7wZMzLLTPYhTb5+HJFAumWCtn8GPEZsLjnCHP0BkeRs5g1njjOvkugvbxMY+h+d7s4klckW5i9qSLaFe82NXS8FBE9oC/B/xlEXkfWD3p5FpK5pAnNqG/jFqkp7nnM0Vyzvngpgulqy6cngcP3YFHmEizS0t2TWR0mAw6zdwRNUx96r54+SVc2io0XTtcjguTMS/MINKdHw7alk676JrzeDx+YjpR74UdeEzfx2UK/emIbfB0R+dM78x3dEWBelkRqWuhG1blU0RuP+4U8Aee51wSPROsgCeXnS+xZfpK93xGrl5Cu/xezsj6K1ryduEx7XMZj6l8i0Gvl/CYyv+pPDqXx+Pb8dh0iOm9Xvaspud3eB/n8rhA1p+xEk8k81QpKDpOjy073MUUT3b4Rs70ueSSqUVqp3c+/HYFuORbLNflheevTDcMK+B68GJX1cR3AWvgPyQGS3yRV5U/nxtNd9lipDMDisTzvbZf+z7Q/dAu095cD48zE49/8xmYdL0R3NfuI77P7Vw8CrhUIVgEbJ1SrKa2+HiMTs73kqw1/+Jl0IZNmXdaEgM8qpbUIfqUCsLKF6lWQ7zJrWY88gtCaivCcYhuO61/6EpD3wY26vGpHYCAoenOx3F88vtUBKOR+zC/d6uTIh2f0Tt2qsJc6jRS/G9fhTS2HYN6CnH0c2wVY4k8z+puhgAu5dlu29bFPNztsfY1drMWwGnfBtSGvq1Q70Vzcnddl6Upye8ZaCy5jSG94yp071wtNDPTfX8oUegPsF4/8zY4G32f+0cQv9X0d510QzVHHwA/D/yfg7+fT3+vP/fZvMKKF0adD/kLkfVPzkOGfUY8otRzX/kQQi+vFh8EZKCvsSV9W8D4sewiSOfbIsS2ejOSJesy1mFopf8H2/0OCwTlfr1Mbjrxiod+SRUyQjq2VcMmmJQaNdZ13qomq22U5Rv1qGrXbj1qepuFpLAuIajgxHRY0tb0IVmFFYPVgB05NMFMBvhBTC8OLX5Almr0tHiSSYvDZ/FEENAhvsRvrHB1mmUkYwayn16h370Do93/G2LF7tadTNIJX4xePT6l6+4+o5SytW3bun/nAuhMonmoxQtHZ5ESFDIHRT4ao6PUDtdomd4VK15GvNjV0vBnUpXPAPx3ANdV5fNpqdUiwcupSdqFztRTGPrutf9tz037dMcv3tmfrdkwGHNnHpOJ7y2xe0sky9AQCNst5mAP2Vti3ltTvT5HncXUyvI3G1ZvWJqDKHHcKeg8xjIEJ0glGB8Ic40eO+mHrkWDLTxio4C2JuCygLGB0zLj7nzNIq+Y2YYPyj3uFsccupLCNNzzexyy4pZdk4nnw7DHSjPecg8BWJGx9Y5bpmzjedmoZ09iqr8G5VRr5uLwBLYpT2imUTxu27R7AbwaTtR2gFGpZa05IZmkq+BYhyIFOcPWOyosjTpyGjYho8Z0AWlVgFoztiH2r72hDo61zxPoKFWwbJus4xFLHljEwCKvONkUNMFE4JNA8AYfBA0GnEcbATUgiloFTwe26iJ0UZqYZlUk9ikFE+KGoDyKvqlAr55ogAAahHJPmd3zZI882aMIZtW+pbqVgwj1fszHPrtXY5qQlGKB4AzGB+RgH77lG9Ff+P+6DUX8HJ+hJL55GTF+Hfj287ITiciXz+n/rOnGY8XXK0609EJk/WU8ropZRmC5xB7s404a3Bc/YP0NrxMywZaw9xXPyds21nYJYFcxGLrJlZSdNKaMzhTN291BqiMgim8MEpRiXpNlnkYNJ2XBG8sTFq4BEd7b7vPJxT0WrqQwng/9kjfcCTOpcOL5ij/kjjlhaWo8hq0Ke1Kzb3xKfeFZoywxVMnWsdaGBY5KfRf/lqmhIdCWCvXBUhFjGgAaNdS4lHZV8Opo1LLSIrpJqWcVZmxCjoqQU7MKOY06VGLBV8TLEAAAIABJREFUtzI4PBbFkJmGVZNTBkuV0niXXqi9Y+3jat57oQmWrY88VZWgQuMjfhRZw7YcFLiTEGMbNMlfAa1MxJA6CX8PEgRpoF4q4sBVQhuCJ0WMhVMDYS40hZKtYowjNioKs7VimliayWzT5iMotoImi8esj7y8E2S1xr73CPOb78SAvwu+3Wt3Z715WAHXgBcvrCL0K9qdpjED5/muTnfPV/VvPcPjHP+/XeYxJHd0iKS6DGIM9s3Xkb2YClhIP+52bIjWhoHKWwJIJoO2jDYMAG7eILbXQhSFx9pe+zJzgblr0vMRVC0zG7rnVasjM/3zq9R12iOIgWG19np5JdZdH9aCXql2GwaIwWvD2pINwonmgzGEU50xqLHMo7DoNgwApRY0SaBHE7HDt6p5Ys2JchDwrAibMBvpidZNPuLhtdcjxUxbNt1run+1aGuNkaghGr5yEVJBu3TQgPEDn1eJ77TTnhhiau/BZ2KJAl/6SVCc+m7WtppUbnVxs9jfFdgm9DycxRTjJLdXjbG5EukOf8+XfhS49Zhzf/55TiTRK6y44fQsZP2TXH+2z1DYGOzRIZIilk3tCZn02uMAVmEgehALmvXHsEAxMCeQtOCdEUXIc99pxlWFvazCmbbGgmFhGwrTJ9QQhMz4Dk+2muPp5XKphnoQN9AA24FzVEBZazNKmLFNCqiWNmrYaKenp8ay1j74OGBY6byX5cA6FJ2sF4m4poO2Mm57YhalIZ5sfc9DEepRQo24qRjjB9351oLcvT4B4w3UPWCLgvU9fqgDBu9LXbQw9yYLIRTSB5gI2BCVUi3nrALXldoQMPG76ObiA/Yr78Uq4s+bdsGKlxAvLrQ0iMj3Ad8PfEZEhlU994G/vwuD50lfr5qk6Q54WtgNzu6ez+vz1Dx26NPR4R7N3QPMqkS2Vaz0MrzWCI3rxUx0ZRGyE6VaghqJQqVUyECTedKshVBo+nKVZpVhch+rfAKbbYazHpd5RJRHVU4ZhKNigxGl9I73tkvu5KcRHFT4sF5w5FZkEvAI7zSH3LYr5qamUsNXwz5HpmQpFQ2Ge75gIQ2HpsIjPAgZFsuBiQV5jkPU7u+ZCgM88HNOQs6+KbEETnzBic6YmwongW1wlMHhJGBQGgxV0lIYFI9w4qP5PDcRXqrgOo26KpTeDSpqwqZxNMFiJLoa+SDUjcEYxYjSeMO2siCKMa2GiyjZNQIowfTJJhSoTSy+1L5KT7QuBBCVqN1rY/kUTAOyibI8OKJG6DReGrNlCc3Ccvy2sPxqhTRKMzNoo138t2mUZuGwW4/xCo9O4cvvwhu34e4t2JSEzXCL9gw0RgO6SYFrAKr64xec+7HnNY+XCSt+O1inL6Jrl/XXxqNvy3wclFofFfhCEE/n6pjfC5gFVPsSi4BtwXmhWcRx3KmBtRCOfKpmL/gkW0QUsYGqcrjMY41iJPDBZsl+VjF3Nc547tVzlsFwkFJ1xxSmC/btOi7WdUalGbftKZbAVjO2Kty2GzICm+B4gOOWqZiZQBkMJ8GxbzxzCVQIH/qcuXgOTE2D8KEvAGXPlCjCh80+FY59s8agHPsZ65BRmBqDsvUO6dBAqMMEaxVi3iQhIHg1VF66a4IKtbe0Ke5UhSaYFP8Q2z5EO296OzSNITQGMWmFruArEwPCbcSQEGSUZtt4M6pMbsIgilB7l+TE4mxMQIA6i/tGU4NpAsX9GhWhPHKoEUJuKJ3iVor4gHn3PlpdtcjI9dBNwwq4Hrx4kRWhX9EVaJR/Pi2wWx+8NpPAKD998qeM/eWsyRq6H/u18/jcJ+FoH4xBZxlsasQrJD/zemapj2KVZSNCY5V632LEIFVchJ6+HTVHEsCWil+QBJBgNqDLEDcV3qBbA9ogWXTBaTQKr+W8ogmWVe3wwfD23kM8jpW3VFvLZ5cfgBhKzfigPuCN7BgrwhbDO96xr9suhuD9kFFIK9WEUjOOfdZlXRKEjbepv0VRPvBzTsIcxRJQ7nnLVotkJoYTP6dMpmZFqNVQBsc2aYAE4cQ7Hvk2jZ5QeZvS7sUaCY3Cw7J9OHHD8KCc41OqvSZYQjBd2rwQ4LTM2JQZbZxF3bR3FUf1qoTaYCTuADQIbE0EHQWaZGHQtEmQaBYWH0FCgOwY3EmatYDdKnbbKgoFaRQ/A2aWujA8XBhmD2LFbFGQWrGVRhxzhmbPkP3CF5H3H0IIyHqD/ta70DSgOvl2o9rr+s3N/YLlppGIfPc5hx8Bv6iq7z+HKbzCihtAj5P10xo+zwJPrsYjSs4hjzag1r7xGiaP8kmB1Tfeobk1j8LDgra5+GtwJxHENnejBtpWgqmj1jpm8VH4AKrXfJfVRxvI9hqMhaCGqhIOFlsyF6iD42HpmNuHHGYbanUcN3GZ9JH8mCZZfOvGcug2CEKlGWXjuG1XRMGjvNMsmXUpW+E9PyMPoWvfT/EPTYpRKNVxzxeUySIgwLGfdfihREVQtCBEfFn56F4UUn+jykmYdS5HIDRBaLCtEp5tY3lYLwDBIFRBeFjNUyvSukoWB4kL3zqYGB+YOmzXjiY9E4IQuhTdEbPCFmgiJkpy5bVb070PVbB1tDe0Hjz5cYxFaWm4gRCIrk0+4o0Wgqxrll+tIp6IIE1g85FZ9x3Vdc3yb/8Spmoij+7bPcc9KR1PJpjroRuMFfB0eLFTRWgR+SHgXVUtReTbgG8WkZ9U1YdPOulnSV+PmiQN/cJIlZHQHgbwjArpQJJhj1k8TQ5HHqCBJ+dhBG4d0EqYdmHatVUJexm0AlyVkEf/oFYL4Wdxw9D95gxn07IOMsfF9GqDuSrMct9lflBVFl2NhthvYeskJiMXm7T8LZeYurQfUxMf6e5LiXaAkFr9de2YjTpCEvCQAqUnKe9qHftblQOTsaJsQjFwOVKaQWE4gDo4wHTPqlGT+vQ0zLOtwLbKujEDdHeqiWfwcSfQaYWC9BuGwUDDpozqOoBbD7wAFLKm/39QQos5iYcJgljTf2caQWJI8m6/9lQfOh/VOO7w9zGZ3HXSDdQeJfq3gX8R+Dup/W3EILdPicifVdW/9CyZv+xY8fWAE49VDnF2ofTEeMLj8eRcHmlOZ3n0v90Rj8zFGLiB31F9ez7Ak7EbjAal2Y+ailbWt+Kus7xmcQPROgmJUcSMI5+c7ecTgMNi0y2SA3CY8KOV/K6r2RBHcdJmJepdY8MAQ5SIS+2YCtSTdNwVeddbgTL08WhxHqbbMMRegp94mPcbhtjDD5Z4irLxYzwpfbs5i2M2yT01dD1IAeE91c1wTGKcW/vsFCTYEZ60crzHk6SvbBfyPm0YBi9kmNxKoXNJannMH3n6kkWK34+VXlse7t4ppm7QZgwij10KKWS5+21RpyHRE+PFro6KPwV4Efkssfrkx4iapVf0HOnS/NXn9LlqBhmZJsd/Jjwu7395/ukzF4zInLn+7Hhn53EJz6v2P6fPLnd+VR5n3seZa3Ry/uLrd+NxSf8z7V14XDLIZXO65mxJ55Lu8PdiyAHfqKp/SFX/EPBNaTa/m+cbhPwKK14QXTWW54l+91eV9TvM6SyPK7E40/+8y68qj6bnd8Ojq8r6q81pNx4XCyDZYcl3ZVk/xd4dbuOq+P5EmDWdyCWXZMWueYF2pF2w4iXEi103DSEVmvlu4MdU9U8Bbz7FhF/RjjT8zoMP4x/LVO0iE9/RqYtGq7wZ5c8c/7jO5TGZ0KU8QuDMNIc8aj/+/U6LdIXoItO1kVEXOedfDeONQh3GDyeopPSqkbxawiB4OQYED+2JUy6tpmyoVQkDrQ30qVb7S4d5tXsv0sfzkKT779vNSMBO5Z4B/OA+YjKRgaYwXTES0hJG43gdt83EGVMEfJi8j9Ec4n2NxpQxmDSMq6FO/T3FgB/6NMvknYug1oy+o9a60NL0230Wm4jrSqEnIn9BRN4Xkf/nMee/TUQeicgvpL8/c8mQH1PV9wbt99Ox+0D9mGueBb3CihdEU7kMV5T1UzpP1p8jiM+kSn0KHm2l4xGLMJYtynhhqpPkCVPMEmWEJ4JMZGR/vKU6yOAMVMoIP1TNqB3UoNqrqRUzsci0fUdpHkZtxY94niVBJxg1Qc4JL2CCJzEtx+AKOTvC9FnpGTwZFylVHbeneDIlM/AqAM66tU2/XcZWGkgJmQYfgaS4iY4KR6gnmVaGPNqA+sGg6+PNBbO+Ol1XytVngBXwFHixc0VoEfke4HuBv5GOZRf0vzH0hS98YeSu9LJR901J257agB9/7RQwzBRApj/tx/G4gM7wUOAf/QqcrEA1Vu2dDJc/KHGn0WlRDdQLoZkNNt6aApla86WC3fQmSoPBnBiM73mHytCunUVgWzvqpvVpUk7qnAflrDNnbkPGu+UBXmOauK0W/EZ5JwYYa3Qt+lp9xDbEAOQ6OL5S3+bExzG8Gt71Bzz0y5TKVLjnl9xrlgSNebYf+SUP/DzWalA4DXMeNMsYw6CwCQUP6iV14lkHx6nPqYPteFTqYhrUZIrdBkvpbddWBJecQdtrctNEo7ZGMCsbi6Y5hBCD10Y43mbqSC8geOmC11DiDmAQwCYBaNIrV5AGpE4CUGMGJY1eU901CJ3bAApuFXAnoeOhRqgXMlh4gJ9JN4Za2H77P0N4/XD83Z2h/mO7aiGq50x/Efj9l/T531X1d6W/P3tJ358Vkb8hIn9ERP4I8NfTsSXwPF2DXkqsaHHiZcYKYHdZn+ipZP3EKn0deIL3+Pfej7FKiefB//Uu7rgC1Ri3UIbedVGguK9kx/Q//SSfWt5qgFr68yrUqyzWb0BxFsrapk2A4kR5b7PPuik6sffV7S0+rPdjXQWFD5sl7zV7SRkFj/ycrzZH0TVU4dTP+EpziyrEtKSrMOeL1WusQhyzDBlfa444CbOEN5YPmn0e+TlBwavw0M951MwSvsDKz3jkZx3PdSh42Mxi/JrCNmSsm7xrN8HyqJ5ReZewQDAS+P/Ze7dYW5b1vuv3VXX3uMzbuu21b+fY5/jEcQJCgGMQdiIQCg8BBfEACuEJwkMUhJQHREDBQhYIHkykPAQLIoMSJxLKSyKcl0BIIkGEUISN4zjGsvG5n31Ze13nmmOOS3dX1cdDVV/HvIw515xrr73P+qSpMWt0d31d3TW+f9V3tRJSu9kcaYcvLmNTm7btvKWuM2jwwxtMFmLAc/POhdhu8KISxHV4QhjOATWKzzs8MQF8l4Cqt5DW9ni2Uazr3vHyg4L1gwyVBF9BMRtP87DySjAP7kOTfUuaTcJovp2xQXkD6Ze4WayAV8CLXe0xfwL4U8B/rarfEZGvA7fqI/uWRqTd51l+poNzGJ2TPhpf1eb7ZlGlQYc78fN4nLFbP5NHWaH/+JuYf+r3gSTdRN9PFshOSlbv56zvZa3GwE00BroZiQJiCWE/YoBRIWwUpqCiGBV0Adz3ac0ruEqY7ldgQrtxmGaeIqsRgaWbkBvHO9M1IsoqTPhkY7hbrNPtW75T3ue9/CRm4wAeuYyCWfIdFR65grmZ0pSzKRWWOiHq9yOIvqxnMZd2aj9yE1zI21R8T+qY7q5O/qfPfY5XwaWo4lph4/eoeqlfSzej6qV+rZyFVNrHpNX70k1xKWDN4DmppoQU/LHxUFa9dLImQG2TVi9p9esM72h5UivibbsZw8XsJBrSO62UfCkxFS5ABZNTYoYTwBvIS1CfIj0M5KUiZWxbp0jw6MzGuZgJ9VTJNynjh5GYZSn9TzFl/TM/wexv/kqXczvoQFOVYilvx+zbbJhuoivVvy8iX7uZ3gD4D4na/T+U2n8F+Bsaf+T/8g3yuYzeYsXnSa9V1ne/wRvhIenaqsZ99oTsg/fQALb07P3GZ5z+M+/Tbk3KgB4YgsQFX3GsaC5UKfxBFFwW8AdKMCAqqFNMkRaZaqjXlsN7JTblbi29cH+2wpoYi/GknPP+1CWljPC4OiCoobAVIFRJwbNv66SbyvluVTA3jsaW/l1XkBHadNrfrifMpUxYIGz8hCe6nwKi40L7mTukUtOmUF3VBZC17vZPXD7Al2cuYxOKFk9qDxs/TTV9hFMP4guaMm/WBE7qKUvXxY6s6oxVOcMl68mytGgvgcamEkIQtNH82EDYpHg8ifZxs7ao6zaP1gkhpLZpvAHSKzcRMSdLoTGweAPZpjc1FPKFtsX68IqZp3gLI6weFmhhMesARrAeio8WzJ4sYe1gOoV7d9Hnz1trVosT5/w+bozebKyAV8CLnTYNqvpbwJ8GEJGfVNVfA37+2rf7OdAXOuDtthZBt0wSI4d77eTO0+z6t+zI4+uT1qGn4Bq2r64d0FShubkyqODUYptMFyqUmlH0VFkbzcnoXK42IScX17ZrzTD41tTr1MbvJPT6zAftTcixPdtkFSxp7xP7SMFkTdsn60UbSNdsIKQReo2LVddnCGbwTIMONYON9aHrc/ywpNXGxTZx5Z/a0h7vyDTZ7S43BMTTQmTcft0k/O7fTOVg2lNWWzsIgn6t9Hp/hz8tIv8I+AT4j1X1/z3vRFVVEfk/gYp4l/+3fg6mlrdY8ZauTYONhqLeI1nWHpI6QNYV1REfFQ3t5ZfMdtHGuaZ/4vCiscLZB4OVTvY7NWQqrWeMVzPAj6BCpdkAT1ZakPcieVdaDPFEc0yvXWmGUwY8AhbT67Ma40vIB65GdbDE7EXdfdNzEdUz8KQf8KwqvRo/qR3M2RbqFkxHD09lgNfAtrtTGL6BrfPHeNGl8Iosbaws3lwiAcTdannP3ekNxQp4Nby4TuTH/wj85DWue0vXpbSwG++SzyVhkLmiudaYmPKu+Wy0P+3nZTz0LB7xmqYPY4RgLNlX30clplzTEJCygk0VM+TMpjArmH1UM33kWHylIEwNxUlkUB0J9Z5gnCDHYGZQTSMPW4FYqPMAE4G1xVghFDUmh7K0GGPJ8ppp4fAKG5eT25rMKKd+wvJ0wv3Jiv1sQ8WE082EQ1uyl61Z+n0e1wfczTY8LI55Xh9yGnLmxvEwO+aZO+RJvcfUOH5s+hmVFjyqD7EoXy2eIgI/KO8TEB5kC3JxfFzdp1LDkS2ZmQ1P6yPWapkZz9xsOPUz1sGSizI3G9ZhwkozBCFXx9oXnPgJAhQS6zSUIUch1pbQmHbVq2ARPLDYzPFBsCIEAhoyjESh7YISqhxXmRQr4BFtNDiAV/CClNFKETWAkJUxpZWYKPgzJzEzCRBKmK6T0JZoKbAhedRaQV1ozcuaC9TJtuNATwIyjRU8EaGegvWCebZi9mvfhqcL9KsPKH//V5g/WWHefYg4h3v8FILfti7cprDere8HIvKrvfYvquovXpHTrwE/qqqnIvKvAb8M/Ph5J4vIHwP+HPC/E5/Gfysif0ZV//oV+d4kvcWK10wDOXzLsn7M69V50P6OBcV/8gi7vwd3jjBH+8w/XsEsY32noFg55t+t0Knl5Ksz3GGOOYXpCspD0Alka4PdKG5fcTPFBAO1YgohTDy2UE5XUya5UkxKMhGervaYWGWvWCFYvre8Q2EC785OyE3g0/IAyz4PigWZCTx2+wC8m5+wZ0ueuwMcwn27Ys+u+F75kGXIuZdteK94znN3yImfsGdq3s+f8dLv88zNKSTwMD9mHSZ8Vh8iKHftEqeWT6s7EU/y04gnm3tUaribbdjL1jytDtkEy9QEJnbD0u2x8pZMYGJK1m7Cqc/JRNnP15Su4GUdC7eJBnwweLVM8praGypvULU0mwzvBL8p8B5sBpo5JLksNdggVVJyGVCfXIkbg1OI1uesjJYUCgGvzJ5LdHPNogdsURJTq5pofDAC9b5gA8hKYxE+H2u9eVFsiF4Jbs9i1w5TgkwmbL4yIXt+ijx+gb44Rn04d+6OP2+Udu/uVfHiSlgBr4YX19k0XF29+wbRF02L1DzsboN9flq9llrF8PC81nx83mXX4jG6GDA/8WOQ2e7gYtXpF3xAM4EiadadMn/iqA87TXK2Ueq56ZQYFTDv8Q4xLWssSiaoB2z64RNNokXmmRRNnmwBNVjjaOwbp/WEie2sBad+SqWdJuWlm7EOWfKFVNah4LfX7yEpMes65Hxz8y5FSt3ngO9V79APCX5cHw1cjE7cjKfMWwvJ2ucsfYYko61TwzO3hyEKbAVeuBl1yNtnvQm9dISpj0XVVe50wfB8PW15hCDUwaYgcQWUelUgCRRUAWe7iSbE9KmbLjWsOMGWndbJBFDXmX9UYfqyZ3oGTEWPY8ypbns1dtR2ebmFmJc7mPTCBagrZn/vH2OSFcJ8+px9LTC5jZvRIgdrovWqFzczCKq7YQtda+m6nJ6q6k+9Ci9VPen9/7dE5L8TkQeq+vScS34W+OeaHNsi8g7wd4HPc9PwFiteNzUy/nXI+p15nGHFPIfH+GJdr+FHP0QzGy3PG8/s01VbS0I2HvGpZg9RaWGrpnhktC7YDXTFjQXxip25tl3XMJm09lMqL2QuJzfxxqpgWbqCvTzGh3qE5/Wcopem9ak7YKldQbpnbo/vVXdbOXzsZqzDwxZPVmHCt8p3sUlIVWr5XvkAbRN/C59Wd6i0q8L8uDqgCl2K7mM344XrZP3GZ5y4Q0ySxF6FZ+UeJDxxKjxeH7QuqwCVz6h83r4nK+B9Ly14EKpl0fJQB8FlXdIRBVYmun+lZvQQ6PwIjANbdqIgW0H+srMOGJfwQnrXp/tRgWBB8jTqxCSrU/xkfGVRwVYmPycTFVDmydOmsl1vXnE23fB+4QpYAa+IF9fACngFvLgwEFpEfuSMr79AEvSLT/15Z+woI8MZkGz6lZebnfVQxm/78/Uz47wij6AgxvR4pizQfR6TfPgbtUMmQQTbz5LDcP0XUhLnfi5pMcOsQcVoOyyjX7BIGATyKcMsH01fOvg0g9wVVob1EBph3/VB2gB0ffarP2gaWZ+HYAbjQLMLjOnRHG574/DoILtR01c3nijQ+9krmqJHLY8tMzOD9xF0uEFoC0n35tnYwyhDBjc/FqhKtIS17dINtD/qQpxXvlkUgYRwxtwddXrTpDv83QCJyHuSJqSI/PPER/7sgkvMqCjPM3ZPdPHK9BYrPn/qz/9ryfpz+mxJm03GVXlcft/n8QhBY5HQAZ4MeTAfysjxrFcDtifbVYZyuMORTkYaGeHJ+Je0LSLppzP10TloSw4PZf2QB6mIG+01w2x8QWWAJ5HnGE+GvHSMJykOrt9nn7wywJPQvI/2nuLg+9hrdPiOx7i4JRP9Np70N5Zh9BmRlxGPIZ4wrvNQ1Zis96zSImIQd9P/VGV+OONGaResuAG8uAZWwCvgxWWWhl9mZF5W1V/epeM3nX7u537ui6VBgm0Bf8aEG59zVbfmXXiMUWCLJyOZOu7yjPMvYXFl2uX6V/+9Xt7DlrXnzD7k3OO6/TS3e7j0ecqFx3fxAN3lnV3cwS7nvNobuXUXfuXGgttE5K8RC+o8EJGPgJ8jZRlS1b8I/FvAfyAiDlgDf/wSn9P/VUT+NvDXUvvfBv7WzdztTvSlxgr4AlgcXlXW79DnVb3FXwePXcZ9HZl30fVnn/Oq8ucm8OTVjXs7rQEuOLzLO79KFsjr0lXH4fopWl+Z+RuNFfAKeHHZpuELbV7+MtHA505SruPUbnIOa9CBj2n8ki1/vda/b2Sa3pVHuIiHgGxKZDZNWm1FTSzzjsSgJV2WcGTbtpQB5tFaAGBrpQoBYw1BU0VJDybr8msbBxrdKuMkrQ0UobFOsi4t+5lr/fjrYMjVt6nX6mDxqmQSqzoHFXwAa2K9Bk3ZjDIUEUXVRM1NGzxmKL0wsU06t1gxWQhdukONOa+jRSPeeUx71+iYBK9RPxVT1nUatqQ3wIqjCkWKD472FtXOOmAk9Ko6943bZiCYG0uNIJjM4+v40zek9yi0PEIWoDKtNUElWRfG+bWbPhVCluITmu+lsyYYEZwJZDQ8YsycEDV6qtFNLXiN8SlAmE8I0wKrVazqKaDrDTKd0GTkYjIBt4o8evNREpMb91GFGwM3Vf13Ljn+C8AvXKG/PyMi/ybwB9NXv6iq//Mr3OJV6S1WfM4Ugsb4odcl68c8xt9fxkPjwu5CHkagrmImnOR7HpK1AQWxQv6sotzLomwRwVQK8yRbAKnpeKDRgd4LknWLV+cMeaaQrBCVNxTWtzLxtM64Y2qMRAuCC5ZMtAtWDoI3jZY+RvdGDXqHJ3WwZEZjALMaHNG2EK3gEesC0uKJkUAIvfS5REtAiycarRE2PQxt8UNSnxEVmoDniDQeVdv2aVL61cZtyoqmPIDRQiEpG0nf3aivxxKIwWvOpP4EL9Ha3XiahoTrjaxXS8ykdEm9hvZ7tvHEB8VowhOBMBF0mcYRFJ1PUT+aV/052QFiO9+qTbXN/FXoDcWKdM218eKyTcOHIvIXLmD8p3e8xzeSvjAaJEa7Zh2aNfs+3cDgRwLbi6e+qXdnHqM+tngokGWYu0dQ1nEDYw3h6XP0+TFMCux7D6HIMVmGrmv8foEvBL+XIV5jmtMkjKbPlHo/oIWQbZRiAeURVHeje03+Mvqp1ochJnGobASCuQMTNwUnyyn7sxJEqH3Bus45mpZkJlCFnB+c3uH+ZMU0q1nUE5waDrINh3nJwk2oQsbcVhzla17WU07qGYXx3ClWlD5n4fbJxfPu9BSnhqflHiLwTrHAiPLZ5gCnljv5iol1PCv3KEPGflYyzyqelXvRVzaruFesWNRTTtyEwnjuTiKPkzoFQBuPImx8/MlmRIB5vp7h1FCYgCHwcjOl9hYrmgqmmdYELQq+tniXzLYBggeqGD+ihcZA52UWAT0Je/GgVuO5IWVISlNOPExeglkT312WpmKT2bVUbBnIT+OGLhQRyW2VFg9BER+YP94gLrD+cE51mJEvFfcz/yT63UfY3/kobsCePEVmU8zREWG5RJerM+fjbVocruCn+tpJVf8GsSLz50GHXX18AAAgAElEQVRfaqyALwZe3Iisvy6ebH3/CjxECPtT/O/9CvrOEWbjyRclfi/HzXNMFchOS9xhTnVvSr5W3CxuCmwF06cRKzSLS+f8Jfg9hUlaxJ7kyNQjM48Gw+nplDz37O2VBFWWvqA0gf1JiaC8rKesXMH96RKvlmebQ0SUr+69pDCeE7dHKIX7+ZKprXm0OWATcvazkllW8WS9z6mbcKfY8NX5MQsXZf3EOO4XpyzqKY82h+TG8950Qe0tP1gdAcKD6RIR+GR5gAuWB9MV06zieTmnCpb9vOQgKzmuZqx9zsQ69rOK07rgtJ6QmcA8q6h8xqKaIKJMrcMHw8v1FFVhVriYhrzM0eS+GlSoyiylqI2bEkK7C41qqlqg7uITWvefFLTc4IVa0Cr+X7xImJIpmBjP1uBJ8tmKC3kBTHyfpkrHUGyVavwYqA4z1EKYGtx7OfOPl9iTEvPb34MQGCv7x2mDOWfu3gS9yVgB18eLyzYNa+D/udYdvaUbo7EGx1gzrNSp27v0rXNelcdl5xhD9v67jYocaof/1kfdxWWFzKdonoKuFHRuqWddSj1F0TwWZBOFbKlkXlrfxuJUcYeSFrMxk1LdxYVBMOQZaEpvF9RQuQIxXaq6dZ0zLxyNjulFNWMWilbjtHBTau2Csld+wknd+TpWIeNZedieX2vGp5s7nVZf4dPNEfQ0/c+rvUGswqKe8rTcb/tcuglrXwx4PN10PBQofTEA3UWds66n7XerOqOuJ201TxdkKwVeucq7hyVgSkvoOadmSxs3Bg15sCTtnMSXljvTaYc8zD/rCUdlqF0RmC9CLKeajufVsOKrPa2ZvawIKVZh8niDDfMo7EXwHz7A/u7HrYOrrjf49WYwrnO1SbdBbxgQiMiCs+8qQr3q4Wu6lbdY8QWhnfBkBzy4eR7SygHNLO6n/wlI8Q5hllHOuuVKmFiW7x62WmxRsAptfoYAZgN61FlYpQb2Gn6ClhZybUViXWc4Xw/SXnvNkORnUqvl8fqok8MKn60POCjqVlY/rvZBbXvOST3l0br7CR5Xs1Y7DjEL3rdO3yUNmypkfOf0HVzvUX2yPCRo1vb5dDMnt7OW52k95bTugrBLn3NadW0XLC82+y0PVeFkvUflO3xYlnlrXYjXCKEs4iZA0iag6lWcVpB1L2CaWEupiWdDwAbpagtLXPjPj3vWJjeyNiitS09jScpPewwUJovQZt4jgA1KnadxWMFtTpn85vcH1aBvFQ8uojcMK+Bm8OKyTcMzVf0r17m5i0hE/hzwrxPz4nwL+BOq+jqrlg7odcQ3vMqYt7T8ZwjvsXL1KgJ+Zx4XnaOKP1lgDvYRE4WLHOyjqzX4+AP2z14gR4et65L66JaimbRuMINgp43HrBzlUSwAp4BdgtsjFosRjUKpV33YrQ1mokgW3XiqjcVmkBUxNWftDCvNmBYRHGpv8N4wLypEwHnDiyrncFJijeKD8HIzYa+oyUwgKJxWBdPMUdjY58uyiNqcokYV1i7DB8s8j32WzlL6nP2ijDyDYVnmzIo6BtwFYVnHdmbifS+qnCJzFDaaj9e1RUSZZJFn5Sy1AxO9vPDeUNcgqa1e8LXFFj6mwQvxedOkOdfUhtZEq0nj08TaiSMu1ptqnWGoLRRP0kI1Ql6xa/ATWlt0kzqvKRjqsmg+bjInmcq3CwUAaod5dkq4uwcmZjqR+/eitcqf7XN6mSb0xmi8KXoDSFUPPu97SHQrWAFvHl68DrrNMb+yrL81Hn3Tg2K++4jwIw8hz8B5suMV/nCGFjHwubVWJI2ZWQVsLviJafGkxYdmM1ECXbI5tDQw6VJylpuconAYG+XwapNR5I48i3J44wzGKFmSy7U3nFZ5K+vXdc66KjicraNnlTesypxpT9Y/Xc45mm0obMST49WEInetbF+WGfRk/abKcCFjkjDLeUPlDLOEWT4ImzpjltcYE0V25QyZDa1L0KYyGKvYhC9lHesyxMJ24J3BB4PJo0tW8IJ3sZq2NNr/hB8NFpgmK1Xjx+rSsabd4Enz7D04qzEtN0TZ3uJLlPXZOuBmJsp+p+THFW4vi8pEIn6YBj9UMWuPDQY/i8etN+jhITx7ce48ey30BmIF3AxeXLZpuGEnr5b+DvBnVdWJyM8Dfxb4T2+J15tC1x5z64/a8yNMFkKgS/LZ913d8mEdLcjPbY94IL0+L+IRAiwW+MUC884DzGSCvXsH7hzhn71AVyt4+pzw5Dny4UP8Nz5AVMg2IVbyPMzigpbIZPqixi49iDB57jj5sSlYw2QF+QpWD5X6AKwzqAMtYto9LTNcabGTQBCSdiUnzz126qkUNgKrsmA2qamDYBAWZUzBunY5gnC8mbOXb1jWE0A4Kafs5RVlyEGFkzKaeDdpgwDKNPNYGyh99OhcVBMsysrFmgsvNxOmWeQBwqouKIxj4yKKnZZTZnlFFWKwhpbKNHMEDCGkVK+1p3YZpYvSW3wU+JWLxXwaFyJXxrYvLZJ7tI5tTcdlE9PkQdTu2I1pMyCJp40jEUnuSMnc3LiC5kuwK6JfsIJZK3kZ50tWQchDNClngreCCRAyaZRUSBWYf7TCrFPaW1VYl2RlDUbIny3x9w8wVUDefwjvPsB/+3uw2pwxd7vfR2cmjxomzt5nXJtuKrjtS0i3hRXwFi+uiBeXyfqz8WRnWX8TPPp1H87gIbUj+9Yn8J1H6Dc+JEtV5PXJks2P3KF+fy92HUA0RHdHhRzFz5TlexZsTPUsDtxBvElbClIq7iDF2NUWrQ069WCEelNQbXKKaY2YOKlXZcG0cBijratnZgK5DdSasa5hUU7w3rIoC0SEF+sZ+5MyVXMW1nVBZhxlwpdnqz0OipJFNSWoQVfKXlHjQly8K5AZj0+yXkRYVzlF5qkTvizLgmlWs0n4si4LpnnVVoKufYxxq1x0OVIHVpSqygjJCh1TqBpCY3WoLEYUV6eohJSVSOqUDSpEi0G2jClwtQI/0fYdQ8QOW0qHJxrxQjxoAWRK8RKkjvPGVgohkK3jO87XHtQzfebiOz92lPcL6oOMMDP4qSHbBIoTh62VfBkIGeTLCusK+OBd9P4d9NvfR53fee42yrSboi8rVlyYYklV/4XbYKqq/5uqNlnb/wHwldvgcxVq/FVvi15lzO2ET7vXzk+107Z0p+jwGrpzd2qPeKBpp74Lj6AxWK3oXG0QQTeb7rgq/p0j1KS0bAquMATTbc7tymNXPjaCEjLBF2nBGVVM1Ie0C1Cg9b9vxJcP6fmk74IoXtsuCRo1/hADb70KK1egqR0tClNiwlhQ4vGQiqApwmlVUIeYwi4glEHYeNv2UXnDyuVdn5iWR7wPYd1rK8LG5QMelc9wwTRunqyqnI2z7TW1N9QuqtLSo8GVXRsErfvtuGGgn0e7lBi713zhaBfbmh5gUyBbif9nq24DAUpW9uYPYDdRaLZ7ykyiJSmdZo8rzMq180orFys/07ygaGlqXZ+MgdWmu6f+vOt9oaO5e9PUVCm/6O+HkW4LK1LfbxRe3DZWwPXG3N8wD/va6jv9w/Vl/avy4HIeBI0WhpWPGumgiCrVg+lAlphKWz94FKq5kEK/UI1JGprSBK1irLE0NLySv1ArM7vuaBJWeO1/38MTYF1nLMpJlPUa6+Us68lA1m96WKAILzazKNs1ttd1Rt2T9euerA8aLQp1D1+i5SMf3G8dspYnCJs64kkzzrKy+NCd75zgfdcOIVqoW7xQYpKRHn7Y0wjW7bPTwQemGuKJ2cQNQnONVMmK3byPAPlKo+xMMnT6xMVFd4htlywJGoeFXYfBO89PauzCpfzfAmWFhqZi9m5zt/GQuCnaBSu+iHjx2vJ4X0D/PvC/nHdQRP6kiPyqiPzqkydPXuNt3SrtPOaasv1+kM8aOpTokRlN/CYTxq70yjzOYjf6bovHWfchwz4NF/OQMQ/Zgcdo+l/2pK7FY3TRpW9jdIKMme7C44pMxteftdjeOufi5vbx0TyUM4KVzXiuXjKQ8bMZz8tbCYjWHf7e0m3SW7zo0QAr5GrGnp3k8FjW7yCPbpvH9u/+ijJSdpH1l8jIy3iwA48tOXzzeLLFg7GMHF2gl/MY9zk+bnd5Vluy/uLzx+/Djq4/S9QbO4y5uIzHdG9y8QlXpV2w4guIFxe6J4nI11X1O9fpWET+LvDeGYd+VlX/ZjrnZ4l6zf/pvH40ltP+RYCf+qmfeqMf8W2M+VDutWPe8gs942mEMDznqv58Z/IYme0u5LEDu+B2GUe/T02+9GP10OCUgVAIGi4VEuNzLrv1ziWsuX58xbZ904+1HNfgMcK78RVb93H5Wnn4sGIKwxGTUR/jcYyPjx7/1vEmrWvHQ86YV/2LdPthjEcxuqetwH2RmxXKX1Ah/zroVbAiXf8WLzraecz98R7JPb10FdmjXeIUtmT9FTfit8HjrEx+F8me5pzOFLOLHNYtOXzh3kS28WCMSVs8RlLTXyrrr4EnY1k/am+JSAE/jhE7o88+XoyP+/H7GJNsx6FdNhANOigC64PS3xJsw4QQxjFwl/BYn24uPuEq9CXGistiGv468AdE5O+p6h++Sseq+q9cdFxE/j3gjwJ/eIdCFF8IurUx9xZX7Y88/fYb/9DmvPbU0YJs26+v1+4J03N5jP1Tz+JhBBVB6xoping4BKQo0M2Gpnql/eQpYW9GKKIZNF8HyAWXp0oEhUUzj9QxrZoJgt0oftbUSBD2PlZW7xMDogG7EMK+tm1ZW2QaUBPFZihzDA7yFJSdTMHZpI4CMEBVWfJJrOWpKpQby2Tm0vNQ1puM6SRgTBS9vjaIAUnZmuoqI8sUYx0xvsBQV5ZsknwzVahrQ15EHiiU65xi6tosEq625HlAJN53uc4pioDJAoqi3qDeIjaOQ5xBKwN5iK8nCFKB5nS+mhtBp50PcaznkMzxCpopJgxrRBhJecahNRGHFHNilBjAbtP7UEFNQIK0PNwEsiYQLk2mfqXX+l5BVnrsSR15zCeEXDAna9obW5cwm6TA7gCzKaw3F89dI63L3lUWULuQ3HyXXya6NlbAFw8vbiJ5xk2PuTnhlWX9eIPfXHeTeLILjwZPqgqZTFoee98/YfX1I9TGDHWmCvGYjfJl/ixABuWBAQP5Jsqzak4b1GuXgt9Li/QgyLElHKWkEQpunZPPHI3/SLXKKaYek+RuuSnIbSArIn6IN4TaIFlSRAWhch2eoFAvM7KZb6tN+02GLULLw20ysiIMZLt4g2ZpU+UNoQYzaXxFBVcZ7NQn/IDNJmc6rWnq9oRaMFZaHjE2wbQZBo03qDMEG/FDgkAthEw7H//QvLf4v1pi3BvxWdkyBUQn2Z9torInpPTbVsH4Hn6EGMfgs4QXAr4QbKXtXNncL5i+qDEaA7dnTyo2DyfxHaNglVQRI92E6bIkaUDyLLq0Br/z3L3JgOkvM1ZctmkwIvKfAb9XRP6j8UFV/fPXYSoifwT4T4B/SVVXl53/ZaBXGnNvLjfK10YIj3Nht8AxvOwMv77hdcNj5/DQM0+PZAxydIjZm7e+ge75i5g9KWmS5M4BZjZD8hzzg2e4r9wjTHIISn7sMLnERXipBGvwE4PfM1T7MYjXbDQuhjXWBsgXsPiRGKgratCl4u4qGojFaDaC7AXURJ/SqiqQwiE2ZoeoEMwqIyuUqoobmHIdyHJPneICyk1BMa2pa4uqYbNWptOa2llCiGkiijzmgw7BQAmGDKOGsox9mo0nLzxV8hXdbAK5VarEo9rkTPYqnI++o5VTbAKE4C0rlMnE4RVcW5QtYILBbVI6CwlYK+jaYIhB1FjFVCmArVTCRDFOUqEexQRiJgsVglGMl6jTJAljFwW8Jrmb1UqxgeykmV9xU2dLUnYkpZ5HsMAY/FQpTpsFvLRBcRqUIMLygzn5tKJYB0JmQKZIkVM8XUT3papGNyV+tUKPT9p5tDV3+9ai3mLkVnJvf0mD226AbgUr4C1eXHXMNynrb4pHVyny4nNbOgNPwqSAIsM6w/7vLqjemWIqxbikGNrLCMQF6uHCc/p+RnXPxtTRa5AN1EeAClktmEYmlklGLg3VuynOCkNZG+zEE7ygwVCulWLqCYG21k2eO4yJsQIgiAlYG2MFomwPZCj1OibQqE8VO6/xVcSTeqVkhSc4g3pDvVSyPBC8EOqkS898XP+mPll7TKH4KiXD2ATszLexCPUmJy8cLvEAxSqEKvIAxViDekFdV3lNDGhN3ATV2uawjQHQEUuaZxVfSMIPUqajMmY3tHWK4yhIQdFpGKWnKCFbpA1KAW4/ixuMIm5UjNeUMEM43bfsf2+NALYMzH+wxu1bTBmwdXznWVCoPcal4qkvF8jLE8LxyfXm7g3SlxUrLnOi++PEkMgMODjj77r0C+n6vyMivy4if/EV+roxuuUAt2uPecsf/IyAnS2X8ysG9ezEY3TOwBdVBLO/N7hOT5fQMz2bvT2kqdMAmJFZ2tYatQ3N9VbwB3k7uDZ4qOkvgK2kywKEYGozaIuPQb/tPTkLvskbB8FbXNXVL1A11GWvnoEKVZkn4Rt7raoibRhi2wfba4NzWbvpiDwMrh7yqPo8EJyzw3aZE7xt23WVtxsGgFBbQo8H3iCbri0qmMq0Av6sZ2NC10a6bHoNxXckg+ddLLu2KGSbVokVFWx5MlNAfG/W0J+cYw1MmFqYZN05uUV6ZmhU0RcvB/NoTNsxHbeo49Ed/n446bawAt7ixY2O+ZVl/TV4nOWvf2Fswhl4wiSP2mOiDClWAeO0bVtHm8oZosbb+h5euFhLoJVfQbBlT0Y2xct6cljrDO3L+irrimMSazvUffwIJsntHp6sig6DVHCbIZ6EKk+L+aaddRsGiNWWe/ig3qBV1uMh+LqHBQj1CLO0zAY8pLZtFWcA8YJxvWcRBOvtAD/6zyqe0+coFEvB1s35xE1EF5KJrWGy1N7zHy1AjRDyHl4YGYCFAJNV3DA0beMCpu/u7BycLvu93i4eXES7YMUXEC8utDSo6u8APy8iv6Gq5wafXZVU9ffcVF9fFHqVMQ/KnpP8REemhMFmWUa+pGOzw448xqnJBprbEQ/RgPvkEebOETKfISLYD95DT5eEkwUA7tFjzN4MOTyESY5isLXHZzG5dL6sQMHtFYTMIJnBLhxhZlALsxc12Yln/TCnOrRoJkyOwU/BTaNWo3is6FQoD+O92pfRPFsfxHvNT2zUJN1xqIX8JINK0LsOnShmbdAypeDLY9suLOHA4w88OEHXFskVnTvwQjjOEKvoQaxkIyd5BJ+ZAwG7zqIG/9BBrthFBqeWsO/RaUDWBnk8RQ494aiO7kYnFpNBmPi4FFubqG2ax6xSdmmjOSAlqipODVIrOgPNYpVNqeP/apLGpwbNBV9oNC9vFGMEn8W2WUR57QqQoMyegTilPIx95KvOb1dUydcBu1L8RAh5BIHZ4xCtQ3tCtlKmn9XoxFLuG0wZ2PtkhVph9V4smDd7ssFUAd0rUIHsZIW6zqKgp8vR5N6e02E0d8dz+UbpCyjkXwfdFlakvt/ixa50TVm/hScXyPrb49G1zeF+p7cBdG9CmBcYH6DyhIml2s+wHszaoZlQ78W00WYT4ho9E7KTkKyfgvGQP4upukOebscz0GQUTyxhT3HzgNSCXRpsodR7PslIi7HgDqNJNju1EIRw6MCCbKIGn1kAq0hpYv2bRiOjRBehjOgCVEvMRpSDm0ce9qXBGqj3E2atEmbte9RAtrERh/Y9mil2ZTGlwe97dBaQjcGcWpgH/NwjpWBfGGwO9SykcQhWhHre8IgbpjCP7y1fRauz7oHmkK8FUxErO0tMu5qvQYuIF9lKyZ8rWKGeRbzJFzGtbTWPDzfzUBfRwoMCRjBLj04ivmebgCkVN42Wh2zpB/NFNhXycomdFPjDGfiAHJ8Cgs4mqPPw/HgYyyA7WpxvAy++pFhxmXtSQ/+XiPx54F9M7f8D+C9V9eXt3NZbGtOgciIMc1pL8vPTy88FWt+/1gcw/WDG1/V3DZIcHPUcHgB4j744Roo8pl3NMszdI7SuY9pV72G5wh/M4Z0HIIIExS4rbNnlU7ariur9OcbG48Wpp3hRYRHUK3uPaqojixYxF3S+EaTSpLYQzDrWIwipkLMJIM8tSDek/EkOKEZihePsaYafa+sbatYZ8iJWtVSF7GUWNxOpWLRUJro/aUIcB5Sm92zALHIkdDzMi6KrghkgO8nQkxhfoAry0qBLC1n0FzC1QEmrpRIP5liihibxsCsh24AljWMZhbyRWMnZ1HG8hvhp65QSr5lXQZAF2JR4JWrwlOKUdF9x8wAxK0ZI5p7pM9/ed1YqwUvrD2yXyuRpjU0xDRIC2bMN2aqOliIjHHxzEa0bAhogf76C0xWmSb0bAv7Tz0iJebdyuY/ndHzGvU3s6PiNkHZWlbd0Lr3Fis+RrivrB3jSjwtiBzxpfpPjCs9GBhuBnXgYwb73LibP2kDi8OAQiih41Vrqg4JQRI20ZkI5kSgzU3KFKreEiSBGMA7ypVLeifV48EpxApu7xArU6R4U2viG7FQwq0Y4gdkYZN3tLMRB9jQH08l2+yIjTLpxyDLW7olymOTv2a1NxUO2NNElVKNMllWUx8158jzWmmieW3acxfizBrNOYpEcS8SC7CRDF13smFlazEmyJjc81jKouly8NIN3k5+YGPNhYuXm4iRdK2mdH8Aso+eSpj6njz35miTrwawDpuEhMHkZ2v/VgJ/Euj3Ns8o2HqljWwMUa8U+qbo+jCDPFph1BaoYrZDlpt3oiQjh8TPCk2ddHEZ//vszfg8tjpBc9G4YL77EWLGrzfEvAQvgj6W/E+Av39ZNvaVtCknwN9QXzgPha2SQmUDO2Gm3ebKb73sa24t46A48VANkWfsDVAWtqqEF495Ra4JUBdNeGz99YTqBTtR2G6X78avip13eaG1Wxf1nVfTG0ddu9f5vhG9kEk2soWOB+C5wVxX66RqCKrbn0KNNJ70+orDt8VBi/EDo8dAejxCFWvustAtYbnmEIQ8J3YahT01boS2yE79n67ysp0RUkpVCGQhRS/fOxdPGJrTPpmeV0qBY1z4UNCi5C60Q1ZRdI/aRGHiP6Qt470FDq+Hs5lNv7vasziGEc+fujZLu8PfDTW+x4nMiM1q0wzVlfTgfT87S3A5+kz0K/ho8bIZYO5C7mmc9WU+7YYAkI3suLaqgaQPRyrws8WjuM+0v+jJy8NnsIPpyWEZyOD2Mfp9bm6s+FpzBo9kwNDzsWNY3lpYeRp2JJ32MQgayXsIYT7bvZcBDk3KoN1+k7bvB63471ejptfspV1W1NeQMsLf3PpoFdh9PxGkLVhoUU9ZtB8GHDieb68q4oRhvWvsbhqav/nj7mDWZv025ugvtumn4hqr+nKp+O/39F8CP3eaNfV70Ogr3XJe2fPPOctXbOudq/nyvh8dOZ13SvtrV1z3n4gsu7+HKPK7TwyWvbCyXtnu8Oo8beHg7sLj4mq184a/Bd1XC5X8/5PRDgxVvHF1x+u/0exnHC11VDl+HxyWXXEd+XVUyvA4e29dfR0aO6JJF6I3weMXzd+rkkuM7ifpd1jI9sjvUFLkK7YIVX0S82PUprUXkDzUNEfmDwPp2buktnUUiXZxBS2cIiLHvqfY0Sm18UdMef94ADyMCITTxavF7myG2MYNCOF0jvcDWaCnpaQi8DnJ3dybF5llIdPNpzMUAo6CqUOvQPHjGrr6fj9okjX8zdJMMyQMevuMhgNcwlEMjHiJDHpLcfdo+k3a+0WIJUfPe5xFUt82cY2tBfxwwGEcXU9bjoaNxMMyrrT2LSnsfZ1iWWh4mDkR6PGKWvu4a3xzobnrwzsWazu85tVXD9hy1XbtxiWi77F1/W/RlrPB5w/QWKz4n6mv2ryXrz+xzJOv1LB7mxngIIVpBWlkPhK5GgkTBO5T1biSH/RhPwPfxhGRRTUyavvqyfksOX1J7wOhY1jdytocfI1kf0BGenFFvoo8n5/BoMat5H/1xMJL1Z9W0uBCz2MLF/rMTYl6R/jse13mIJa+H+DGIX2ky6/WxSkbzyJjBBjT43k0BZHZrkzDAg/QwtuZo73O1uFkx9WWtCL1rTMOfAv6qiByl9gvg372dW3pLZ1Oc9Tu53TXSYkvFLJzZQfvVq/NQIHzyCHlwF6YzQLDvvgOLBf74JN7Dtz9CT1fwtQ/AefSb38cr2B/5EKxFFIoXFe4wJxgh/2yBfbaC+weEPIPccPCDmvJhTrkXU8DZtcag4CzeWvESwhSqw9i2m2hC9inOoSlrT55My2uYnIC/2wR2gXkJzDQGzRnBrgSdAEVcyJu1oAYoosYgexmfiztKwnoF4kFnMUd1vlDMCvyhEjKwHrQEmcRVvAlC8UzROYS9mO0jfxaD9qqjKGDy5Bnu9uN95wuwpRLmQjAxtWC2SeOIbrcER3RCJW1cnEZXKyH51qZ3V0Qe/YwX0E2ZNmf2918y+XiJ+/Aubl70hHcEB/v0BPPr30HevY9+7f3IMxOCWkzpwHn8Dz4GwP7oV8Ba1Fo4mCOrNeoCuljGhzhG6vF8a1zYmql9m0L4C2pOfs30FiveBHoNsr7hods/youaF/LQ2uM//Qx55x6aF8hyBR8/Qr/+Ady/gwQonqwIBwVhL8d6pXi+Jkwt1b0pEpS9pxuCFdYfzlEjZKuA2UD5ICNkUVZPjsHvRzmcVSArCPtKKKIcNiuFSUwbajyYE9AMXMoDZmoBUbSIj8WuBHuquCMIFkwpUCvMibIdwTttV1xZLZglsZ1HOWwX8Vw/S3J4E5+Ln0dZb1dgA/jDiCd2A1KC7sWFu/FAlfDExnFolWS9ieO2y8RjGnmYJOvDJPE4BXERf0KTTrsEnYO3MX5NViCFglXyjZAtQ0yoYVN2qnVcrGsuiB0bnwwAACAASURBVAvMvvMCgPU37qFWMCnrVZjZGK7X17arYuqQ5lrEE6kcUvtunmUGnU5iIPSmBBHk4QPM0QHh+x+DGxV1+zzoS4wVO20aVPUfAf+0iBym9vlJcN/SzZP0fA5hK+Bsi3R4zvgzjL9PqoRLeTTgsQMP9+Q52Vc/bIOOODxEliu0ToUAHj9Hnr+MGQ8S+ecvkB99H5WY6aL4dEl2soQ6SpXwfEH9jXdiIReF6WeO6itxl2AgCszQrTXNOuKaizHQcf25ArG9YayhcA2wCfYp5NJo9wVWwBFt1WQpY9Bw1KQLhLjo1rqrkinPFMlBmzR7L5VZJa3/vhwDM5q4PSghcyQdjsASihNN9REEW0NeNRaB2Kd9qmT9gmwnMOkp781z8HciEAjEvpIgNgAepgvAdevy4gRwPU2U0qZXB7DHJXd+6wWUKXPIRy+ofv+7KdYj3kvxt/8hpkrv+LufYu8eodNJ3DjkFv3Bp4TPnrXvPDx6gv3wvcjD2ogfn31GSH2cN7/6Ptpb66P+PL1p+pICwU3RW6z4fOnzkPVbn0mM9X+Tu/GIGn68xz16Mvhh6+9+HzOZISbq0c1JRXZatxZPe+ooVsvWImABfbTG3Z+m+AHQF57yXUuI632yRas7AcAsQKcgjfp3A9PTeE8C4EEycFljORBkGWPCtEmI8UJh0thYBV0qMk2KKQQcTF4IpsGGCvIRD+vbemqxl5MUiNzI+hdgsq6tp1HhAwkHy2ZcSZY7mGxo6+2cxcMsYnajFj8WQ56cRgxqNeMlHDzxbbAyXinWxGKu6avJx8eYF6v2mtm3jwkPD7s+Vx4mprVkqFfyRd1erwr20QukP28O56gk00YGTPM4Z0RiQbeHD5DPnsRioGle9efZpXhyk/QlxYorOXGp6slbEPgc6HVMvlvgEcpqmJEg72ouxBNGDn3eo2XdNiXoIHK3UT70o7KyzdDNxdQ6EDLiGbSNV0zdOx7iOX0mxkknZFIfg+fju9oQzUmD/NUqMQCtz8ONefT6TKDW52HSBqjtw+mw7Yf3LUFjnz0eTfai2I41MC56VsaNnt2qwiw6s4PUHl1v+izavtt/96folh9077byDC16uopR/ndV7QT+jvS6ZLNwtnn5i25uvg36YcGKNzkG7lx6U+fo+L7GJorVerjZqX0sAtl+Mbp+pDyQoNiy94VqzPnfw5OxjIzFL7tLsnU6pznuBfF9WS+YDsJAIdvIQNb3g5PhbDls/Oge+rJfx5iVxtULLN7CkxEPW45xcIwvo2cVlHzhhs9q40fPSgfvI/bXeykio/cRMJvew1JF/CDKGowZTotxEH5VDxSPF9XzeZ20K1Z8EfFiV/ekt/SG0KVWhkSDNHuja7fSjY3Pk3N23qOvzkrl1+cRHj9B8gxz7y52NoX790AV9/QZUlXbPB4/h6cv4P13kLt3sIt1FCRG8JMM//CoLdbjJGCDsP9JzNG9fGDJ1UAZnVGrfYNk0ZKQC7h5rEVQxJIR1AdKNYN8GdO2kkNtozaFAGRQTkDy6KqEATchavddHK8vorm4qaLcZIFotTlWkSqawkUVCqEqlMwJbCLPcpZ4+sjDG2V2IpjEszyI1TwlmardPFpBslNAFZ0qXqJrFarkU2Ezi6li7XPQQijngdlCoNI4rv3A/KVgTuPNVnNlUip2EdtuCtm3HzH9hx+BQv0TD6nv7jH/le/ia4/ZmxPefwf3I/cxPm6SvFXEGOqf+X1IWZP/+nfJHtyLVVybqZMb/O/5EH7sfeS7n2DXDvPugzStlHC8QD/+NJqdz5tX53zfTd7teXqTJLehkXpLb+mm6Aw53Jf1u2TXA64s6y/kYXbk0f89n4Un3/kBUuTw4XtYa9Gyjt/PJnAwwzdFIetAmFvqO1H2iCo4ZXqqTJ973JFl+cBQLOPiO8pIjdr4GshgfVcxxqDJ8qqq7D/2TJ5GAHr59Yz60CY5HC0UroAipR7VmVBNlOmxgI/Hl+8oxSa2Y1CDMj9ObkpJ1ud1dGtFoDpQQtFtQkyWXIRS2+YxDmL6EghxHKu7ShZMjOsw4Kyy91Iwp4nHfmD+PLTjOP3A4g4sdhUtE5orVMr8STTZ+31DlQfufHOFKQNhL2PxwYSDT0rM0oE1bO5PsMZClTwCjJKvawQLh3toWeGnGf7efpqiipyWZE9OIoYdTqnu7lGc1lE5Zg0uM2QuoPszCIqerjHETxEhTHJ4cUx4+gIE5N2Ywp3PnrbWtIFSrpmTl+DJTdKXFSvebhq+6NSf7KOJv1UuvTHFNcu03o/q/E535NF8jHnUDoxNP2SJZsSRxmHQb1Dk+BRruyqbIbPU799trRTqNWU/TencaqVYKpLFUvIEkhZF2nsrTmKquNYtcpWEd8O+hryvIUq1bdvbDMnXX7rx2nL0aEZjytYx5qA9oU5p9Rrli4sF09rHEWD+DGzQtp0v6dyptNv0NH2alZC7jrFWTQrCdLwKzNZEgZt47n/SayvMXnR5tVGY/eYjst/+CJLGv/jtzyhUW54heKqvP2itBDrWDM0m8BNfi4WTmm5zE6tDA1iL+eA9zKp74Hq6Qj/6pEuzN5pXA+1T//sRCdLOPTHDd/zKpOfzfUtv6Y2g8+Tw6OfT0ti371Vk/Xk89IbwREHLCrMu27oNUXGSQ9ZZLN2dAr/XLW3Eg910TLLTwCSXFk+k1qh0EWndeUwNZN01Bz9wTE5DKwOmL7ST44CUkPcsvWatzBa94xsoTqST9QrzzyDznayfvmRwPOJNrw+flFQNj0rJF90pUmmMz5Cuz/0nQzw5/J4jq7Qdx2TBIJV4dqoUx1167PylY//RquVhl46j/8919RBcwG4UyUP7GouTElMlwSuCv7NH2Cu6caxr8scn7USR04oJWYfvIWDLVKAP6SK8e1igHz+C9Sb2oaCPnrbHzqJwLp4w/Lwp+hJjxU7uSSIyF5H/XET+h9T+cRH5o7d7a29pTMaai7WqGs/pt/vZZfrfD5p9lxVrhpuNG+ARM+H0xjHyqd26J+0ycjSHzuAwoJhNp2tbK4MuZfQj3sonDQPPqbNkz3ivY0QulAsa2MrvfRaPQZ865G1H49oK9lVtM6TsykMY8RgPrHbthgGI2Ut67yMYMzBDa2I6GAejth2ZmccP2PmtecVZ4+jPKxkVkxrN3dvwU/0ymptvkt5ixedH5qyUkZfJ+vMKCaT/t2T9WamQroInN8CjX8cBwGR22KWMRaRibF9G6oBHE882IBnK3UxlELC7tS8iyup+n1eV9WYs68c8dMQj7MCDIY9MhuPYcuYJQx6Mam3Q7JX672OMvWM8GesIfRi+jy3QGn2yvWYwKNp3RdLRwzxjXp2NJ90188MZN0lfVvekXWMa/jKxNu1Pp/bHwH91K3f0ls6lMPbXO2PCnVfc50Z5nGPBOI+uev5Z51x2xZjHLsMeF0Q7R0lx7vFdKkhujf2y8y+5ficeo0su23Dt9D62xnFxr9vv4+rv/LKHdZ159cqkO/ztQCLyl0TksYj85jnHRUT+goh8U0R+Q0R+8iZu/zXQW6z4nEiv6M+9JevP6nNLtlztN3Yt2XJFQay6wzgulS1Xw7CzKIwx64rCfpdnNeaxddujDdf4HnaZItvP6moD2brHG+BxnXl4VTyp+vEVN0G7YMUOP6c3DSuuUtztvwFqAFVNXndv6bWQMHDpESOD3X+/HX1Ley4wwvBNXfDWLuJhjLQa7bYuw3k8RqSL01YoCAqz6bnjMEbQskSd65lda+R0Tb+UsqmGQVn5om53CqJglx5J+bwNxNSoGjX/RmKgWHO80Zy08QipnW3onocOg8uEqOlBaXkASIhadkNMvUfoPZ6Qgo37WppRgFsoem2I8RI9HkIMaDMk7Uvjo9m8Bg+2X6OicdXqjaPvLSAhoFUNzsesJEB45wgyi1gTLQwCGIOk/2W5wRyfxqA1iJqjk3VrnRCFkEvvnYNsHKTMGoLGwtY+8hQBptOoDNp1Xo3mdFO3Abbn1U3SDWqOfgn4Ixcc/1eBH09/fxL471/lvl8jvcWKz4l0pE29rqyXV5D1Yx7n/SbNGLMu4CH9BBrGEKpqcK+sq8Qv8rRliDJSGk27omXVWkelBlN3LjiMZDtAttKBrN8cmZiJLt13vmp4SJuNTjxXwhM3YYgnaREpySPH1g2eJHwJHQ9Icny0grO96sziFSV0uAjUM+nGIVAsPcaFlkewROtCw2MsR1WHNXY0IMerTvY3faQYFxGQ0iM+8hCBUGSdRbwB0v47DwHKsof3yfWphydMzl9DtPNqZFG6DE/8DadqvUFLwy/xBmHFrjENlYjMSNNRRL5B1Ca9pddBGn8AbfMSa8JgFz6elBdM0otcO7a0KOECHiMKJwvCao25c4iuN+hydfZ9N76XeNwnj5D9PWQyIRy/RL7/MfLOXfjaB9gXS0zlCJMcf3eOfbnGrCuKiaX82j3sypMvajQTFt/YRzNpaw+ojX6reRnIT6HaE/zMxExDgEoEMFsqZhGD2zZ3hayMQjxYqA5icTlbAxIX+uKU/DT24adxYT97HJAAbiqEQphsYoYKN4Hq0MasGCThj5KvFVvFxyma+myCrieCqQPFcRSk1R1LKAzFJvrZatpFFOuAnMRg5npumD73ZKXiC2Fzz5KtlKxUVCCsFtiPnpN/6zHkGf6f/QYGIf/sFN57Dz05gRCwR0dghHB8DIslgpL9yu8Q3jnCf/Udsm9+gjlZEe7s4f/Aj5OdOrK1j24CBthUZMsSjOD2C3S5Qr73CK8gDx8geUb49DH4MJxKF80rHf5/225JDZ+bquCpqn9fRL52wSn/BvBXNQ7sH4jIHRF5X1U/vZk7uDV6ixWfJ+k5/49Pu+D30snhM45f4ae1M56Mfr/NilKmBebOEVIUaFniVyvMg3vIwT7qA1pX6LRA55PEL15sHBTPKpxWsF4z+a0fQO3Rb3yIfPAQ4wL2kcPNDNWdnGyjmBBle71vMT4GReensLlrMDVIMKwf5kxexnoBfmqYvAi4WVwcTxdRDlf7gp8b8nVSnljwWQxeNivwOdRzZXIc4/BUwE9NdAFNa9ZQpAJqCsWx4qfxeUwSj/r/b+/do2XJ6jrPzy8iz+ue+751uVVFVUlhg7S2I6NXfGCP4qCgPS1iqwPjtNA6XeqIjk53z4KhF4XtWjPqaDuzfE5poyxawSdaKgOCDmI7rVAgQhVUQUk9b9V9nnveJ09mRvzmjx0ZGa/MjMyMfJ7fZ608JyMyYn/3joj8/XLv/dt7rwt6TJyvaNtYBfXdrHu0oHbQYP3SIas3WgRLHrt3HwcBFaFx0mflZhO/HlA7aLJyFQ5uXUGXPNYu1ZHAlS+suTWSpBmgNd+NIzlsIEGI+h6hhuiNTWRnF291Gb7o85F6y01gIrixbaHi1Rt4NyE4s47UPPzruxAqoS/oUo3w1LpbnO2wiWxso1euu/EKa6vIc25BNzZhZ9eNhTtzCq0fort7XZ/d1HNFyWc3+wyOygL7irKVhnuB9wJ3ishvAC8FXj+ODBl5sjPEeO3Vc5PHdBp2ux4zskaJY7rSaqEbNwfS0N095MC1YAvAxja11WNxq5N32MS7vBWf6x8GHLt8EE/5KS1ldSegcSwxKK4Z4ncakVg6VLwlof1zVUJSU83VGtFgsXZDVwArB4lYUIWVXTozKAErmyHLdeJ1GWqHigQSh0z5Dddi1E5T1K3LkGxoW2oq2p52T2FtK4xnpwCo7YUQdtKUlpsvvL1dO1BWtjqZ8hvK6kaY0lz72ydh21XgaLZYfew6rEVxnZ4gp0+lZkKRtTXkoE4Ytch417bwrnWuv7e5x9KVg3iAtCj4W/udKfFCpfbEFYKd3fgcvXLNrRaeXBW25AxhsW5utpbBzh+IMSVbwHOBpxLbT0f7Zr3SYL5ixhm7rR9aQzorBXse/vlbYlsvKyv4zznX6XHwPTh5Mh2/n7RVwNKzW8jTl+MpnOX6Ft75c4RRGrV6iO5pyrb7jfT22m7C1ntCcMJ3s+e1NfZCatqxw0sHSk0kDsf0AuLKALiGptVLCVunUGtqamxBTdPzN9T2lBoJ215XvJrXCfkU0FqiFyeEsw/uxT9a/WbI8mFIa8mLy+GHIf5Bu6UMjj17mLp+tZ0Gfv0wvmfSClKLpkkQok9d6rT+1xssXb6J+rU4TaknZkhUqG3spW2178O5k52CeoI+ealT0IM6+sTTnc+DADK/IYpsffZZG6s/6MWC+opS4Umq+n7g23DG/53ARVX94DgyZOTJtdIUGO9c7OKgc92X0ShxzEAaBQGWPTU0JNi4ibaihb+imXo0GvCknhAs+2h7kJUq/pVdlq5G4VGqLG8dsnTzMF7avrZxwOpnN5Bm9EO4qfj7gZuyDtz81LsB0uwYP6kr0p6xSNWtcHkYxhpePXQL0qnGYYupmP5QWd4OOmlEU75p4nMaiRqAKlJvOcMdafiNAG+3mbrxLem0q3iNAH/3sPNjfL+O/9DjyOZuR+PW83CmY7SD3T2CnV3Xna9KsL1D6+ZmHCstng8nT3TWVlhexjtzGmqdSlnw9LNxTxJhiAaZLt/VFeT4erwpx9fhxHGSsbiDj8Pp0dpUIULp7uZbROSBxOuesWRoBjFfMftMxdaX0khst2dHah/bbDrbsn8QbbcIP/c0bHQaLdK9Fy5EU44f74SkHFuDZFjr9i7+Q4/Dvlt7Rg5brD5xE283Cn8KlKXrDfy9qPElCFl6civnT/yb9Y4/2W2ycmkPabny+vWAleuHSLRAqTRCatvNeJsgZOnaAbXIlivQWHE93ArO/zx6Gf/Bp5wtV6W202T1yaRGyOq1ZrzugjSVxsllwqiSoAJSD/EakY8KnT/JEi8ECOiST/PEahyepIeHBDc20