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fMRI Fear Conditioning FSL Analysis Pipeline

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megq edited this page Jun 23, 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/fearConditioningRun1/design3_run_feat.sh

Requires:

  • Subject list in the form of a text file (list of bblid/datexscanid and fMRI order)
    Example: bblid,datexscanid,0_2
  • Templates for the model (template design.fsf which will be populated with subject-specific information)
    Example: /import/monstrum/conte_815814/scripts/functional/fsf_templates/order0_2_run1_linear_mini_block_design.fsf
  • Biascorrected brain output (either from ANTs or a different pipeline)
    /data/joy/BBL/studies/conte/processedData/structural/conte_design3_n118_structural_201706051547/$bblid/*$scanid/antsCT/*BrainSegmentation0N4.nii.gz

Output:

  • Subject-level feat output (mini block example)
    /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/subject_level_feats/[bblid]/[datexscanid]/[scanid]_bbr_linear_mini_block.feat

Steps:

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/design3_run_feat.sh

  1. For each subject in the list, create variables for their fear conditioning order and the subject's scanid and bblid

    for i in `cat /data/joy/BBL/studies/conte/subjectData/design3InclusionsSubjectListFslFeat.txt`;  
do  
   
bblid=`echo $i | cut -d "," -f 1`  
datexscanid=`echo $i | cut -d "," -f 2`  
scanid=`echo $datexscanid | cut -d "x" -f 2`  
order=`echo $i | cut -d "," -f 3`

  2. Create a variable for the design template and task depending on what order the subject is in

      #if the subject is order 1_3 and you chose to run run1 then run the following feat design (the one commented in) template  
  if [ $order == "1_3" ]; then  
  fsf1=/import/monstrum/conte_815814/scripts/functional/fsf_templates/order1_3_run1_linear_mini_block_design.fsf  
  #fsf1=/import/monstrum/conte_815814/scripts/functional/fsf_templates/order1_3_run1_linear.fsf  
  #fsf1=/import/monstrum/conte_815814/scripts/functional/fsf_templates/order1_3_run1_split_half_sec1_sec5.fsf  
  task=fearcondV3R1  
  fi  
           
  #if the subject is order 0_2 and you chose to run run1 then run the following feat design (the one commented in) template    
  if [ $order == "0_2" ]; then  
  fsf1=/data/joy/BBL/studies/conte/fmriDesignFiles/order0_2/run1/fsf_templates/order0_2_run1_linear_mini_block_design.fsf  
  #fsf1=/data/joy/BBL/studies/conte/fmriDesignFiles/order0_2/run1/fsf_templates/order0_2_run1_linear.fsf  
  #fsf1=/data/joy/BBL/studies/conte/fmriDesignFiles/order0_2/run1/fsf_templates/order0_2_run1_split_half_sec1_sec5.fsf  
  task=fearcondV3R1  
  fi  
           
  #create a temp design.fsf which will hold subject specific data to replace the standard template for feat  
  temp1=/data/joy/BBL/studies/conte/fmriDesignFiles/order0_2/run1/fsf_templates/${task}_temp1.fsf

  3. Create a variable for how you want the feat output to be called in the subject's directory

     suffix1=bbr_linear_mini_block.feat

  4. Print to the screen the subject and order and create a variable for the subject's functional nifti image, print it to the screen and if one doesn't exist print an error and move to the next subject

     echo $scanid  
 echo $order  
           
 #create a variable for the subject's functional nifti image, print it to the screen and if one doesn't exist print an error and move to the next subject   
 func=$(ls /data/joy/BBL/studies/conte/rawData/$bblid/${datexscanid}/*${task}*/nifti/*fearcond*.nii.gz 2>/dev/null)		  
 echo $func   
 if [ -z $func ]; then  
         echo  "no functional image"  
         continue  
 fi

  5. Remove the nii.gz appendage from the file name and append the suffix you chose above for the output name then print that to the screen

     func=$(echo $func | sed "s+.nii.gz++g")  
 feat1=/data/joy/BBL/studies/conte/processedData/fearconditioningRun1/subject_level_feats/$bblid/${datexscanid}/${scanid}_${suffix1}   
           
 echo $feat1

  6. Do the same for the subject's biascorrected structural image and then remove the .nii.gz appendage

     #create a variable for the subject's biascorrected structural image, print it to the screen and if one doesn't exist print an error and move to the next subject	  
 mprage=$(ls /data/joy/BBL/studies/conte/processedData/structural/conte_design3_n118_structural_201706051547/$bblid/${datexscanid}/antsCT/*BrainSegmentation0N4.nii.gz 2>/dev/null)    
 if [ -z $mprage ]; then  
         echo  "no bias corrected mprage"  
         continue  
 fi  
           
