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fMRI Fear Conditioning FSL Analysis Pipeline

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megq edited this page Jun 8, 2017 · 9 revisions

Motivation:

To detail the fMRI Fear Conditioning (version3/design3 only) FSL Feat Analysis process and scripts. Analyses were run on run1 only by MQ but scripts will be analogous for run2 reversal if someone were to set them up. The fMRI fear conditioning paradigm is detailed in the wiki "fMRI Fear Conditioning Paradigm" and the model design themselves are detailed in the wiki "Fear Conditioning Model Overview".

FSL Feat Overview:

Various FSL feat models (mini-block, linear, split half cue and outcome) were run with the main contrast of interest being activation to aversive face when scream is absent relative to neutral face (CS+u > CS-) or the inverse (CS- > CS+u), as is consistent with most fear conditioning literature. See the "Fear Conditioning Model Overview" wiki for more details on how these models were set up.

For more information on FSL feat in general see "FSL Feat wiki"

Final statistical testing was done using FSL randomise (see: "FSL Randomise Wiki")

Run subject-level FSL Feats:

Script: /data/joy/BBL/projects/conteReproc2017/behavioralRatings/pre/pre_get_ratings_design3.sh

Requires:

  • Subject list in the form of a text file (list of bblid/datexscanid)
    Example: bblid/datexscanid
  • Templates for the model (template design.fsf which will be populated with subject-specific information)
    /data/joy/BBL/studies/conte/rawData/*/*x*/associated_files/ratings/pre/*-Pre_task_faces_BL_wheel.log
  • Biascorrected brain output (either from ANTs or a different pipeline)

Output:

  • Subject-specific pre ratings
    • /data/joy/BBL/studies/conte/rawData//x/associated_files/ratings/pre/_pre_ratings_data.csv
  • Aggregate subject data with pre ratings
    • /data/joy/BBL/studies/conte/subjectData/behavioralRatings/pre/pre_ratings_[date].csv

Set variables and pass to logfile script

Script: /data/joy/BBL/projects/conteReproc2017/behavioralRatings/pre/pre_get_ratings_design3.sh

  1. Create a variable for the date

     date=`date +%Y-%m-%d`

Merge subject-level cope output:

Run group-level FSL Feats using flame1:

Test statistical significance using Randomise:

FSL Randomise was run using the output from the mini-block models detailed above. These were the models that showed the most robust results for the CONTE study and therefore they were utilized for the final analyses for the fear conditioning run1 Quarmley et. al manuscript. Randomise was run several different ways including voxelwise however detailed below is the 3-ROI-mask method that was utilized for the paper as it showed the most robust results.

Steps:

  1. Make a 3 ROI merged mask of the vmPFC, bilateral amygdala, and bilateral anterior insula neurosynth ROIs detailed above. This was done by running fslmaths -add and merging the three ROI masks together. This mask is saved here:

``

  1. Run randomise with the following parameters:

  • threshold-free cluster enhancement (-T)

  • 5,000 permutations (-n 5000)

  • the 3 ROI mask made in the previous step (-m [mask path])

  • A merged subject-level cope (-i [4D image]) (the example below uses mini block cope11 for all 96 version3 included subjects merged together)

  • A design matrix which is simply the same as a higher-level feat design matrix.

     /NumWaves	1  
 /NumPoints	96  
 /PPheights		1.000000e+00  
            
 /Matrix  
      1  
      1  
      1  
      1  
      1  
      1  
      ....96 times

  • A contrast matrix which in this example below is two-sided so that you will obtain output in both the positive (CS+u > CS-) and negative (CS+u < CS-) directions

    /ContrastName1	group mean   
/ContrastName2	group mean negative  
/NumWaves	1  
/NumContrasts	2  
/PPheights		1  
/RequiredEffect		0.484  
          
/Matrix  
1  
-1

The template for randomise is:

   randomise -i <4D input image> -o <output file-rootname> -d <design.mat> -t <design.con> [options]

An example run from monstrum with the parameters detailed above:

    randomise -i /import/monstrum/conte_815814/group_results/merged_images/n96_bbr_linear_mini_block_run1_cope11.nii.gz -o /import/monstrum/conte_815814/group_results/randomise/fmri_paper_2017/cope11_3roi_merge_03212017  -d /import/monstrum/conte_815814/group_results/run1/single_group_level_feats/all_subj_single_group_level_feats/single_group_all_subj_linear_mini_block_run1_n96/design.mat -t /import/monstrum/conte_815814/group_results/randomise/design_2tail.con -m /import/monstrum/conte_815814/scripts/functional/roi_merge_masks/3roi_merge_fmri_paper_03312017 -n 5000 -T
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