GYzvh/ZHx2tm1uE0VoaGoYENzcJdnY6/mQn7U8IQ9jc7fio3f2+gxHL2PrsszaNCkNZXzGP/qJUT4OIvBr4c1X9k2j7tIh8q6r+wVhzZwCkZ+1RV3OOu9OiOE6gM2c2buXg1HzFiTj2sWkMWo52mr002jPkCG6+5v19gv0D/M97LpyIeh2WlwgFwmPLbg5ncF2iG3ssCyxtCivXD+DYajy1aG3rEA4P8epu7MTK5T3qX/Ac3KTirnUmXOlkuNYIaR5zXbYi0ZiIuuIfhG6lTwU9dAv/eKHr9glD0LVoGj9x5WpP++oHSu0woLUezXcXGWevHlDb1/haeY0Qf68ZaWj8I1wQEMU7DGieXUGjGFX1lJVrdfyDltM5DAg2bsAz11xl49kbrpv+3FnkltPomZNufYzHLrk40kaDYHfX5TlwPRPB3j61225FVpbwVpbwjh2DViterM87vk5w7TpaP4TNbcLtHbxbzuGtrcb3sX0dvfVjeGtr6MkTLgTBd8t1y7FjhFeuuhVFk/c8+Vx1ec6S4xmSzy4J3coo14V9XVUvjqh0CbgzsX1HtG+mMV8xA8yirS+jEX3v/fPnkNXVuCEh3Noi3HYLIoRb225M3EHdZeX6TbhwDj7v9ihNhWYL/8aua+Q4fRI5eRzWVmBlBQkU9hroM5fh5ja+hvjP3ECedzte4CZsXto4IDh/HF1aRoDlPdCrB9SubLsZjyDnT5a2G7DkIU3XY7y02SA4tRKPJ1vaDQhXPLxDN5ai1ghR1Nl2Ad1pcXhume3nu1WvFUXqB5x836fxDpqupf6J63j/8PnOvwArNxo0nnMsXih0ebdBa91DWs4XHq4t4R+6Lm0/AG8/RMKQ2tYhXvSbIO6sEPcnDBVdW4r9UbC2gjz8GLq9ByjB/gGysowedsaVxNTrhPU6UltCg86YxGBnz6Ufuv78sO1PghDvoIHsHRLubKNbyTlqi5+zQlsfPUdI2y+2n6v2T5/EOanE0hrVT9Fd2vmM6i8m6ivKDoS+V1XjKr2qbuK6oY0J0B4gFW+HmvriaKj5WL7s/Gt9nt9KNMZRjrC4HJpsnRYhPLZMctpPb9fN4ayhuvCmUJCWxhpy2MKvt1xFJFRY8iGxUme7lSeZl9BLl8OvuzELrhzuB36sobhY0ygtLboPqQ8c/oGmNQ5aKY1214VGZdPE/OSquPCrqMLg9ik8fTVqCcNdixMnOhqeBze3O/cjCKHZQpstNAwJgxBZXo5/BcTntPe1s1RPhK2H6gYvtu9fu9La1gDXO+H78SXQRiN6U3DPk9ergLaGJLZd61bx8aMwwSn07ge+O5oZ4yuBrTkYzwDmK6ZG3Fk3z7Z+aSlVYVBVwq2d2DYQKuwddOx2EMLZU/GxqiB7h9AKOjZzeTlOUxWoH6I3t6IBvUAYuhWTIw0JFaTWmaY7hKUbrmVfgzDyJ6T9SejsfztfooLXXg8BZxf8enrwdW2v6SamCF25Ds4vo54LlVVg6dGryHYdbQZoK8BbXsFvqbsGoboGm4QG6taXiDVEXCNYu9wKtd2mGyAdpu2jtu+J77kwofbtqzfQnb3O9Vd1tr7AuGo7X82mG58WvWhF/iTalqWEP4mO182dnL8tfK6KbH3CX7T9Yuq56uYIMrsLpywegQlOuTpRX1F2TEPR1bSF4SaM52XiQgtq4+JJJxa+vT1QfPgUNArIaUiX1oJURjPnd/+4kyZJjWxRpeD4Dl7m/CI8TwjCjEaPU6So9aOfhghBMtG+Ivnz+wUfFK4k268cWY0+v+LF89AwueIePYuefSbE8+L45bGguLEoFSAi7wS+Dtc1/TTuh/USgKr+MvAe4JuBR4F94F9Uozx2zFdMCfG8gon3uzMeO1yFRum6SaTR335lfVTO1mSPz+Qha/u7aiTLQb9ypI9IrrfTPV/pNRFy+VJNG9+sPylaAyOrMfC1ytrh9HUowpN0HaHfczWMrc8d0+eGrKwtQ0EHylAssK8oa8wfEJF/D/xCtP2DwEdHFReRfwX8NHBeVa+Pmt48MEqZwyBM/1hT0l8EGTKeL5HGVDQynxdq5JpFSP/41dC1qkQEqvipcmSqAyLpMRXtlumUTU3v0FCRxKJxQehWpo5TzthjIV1hKMhGDqW9kI3bDsNMOTIIEISJVX6iLv6erqEVRiYn0pCMUS5osQyD9EpCfcsRhC48oH1+u7u4WzlECLJOoJ9GJlwi+1xV3t1cIk+lk1F9bZ/PFWdn542x+Aowf9GvzGEQ5mxQPkGqs8Nj0SiywwUSCXsVNprIetIOS0pDyNgvkb4/OJ2t7+Tb+ZPeP2xTYzKIelayq68lbSiasvXaCBD1o9n0QGq+G/Ddzquoa7hpDw6HvEa2HJq+lGEYpn1WBhFNXyuvxLUqtMM9/EmmHM5nVa1RMI6mz6N7sHPQ+4BBWVBfUbY/5odwdbDfil6HjJhJEbkT+EbgyVHSmScqKXMPA5H9JDtXfdzKkP1xm01zljU8QVZXoNFoW00XX//o08jeAe0BanpsNTWYTg4bSBwCo7h+y+hDVbydA2qffMythuxkkFYnL6KweqPp1kAIFQlCVi5ts7TVGQTnBcQxroA7rhF1gSv4oeDVg1jXA2o7QeccddOzxnNjqyKt0MXhFqFKbavO8jM7zrEEId7uPsGzl91ME6p4KN5tF5ClmitUzUe29/Ha1yEI3CwWSUuQbmJzazXEnk2h2SDY2Ijn0fY8wb9w3mmIgO+hraATYxyGhDe33CDG9v3Z3UV3OwMKc5W63HOV/t/Z3/25qnQKvXby2v91xKncV4D5iyHOjd502d/Z0T2N7PYkbH0QEFzfiNcCkCBw03EKkf2qIc+9gBxb66T76NMuxDIMna3Z2XVTcrZtzfYO4eVr8ecShu7HeJQ3WV5ydl+Idbz9hgtTighPH0fXlrtXZjKNTV4Q4m3udjTrh/C5p5DDyAeFIdQP48HNhCHrf/U4y49vuH2hEt75HLjjfLRog0d4YpnGqaW4UuGJ4DUSk2O0ApYvbeI1AxciGoT417fxD5tOLwgIrlxDd3Y6NrfRQBrNTp42tuGJZ6M8hJ3rn7mV/e1w6nKkqR8S3Nzs3GPVzuQaVWlkDy3hT6p2F4u6InSpngZV3QPeWLH2zwL/C/CHFac7y4xc5njgWEGMaa7bMtfKnT+nnWZOI9lFmai9V6tBPC1pKY2aj/fc2+DkcdqLEwVPPA1RzKXeuIn3j14Inu9al9ZWneFvRjH++4fgNVw3fjR9XHhQh71dwv26+xG/tUvrpf8I9T1XJ2kofitEAmeH/CtN/O0d/M06XjQDhnfXWcKVpc68zD5IzUMV/BZoM8BXIBokJ82AcDUacA14my1a625bEFrLytrlw2g8gyu7t7ZEmDR8e3Vq+4fQaLEM+JdvEjbqcG3L9T7c3Ma/8w40CPCWltBbL7hZpY6vu27b/Sby7GUXS9psuT6VbA/TsTVqJ0+48QtAWK/Dzq67ZkDr4JDa7beiCLKygnfrBbReR1ZWnIYq4cYm4f5B5Mxxg+iiOFcAtnfdtaon+oWl4HnR9P/Uc5VogUptV22QtSADRoox+QowfzEQ82jr4+/9wQGtS3VnKxJjpby7bocTx51tOXsGHvl7aDRdzPzDj8HJY3DYgsOG62Dc2kF8Lx60G9zcQlaX0d1odjdP8M6eRaIJG1BcY9NSzZV3vwnr0WqbvkfrzHH8lSVoaafSEeU7LlOzhTSbaKPl2mF264R7u2h73NjVm3gveB7SaLnxDAcNdGebcHsPCQKOP3ud1a94IeGpE/hNCJ5/B8Ed56EG4aljrhxrLVa2W25WJAVvv4m3vecqOoC/XSc8voK35xa0060DONhDN3eQZosA8G97jhsY3p4x6eAA3d9H96LW9us34dgqutWZHtvd8/T/1D3vZoeT99yLPtvdo7V/gKwsoQfpZVxG1Ug9a9H9KetPKmOBfUXZ2ZNeCPxr4HnJc1T164cRFZFXAZdU9e/6xdhF00/dA3DXXXcNIzcTDFvmVY4VxBjmYwazXcBl4gpT52c1CuL1h4ld7K3hoZlogbi1SgAAIABJREFU1J4aIsiJ9XTzQHuQVoTXCtDlRMuI76GJ1eElVDqrMkQ0OgdIpktTSM+zLUQzZTQ7x/nNEF1KH5NaDVXBDzrhxkK6hUHAVTjibg1xFZXktSKNRK1wsf9tBvg3dztXM1Sk1YqvlYikpjYVgL0DtNmZei8bAyqeh7e6TKrnuJm4mAUhX9Je5yF5fCIBaTbTBrzVQtTLxw8PEjedGS8x6PmDMI8tQ5Okal8RpWn+ovtxKV9RKu0ZtfWel1inoT3gNnn8ieOdNWBE3Hz8ic+9vYN0iFCr5WIvE9uy37GZiOCtdQZdA9DuLSWy/aSHichSDdV0zGOuHEHYsfWqsLmTOt6rN1Maul93Pb4Rtb0WQfJWrq/CWiJWyvdgyYunBRfAP0iskg1uuu3Etre9R5i09a0WLC0nrk2A1A8716bZwtvZG2SITP46FIxj88QjvjphCEWzMPWgjK3PPmvj9Ae9WFRfUXZMw+8Avwz8KiWjhEXkA8CtBR+9Gfhfcd2ufVHV+4D7AC5evDjTt2EcZT4pZzXbGhTHrvboZShc3KR9TvQ/tZBaGY3MQNiJa4QhwUOfwbtwC3r2DHJjw3VvJtJsPfI5vBPrcPutUK/TfOpZxPfwzp5xkTIbG27q07Nn8NZW8dZW0JULyM1N9KDO0qlT1D5zheCWEzTOHaPWVKQeIEsegYC/W0cPW3EsanhwgH70WfzVZYJ/+DyktsTqNddVfnj7acL1ZfxAO7+bQ2X5oAk3WgQnl2keq7G012L1mQbBqWUOzixTa4SENQ/xPKTuZtjgoIkfrUMB4Hk+4fox2K8jrQB/dYXw1qgce/sI0HrqEv7pU+jxdWR3l+CpS3hnT6EXzsPWDrpfT8cGZ+6H7h/QeOrAtcatr+Hdcobw/Fnk2SvoxlZH49RJ9MRxp7G5jX9iHT15Eg4OCA8baY1s6xCZe5xp0Wzf23Ya7cpx6jnK9L4VTtdaFTNtgWaCgX0FmL/IULrMWV8x37Y+/71vt5b4p0/B9h7e6hLB8jJ6cws9OMzYr/z3PtbwfWrnz6K1pdjW186doR2SpAoSrSwt4myKbmyiD15FTq2jd9/hGnY2d53m2iq6XENXaqiAd9hCGgFy2IivlQYhurXlfFRbQ8TZzBPH4dRJZ+PP30Kt2aR1fcNVHj79OP5jzzp/srzE2lPbqIYc3nnardL88cfQm7vwvNvh3GlqOwfu+kf5Zmenrx1uXb+J1Hbwzp1xed3ZSTcYtdPK3I9kOTTje7NRENOy9b01XC9EL43KmGnrMzxlKw0tVf2lQRJW1ZcX7ReRLwbuBtotKHcAHxORl6jq5UE0Zo15KXM8o0Uc1zdMTTz9Tc3+RhuLhkZTaV67jl6+BrX0kJy2hu7tow8/Gk/RoEFAcPlqKrlwextv1YXR4Cn+uTOx8UDd6pX4vmtZArxA8a5u4bVba0TcQnN7e/E0gLVPPYF/7mycn+WrOzRvPxPP5CSq1DYP8MXN9lHbbeBvN/Ciz5d2m8hh6KZ/RcAXFybVarfMqJsmtnORYXXFLYojHuIp/ulTrts3ajsMt7fRzS03nZwqbG2jNzbdKsxFNy75PqrphHu7+BfOueuhilw4T7C53dHY2UG3tuMp63T/gHBnz13bfhq9bznSVol/oDgr39nsMn/8GAy2qKZ6f4xCBvYVMD+2s0rGUeaJ2OGMRuo72Euj5/c+3VUeRQu5HoVbL+At1Zx9bgSETz6OtHs8y3zvl5fwn3Pe2TwlZeu1bVOOH3P+JPpBHD72JHLgwirZ2sP73DN4p05E6Sth0EKTa0l4gr+zRzybURgSPPNsPCNSnK0ov7q/D6ureMdWwfOQ1VXk2Job5xWEsH9A7dMdfyLA6iNXCK5cdUmEijx1Ga+hcY+LAsGzl914NfrYYVW3YF7GL3a9H+1D4l/ZbkfbPg9lhydh63MaUS9EF42qWGRfUbbS8Eci8j8C78YNbANAVTcGFVTVTwLPaW+LyOO4VUMXdjaMKsqsoToH0G6yTrTwJMktoZ6sabd/MMctIdrOX0cjef4AGuEgGl7++Cy9NOI0ox/TcUtHWyMIc+dntcT3XT5iwyikxvsGIXheqoWj1grieeI0VLyg1ZkdSXFrEyTlam2NaEeg6WvVtl2JcsmKlypPTfoYszBdVo1mlWg/J6mWPiBsOdF2q5K755Lu1s8gS0tOI76+Qao3K6cR/UjRRB6yU7YWkbtnQq4c6RanHs/umFjULucKqcxXROeZvyhZ5iK72tsOZ/xJUZolbL1m7XI3jW75LtIIEt9334/tchiGbl7/QexXrYbve4kpPjO2PlQ3S1CsocjhYUIjpLa2ko7GXPLjCgPgentrnrOvgLZaiCdpX0raVtZWlhKx9ZE/iX/Uu3wn0UbTXc9oPF7793OYtI2tIA7MKbTDRdeqj39HMhrJcoThUHZ4ErZ+UI21E6uw3TPJgVhUX1G20vC66P+/SexT4PnVZsfozQSewoUZvDOv5WjX1CYsWcUx4zt9YmnOpuhcYb5ibhjmYR70nPn8wgyV6+xJs1j0UnkaLOOTKOZENKoWmcX7XwGlplxV1bsLXpU4AVV93iK3GhUxTJklt7hWcRN0GISp6cVUXWuPO6mTFnQWk2kPtBtIQ9IaseYwGgUUlaOrRhQW094WL92akpKI3muzRRDNIR7tiVs04nOaAe1VGTwg9D3aF0Q8IawtxYmLSGdV43YajZYrR1uiHbMZl0vRMOxoiKCtzvEikGoUKrpUUWtWfB1rblCx5/e455ren120LXetDhsEYeJa+VGogC9xHoA45KlIIxu/XFSm7D1HE89P5r/nF5Qju5DPGFjEKfSqZJy+Ikrf/EUXwnBEW1+UZs7WM1FbL564AcxJu1xbynzvB7X16XKI4FZ5TqaxspLSaO3XU+lKo5Wa4EF9IQiChI10s8d5fvdrFdYPif2JQOh32nBF6PiT9r6aT9gKOvkOQ+c/ktfG90ey9eDGhuTssN/FDhdpdLHDktIYj60fVkN8ob6buMcVsKhTrpaqNIjIMRH5tyJyX7T9AhH5b8abNaM33Z+2bDxpPJOApj/P/s8r9Hqis12cmS7rCjRy5xRpiCDH191UrKdP989pe6PZJHj2ClqvuzEP1zYInn4G3d1Dw5Dw5iZ87FPoU1egFaCXrxN+7kmC6zddPtSNH/DOu1h/lpfwTp0kDsYFvEaL5Sdv4NUbbjzCXjRTRDTOQp+9SvjgI4RXrrl9hw28q5uwsRPNfhTmZnNKXYswhHjWjHZlxse//VY4tpa/Vsnr3eWyF+6uHxI+/Pfo9i7aaqHPXInm2AZE8E6dxLvjduTkieE1unwYJrqkU/+1+PixorjwtH6vI4z5iukyy7Z+EJ/VPlZDJXj2CsHmNhqG6M4uHB6Wsy1ZW5+dqSfU2NYHn/4M4ZVrzi5f34C9vdi+ydoq3vH16Bw340/4uafgk5+FvQO33sJnnyS49Cx64PxJeNMNgM6GBiftcHh9g+D6htM8qKN7+6ms60Gd1uUr0GygrRbB5la6rEFI8MxlwrbP2tyCIBjN1nehW3hSnEaJxHKHdLP1WZvfz9Zr4duBNCq322V9xRz6i7LhSb+GW9Xzq6PtS7hZMv54HJky0mRbaovi+VLjljR9jGZ/d8YGbECN5EwJWY1imz+SRq4cBRq1O293hh3g+Drh7i5hYgrVnhpBQOvKdTcQOdJs3bgJ3Owc/PQVePpKbHt0fx85cxIV99XxVlep3Xl7Ll4/ngkiCPGvbLN04lhsH8Jmk/CRv48NRnjlBrVlN6jOA1cRWG6giWaT3JiARhP/sNEx5to5RjwPTp50A6SzDj7ZQNPvfiidmORWi+Cxp1LXShFqd9zWad45cYJwc7twRoyu5cjd4y7zxmfoleZYZsJopz3eIROLgPmKKTLftj45liEtEW5tw9Z29/OhMF4/aeuDzS1qF853xi60ArcQZnu2nyvX4Uq6Q2fp9gtoogdAt7YIdvbcRqsFn/hMeva+q2l/0vVatet2+weE9Xr3a9Vs0Xr2qmshT46na9s8VYKszypj64vscGL8Sdmxh2Ox9dk66iC2fliNMbiLRfUVZVeE/nxV/SmgCaCq+4wtAMDIkluYpGBgWa5RfsAfTfnFT4o00sf0G+DWV6OwHINphPsHnYFeqrnjS5VjQIuhmUc/qB+iifULtNGIup5jQbceQrt1Q0DPnY5X9mwf00kfQl9Sn2uzlZrLWw8PCRIa2moR7B8kWlDCvpawzLUOM9cvda1U3fVPtt5ke7lKXOv8PRvsfmTTHOuc3O0y9nodbcxXzDiza+sHtMMlbEvyGFUlPKh3t5EicPJEanXioF5HWwm728rPIjyyP8nar0I7XK1GoR3uZeur0piArc9ev7H6g16U8RVz6C/K9jQ0RGSNqH4mIp9PYmYMY3LkF3pLtCh0QuXJTU2W+Cz1HnJT5BUuJhdq/tz2dvK5F6rRSLYI9ChHuHHT9TScPI4c1N10df2uleZ7Znpeq/b+pRq187e4cCScA9HNzXiFZO/EOqIQ7LqWKO/YGt7ZMy7EaO8Ar+bTPL1OcPYknD+BNO9AHn6K2tpa7Ki05qPnThBG0/95hy38nX20Ga0gulxDN7cIb2y6rK6tIks1wu1dEPCWlghXV9GdneHvuUTF1uJrFZ+rSnjzJrK1BcfXYXcvdb36acT56aVRdD9KlkM8GWClgHLMYwzqhDFfMW1G+d5LSTtMYntCtn6wchTY+sMGwbUbeCvLhCvLLtSpne76Mfy7nusWTguV4Imnkb0Dwo0tYMv5l2ar0xjUz0Z2s3WZaziSHe6lMci16qXRyw4PUI74vDHZ+vRvn+E1qmRRfUXZSsO9wHuBO0XkN4CXAq8fV6aM7uRaJbIPpg74vjDN3tu90ure/TegRhk9jf6oopvb+az0uFbDXDf//C2QmAYvvHnTrVra7t7e2U2dk5y7HIVAIDgRze0tgi4v4R8/nspGcO4E1KIVrQWkvWpzO83rG4RbO7GmHhygB8QGOTxsFK+yOej9SH3W49xQ0TCAzfxcdYPc454aIzy7laJ5fSOH+YppM+z3RSE3tiH7o75IZ2K2vmSa2sN+qbof/pnVpr277yIOs/SBw6a7Fm27u7Xjen4TZU71BGfzlwmZ6VoO+pSjCo2qbH1FdnhWNbpN/DIUC+wr+lYaxE158DDwbcBX4szI/3TUZrCYBYrmHc5+AXJzXg8Y3z0VDUpoDLgoUV+NgmP6aYiXjubzRAi6GaP2MZ6X6HaXvovItBeCS2qkJKI41qTmwNcqk4misQy5ckxBo9/FyuYhe37V4xoEkDnsTp4U5iumi+d7MEAU0UzY4Vm19WjKzI5Do6+NnIRGBba+nx2ehK2vQmPtxCpsdf98EBbZV/StNKiqish7VPWLgT+ZQJ6MLuTiSgueyXws6WAP7lQ0CsjH3VavMWh8pWp6NEM5je6tOsUaec3+GgPGcGY+Lnc/RtMoitPtp9HvYpWJBa4a6bGI1FHHfMV0GfT5nxU7PJu2fnCNUW3kOMoxCVs/zPiXSdj6QTUO9wt66EdgUX1F2YHQHxORLx9rToyuZOe3Fi8KRJR2pIuk5yMWcnNp54YiZrYH09D50yCvIbS1y2mEm5uQmBfbO30K8X3E9xDPQ5aX3Mvz3JoFQQC+K5wISL2Bd30rPb/3ibWUhre156ZbbWuur4Ln8iwo3uqye58tR0HZB7pW0bVOXovcczVujcSaF8NoaEaj27zwQ6MlX0cb8xVTIv5dNct2eMwa8RoRpW19ZJ+3OyGfhCFy8vjAGu10h7VfcR77laMgX7Nk64vKMW5bX4XGoIP9e1LWV8yhvyg7puErgO8SkSeAPYjGzqj+F2PLmRET17Lb/xItM9r+q+ljs+d0jUcdSqP4nJnX0C4alNPQvQNaB3Vqt98GnocsL+Pdfiu6s4t6Ht76MQDCeh1qHnLuHOoJur0LmzuEW9vI04p3+jh8wd34hwHi19AT60g0jZ930EDqNwnPncRTRQLQE+uEV2+4NSQSc3l3vVZdrk3fa1UU31yRRuH5WY14msIKy1Epmm+CNLKYr5g2s26HJ6FRxtZ7ghw7hnfyBKKC7uwRHjbQm5vxmIeRNLrkexAb2VXDbH3lGtWGsy6uryhbaXjFWHNh9GTgdRq6HDOyRolj5lIjO590L0JFNESjTjoRwT99MnW+d+oksr6aaPkT2NuNb5Ds1t1Cb+0xEn66w09U8ZtBZ50GEaTVJExWGMjPSz5QOSh3rZJzd1eiUXD+yGNkJrlOw2L6gSoxXzHjzKwdLlhnYTCNHus0tI9J2goR/DOnOy3SoaKXr6Yc6cgaQ5WjyA6n0xyLHV4QWz+qRlUsqq8oFZ6kqk8AdwJfH73fL3uuMTrZB77IeOdiMAcw8KU1ShwzlxoDGpTmtQ2CnR1UFW0FNK9eJ9jcRMMQDUPC6zcInnwGbbm1GUQVTp+JZ16SE8djRwWgu3u0rlwl3N/vtIrs7kcrnyrabHYWFEqVbbRylLlW2S7bkTUKzs/qDj5GJn38WB2Eav/XEcZ8xewzs3Z4wBjwMuf3sj+yupb+rF7PfX9H1ShDOTtcsUaRHV4QWz+qRmWU8RVz6C9K9TSIyL3AReALcCt+LgH/ETednjEBkrMHSBQj3/4CtWP4UvPTa9QtJ+34gHZCdLrnku9J18hH0khlfFSNaBaLWdJoNCBoEURTn7bjTuP1GcTNgBTu7lO77YLLz7FVZPUCHFuF5SWI7qc+9Qy6veOmLm1uwmlFjq9DEOIdNgk2tghvbLhw4EQry7DlSPVIRflOpiH0vlaq6q5DRRpeNFtUoUY2rQE0kvmuDGVhB7dVhfmK6ZL9XpitL9BA8C+cR5aXAGdvwo2b6P7BxMoxa3Z46hrZtAbQyKY5rAYi1a3rs8C+omx40quB/xL4GICqPiMiJ8aWKyOHRj9ONTLeqVC+Pt11qS9Jt/dROkNpaC6pPhrRD+Z51gjChAapONYwei8rK87YtjV8H1aco4rTSaxvoGGIv7aazuuuGxitmSwMW47ss+Ceq6gcoU5co19Xc7eGmEKN6Pzx9jSML+kFwXzFlHDfscQOs/XFGks1ZCljhwvGio2zHLNmhyvVSNjh0hrZtAbQSB07gsbScq3axUAX1FeU7TZuqGvmVgARWR9floxuZNcJyM0CAPnZHgacQWYoDW9Qjf7Hj16OMWhkJ2Eoo5GdkaLogvbSKFHsiZRjChp9L9WIz8gwSBRu1ut1xDFfMSUGt8P93f9M2uEKNPrZlnmxkbOgMcz9m4StH1RjaaVsG3o5yviKefQXZSsNvy0i/zdwWkT+JfAB4FfGly2jiDBq3Y5R0l8EycTzCYWtrr1+9E9FgxIayS+XDKmR0RtYQwfUUM0skpPWEBfXlEpDW2Fag/xUeBMvR1kNr1qNbIhAthztFqduGmOpRCRjUbu9SiAirxSRR0TkURF5Y8HnrxeRayLy8ej1P1RelvFgvmJKFMXCj9UOT0JjDLY+u3BbOzRlIjZyDu1wyme17XAizX52uJ+GVGDry5QjpSH5cuxvH3RPbxjK+Io59Bc9q1YisqKqh6r60yLyDcA2Llb1Lar6/nFlyuhFtu+0+0fxOIjoCxN317W3E+Mkxq6R+36PoNFtO6fQ4ws5CY36IcH1G/jnzropWlXR3X1kfQ0VQcIQ7/ZbYWMDPTiMYmyj9FQhDNEg7GlY3OoNnc89EcKKy9FNox3ekE2zCo12GrkY1Ew5Mr4grVd1K44y0Iq73RARH/gF4BuAp4GPiMj9qvqpzKG/papvGF1x/JivmBFm0Nb3nIJ0CraeICC4co3aLWddyGiz2fnlWrGNnIQd7pbmOGx97viuz9UARImPYuv7S2T8Sa4SUfr3ezkq8hUwe/6iX3/Mfwa+VETeoar/HDDjP2U0aax7kDqm/S/ajv9rYjvxJeqqUdAiUFojmIBGphzOsYFmv7xFBmMMGqoKB3WCazfwL5x3sbEour3nplsNQzzfR2+5Bb/ZcrG24sob3tggPDiIdYquVb7nIjFGYJhyFDiA3L6Ehg6lkTfOubJJMq3eGmGYdjZJx1w1giLVLAD0EuBRVf0cgIi8C3gVkHUC84T5illgBm09ZTXGZIcLNRoNWs9cdjPatVpJyeFtfUJjJDs8EVtfYIcrtPWFDT5MxtYPp0GlVOgrYMb8Rb/wpGUR+e+ArxaRb8u+RhEWkR8SkYdF5CER+alR0poXhi7zGKIsjg7Tv3hu9oYwucO1bkWICPheJkyp1f+Hb9VFK5He9K/mlCnX3XyLiDyQeN2TSeW5wFOJ7aejfVn+mYh8QkR+V0TuHFOJqmJsvgLMXxyJMk/DuEQVBiPPkbf1o1I+PGmu/EW/nobvB74LOA3808xnCvz+MKIi8jJcTelLVPVQRJ4zTDrzxEhlzrQM9J0dJqpZx+EjUc276/84zKSPhi6ohia6L6O02ovIiC9ooAVd8UmNgnuQ1Gg2aT31DP7pk+jqGmxuEtYP8U+so+vHkd1dgt09vGOrcPIUHOyjjWbfcqR0M2XPlif7v7gcBRqZlqdk126/+1GoUXQ7Mq1CDKiR626uuNWok1HKdjlfV9WLI6r9EfDOyFZ8H/B24OtHTHOcjMVXgPmLQco813Y4pLTGUHZ4Era+n8bQdrhIY4RrVXQ7KrD12V6snMYEbP1IGlVR3lfAnPmLfpWG21T1B0Tkb1X1vgp1fwD4CVU9BFDVqxWmPasMVeZ2bb/9BSwVQ6ipf/1/TEXWpKNR/IUvp9HlxEo1uhw/kkZaLL7GmfPb138wDXdAuL2Dbm7Hg6/CvX10Zw+JVoTW+iG6f6WzPcq1ymhne3GHK0e37eITK9HIpRkdkv0+tM8bU2hSrF9NP/Yl3AJobe6I9sWo6o3E5q8Cs97SPC5fAeYvypd5YBs5ij+Znq0fxbaM19b30ai0HOntSdj62P52KQejaOTSHMHWtz/r9vyQqeOMwWdUODPSTPmLfuFJb4r+f3/Fui8E/rGI/I2I/IWIfHnF6c8iQ5U5+dh5vpdrDcji+Ylb2q5ZR+ek4g8L/nc0EgkOrMHgGgXkNGTEcgysUVAOGU3DtcJJPt/RLA+xRmLWh6rKEVZYjpxG2ClflRqpWNTof3tFVhczKzmNcVYYIuH+r/58BHiBiNwtIsvAa4D7kweIyG2JzW8BPl1ZGcbDuHwFmL8oVeakfyhvI3s/rwPbyEKN/vnurjGftn6aGtXb+s59iO1vQqPIDk/d1mtvjTDzX1U5dnItm8polPEVc+gv+vU03BCRPwXuFpH7sx+q6rd0O1FEPgDcWvDRmyPds8BXAl+Om6bv+VpgwaL4rnsA7rrrrj7ZnS7jKPMqx+L9uUOLnrfcMYP9ihpGY9DZE0odn9UY8NfgWDRyl7a/RvaYhdEo2To5ikb/Yoz2HA6MKlQwuE1VWyLyBuB9gA+8TVUfEpF/BzygqvcDPywi3wK0gA3g9SMLj5ehfQWYv8hQuszJ8q7JsWw6PZlbOzwJjXmxwxOx9f0kBrfDk7D1g2oEzQpXdqvIV7ikZstf9Ks0/BPgS4F3AD8zSMKq+vJun4nIDwC/HxnAD4tICNwCXCtI5z7gPoCLFy+O+VfBaIyjzCflrALpmDshVfNuD6BV1VTcpNtHLl4vGwsZ7++l4bm+wlgjiv90IuU1PN/rzJ/cTyMzY0dpjSh+dBY0kvGs49SYeDnIp5WbirGkRrvLuadGl9jUIg0RybWwVUZFE2Ko6nuA92T2vSXx/k10Wu/ngaF9BZi/yFK2zFlfIf4M2foB7PC4baTZ+mpsfXbK0lmz9WU1ispxWG9QKZVNnjRb/qJnpUFVG8Bfi8hXq2rOQI/AHwAvA/5fEXkhsAxcrzD9WWSkMqd+AGm65q1uR/GxRdta4rishmY0guE0Uguu9NMYshzhIOUYVkPLa4QT0JiFcjBOjfbuEhrj7HGYxxU8J8EYfQWYvyhd5pmy9QNoDG0jzdZPtBxxEjNq63Ma2c9LlqMKFtVX9Fvc7f9U1R8B3iYiuSvQr8u5B2+L0nwQaACvK+pqXjCGLnPP1noA7bQudD2mCo1+x8yrRqY1YnANieMvx6eRL0dWdywankeY6GYdWSN7vuZ1B53Jom8PSJUsvJkajjH6CjB/UWmZZ9YOZ+3owBqTsMNTsvWZNMdihxfB1mc1Cu7PxFhQE9UvPOkd0f+frlI0apX676tMc9YZpcw9W+vj9Ol7zMgaJY6ZS40BDUpeI39+9Rr5cmR1x6KRicscWaPg/KzuoAa+bw9IVSgwDeczH4zFV4D5i8rTnlU7PECFoez5C2Prq9YossMLYutH1aiEBfYV/cKTPhr9/wsROR+9r7rr2ehDcoaE9vpfGvUu0I4ZjPYL7tj4nII5mtvppLfTGkrnvJyGtOMCh9SQOKnicmjn2LxGeorAYTRyaxnk0pyExnDXKhkn2tFIa82MhnSex2mUQzyBCse2gVY2uG3RMF8xO1Rmh0l+R6u39V1t5Azb+iK/aHZ4QcoR3+AqWFxf0W/KVUTkrSJyHXgE+IyIXBORt/Q7z6iO5AOv2nngVRO1aHWvdgxhfE7i2HSavTXopaEjamifctCrHOlEh9HQbhrZNMaqkU2znAaFGmXz3UWDMWnoBDR6lGMsESxtgV6vI4r5iumS+hGVYGg7PGZb39VGzrCtL/KLldmvcdn6KdvheSmH5/X9OTwYZXzFHPqLnldJRP5n4KXAl6vqWVU9A3wF8FIR+dFJZNDokJp3GDpeInlM5sFvLyQ20xpFx2Q1ZAY0Mh8vjEYJYzkNjaJnr1cecudXbY8V1+Xc73UEMV8xfaSErUgyt3Z4Ehpm60tr9LXDJTRGtvUVaKyur/RNszRlfcUc+ot+V/6fA69V1cfaO1T1c7hYy+8eZ8aMPLlYx4LnLRuXOHDM4DQ0io7JagxYIx+LRubjiWiU6OJc1HKTgjp/AAAZy0lEQVT0+9GfzUP2/AF/e5RAQcP+r6OJ+YopowOOO5hbOzwJjTmxkbNQjn52uIzGqLa+Co36br1vmuUp6Svm0F/0qzQsqWpumrcoVnVpPFkysiRr2S6Wsr2RaOWP9rcPzf5gytbUU9uS15ASGtma+6AaZcrRXyO5MWA5EueNUo4yGqPej06McV4jed5QGjIBjQHK0TPNAo3UuZ4kNCqvNSxkd3NFmK+YMu0nb3Ft/eh2uCqNIvtVlcbC2/qyaRZodLf1o2sMOvC7LwsantRv9qReq11UvBKG0Y1sHJ8I8YCsfrGmRWnktpXMfMp0vngzr5HcKKkRHzvGcmQ0slqlNdppSEIvc2w35kVDcnI90izQyF3/aLt6JwAM2Jp7hDBfMSPMja2PtyegUSwxtEaRjRxZ46jY+oROVbY+V46kRq80C7YrYYF9Rb+ehi8Rke2C1w7wxZPIoEFuzIAUxPPlWrIHHNST1SiKGSxzzFxoZOMjB2yRzmvkz69eI1+OrO5YNMTLbI+oUXB+9lkdOKa5T0tSpSxgy1FFmK+YE2bWDg84Nm46dnhKtr5qjYLzF8XWjzrmsjKOYk+DqvqTyojRneRUpBDFJSa2IfPsyZDzavfTSLbcjkNDpqQxzHzUKQ2dgEa+HKk5w8elkYwLLauRSCOnEeavVeoeZ69dCY1smlnN6phPIz8JzFfMD7Nr6+fBDvfRYFx2uOJyFNnhRbD1FWhUw+L6iornmDLGRa8atniS2s62GOR6KqJj+7UMTFxDpNP9OCGNXHzjVDSK96c0ZEwa3gAauR6vLhq98jKkRjKWuEijVzkqQ3Fzb/d7GcaUqMrWT9oOz42t72e//AloTNHW97PD07b1uedqEI0qXUZZXzGH/sIqDXNCGGrqS5asPWuocVyeW2Y9sUy75Gva8dzbBS0kw2gU1eaH1WjX9iei4Xu5lqjpaOTTyGlodw3xZHiNYACNYDgNLanh9dCI0862MJUoR6UsYHezsRh4WTvAnNjhebL1I9vICjQmbesTafWzw0Pb+gE0ypbD8yTXe9VLY2VtmUpZ0PAkqzTMEbnW06Kace6YwarPw2gM2qpb6visxoDNAMO0NPfVyF3a2dDItfbMpEZficGfVUZ7DgdH3eC2fi/DmAYDPv4zY4cXxNbPhx0eQqOvxBB2uMzvjAlr+LUqIyxL+oo59BdWaZgXxMXqpb4HBZXUoni++AsW3e24iy7zfySNzHRnnj+ARgE5DU1oROXwBinHoBptqWTamu5SrVxDqtdo3/v24LBxlKOj0eta6cAayTQ8L1+O1LVsnz/OeoOCatj3ZRjTwH3HRrD1hWmWsF9m6wfXmCtbn7TDXrocXezwLNj6MhrJ52l/+6B3goNQ0lfMo7+wSsOc0P5+lOrN6vJlytXW4zS1Oo3IWuRmP6tSI/ogm8Q4ypGj3YUrY9CIM16dRmd/9oYMr5GTaqeR3d++HxVoaPw/92ClNKof/Jwh1P4vw5gyZusH0JhTW9/dDve2kaPZ4Si8J3v8rNr6WGOcrUldKOMr5tBfWKVhHpD0l7DvFGLtGnUctxd90cP0/3h2BC2pkdyV1QiLNXQeNKJWiKI0w6xWNDd0WiMvUb1Gvhwp3XFpJJ1dpJGNme36v+h+FN0OL63BgBqS0Rhvb4P2fxnGlJhvW8+IGnmJhbH1BRqV2+EFsfXFGn3uwzh+vC/omIZ+i7sZs8AknqtZ1Rg5X/P3pSxN1UUrkd4CX83+qM7lbBeGkWJWv8Szmq+J0/9CzKq7ng1mIOcL7Cusp2HGyVakc7MndEFD7Uw31v6XjePLxKL31cjsKhrLULlGQVxoXiOxP9XSIRSGDOY0GF7DG79Ge9xITqJqjYJWj9y+kTVyEvmyDaiRa3kao8/QIOj7MoypMSu2Pmcjy9j62bbDk7P1BRLZyzcOOzzrtt4vZ+tz16+Exjh6p8v4inn0F1ZpmHFy34nsDunyHgjjfr/oX2aesbjLbgQNnQmNbOJdGEQjLKkxQDnoqpH+OJ9tye7oqtG9HANqdNObkkb2fxmNvmF8A7OY3c3GAnFUbX0ldths/TjscGUaGa2URlZuQI3qG5pK+oo59BdWaZgjPN9LtwZka9rqjklu5+L7yD+nydr/VDQS53XVkAlrtHcnz5HRNLSrRvp6j6ccA2pk9KahkTT88Q+SRBquJ6m7RuVxqspCDmwzFoPc94cFsMNj0OhuhxMbZusLNYaxw2U0RrX1uR/9Q2gcO7nWPc+DUtZXzKG/mEqlQUReLCJ/LSIfF5EHROQl08jHJKmizJqNkSt43rJf1p5f3qo0BnzwS4VXjVyOMWj0cFxT1QgXU6Nf68+oz+FQaNj/VQIReaWIPCIij4rIGws+XxGR34o+/xsReV7FJZkbzF+UK/Pgdrj/szqTdngSGmbrS2sMY4cnYesH1WgeNvumORBlfMUc+otp9TT8FPBjqvpi4C3R9qIzfJklqmlHD714kmrdF0/iWNU49jTZc9erG3UADemmIT00Mu+dho5Xw5uuhtdHo9c9GEpDnUbcIjSChlexhohMXaMqFHc/+736ISI+8AvANwFfCLxWRL4wc9j3AjdV9R8APwv8ZLWlmSvMX5Qos6oOaIejzSNqh+fS1jO/dnhaGmXvR6tZ3fiCsr5iHv3FtGZPUuBk9P4U8MyU8jFJhi+zOmMRb2ZbAsL0jMapzzVTwe72jJbQ6LrdSyPzfiIaPUJUJqER9tHoygxoJD+uQkM1fVGnoVEZqlUNXHsJ8Kiqfg5ARN4FvAr4VOKYVwFvjd7/LvDzIiI6aDPsYmD+omSZNfP97vbe7PCc2vrMsfNkh2ddo1LTWp2vgBnzF9OqNPwI8D4R+Wlcb8dXTykfk2ToMmdnn/B8L73aIeTmLy46ZmSNEsfMpYYnOWMzmIYQBtr7mJE18uXI6o5Fw/MIE2EMI2sUnJ/V7TrbSheyaQ56/kBUs4Lnc4GnEttPA1/R7RhVbYnIFnAOuF5FBuYM8xcVlnlm7XCBHR1MYxJ2eEq2PpPmWOzwgtj6UTUqo7rVnmfKX4yt0iAiHwBuLfjozcB/Dfyoqv6eiHwn8B+Al3dJ5x7gnmhzV0QeKSF/C6NdrC8Y5qQxlfnw/cHvPJg6oFVwUvY7UXRML7KV4qLzyxzTX6Nzb8an0fv87DGDNgikj7+FVsGzVq1GcTmy+6rRSH93qi5H0flVlyO9PdR3uYgdbr7vA/q7t5Q4dFVEHkhs36eq91WVj0XE/EWK0mXO+ooP6O8+mD0mx+Rs/agave/L5G39qBrF5ZlGOYrOH06jU6bp2/oqNCrxFwP4CpgzfyHT6O2OakGnVVXFBbVtqerJfucNkP4DqnpxWud3SXOoMo8jL9PEyjPbWHnGj4h8FfBWVX1FtP0mAFX93xPHvC865j+LSA24DJw/iuFJ5i/KlXkWn/VRsPLMPotWplksz6z5i2kNhH4G+Nro/dcDn51SPibJUSyzYRh5PgK8QETuFpFl4DXA/Zlj7gdeF73/duDPj2KFIeIo2s6jWGbDMPLMlL+Y1piGfwn8X1GNqE6ne3WROYplNgwjQxRz+gbgfYAPvE1VHxKRfwc8oKr348JR3iEijwIbOEdxVDmKtvMoltkwjAyz5i+mUmlQ1f8EfNkYJUaNB6s8nmyEMs9sbNuQWHlmGyvPBFDV9wDvyex7S+J9HfiOSedrFjF/Mb18TBkrz+yzaGWayfLMkr+YypgGwzAMwzAMwzDmh2mNaTAMwzAMwzAMY06wSsMMIiLfISIPiUgoIhczn70pWir8ERF5xbTyOCwi8lYRuSQiH49e3zztPA1Dv2Xd5xEReVxEPhndlwf6nzFbiMjbROSqiDyY2HdWRN4vIp+N/p+ZZh4No2rMX8w+i+YvzFccXazSMJs8CHwb8KHkTnFLh78G+CLglcAviltifN74WVV9cfR6T//DZwspt6z7vPKy6L7M1LRzJfl13PciyRuBP1PVFwB/Fm0bxiJh/mKGWWB/Yb7iCGKVhhlEVT+tqkWLEr0KeJeqHqrqY8CjuCXGjckSL+uuqg2gvay7MUVU9UO4mSOSvAp4e/T+7cC3TjRThjFmzF/MPOYvZgzzFcNjlYb5omg58edOKS+j8AYR+UTURTiPXYCLch+yKPCnIvLRaKXZReCCqj4bvb8MXJhmZgxjgiyKnTJ/MXuYrziiTGudhiOPiHwAuLXgozer6h9OOj9V0qtswC8BP44zOj8O/AzwPZPLndGDr1HVSyLyHOD9IvJw1CKzEESr69p0ccbcYf7C/MWMYb7iiGKVhimhqi8f4rRLwJ2J7TuifTNF2bKJyK8Afzzm7IyDubgPg6Kql6L/V0Xk3bhu9Xl3BFdE5DZVfVZEbgOuTjtDhjEo5i/MX8wS5iuOLhaeNF/cD7xGRFZE5G7gBcCHp5yngYi+jG1ejRvEN2+UWdZ9rhCRdRE50X4PfCPzeW+y3A+8Lnr/OmCuW2UNYwDMX8wGC+UvzFccbaynYQYRkVcDPwecB/5ERD6uqq+Ilg7/beBTQAv4QVUNppnXIfgpEXkxrrv5ceD7ppudwem2rPuUszUqF4B3iwg4u/Cbqvre6WZpMETkncDXAbeIyNPAvcBPAL8tIt8LPAF85/RyaBjVY/5itllAf2G+4ghjK0IbhmEYhmEYhtETC08yDMMwDMMwDKMnVmkwDMMwDMMwDKMnVmkwDMMwDMMwDKMnNhB6BokWS7kHYH19/cte9KIXTTlH0+eZZ56ZdhaMAbn99tunnQU++tGPXlfV89POh2GMA/MVxZi/mE+m7TPMX/THBkLPOBcvXtQHHnhg2tmYaX7sx35s2lk4Mtx7773TzsJAiMhHVfXitPNhGOPGfEV/zFdMFvMXi4f1NBhzTzfDZA5icObNyBuGYZQla9/MR4yG+Yujh1UajIWlbdDMMXTHjL5hGEeVIvtn/qIY8xUGWKXBOAJY5aGDGX7DMIzumL9wmK8wirBKg2EsKGb0DcMwjEEwv2H0wioNxpHhqLQgmdE3DMMYDfMXhpHHKg2GsSCY8TcMwzDKYP7CGAarNBhHjkVrQTLjbxiGYZTB/IUxClZpMIw5xYy/YRjGeFmURibzF0YVWKXBMOYMM/6GYRhGGcxfGFVilQbjyDJvLUhm/A3DMIwymL8wxoFVGgxjxjHjbxiGMV3moZHJfIUxbrxpZ8AwDMMwDMMwjNnGehoMY0axViPDMAyjH+YrjElhPQ3Gkefee+81o2sYhmH0xfyFcZSxngbDmDHMIRmGYRj9MF9hTBrraTAMwzAMwzAMoyfW02AYEdOeHcNajQzDMIx+mK8wpoX1NBiGYRiGYQyAjW0wjiJWaTAMwzAMwzAMoycWnmQYU8RaqgzDMIwymL8wpo31NBiGYRiGYRiG0RPraTCMDJMYEG0tRoZhGPOP+QvjKGE9DYZhGIZhGIZh9MQqDYZhGIZhGIZh9MTCkwxjglg3s2EYhlEG8xfGrGGVBsPoQtJgT2vBN8MwDMMwjFnAwpMMwzAMwzBGwBZ7M44C1tNgGBPAnIlhGIZhGPOMVRoMwzAMwzBmBGtkMmYVC0+aQUTkHhF5QEQeuHbt2rSzY2Bdz4ZhGIZhHG2sp2EGUdX7gPsALl68qFPOjjECVtEwDGNciMg9wD0Ad91115RzY8BkFnszjGlhlQbDGABzCIZhzArWwDS7DOMrrJHJmHWs0mAYY8CMv2EYhmEYi4RVGgxjCKzHwTAMw+hHGV9hjUzGvGCVBsMYgaxDMONvGIZhlMH8hTFvWKXBMCrAjL9hGIbRDfMRxiJgU64ahmEYhmEYhtETUbUJF2YZEdkBHpl2PirkFuD6tDNRIVae2eYLVPXEtDNhGOPGfMXMs2jlgcUrk/mLPlh40uzziKpenHYmqkJEHrDyzC6LWJ5p58EwJoT5ihlm0coDi1cm8xf9sfAkwzAMwzAMwzB6YpUGwzAMwzAMwzB6YpWG2ee+aWegYqw8s42VxzDmk0V71q08s8+ilWnRylM5NhDaMAzDMAzDMIyeWE+DYRiGYRiGYRg9sUrDDCIi3yEiD4lIKCIXM5+9SUQeFZFHROQV08rjsIjIW0Xkkoh8PHp987TzNAwi8sroHjwqIm+cdn6qQEQeF5FPRvdl7maREJG3ichVEXkwse+siLxfRD4b/T8zzTwaRtWYv5h9Fs1fmK84ulilYTZ5EPg24EPJnSLyhcBrgC8CXgn8ooj4k8/eyPysqr44er1n2pkZlOia/wLwTcAXAq+N7s0i8LLovszjNHq/jvteJHkj8Geq+gLgz6Jtw1gkzF/MMAvsL8xXHEGs0jCDqOqnVbVokZ5XAe9S1UNVfQx4FHjJZHNn4K75o6r6OVVtAO/C3Rtjiqjqh4CNzO5XAW+P3r8d+NaJZsowxoz5i5nH/MWMYb5ieKzSMF88F3gqsf10tG/eeIOIfCLqIpzHLsBFuQ9ZFPhTEfmoiNwz7cxUxAVVfTZ6fxm4MM3MGMYEWRQ7Zf5i9jBfcUSxFaGnhIh8ALi14KM3q+ofTjo/VdKrbMAvAT+OMzo/DvwM8D2Ty53Rg69R1Usi8hzg/SLycNQisxCoqoqITRdnzB3mL8xfzBjmK44oVmmYEqr68iFOuwTcmdi+I9o3U5Qtm4j8CvDHY87OOJiL+zAoqnop+n9VRN6N61afd0dwRURuU9VnReQ24Oq0M2QYg2L+wvzFLGG+4uhi4Unzxf3Aa0RkRUTuBl4AfHjKeRqI6MvY5tW4QXzzxkeAF4jI3SKyjBtseP+U8zQSIrIuIifa74FvZD7vTZb7gddF718HzHWrrGEMgPmL2WCh/IX5iqON9TTMICLyauDngPPAn4jIx1X1Far6kIj8NvApoAX8oKoG08zrEPyUiLwY1938OPB9083O4KhqS0TeALwP8IG3qepDU87WqFwA3i0i4OzCb6rqe6ebpcEQkXcCXwfcIiJPA/cCPwH8toh8L/AE8J3Ty6FhVI/5i9lmAf2F+YojjK0IbRiGYRiGYRhGTyw8yTAMwzAMwzCMnlilwTAMwzAMwzCMnlilwTAMwzAMwzCMnlilwTAMwzAMwzCMnlilwTAMwzAMwzCMnlilwZgIIvKtIqIi8qJp52UUROSdIvIJEfnRxL43i8jHo1eQeP/DJdN8vYjcPuhxIvKrIvKFVR1vGIYxC5i/6Jmm+QtjatiUq8ZEEJHfAm4H/lxV760gvZqqtkbP2UCatwL/SVX/QY9jdlX1+IDpfhD416r6QBXHDXu8YRjGLGD+ome6H8T8hTElrKfBGDsichz4GuB7cathtve/S0T+SWL710Xk20XEF5H/Q0Q+ErXSfF/0+deJyF+KyP24BYsQkT8QkY+KyEMick8ire8Vkc+IyIdF5FdE5Oej/edF5PeitD8iIi8tyO+qiPyaiHxSRP5WRF4WffSnwHOjVqF/PMR18KMyPhil/aMi8u3AReA3onTXROQtUd4eFJH7xFF03AdF5OIA6X5QRC5GeXmliHxMRP5ORP4s2ve1iVavv5Vo1U/DMIxJYf4iTtf8hTF7qKq97DXWF/BdwH+I3v9/wJdF718NvD16vww8BawB9wD/Ntq/AjwA3I1bwXEPuDuR9tno/xpuKftzuBaqx4GzwBLwl8DPR8f9JvA10fu7gE8X5Pdf4VbtBHgR8CSwCjwPeLBPWXd7fPZlwPsT26ej/x8ELmbLFL1/B/BPuxz3QZyhL5tu+/jz0bW+O3MN/wh4afT+OFCb9rNjL3vZ62i9zF/En5m/sNfMvaynwZgErwXeFb1/V7QN8P8ALxORFeCbgA+p6gHwjcB3i8jHgb/BGfYXROd8WFUfS6T9wyLyd8BfA3dGx70E+AtV3VDVJvA7ieNfDvx8lPb9wMmoZSvJ1wD/EUBVH8YtKf/CUS5AxOeA54vIz4nIK4HtLse9TET+RkQ+CXw98EUVpdvmK3HX+jEAVd2I9v8V8O/Fxdae1gl35xuGYWD+oo35C2PmqE07A8ZiIyJncYbsi0VEAR9QEfk3qloXF0f5CuC/peMoBPghVX1fJq2vw7UcJbdfDnyVqu5Haa32yZIHfKWq1kcsWl9ExAc+Gm3er6pvEZEvwZX3+4HvBL4nc84q8Iu4Fp+nROSt9CmTqt7sl24ZVPUnRORPgG8G/kpEXhE5QcMwjLFj/sL8hTHbWE+DMW6+HXiHqn6eqj5PVe8EHgPaMZ6/BfyLaPu90b73AT8gIksAIvJCEVkvSPsUcDNyAC/CtYgAfAT4WhE5IyI14J8lzvlT4IfaGyLy4oJ0/xLXRY6IvBDXLf3IgOVGVQNVfXH0eouI3AJ4qvp7wL8FvjQ6dAdox4O2Df71qEXr2xNJJo+LKZlukr8G/isRuTs6/2z0//NV9ZOq+pO4azjXM5cYhjF3mL8wf2HMMNbTYIyb1wI/mdn3e9H+D+GM8juAP1TVRvT5r+LiQT8mIgJcA761IO33At8vIp/GGem/BlDVSyLyvwEfBjaAh4Gt6JwfBn5BRD6Be/4/hGttSfKLwC9F3b0t4PWqeuiyMhLPBX5NRNqV9TdF/38d+GUROQC+CvgVXLztZZwxpstxg6YLgKpeEzcI8Pejc64C3wD8iLhBfCHwEC4cwDAMY1KYv+hg/sKYOWzKVWMhEZHjqrobtRy9GzdQ7d3TzpdhGIYxW5i/MIxyWHiSsai8NRq89iCue/sPppwfwzAMYzYxf2EYJbCeBsMwDMMwDMMwemI9DYZhGIZhGIZh9MQqDYZhGIZhGIZh9MQqDYZhGIZhGIZh9MQqDYZhGIZhGIZh9MQqDYZhGIZhGIZh9MQqDYZhGIZhGIZh9OT/B7XfFW8moiHHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1162524d0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.489637\n",
+      "1.22929\n",
+      "0.746898\n",
+      "-0.491454\n",
+      "1.15151\n",
+      "0.780264\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116389290>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.452502\n",
+      "0.889581\n",
+      "0.87003\n",
+      "-0.452502\n",
+      "0.889581\n",
+      "0.87003\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1150cb3d0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from lib import bland_altman\n",
+    "bland_altman.bland_altman('Bland-Altman Plots: Parametric Analyses', afni_stat_file, spm_stat_file,\n",
+    "            'AFNI - SPM Parametric', 'AFNI - FSL Parametric', 'FSL - SPM Parametric',\n",
+    "             fsl_stat_file, study=study)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.225189\n",
+      "0.944374\n",
+      "0.852324\n",
+      "-0.235937\n",
+      "0.919563\n",
+      "0.859935\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116001390>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.445858288306\n",
+      "1.10640004934\n",
+      "0.787095551835\n",
+      "-0.44153854146\n",
+      "1.01664395193\n",
+      "0.821947557043\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1167fd890>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.221995142876\n",
+      "0.76094469844\n",
+      "0.896268910828\n",
+      "-0.221995142876\n",
+      "0.76094469844\n",
+      "0.896268910828\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11600cc10>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bland_altman.bland_altman('Bland-Altman Plots: Permutation Tests', afni_perm, spm_perm,\n",
+    "            'AFNI - SPM Permutation Test', 'AFNI - FSL Permutation Test', 'FSL - SPM Permutation Test',\n",
+    "             fsl_perm, num_subjects=num_subjects, study=study + '_perm')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1155f0e50>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10d382a10>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10ee816d0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "reload(bland_altman)\n",
+    "bland_altman.bland_altman_bold('Bland-Altman Plots: Percent BOLD', afni_bold, spm_bold,\n",
+    "            'AFNI - SPM Percent BOLD', 'AFNI - FSL Percent BOLD', 'FSL - SPM Percent BOLD',\n",
+    "             fsl_bold, study=study + '_bold')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1164c9990>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11533fd10>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x116692bd0>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bland_altman.bland_altman_intra('Bland-Altman Plots: Parametric vs Permutation', afni_stat_file, afni_perm,\n",
+    "             fsl_stat_file, fsl_perm,\n",
+    "             spm_stat_file, spm_perm, num_subjects=num_subjects, \n",
+    "             study=study + '_permintra')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "lib/dice.py:101: UserWarning: Resliced 1/2 and 2/1 dark dices 1 are not close\n",
+      "  warnings.warn(\"Resliced 1/2 and 2/1 dices are not close\")\n",
+      "lib/dice.py:104: UserWarning: Resliced 1/2 and 2/1 dark dices 2 are not close\n",
+      "  warnings.warn(\"Resliced 1/2 and 2/1 dark dices 1 are not close\")\n",
+      "lib/dice.py:98: UserWarning: Resliced 1/2 and 2/1 dices are not close\n",
+      "  dice_res_2 = 1-scipy.spatial.distance.dice(data1>0, data2_res>0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AFNI/FSL positive activation dice coefficient = 0.722774, 9, 2\n",
+      "AFNI/SPM positive activation dice coefficient = 0.642180, 0, 15\n",
+      "FSL/SPM positive activation dice coefficient = 0.755689, 1, 22\n",
+      "Permutation test AFNI/SPM positive activation dice coefficient = 0.672231, 0, 16\n",
+      "Permutation test AFNI/FSL positive activation dice coefficient = 0.692559, 10, 2\n",
+      "Permutation test FSL/SPM positive activation dice coefficient = 0.768594, 0, 23\n",
+      "AFNI classical inference/permutation test positive activation dice coefficient = 0.898519, 0, 0\n",
+      "FSL classical inference/permutation test positive activation dice coefficient = 0.808453, 0, 0\n",
+      "SPM classical inference/permutation test positive activation dice coefficient = 0.970059, 0, 0\n",
+      "AFNI parametric/FSL permutation positive activation dice coefficient = 0.668829, 10, 2\n",
+      "AFNI parametric/SPM permutation positive activation dice coefficient = 0.649645, 0, 15\n",
+      "FSL parametric/AFNI permutation positive activation dice coefficient = 0.749855, 2, 9\n",
+      "FSL parametric/SPM permutation positive activation dice coefficient = 0.759244, 0, 22\n",
+      "SPM parametric/AFNI permutation positive activation dice coefficient = 0.692919, 16, 0\n",
+      "SPM parametric/FSL permutation positive activation dice coefficient = 0.771524, 23, 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1106bab50>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from lib import dice\n",
+    "reload(dice)\n",
+    "dice.dice(afni_exc_set_file, spm_exc_set_file, \n",
+    "         afni_perm_pos_exc, spm_perm_pos_exc,\n",
+    "         afni_exc_set_file_neg, spm_exc_set_file_neg,\n",
+    "         fsl_exc_set_file, None, \n",
+    "         fsl_perm_pos_exc, study=study,\n",
+    "         afni_stat_file=afni_stat_file, fsl_stat_file=fsl_stat_file, spm_stat_file=spm_stat_file,\n",
+    "         afni_perm=afni_perm, fsl_perm=fsl_perm, spm_perm=spm_perm,\n",
+    "         )"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 2",
+   "language": "python",
+   "name": "py27"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/figures/lib/download_data_amended.py b/figures/lib/download_data_amended.py
new file mode 100644
index 0000000..39fe92b
--- /dev/null
+++ b/figures/lib/download_data_amended.py
@@ -0,0 +1,178 @@
+from urllib2 import urlopen, URLError, HTTPError
+from urllib2 import Request
+from shutil import copyfile
+import json
+import os
+
+def download_data(nv_collection, study, output_dir):
+    request = Request('http://neurovault.org/api/collections/' + nv_collection + '/nidm_results/?limit=184&format=json')
+    response = urlopen(request)
+    elevations = response.read()
+    data = json.loads(elevations)
+
+    pwd = os.path.dirname(os.path.realpath('__file__'))
+    input_dir = os.path.join(pwd, "input")
+    root = os.path.dirname(pwd)
+    data_dir = os.path.join(input_dir, output_dir)
+
+    if not os.path.isdir(data_dir):
+        if not os.path.isdir(input_dir):
+            os.makedirs(input_dir)
+        os.makedirs(data_dir)
+
+    # --- Download all NIDM-Results packs available on NeuroVault
+    for nidm_result in data["results"]:
+        url = nidm_result["zip_file"]