 #remove the nii.gz appendage from the file name and print that file name to the screen  
 mprage=$(echo $mprage | sed "s+.nii.gz++g")  
 echo $mprage

  7. Copy the design template to the temp file created above

     #print to the screen which feat design will be run  
 echo $fsf1  
      
 #copy the design template to the temp file created above    
 cp $fsf1 $temp1

  8. Replace the standard template "fillers" with the subject specific paths to the output directory, functional image, and mprage image

     sed -i "s|###OUTPUT###|$feat1|g" "$temp1"   
 echo "$temp1"  
 sed  -i "s|###NIFTI###|$func|g" "$temp1"  
 sed  -i "s|###STRUCT###|$mprage|g" "$temp1"

  9. Run the feat design for that subject

     feat $temp1

Merge subject-level cope output:

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/conte_merge_images_mask.sh

Requires:

  • Subject list in the form of a text file (list of scanids)
  • Standardized stats output from feat for each subject in the list
    Example: /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/subject_level_feats/[bblid]/[datexscanid]/[scanid]_bbr_linear_mini_block.feat/reg_standard/stats/cope11.nii.gz

Output:

  • Merged registered copes (with as many volumes as subjects in your list)
    Example: /data/joy/BBL/studies/conte/subjectData/fearConditioningRun1/merged_copes/n110_bbr_linear_mini_block_run1_cope11.nii.gz

Steps:

Run the script first with the following parameters:

    copes="cope1"  
    maskflag=1

Then after the first cope is merged and the mask if created, change it to maskflag=0 and copes="cope2 ..." with as many copes as your feat output has and you wish to merge. This is so you don't spend extra time and space to merge the mask each time you merge a cope. You don't need the mask to merge 32 identical times, just once.

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/conte_merge_images_mask.sh

  1. Create a variable for the subject list you pass it, the path to where the output images will go, and the suffix of the feat you wish to merge images from

    scanids=$(cat /data/joy/BBL/studies/conte/subjectData/design3InclusionsSubjectList.txt)  
outdir=/data/joy/BBL/studies/conte/subjectData/fearConditioningRun1/merged_copes/  
         
#the suffix of the feat which you want to merge images from  
nickname="bbr_linear_mini_block"  
         
feat=$nickname.feat

  2. Select the copes you have in your feat output and wish to merge and the run of the data you wish to merge (V3R1 is run=1). Note the information above on running the script first with copes=1 and maskflag=1.

     #variables for the level1 feat copes to merge-- can be multiple copes e.g., "cope1 cope2 cope3 cope7"  
 #Note you'll see cope1 is separate in many so that cope1 can be run and merged with the maskflag set to 1, then can be commented out and the rest of the copes can be run without the mask flag to speed things up  
 #copes="cope1"  
 copes="cope2 cope3 cope4 cope5 cope6 cope7 cope8 cope9 cope10 cope11 cope12"  
 mask="mask"  
 varcopeflag=1   #if=1, also will merge varcopes   
 maskflag=0 # if =1, also will merge masks    
 run=1 #the fear conditioning run you are merging (run1 or run2 reversal)

  3. Create a variable for the output log directory and remove any existing logs

     logdir=/data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/logs  
    
 #######  
    
 #remove any existing log file for missing copes  
 rm -f $logdir/conte_missing_copes_for_merge.txt

  4. Print out which feat will be merged and the number of subjects to merge

     echo "feat is $feat"  
 nsubj=$(echo $scanids | wc |  awk '{print $2}')  
 echo "$nsubj subjects"  
   
 echo "*********"

  5. For each cope in the list, print out the cope and remove any existing cope and varcope lists

     for c in $copes; do  
         echo ""  
         echo ""  
         echo "********************"  
         echo "now working on $c"  
         echo "********************"  
         rm -f copelist_tmp.txt  
         rm -f varcopelist_tmp.txt

  6. For each subject print out the scanid, and create a variable which gets the fear conditioning standardized cope image for each subject

     for b in $scanids; do  
         echo ""  
         echo $b  
                   	
         #create a variable which gets the fear conditioning standardized cope image for each subject  
         image=$(ls -d /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/subject_level_feats/$bblid/*$scand/${scanid}"_bbr_linear_mini_block.feat"/reg_standard/stats/${c}.nii.gz)    
                                	
         #if that image doesn't exist then output an error and append it to the error text file  
         if [ -z "$image" ]; then  
            echo "COPE MISSING"  
            echo -e"$b \t $c" >> $logdir/conte_missing_copes_for_merge.txt  
            exit 1  
         fi  
                                           
