-
Notifications
You must be signed in to change notification settings - Fork 0
/
level1_SPM_ABCD.py
198 lines (158 loc) · 8.63 KB
/
level1_SPM_ABCD.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
######
import json
with open("ABCD.json") as f:
groups = json.load(f)
for subject_id, runs in groups.items():
for run_id in list(runs):
print('running for subject ' + subject_id + ' run ' + run_id)
######
# Import modules
#####
import os
from os.path import abspath
from bids import BIDSLayout
from nipype.pipeline.engine import Workflow, Node
from nipype.interfaces.io import DataSink, DataGrabber
from nipype.interfaces.base import Bunch
import nipype.interfaces.matlab as mlab # how to run matlab
matlab_cmd = ''
from nipype.algorithms.modelgen import SpecifySPMModel
from nipype.interfaces.spm import Smooth, Level1Design, EstimateModel, EstimateContrast
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.fsl import ExtractROI
import shutil
#####
# Set variables
####
mlab.MatlabCommand.set_default_matlab_cmd("matlab -nodesktop -nosplash")
# If SPM is not in your MATLAB path you should add it here
matlab_cmd = 'users/dima/Applications/MATLAB_R2018ba.app/bin/matlab'
project_path = abspath('../data')
layout = BIDSLayout(project_path)
data_dir = os.path.abspath('../data')
project_dir = os.path.abspath
contrast_0 = ['AB', 'T', [u'AB', u'OFF'], [1, -1]]
contrast_1 = ['CD', 'T', [u'CD', u'OFF'], [1, -1]]
contrast_2 = ['AB>CD', 'T', [u'AB', u'CD'], [1, -1]]
contrast_3 = ['CD>AB', 'T', [u'AB', u'CD'], [-1, 1]]
contrast_4 = ['AB>CD_vs_OFF', 'T', [u'AB', u'CD', u'OFF'], [1, 0, -1]]
contrast_5 = ['AB>CD_vs_OFF', 'T', [u'CD', u'AB', u'OFF'], [1, 0, -1]]
contrast_6 = ['AB_superior', 'T', [u'AB', u'CD', u'OFF'], [1, -0.5, -0.5]]
contrast_7 = ['CD_superior', 'T', [u'AB', u'CD', u'OFF'], [-0.5, 1, -0.5]]
contrast_8 = ['AB_plus_CD', 'T', [u'AB', u'CD', u'OFF'], [0.5, 0.5, -1]]
contrasts = [contrast_0, contrast_1, contrast_2, contrast_3, contrast_4, contrast_5, contrast_6, contrast_7, contrast_8]
output_dir = os.path.abspath('../data/derivatives/SPM')
working_dir = os.path.abspath('../data/derivatives/SPM/workdir')
preproc_dir = os.path.abspath('preproc')
import pandas as pd
events = pd.read_csv(os.path.join(data_dir, "sub-%s" % subject_id, "func",
"sub-%s_task-odormixture_run-0%s_events.tsv") % (subject_id, run_id),
sep="\t")
confounds = pd.read_csv(os.path.join(data_dir, "derivatives", "fmriprep",
"sub-%s" % subject_id, "func",
"sub-%s_task-odormixture_run-0%s_bold_confounds.tsv"
% (subject_id, run_id)),
sep="\t", na_values="n/a")
info = [Bunch(conditions=['AB',
'CD',
'OFF'],
onsets=[list(events[events.trial_type == 'AB'].onset -6),
list(events[events.trial_type == 'CD'].onset -6),
list(events[events.trial_type == 'OFF'].onset -6)],
durations=[list(events[events.trial_type == 'AB'].duration),
list(events[events.trial_type == 'CD'].duration),
list(events[events.trial_type == 'OFF'].duration)],
regressors=[list(confounds.FramewiseDisplacement[3:]),
list(confounds.aCompCor00[3:]),
list(confounds.aCompCor01[3:]),
list(confounds.aCompCor02[3:]),
list(confounds.aCompCor03[3:]),
list(confounds.aCompCor04[3:]),
list(confounds.aCompCor05[3:]),
],