+        study_name = nidm_result["name"]
+
+        localzip = os.path.join(data_dir, study_name + ".zip")
+        localzip_rel = localzip.replace(pwd, '.')
+        if not os.path.isfile(localzip):
+            # Copy .nidm.zip export locally in a the data directory
+            try:
+                f = urlopen(url)
+                print("downloading " + url + " at " + localzip_rel)
+                with open(localzip, "wb") as local_file:
+                    local_file.write(f.read())
+            except HTTPError, e:
+                raise Exception(["HTTP Error:" + str(e.code) + url])
+            except URLError, e:
+                raise Exception(["URL Error:" + str(e.reason) + url])
+        else:
+            print(url + " already downloaded at " + localzip_rel)
+
+    # ---  Copy CSV files with Euler characteristics and Cluster counts
+    euler_char_files = (
+            (os.path.join('AFNI', 'LEVEL2', 'euler_chars.csv'), 'afni_euler_chars.csv'),
+            (os.path.join('SPM', 'LEVEL2', 'euler_chars.csv'), 'spm_euler_chars.csv'),
+            )
+    
+    cluster_count_files = (
+            (os.path.join('AFNI', 'LEVEL2', 'cluster_count.csv'), 'afni_cluster_count.csv'),
+            (os.path.join('SPM', 'LEVEL2', 'cluster_count.csv'), 'spm_cluster_count.csv'),
+            )
+
+    if study not in ('ds120'):
+        euler_char_files = (
+            euler_char_files +
+            # There is no FSL analysis for ds120
+            ((os.path.join('FSL', 'LEVEL2_amended', 'group.gfeat', 'cope1.feat', 'stats', 'euler_chars.csv'), 'fsl_euler_chars.csv'),) +
+            ((os.path.join('FSL', 'LEVEL2_amended', 'permutation_test', 'euler_chars.csv'), 'fsl_perm_euler_chars.csv'),) +
+            # There is no permutation analysis for ds120
+            ((os.path.join('AFNI', 'LEVEL2', 'permutation_test', 'euler_chars.csv'), 'afni_perm_euler_chars.csv'),) + 
+            ((os.path.join('SPM', 'LEVEL2', 'permutation_test', 'euler_chars.csv'), 'spm_perm_euler_chars.csv'),)
+        )
+
+        cluster_count_files = (
+            cluster_count_files +
+            # There is no FSL analysis for ds120
+            ((os.path.join('FSL', 'LEVEL2_amended', 'group.gfeat', 'cope1.feat', 'stats', 'cluster_count.csv'), 'fsl_cluster_count.csv'),) +
+            ((os.path.join('FSL', 'LEVEL2_amended', 'permutation_test', 'cluster_count.csv'), 'fsl_perm_cluster_count.csv'),) +
+            # There is no permutation analysis for ds120
+            ((os.path.join('AFNI', 'LEVEL2', 'permutation_test', 'cluster_count.csv'), 'afni_perm_cluster_count.csv'),) + 
+            ((os.path.join('SPM', 'LEVEL2', 'permutation_test', 'cluster_count.csv'), 'spm_perm_cluster_count.csv'),)
+        )
+            
+        
+    for euler_char_file, local_name in euler_char_files:
+
+        local_file = os.path.join(data_dir, local_name)
+        if not os.path.isfile(local_file):
+            copyfile(os.path.join(root, study, euler_char_file), local_file)
+        else:
+            print(url + " already copied at " + local_file)
+            
+    for cluster_count_file, local_name in cluster_count_files:
+        
+        local_file = os.path.join(data_dir, local_name)
+        if not os.path.isfile(local_file):
+            copyfile(os.path.join(root, study, cluster_count_file), local_file)
+        else:
+            print(url + " already copied at " + local_file)
+
+    # --- Copy remaining images from NeuroVault
+    afni_images = (
+            ('mask.nii.gz', 'afni_mask.nii.gz'),
+        )
+
+    if study not in ('ds120'):
+        afni_images = (
+            afni_images +
+            # There is no deactivations in ds120 with AFNI
+            (('Negative_clustered_t_stat.nii.gz', 'afni_exc_set_neg.nii.gz'),) +
+            # ds120 uses F-stats no T-stats
+            (('Positive_clustered_t_stat.nii.gz', 'afni_exc_set_pos.nii.gz'),) +
+            (('3dMEMA_result_t_stat_masked.nii.gz', 'afni_stat.nii.gz'),))
+    else:
+        # ds120 uses F-stats no T-stats
+        afni_images = (
+            afni_images +
+            # ds120 uses F-stats no T-stats
+            (('Positive_clustered_f_stat.nii.gz', 'afni_exc_set_pos.nii.gz'),) + 
+            (('Group_f_stat_masked.nii.gz', 'afni_stat.nii.gz'),)
+            )
+
+
+    if study not in ('ds120'):
+        perm_images = (
+            ('perm_ttest++_Clustsim_result_t_stat_masked.nii.gz', 'afni_perm.nii.gz'), 
+            ('perm_Positive_clustered_t_stat.nii.gz', 'afni_perm_exc_set_pos.nii.gz'),
+            ('mask.nii.gz', 'afni_perm_mask.nii.gz'),
+            ('OneSampT_tstat1.nii.gz', 'fsl_perm.nii.gz'),
+            ('05FWECorrected_OneSampT_pos_exc_set.nii.gz', 'fsl_perm_exc_set_pos.nii.gz'),
+            ('05FWECorrected_OneSampT_neg_exc_set.nii.gz', 'fsl_perm_exc_set_neg.nii.gz'),
+            ('snpmT%2B.nii.gz', 'spm_perm.nii.gz'),
+            ('SnPM_pos_filtered.nii.gz', 'spm_perm_exc_set_pos.nii.gz')
+        )
+    else:
+        # No permutation analyses for ds120
+        perm_images = ()
+
+    if study not in ('ds120'):
+        # There is no deactivations in ds109 and ds120 with AFNI perm
+        # There is no deactivations in ds109 with SnPM (SPM perm)
+        # No permutation analyses for ds120
+        if study not in ('ds109'):
+            perm_images = (
+                perm_images +
+                (('perm_Negative_clustered_t_stat.nii.gz', 'afni_perm_exc_set_neg.nii.gz'),
+                 ('SnPM_neg_filtered.nii.gz', 'spm_perm_exc_set_neg.nii.gz'),)
+                )
+#        if study not in ('ds001'):
+#            perm_images = (
+#                perm_images +
+#                (('perm_Negative_clustered_t_stat.nii.gz', 'afni_perm_exc_set_neg.nii.gz'),)
+#                )  
+  
+    if study in ('ds120'):
+        # R^2 maps created for ds120 to compare effect sizes
+        r_squared_images = (('afni_r_squared.nii.gz', 'afni_r_squared.nii.gz'),
+                            ('spm_r_squared.nii.gz','spm_r_squared.nii.gz'))        
+        
+        to_download = (
+            afni_images + perm_images + r_squared_images)
+    else:
+        # BOLD maps
+        bold_images= (('afni_bold.nii.gz','afni_bold.nii.gz'),
+                      ('fsl_bold_amended.nii.gz','fsl_bold.nii.gz'),
+                      ('spm_bold.nii.gz','spm_bold.nii.gz'))
+        
+        to_download = (
+            afni_images + perm_images + bold_images)
+
+    for image, local_name in to_download:
+        url = "http://neurovault.org/media/images/" + nv_collection + '/' + image
+        local_file = os.path.join(data_dir, local_name)
+        if not os.path.isfile(local_file):
+            # Copy file locally in a the data directory
+            try:
+                f = urlopen(url)
+                print("downloading " + url + " at " + local_file)
+                with open(local_file, "wb") as local_fid:
+                    local_fid.write(f.read())
+            except HTTPError, e:
+                raise Exception(["HTTP Error:" + str(e.code) + url])
+            except URLError, e:
+                raise Exception(["URL Error:" + str(e.reason) + url])
+        else:
+            print(url + " already downloaded at " + local_file)
\ No newline at end of file
diff --git a/scripts/ds109_bold_maps_amended.m b/scripts/ds109_bold_maps_amended.m
new file mode 100644
index 0000000..548ae6a
--- /dev/null
+++ b/scripts/ds109_bold_maps_amended.m
@@ -0,0 +1,82 @@
+base_dir = '/home/maullz/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/';
+study = 'ds109';
+
+study_dir = fullfile(base_dir, study);
+bold_dir = fullfile(study_dir, 'BOLD_images');
+
+if ~isdir(bold_dir)
+	mkdir(bold_dir)
+end
+
+% AFNI outputs a % percent bold image... no work needs to be done!
+%afni_bold_file = fullfile(study_dir, 'AFNI', 'LEVEL2', '3dMEMA_result_B.nii.gz');
+
+% FSL files
+fsl_contrast_file = fullfile(study_dir, 'FSL', 'LEVEL2_amended', 'group.gfeat', 'cope1.feat', 'stats', 'cope1.nii.gz');
+fsl_mat_file = fullfile(study_dir, 'FSL', 'LEVEL1_amended', 'sub-01', 'run-01.feat', 'design.mat');
+
+% SPM files
+%spm_contrast_file = fullfile(study_dir, 'SPM', 'LEVEL2', 'con_0001.nii');
+%spm_mask_file = fullfile(study_dir, 'SPM', 'LEVEL2', 'mask.nii');
+%spm_mean_func_file = fullfile(study_dir, 'SPM', 'mean_mni_images', 'spm_mean_grand_mean.nii');
+%spm_mat_file = fullfile(study_dir, 'SPM', 'LEVEL1', 'sub-01', 'SPM.mat');
+
+%% Create AFNI BOLD file
+% Nothing to be done, just copy to directory
+%copyfile(afni_bold_file, fullfile(bold_dir, 'afni_bold.nii.gz'));
+
+%% Create FSL BOLD file
+% Compute regressor height from the design matrix;
+% Reading the design.mat file
+fsl_design = textread(fsl_mat_file, '%f', 'headerlines',5);
+% Reshaping to a matrix
+fsl_design = vec2mat(fsl_design, 4);
+% Only looking at columns involed in the contrast
+fsl_design = fsl_design(:,[1 3]);
+% Since baseline =/= 0, we get the regressor height as max(regressor) - baseline; 
+regressor_height = max(fsl_design(:)) - fsl_design(1,1);
+
+V = spm_vol(fsl_contrast_file);
+X = spm_read_vols(V);
+Binout = V(1);
+Binout.fname = fullfile(bold_dir, 'fsl_bold_amended.nii');
+
+% Using the formula BOLD = Contrast*(100/B); B = median brain intensity
+% For FSL, B = 10000
+Bin = X/100*regressor_height;
+
+% Write image
+spm_write_vol(Binout, Bin);
+
+%% Create SPM BOLD file
+% Compute median brain intensity of mean anatomical image
+%V = spm_vol(spm_mean_func_file);
+%X = spm_read_vols(V);
+%Mask = spm_vol(spm_mask_file);
+%M = spm_read_vols(Mask);
+%X(M==0) = NaN;
+% Getting the median brain intesity
+%B = nanmedian(X(:));
+
+% Compute regressor height from the design matrix;
+%spm_mat = load(spm_mat_file);
+%spm_design = spm_mat.SPM.xX.X;
+% Only looking at columns involved in the contrast
+%spm_design = spm_design(:, [1,3,11,13]);
+%regressor_height = max(spm_design(:));
+
+% Creating SPM BOLD file
+%V = spm_vol(spm_contrast_file);
+%X = spm_read_vols(V);
+%Binout = V(1);
+%Binout.fname = fullfile(bold_dir, 'spm_bold.nii');
+
+% Using the formula BOLD = Contrast*(100/B)*regressor_height; B = median brain intensity
+% We divide by the number of runs (2) to compensate that sessions are combined in a single contrast in SPM.