         #if it does exist print the path and output to a cope list  
         echo $image  
         echo $image >> copelist_tmp.txt

  7. Create a variable which gets the fear conditioning standardized varcope image for each subject

     #if you should also merge varcopes then   
 if [ "$varcopeflag" == 1 ]; then  
         echo "merging varcopes also"	  
                   
         #create a variable which gets the fear conditioning standardized varcope image for each subject  
         varimage=$(ls -d /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/subject_level_feats/$bblid/*$scand/${scanid}"_bbr_linear_mini_block.feat"/reg_standard/stats/var${c}.nii.gz)	  
                           
 #if that image doesn't exist then output an error and append it to the error text file  
 if [ -z "$varimage" ]; then  
 echo "VARCOPE MISSING"  
 echo -e"$b \t var${c}" >> $logdir/conte_missing_copes_for_merge.txt  
 exit 1  
 fi  
                   
 #if it does exist print the path and output to a varcope list  
 echo $varimage  
 echo $varimage >> varcopelist_tmp.txt  
 fi

  8. Create a variable which gets the fear conditioning standardized mask image for each subject

     #if the mask flag is set to 1 then   
 if [ "$maskflag" == 1 ]; then  
         echo "merging masks also"	  
                    
         #create a variable which gets the fear conditioning standardized mask image for each subject  
         maskimage=$(ls -d /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/subject_level_feats/$bblid/*$scand/${scanid}"_bbr_linear_mini_block.feat"/reg_standard/mask.nii.gz)  
                            
 #if that image doesn't exist then output an error and append it to the error text file  
 if [ -z "$maskimage" ]; then  
         echo "MASK MISSING"  
         echo -e"$b \t mask" >> $logdir/conte_missing_copes_for_merge.txt  
         exit 1  
 fi  
          
 #if it does exist print the path and output to a mask list  
 echo $maskimage  
 echo $maskimage >> masklist_tmp.txt  
 fi  
           
 done

  9. Print the number of subjects to be merged for copes and merge the copes in the cope list for each subject

     echo ""   
 echo "********************"   
 echo "now merging $nsubj images for $c"  
                    
 #merge the copes in the cope list for each subject  
 fslmerge -t ${outdir}/n${nsubj}_${nickname}_run${run}_${c} $(cat copelist_tmp.txt)

  10. If you have the varcope flag set to equal 1 (varcopeflag=1) then do the same for the varcopes

    #if varcope is set to 1 then  
if [ "$varcopeflag" == 1 ]; then  
                              
        #print the number of subjects to be merged for varcopes  
        echo ""  
        echo "now merging $nsubj images for var${c}"  
                  
        #merge the varcopes in the varcope list for each subject  
        fslmerge -t ${outdir}/n${nsubj}_${nickname}_run${run}_var${c} $(cat varcopelist_tmp.txt)   
        echo "********************"  
fi

  11. If you have the mask flag set to equal 1 (maskflag=1) then do the same for the mask

    #if mask is set to 1 then  
if [ "$maskflag" == 1 ]; then  
                	  
        #print the number of subjects to be merged for the mask  
        echo ""  
        echo "now merging $nsubj images for mask"  
                          
        #merge the masks in the mask list for each subject and binarize the mask and get overlap  
        fslmerge -t ${outdir}/n${nsubj}_mask $(cat masklist_tmp.txt)   
        fslmaths ${outdir}/n${nsubj}_mask -Tmin -bin -mas /share/apps/fsl/5.0.8/data/standard/MNI152_T1_2mm_brain.nii.gz ${outdir}/${nickname}"_n"${nsubj}"_run"${run}"_final_maskoverlap"  
        echo "********************"  
fi  
        
done

  12. Print the output directory to the screen (after removing the lists of copes, etc. that you merged)

    rm -f copelist_tmp.txt  
rm -f varcopelist_tmp.txt  
rm -f masklist_tmp.txt  
          
echo "output at ${outdir}/n${nsubj}_${nickname}_run${run}_${c}"

Setup file for group-level FSL Feats:

For more information on group-level feats by flame1 see the FSL Higher Level Feat Output wiki

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/makedesign.m

Requires:

  • A subject clinical variable spreadsheet with the following headers (columns not labeled in sheet as it is read into matlab and matlab doesn't like headers):

/data/joy/BBL/studies/conte/subjectData/design3fMRISubjectClinicalDemoData_no_headers.csv