regressor_names=['FramewiseDisplacement',
'aCompCor0',
'aCompCor1',
'aCompCor2',
'aCompCor3',
'aCompCor4',
'aCompCor5', ],
amplitudes=None,
tmod=None,
pmod=None)
]
####
# Set Nodes
####
preproc_folder = '/Users/dima/Desktop/odormixture/data/derivatives/fmriprep'
field_template = {
'func': 'sub-%s/func/sub-%s_task-odormixture_run-0%s_bold_space-MNI152NLin2009cAsym_preproc.nii.gz',
'mask': 'sub-%s/func/sub-%s_task-odormixture_run-0%s_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz'}
template_args = {'func': [[subject_id, subject_id, run_id]],
'mask': [[subject_id, subject_id, run_id]]}
datasource = Node(interface=DataGrabber(infields=['subject_id', 'run_id'], outfields=['func', 'mask']),
name='datasource')
datasource.inputs.base_directory = preproc_folder
datasource.inputs.template = '*'
datasource.inputs.field_template = field_template
datasource.inputs.template_args = template_args
datasource.inputs.sort_filelist = False
datasource.inputs.subject_id = subject_id
datasource.inputs.run_id = run_id
gunzip_func = Node(Gunzip(), name='gunzip_func')
gunzip_mask = Node(Gunzip(), name='gunzip_mask')
smooth = Node(Smooth(), name='smooth')
smooth.inputs.fwhm = [4, 4, 4]
fsl_roi = Node(ExtractROI(t_min=3, t_size=-1, output_type='NIFTI'), name='fsl_roi')
modelspec = Node(SpecifySPMModel(concatenate_runs=False,
input_units='secs',
output_units='secs',
time_repetition= 2,
high_pass_filter_cutoff=128),
name="modelspec")
modelspec.inputs.subject_info = info
level1design = Node(Level1Design(bases={'hrf': {'derivs': [0, 0]}},
timing_units='secs',
interscan_interval = 2,
model_serial_correlations='AR(1)'),
name="level1design")
EstimateModel = Node(EstimateModel(estimation_method={'Classical': 1}),
name="EstimateModel")
level1conest = Node(EstimateContrast(), name="level1conest")
level1conest.inputs.contrasts = contrasts
datasink = Node(DataSink(base_directory=output_dir, parameterization=False,
container='level1/sub-%s/run-0%s' % (subject_id, run_id)),
name="datasink")
output = Node(DataSink(parameterization=False), name='level1')
output.inputs.base_directory = output_dir
###
# connect workflow
###
level1 = Workflow(name='level1')
level1.base_dir = working_dir
level1.connect([
(datasource, gunzip_func, [('func', 'in_file')]),
(datasource, gunzip_mask, [('mask', 'in_file')]),
(gunzip_func, smooth, [('out_file', 'in_files')]),
(gunzip_mask, level1design, [('out_file', 'mask_image')]),
(smooth, fsl_roi, [('smoothed_files', 'in_file')]),
(fsl_roi, modelspec, [('roi_file', 'functional_runs')]),
(modelspec, level1design, [('session_info', 'session_info')]),
(level1design, EstimateModel, [('spm_mat_file', 'spm_mat_file')]),
(EstimateModel, level1conest, [('spm_mat_file', 'spm_mat_file')]),
(EstimateModel, level1conest, [('beta_images', 'beta_images')]),
(EstimateModel, level1conest, [('residual_image', 'residual_image')]),
(EstimateModel, datasink, [('mask_image', '@mask')]),
(level1conest, datasink, [('spm_mat_file', '@spm_mat'),
('spmT_images', '@T'),
('con_images', '@con'),
('spmF_images', '@F'),
('ess_images', '@ess'),
]),
])
level1.write_graph(graph2use='flat', format='svg', simple_form=True)
###
# run the workflow
###
level1.run()
shutil.rmtree(working_dir)