+%Bin = X*(100/B)*regressor_height/2;
+
+% Write image
+%spm_write_vol(Binout, Bin);
+
+
+
diff --git a/scripts/ds109_euler_chars_amended.m b/scripts/ds109_euler_chars_amended.m
new file mode 100644
index 0000000..104d902
--- /dev/null
+++ b/scripts/ds109_euler_chars_amended.m
@@ -0,0 +1,28 @@
+base_dir = '/home/maullz/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/';
+study = 'ds109';
+DF = 21-1;
+
+if ~exist('euler_chars', 'file')
+    addpath(fullfile(fileparts(mfilename('fullpath')), 'lib'))
+end 
+
+study_dir = fullfile(base_dir, study);
+spm_stat_file = fullfile(study_dir, 'SPM', 'LEVEL2', 'spmT_0001.nii');
+fsl_stat_file = fullfile(study_dir, 'FSL', 'LEVEL2_amended', 'group.gfeat', 'cope1.feat', 'stats', 'tstat1.nii.gz');
+afni_stat_file = fullfile(study_dir, 'AFNI', 'LEVEL2', '3dMEMA_result_t_stat_masked.nii.gz');
+spm_perm_file = fullfile(study_dir, 'SPM', 'LEVEL2', 'permutation_test', 'snpmT+.img');
+fsl_perm_file = fullfile(study_dir, 'FSL', 'LEVEL2_amended', 'permutation_test', 'OneSampT_tstat1.nii.gz');
+afni_perm_file = fullfile(study_dir, 'AFNI', 'LEVEL2', 'permutation_test', 'perm_ttest++_Clustsim_result_t_stat_masked.nii.gz');
+spm_mask = fullfile(study_dir, 'SPM', 'LEVEL2', 'mask.nii');
+fsl_mask = fullfile(study_dir, 'FSL', 'LEVEL2_amended', 'group.gfeat', 'mask.nii.gz');
+afni_mask = fullfile(study_dir, 'AFNI', 'LEVEL2', 'mask.nii.gz');
+
+euler_array = {spm_stat_file, fsl_stat_file, afni_stat_file, spm_perm_file, fsl_perm_file};
+mask_array  = {spm_mask,      fsl_mask,      afni_mask,      spm_mask,      fsl_mask     };
+
+
+for i=1:length(euler_array)
+	euler_chars(euler_array{i}, mask_array{i});
+end
+
+euler_chars(afni_perm_file,afni_mask,DF);
diff --git a/scripts/lib/template_ds109_FSL_level1_amended.fsf b/scripts/lib/template_ds109_FSL_level1_amended.fsf
new file mode 100644
index 0000000..48a7bbb
--- /dev/null
+++ b/scripts/lib/template_ds109_FSL_level1_amended.fsf
@@ -0,0 +1,538 @@
+
+# FEAT version number
+set fmri(version) 6.00
+
+# Are we in MELODIC?
+set fmri(inmelodic) 0
+
+# Analysis level
+# 1 : First-level analysis
+# 2 : Higher-level analysis
+set fmri(level) 1
+
+# Which stages to run
+# 0 : No first-level analysis (registration and/or group stats only)
+# 7 : Full first-level analysis
+# 1 : Pre-processing
+# 2 : Statistics
+set fmri(analysis) 7
+
+# Use relative filenames
+set fmri(relative_yn) 0
+
+# Balloon help
+set fmri(help_yn) 1
+
+# Run Featwatcher
+set fmri(featwatcher_yn) 1
+
+# Cleanup first-level standard-space images
+set fmri(sscleanup_yn) 0
+
+# Output directory
+set fmri(outputdir) "$out_dir"
+
+# TR(s)
+set fmri(tr) 2.000000 
+
+# Total volumes
+set fmri(npts) 179
+
+# Delete volumes
+set fmri(ndelete) 0
+
+# Perfusion tag/control order
+set fmri(tagfirst) 1
+
+# Number of first-level analyses
+set fmri(multiple) 1
+
+# Higher-level input type
+# 1 : Inputs are lower-level FEAT directories
+# 2 : Inputs are cope images from FEAT directories
+set fmri(inputtype) 2
+
+# Carry out pre-stats processing?
+set fmri(filtering_yn) 1
+
+# Brain/background threshold, %
+set fmri(brain_thresh) 10
+
+# Critical z for design efficiency calculation
+set fmri(critical_z) 5.3
+
+# Noise level
+set fmri(noise) 0.66
+
+# Noise AR(1)
+set fmri(noisear) 0.34
+
+# Motion correction
+# 0 : None
+# 1 : MCFLIRT
+set fmri(mc) 1
+
+# Spin-history (currently obsolete)
+set fmri(sh_yn) 0
+
+# B0 fieldmap unwarping?
+set fmri(regunwarp_yn) 0
+
+# EPI dwell time (ms)
+set fmri(dwell) 0.7
+
+# EPI TE (ms)
+set fmri(te) 35
+
+# % Signal loss threshold
+set fmri(signallossthresh) 10
+
+# Unwarp direction
+set fmri(unwarp_dir) y-
+
+# Slice timing correction
+# 0 : None
+# 1 : Regular up (0, 1, 2, 3, ...)
+# 2 : Regular down
+# 3 : Use slice order file
+# 4 : Use slice timings file
+# 5 : Interleaved (0, 2, 4 ... 1, 3, 5 ... )
+set fmri(st) 0
+
+# Slice timings file
+set fmri(st_file) ""
+
+# BET brain extraction
+set fmri(bet_yn) 1
+
+# Spatial smoothing FWHM (mm)
+set fmri(smooth) 8.0
+
+# Intensity normalization
+set fmri(norm_yn) 0
+
+# Perfusion subtraction
+set fmri(perfsub_yn) 0
+
+# Highpass temporal filtering
+set fmri(temphp_yn) 1
+
+# Lowpass temporal filtering
+set fmri(templp_yn) 0
+
+# MELODIC ICA data exploration
+set fmri(melodic_yn) 0
+
+# Carry out main stats?
+set fmri(stats_yn) 1
+
+# Carry out prewhitening?
+set fmri(prewhiten_yn) 1
+
+# Add motion parameters to model
+# 0 : No
+# 1 : Yes
+set fmri(motionevs) 1
+set fmri(motionevsbeta) ""
+set fmri(scriptevsbeta) ""
+
+# Robust outlier detection in FLAME?
+set fmri(robust_yn) 0
+
+# Higher-level modelling
+# 3 : Fixed effects
+# 0 : Mixed Effects: Simple OLS
+# 2 : Mixed Effects: FLAME 1
+# 1 : Mixed Effects: FLAME 1+2
+set fmri(mixed_yn) 2
+
+# Number of EVs
+set fmri(evs_orig) 4
+set fmri(evs_real) 4
+set fmri(evs_vox) 0
+
+# Number of contrasts
+set fmri(ncon_orig) 1
+set fmri(ncon_real) 1
+
+# Number of F-tests
+set fmri(nftests_orig) 0
+set fmri(nftests_real) 0
+
+# Add constant column to design matrix? (obsolete)
+set fmri(constcol) 0
+
+# Carry out post-stats steps?
+set fmri(poststats_yn) 1
+
+# Pre-threshold masking?
+set fmri(threshmask) ""
+
+# Thresholding
+# 0 : None
+# 1 : Uncorrected
+# 2 : Voxel
+# 3 : Cluster
+set fmri(thresh) 3
+
+# P threshold
+set fmri(prob_thresh) 0.05
+
+# Z threshold
+set fmri(z_thresh) 2.6
+
+# Z min/max for colour rendering
+# 0 : Use actual Z min/max
+# 1 : Use preset Z min/max
+set fmri(zdisplay) 0
+
+# Z min in colour rendering
+set fmri(zmin) 2
+
+# Z max in colour rendering
+set fmri(zmax) 8
+
+# Colour rendering type
+# 0 : Solid blobs
+# 1 : Transparent blobs
+set fmri(rendertype) 1
+
+# Background image for higher-level stats overlays
+# 1 : Mean highres
+# 2 : First highres
+# 3 : Mean functional
+# 4 : First functional
+# 5 : Standard space template
+set fmri(bgimage) 1
+
+# Create time series plots
+set fmri(tsplot_yn) 1
+
+# Registration to initial structural
+set fmri(reginitial_highres_yn) 0
+
+# Search space for registration to initial structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reginitial_highres_search) 90
+
+# Degrees of Freedom for registration to initial structural
+set fmri(reginitial_highres_dof) 3
+
+# Registration to main structural
+set fmri(reghighres_yn) 1
+
+# Search space for registration to main structural
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(reghighres_search) 90
+
+# Degrees of Freedom for registration to main structural
+set fmri(reghighres_dof) BBR
+
+# Registration to standard image?
+set fmri(regstandard_yn) 1
+
+# Use alternate reference images?
+set fmri(alternateReference_yn) 0
+
+# Standard image
+set fmri(regstandard) "$FSLDIR/data/standard/MNI152_T1_2mm_brain"
+
+# Search space for registration to standard space
+# 0   : No search
+# 90  : Normal search
+# 180 : Full search
+set fmri(regstandard_search) 90
+
+# Degrees of Freedom for registration to standard space
+set fmri(regstandard_dof) 12
+
+# Do nonlinear registration from structural to standard space?
+set fmri(regstandard_nonlinear_yn) 1
+
+# Control nonlinear warp field resolution
+set fmri(regstandard_nonlinear_warpres) 10 
+
+# High pass filter cutoff
+set fmri(paradigm_hp) 100
+
+# Total voxels
+set fmri(totalVoxels) 33405696
+
+
+# Number of lower-level copes feeding into higher-level analysis
+set fmri(ncopeinputs) 0
+
+# 4D AVW data or FEAT directory (1)
+set feat_files(1) "$fmri"
+
+# Add confound EVs text file
+set fmri(confoundevs) 0
+
+# Subject's structural image for analysis 1
+set highres_files(1) "$amri"
+
+# EV 1 title
+set fmri(evtitle1) "false_belief_question"
+
+# Basic waveform shape (EV 1)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape1) 3
+
+# Convolution (EV 1)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve1) 3
+
+# Convolve phase (EV 1)
+set fmri(convolve_phase1) 0
+
+# Apply temporal filtering (EV 1)
+set fmri(tempfilt_yn1) 1
+
+# Add temporal derivative (EV 1)
+set fmri(deriv_yn1) 0
+
+# Custom EV file (EV 1)
+set fmri(custom1) "$onsets_1"
+
+# Orthogonalise EV 1 wrt EV 0
+set fmri(ortho1.0) 0
+
+# Orthogonalise EV 1 wrt EV 1
+set fmri(ortho1.1) 0
+
+# Orthogonalise EV 1 wrt EV 2
+set fmri(ortho1.2) 0
+
+# Orthogonalise EV 1 wrt EV 3
+set fmri(ortho1.3) 0
+
+# Orthogonalise EV 1 wrt EV 4
+set fmri(ortho1.4) 0
+
+# EV 2 title
+set fmri(evtitle2) "false_belief_story"
+
+# Basic waveform shape (EV 2)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape2) 3
+
+# Convolution (EV 2)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve2) 3
+
+# Convolve phase (EV 2)
+set fmri(convolve_phase2) 0
+
+# Apply temporal filtering (EV 2)
+set fmri(tempfilt_yn2) 1
+
+# Add temporal derivative (EV 2)
+set fmri(deriv_yn2) 0
+
+# Custom EV file (EV 2)
+set fmri(custom2) "$onsets_2"
+
+# Orthogonalise EV 2 wrt EV 0
+set fmri(ortho2.0) 0
+
+# Orthogonalise EV 2 wrt EV 1
+set fmri(ortho2.1) 0
+
+# Orthogonalise EV 2 wrt EV 2
+set fmri(ortho2.2) 0
+
+# Orthogonalise EV 2 wrt EV 3
+set fmri(ortho2.3) 0
+
+# Orthogonalise EV 2 wrt EV 4
+set fmri(ortho2.4) 0
+
+# EV 3 title
+set fmri(evtitle3) "false_photo_question"
+
+# Basic waveform shape (EV 3)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape3) 3
+
+# Convolution (EV 3)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve3) 3
+
+# Convolve phase (EV 3)
+set fmri(convolve_phase3) 0
+
+# Apply temporal filtering (EV 3)
+set fmri(tempfilt_yn3) 1
+
+# Add temporal derivative (EV 3)
+set fmri(deriv_yn3) 0
+
+# Custom EV file (EV 3)
+set fmri(custom3) "$onsets_3"
+
+# Orthogonalise EV 3 wrt EV 0
+set fmri(ortho3.0) 0
+
+# Orthogonalise EV 3 wrt EV 1
+set fmri(ortho3.1) 0
+
+# Orthogonalise EV 3 wrt EV 2
+set fmri(ortho3.2) 0
+
+# Orthogonalise EV 3 wrt EV 3
+set fmri(ortho3.3) 0
+
+# Orthogonalise EV 3 wrt EV 4
+set fmri(ortho3.4) 0
+
+# EV 4 title
+set fmri(evtitle4) "false_photo_story"
+
+# Basic waveform shape (EV 4)
+# 0 : Square
+# 1 : Sinusoid
+# 2 : Custom (1 entry per volume)
+# 3 : Custom (3 column format)
+# 4 : Interaction
+# 10 : Empty (all zeros)
+set fmri(shape4) 3
+
+# Convolution (EV 4)
+# 0 : None
+# 1 : Gaussian
+# 2 : Gamma
+# 3 : Double-Gamma HRF
+# 4 : Gamma basis functions
+# 5 : Sine basis functions
+# 6 : FIR basis functions
+set fmri(convolve4) 3
+
+# Convolve phase (EV 4)
+set fmri(convolve_phase4) 0
+
+# Apply temporal filtering (EV 4)
+set fmri(tempfilt_yn4) 1
+
+# Add temporal derivative (EV 4)
+set fmri(deriv_yn4) 0
+
+# Custom EV file (EV 4)
+set fmri(custom4) "$onsets_4"
+
+# Orthogonalise EV 4 wrt EV 0
+set fmri(ortho4.0) 0
+
+# Orthogonalise EV 4 wrt EV 1
+set fmri(ortho4.1) 0
+
+# Orthogonalise EV 4 wrt EV 2
+set fmri(ortho4.2) 0
+
+# Orthogonalise EV 4 wrt EV 3
+set fmri(ortho4.3) 0
+
+# Orthogonalise EV 4 wrt EV 4
+set fmri(ortho4.4) 0
+
+# Contrast & F-tests mode
+# real : control real EVs
+# orig : control original EVs
+set fmri(con_mode_old) orig
+set fmri(con_mode) orig
+
+# Display images for contrast_real 1
+set fmri(conpic_real.1) 1
+
+# Title for contrast_real 1
+set fmri(conname_real.1) "false_belief_vs_false_photograph"
+
+# Real contrast_real vector 1 element 1
+set fmri(con_real1.1) 1.0
+
+# Real contrast_real vector 1 element 2
+set fmri(con_real1.2) 0
+
+# Real contrast_real vector 1 element 3
+set fmri(con_real1.3) -1.0
+
+# Real contrast_real vector 1 element 4
+set fmri(con_real1.4) 0
+
+# Display images for contrast_orig 1
+set fmri(conpic_orig.1) 1
+
+# Title for contrast_orig 1
+set fmri(conname_orig.1) "false_belief_vs_false_photograph"
+
+# Real contrast_orig vector 1 element 1
+set fmri(con_orig1.1) 1.0
+
+# Real contrast_orig vector 1 element 2
+set fmri(con_orig1.2) 0
+
+# Real contrast_orig vector 1 element 3
+set fmri(con_orig1.3) -1.0
+
+# Real contrast_orig vector 1 element 4
+set fmri(con_orig1.4) 0
+
+# Contrast masking - use >0 instead of thresholding?
+set fmri(conmask_zerothresh_yn) 0
+
+# Do contrast masking at all?
+set fmri(conmask1_1) 0
+
+##########################################################
+# Now options that don't appear in the GUI
+
+# Alternative (to BETting) mask image
+set fmri(alternative_mask) ""
+
+# Initial structural space registration initialisation transform
+set fmri(init_initial_highres) ""
+
+# Structural space registration initialisation transform
+set fmri(init_highres) ""
+
+# Standard space registration initialisation transform
+set fmri(init_standard) ""
+
+# For full FEAT analysis: overwrite existing .feat output dir?
+set fmri(overwrite_yn) 0
diff --git a/scripts/process_ds109_FSL_amended.py b/scripts/process_ds109_FSL_amended.py
new file mode 100644
index 0000000..32a1f2d
--- /dev/null
+++ b/scripts/process_ds109_FSL_amended.py
@@ -0,0 +1,72 @@
+import os
+
+from lib.fsl_processing import copy_and_BET, create_fsl_onset_files
+from lib.fsl_processing import run_run_level_analyses
+from lib.fsl_processing import run_subject_level_analyses
+from lib.fsl_processing import run_group_level_analysis
+from lib.fsl_processing import run_permutation_test
+from lib.fsl_processing import mean_mni_images
+
+raw_dir = '/storage/essicd/data/NIDM-Ex/BIDS_Data/DATA/BIDS/ds000109_R2.0.1'
+results_dir = '/storage/essicd/data/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds109'
+
+fsl_dir = os.path.join(results_dir, 'FSL')
+if not os.path.isdir(fsl_dir):
+    os.mkdir(fsl_dir)
+
+preproc_dir = os.path.join(fsl_dir, 'PREPROCESSING')
+level1_dir = os.path.join(fsl_dir, 'LEVEL1_amended')
+level2_dir = os.path.join(fsl_dir, 'LEVEL1_amended')
+level3_dir = os.path.join(fsl_dir, 'LEVEL2_amended', 'group')
+perm_dir = os.path.join(fsl_dir, 'LEVEL2_amended', 'permutation_test')
+mni_dir = os.path.join(fsl_dir, 'mean_mni_images')
+
+# Specify the subjects of interest from the raw data
+subject_ids = [1, 2, 3, 8, 9, 10, 11, 14, 15, 17, 18, 21, 22, 26, 27, 28, 30, 31, 32, 43, 48]
+subject_ids = ['{num:02d}'.format(num=x) for x in subject_ids]
+
+# Specify the number of functional volumes ignored in the study
+TR = 2
+num_ignored_volumes = 0
+
+# Specify the TR that will be removed from onesets, equal to num_ignored_volumes*TR
+removed_TR_time = num_ignored_volumes*TR 
+
+cwd = os.path.dirname(os.path.realpath(__file__))
+
+# Copy raw anatomical and functional data to the preprocessing directory and
+# run BET on the anatomical images
+#copy_and_BET(raw_dir, preproc_dir, subject_ids)
+
+# Directory to store the onset files
+onsetDir = os.path.join(fsl_dir, 'ONSETS')
+
+# Define conditions and parametric modulations (if any)
+conditions = (
+    ('false_belief_story', ('false belief story', 'duration')),
+    ('false_belief_question', ('false belief question', 'duration')),
+    ('false_photo_story', ('false photo story', 'duration')),
+    ('false_photo_question', ('false photo question', 'duration')))
+
+# Create 3-columns onset files based on BIDS tsv files
+cond_files = create_fsl_onset_files(raw_dir, onsetDir, conditions, removed_TR_time, subject_ids)
+
+run_level_fsf = os.path.join(cwd, 'lib', 'template_ds109_FSL_level1_amended.fsf')
+sub_level_fsf = os.path.join(cwd, 'lib', 'template_ds109_FSL_level2.fsf')
+grp_level_fsf = os.path.join(cwd, 'lib', 'template_ds109_FSL_level3.fsf')
+perm_template = os.path.join(cwd, 'lib', 'template_ds109_FSL_perm_test')
+
+# Run a GLM for each fMRI run of each subject
+#run_run_level_analyses(preproc_dir, run_level_fsf, level1_dir, cond_files)
+
+# Run a GLM combining all the fMRI runs of each subject
+#run_subject_level_analyses(level1_dir, sub_level_fsf, level2_dir)
+
+# Run the group-level GLM
+#run_group_level_analysis(level2_dir, grp_level_fsf, level3_dir, '1')
+
+# Run a permutation test
+run_permutation_test(level1_dir, perm_dir, perm_template)
+
+# Create mean and standard deviations maps of the mean func and anat images in MNI space
+#mean_mni_images(preproc_dir, level1_dir, mni_dir)