  • 1- bblid
  • 2-scanid
  • 3-fmri_exclude (1=exclude, 0=include)
  • 4-diagnostic group (0=NC, 1=CR, 2=P)
  • 5-fmri version order (1=order1_3, 0=order0_2)
  • 6- scream unpleasantness rating
  • 7-cains total (negative symptoms)
  • 8-positive symptoms (sips p)
  • 9-anxiety rating (stai trait)
  • 10-post scan ratings confidence
  • 11-motion
  • 12=CAINS map score
  • 13=CAINS expression score
  • 14=age
  • 15=sex (1=male, 2=female)
  • 16=race (1=caucasian, 2=non-caucasian
  • 17=run1 ratings aversive - neutral
  • 18=run1 neutral ratings
  • 19=run1 - pre ratings aversive - neutral
  • 20=run1 - pre neutral ratings

Output:

  • Group-level/Higher-level design.mat and design.grp files. Example:
    /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/single_group_level_feats/single_group_linear_mini_block_n110/design.mat
    /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/single_group_level_feats/single_group_linear_mini_block_n110/design.grp

Steps:

  1. Create an output directory for your design files and the for the ultimate group-level feat output. Example:

mdkir /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/single_group_level_feats/single_group_linear_mini_block_n110/

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/makedesign.m

  1. Load in the clinical and demographic data

     data=load('/data/joy/BBL/studies/conte/subjectData/design3fMRISubjectClinicalDemoData.csv');

  2. Create variables for the number of groups you want the analysis to include (i.e. 1=single group analysis, 3= 3 group NC, CR, SZ analysis), whether you should exclude NC's from analysis, if you wish to covary for a variable, and if you wish to demean within group

     numgp=3; %1 or 2 or 3?  
 patient=1; %exclude NC's and count patients and cr's as one single group  
 covariate=0; %0 if only running group, 1 if including covariate(s)  
 demean_within=0; %do you want to demean your covariate within groups or across all groups? If you want to demean within groups then set this flage to 1;

  3. Exclude data based on if group is missing, if motion is > .3 (meanrelrms), and if fMRI exclude =1

     %exclude data based on missing values (set to group missing right now)  
 excludeind=find(isnan(data(:,4)));  
 data(excludeind,:)='';   
    
 %exclude based on high motion (>.3)  
 motionind=find(data(:,11)>.3);  
 data(motionind,:)='';   
    
 %exclude based on fmri_exclude column  
 exclude=find(data(:,3)==1);  
 data=data(setdiff(1:size(data,1),exclude),:);

  4. Set the indices for group inclusion/exclusion and if you set to exclude NC's from analysis do so

     %dx EVs  
 group=data(:,4); %NC=0, CR=1, P=2  
 ncind=find(group==0);  
 crind=find(group==1);  
 szind=find(group==2);  
 ncinclude=zeros(length(group),1);   
 ncinclude(ncind)=1;  
 crinclude=zeros(length(group),1);   
 crinclude(crind)=1;  
 szinclude=zeros(length(group),1);   
 szinclude(szind)=1;    
   
 if patient==1;  
 data(ncind,:)='';  
 end;

  5. If you had elected to have a covariate then create a variable for the covariate

     covariate1=data(:,7); %cains total

  6. Create a design variable (for design.mat) depending on the parameters you set at the beginning of the script

     if numgp==3 & covariate==0 & demean_within==0;  
 design=[ncinclude crinclude szinclude];% run most basic group only no covariates model  
 end;  
   
 if numgp==1 & covariate==0 & demean_within==0;  
 design=ones(length(data),1);  
 end;  
       
 if covariate==1 & numgp==3 & demean_within==0;  
 design=[ncinclude crinclude szinclude covariate1 ];%add covariates in  
 end;  
    
 if covariate==1 & numgp==1 & demean_within==0;  
 design=[ones(length(data),1) covariate1 ];  
 end;

  7. Alternatively, if you want to have your design demean within group

     if covariate==1 & numgp==3 & demean_within==1;  
 design=[ncinclude crinclude szinclude covariate1 covariate1 covariate1];  
 design(ncinclude==0,4)=NaN;    
 design(crinclude==0,5)=NaN;  
 design(szinclude==0,6)=NaN;    
 end;

  8. Create a variable which gets the final design

     designincl=design(:,:);

  9. Demean the covariates (not group EV's)

    szdes=size(designincl);  
designincl2=designincl;  
   
if covariate==1;  
for i=1+numgp:szdes(2);  
designincl2(:,i)=designincl(:,i)-nanmean(designincl(:,i));  
end;  
end;

  10. Print to the screen the number of missing values and the design matrix name to the screen

    numnan=length(find(isnan(designincl2)));  
disp(['There were this many NaNs in the design matrix: ' num2str(numnan)]);  
designincl2(isnan(designincl2))=0;  
disp('Your final design matrix is called designincl2');

  11. Create a variable for the design.grp file

    designgrp=ones(length(designincl2),1);

  12. Print out the design.mat template

    designincl2

Copy this design.mat output into the design.mat file in the place for the design matrix which is under the following output:

    /NumWaves	1    
    /NumPoints	110    
    /PPheights		1.000000e+00    
         
    /Matrix

Then print the design.grp to the screen by typing designgrp in matlab and paste this output into the design.grp file in the place for the design group matrix which is under the following output:

    /NumWaves	1  
    /NumPoints	110   
      
    /Matrix

Run group-level FSL Feats using flame1:

For more information on group-level feats by flame1 see the FSL Higher Level Feat Output wiki

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/group_flameo_conte_nogrid.sh

Requires:

  • Merged cope output for all subject you wish to include in your group-level feat (by conte_merge_images_mask.sh)
    • Example: /data/joy/BBL/studies/conte/subjectData/fearConditioningRun1/merged_copes/n110_bbr_linear_mini_block_run1_cope11.nii.gz
  • design.mat, design.con, and design.grp files in the group-level feat output directory you wish the group-level feat output to be saved to. You must create these as per any group-level feat (see makedesign.m steps above for design.mat and design.grp, you must create design.con by hand).

Output:

  • Group-level/Higher-level feat output for the model you run
    • Example /data/joy/BBL/studies/conte/processedData/fearconditioningRun1/single_group_level_feats/single_group_linear_mini_block_n110

Steps:

Script: /data/joy/BBL/projects/conteReproc2017/fearConditioningRun1/group_flameo_conte_nogrid.sh

  1. Create a variable for the number of subjects you want to include in your group-level feat, and for the output path

      nsubj=n110  #eg n61  
             
  #create a variable for the path to where the output images will go and where the design files exist  
  wd=/data/joy/BBL/studies/conte/processedData/fearconditioningRun1/single_group_level_feats/single_group_linear_mini_block_${nsubj}

  2. Create a variable which gets the full list of copes you wish to include in your group-level output

      #variables for the level1 feat copes ----can be multiple copes e.g., "cope1 cope2 cope3 cope7"  
  copes="cope1 cope2 cope3 cope4 cope5 cope6 cope7 cope8 cope9 cope10 cope11 cope12"

  3. Create a variable for the suffix of the feat which you want to run flame1 (group-level feat) on

      nickname="bbr_linear_mini_block"

  4. Print out the working directory and feat name and if the working directory doesn't exist then exit the script

      echo "working directory is $wd"  
  echo ""  
  echo "nickname is $nickname"  
  echo ""  
  if [ ! -d "$wd" ]; then  
  	echo "working directory not found!! exiting!"  
  	exit 1  
  fi  
             
  echo "*********"  
  echo "for all analyses, lev2 cope is $lev2_cope"  
  echo "*******"

  5. For each cope in the list defined above (by variable "copes"), create variables for the merged cope, varcope, and mask files created by the conte_merge_images_mask.sh script

      for c in $copes; do  
  	echo ""  
  	echo "working on $c"  
             
  #create variables for the merged cope, varcope, and mask files created by the conte_merge_images_mask.sh script  
  cfile=$(ls -d /data/joy/BBL/studies/conte/subjectData/fearConditioningRun1/merged_copes/${nsubj}_${nickname}_run${run}_${c}.nii.gz)  
  vfile=$(ls -d /data/joy/BBL/studies/conte/subjectData/fearConditioningRun1/merged_copes/${nsubj}_${nickname}_run${run}_var${c}.nii.gz)  
  mask=/data/joy/BBL/studies/conte/subjectData/fearConditioningRun1/merged_copes/${nickname}_${nsubj}_run${run}_final_maskoverlap.nii.gz

  6. Create variable for output dir within working directory defined above and the cope number appended

      outdir=${nickname}_${nsubj}_${c}    
     
  #create a variable for the outpath directory  
  outpath=$(ls -d $wd/$outdir 2> /dev/null)

  7. Run flameo on the cope, varcope, mask specified with the design files in the working directory

      flameo --cope=$cfile --varcope=$vfile --mask=$mask --dm=${wd}/design.mat --tc=${wd}/design.con  --cs=${wd}/design.grp --runmode=flame1 --logdir=${wd}/${outdir} --npo  
    
  done

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:

/import/monstrum/conte_815814/scripts/functional/roi_merge_masks/3roi_merge_fmri_paper_03312017.nii.gz

  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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