About

This page describes how to perform seed-based functional connectivity (FC) analysis in FSFAST. This is an extension of the task-based analysis for which there is much more documentation. It may be worth your time to study some of the preprocessing and task-based analysis as found in FS-FAST powerpoint

*STEP 1: Unpack Data into the FSFAST Hierarchy using dcmunpack (run with -help for more documentation):

Sample cmd:

dcmunpack -src dicomdir/subject/ALLDICOMS -targ fcMRI_dir/subject -fsfast -run 3 bold nii.gz f.nii.gz -run 4 bold nii.gz f.nii.gz

In this sample command...

• Have all fMRI dicoms linked into "ALLDICOMS" directory
• Arguement for "-targ" specifies output directory
• -run 3 bold nii.gz f.nii.gz will unpack run 3 fmri to fcMRI_dir/subject/bold/003/f.nii.gz
• To get a list of runs, run dcmunpack -src dicomdir/subject/ALLDICOMS
• Use "-fsfast" to generate fsfast hierarchy shown in the image below

*STEP 2: Link to FreeSurfer anatomical analysis: Create "subjectname" text file in the session directory. Write in it the subject's recon directory name (as labeld in $SUBJECTS_DIR).

C. Create a sessid file (text file with list of your sessions) in your Study DIR (optional)

*STEP 3: Pre-process your bold data using preproc-sess preproc-sess

Sample cmd:

preproc-sess -s <subjid> -fwhm <#>

A. By default this will do motion correction, smoothing & brain masking

B. Quality Check (plot-twf-sess)

C.Examine additions to FSFAST hierarchy (in each run of bold dir):

• f.nii	(Raw fMRI data)
  fmc.nii	(Motion corrected-MC)
  fmcsm5.nii	(MC & smoothed)
  fmc.mcdat	(Text file with the MC parameters (AFNI))
  brain.mgz	(Binary mask of the brain)

NOTE: you may need to convert the file "fmcpr.mgz" to fmcpr.nii using mri_convert

Found in each bold scan dir. Sample cmd:

mri_convert session/bold/002/fmcpr.mgz session/bold/002/fmcpr.nii

mri_convert session/bold/003/fmcpr.mgz session/bold/003/fmcpr.nii

Next, I'll outline two methods of deriving a seed region:

1) To use a full-size Freesurfer parcellation from aparc+aseg.mgz, continue with STEP 4 on this page.

2) To split the full Freesurfer parcellation into multiple seeds ("split parcellation"), follow the additional steps here - and resume with step 5 on this page...

*STEP 4: Use fcseed-config to record the parameters you wish to pass to your connectivity analysis.

Sample command: fcseed-config -segid 1010 -fcname mean.L_Posteriorcingulate.dat -fsd bold -mean -cfg mean.L_Posteriorcingulate.config

This example will use the FreeSurfer cortical segmentation for the left posterior cingulate (segID: 1010). For seed regions, we recommend generating the mean signal timecourse by using "-mean"

*STEP 5: Pass the config text file to fcseed-sess to generate time-course information for your chosen seed region (or for nuisance variable signal).

Sample cmd (mean seed region timecourse):

fcseed-sess -s <session> -cfg mean.L_Posteriorcingulate.config

Sample cmd (mean waveforms for nuisance regressors, but PCA also possible with -pca):

for white matter:

• fcseed-config -wm -fcname wm.dat -fsd bold -cfg wm.config

• fcseed-sess -s <session> -cfg wm.config

for ventricles + CSF:

• fcseed-config -vcsf -fcname vcsf.dat -fsd bold -mean -cfg vcsf.config

• fcseed-sess -s <session> -cfg vcsf.config

• NOTE: Once a config file is created it may be used for multiple sessions

*STEP 5: Use mkanalysis-sess to setup an analysis for your FC data

Sample cmd:

mkanalysis-sess -a <analysisname>

• -surface fsaverage <hemi> -notask -taskreg mean.L_Posteriorcingulate.dat 1 -nuisreg vcsfreg.dat 1 -nuisreg wmreg.dat 1 -nuisreg global.waveform.dat 1 -fwhm 5 -fsd bold -TR <TR> -mcextreg -polyfit 5 -nskip 4

Note: if you do not want to regress out the global signal, then do not include it in the mkanalysis-sess command.

*STEP 6: Use selxavg3-sess to run the subject-level analysis outlined by the above mkanalysis-sess cmd.

• selxavg3-sess -s <session> -a <analysisname>

*STEP 7: Choose the contrast file (generated in each session's contrast directory) that you wish to analyze on a group level:

• # ces.mgz - contrast effect size (contrast matrix * regression coef) # cesvar.mgz - variance of contrast effect size # sig.mgz - significance map (-log10(p)) # pcc.mgz - partial correlation coefficient map

*STEP 8: To continue with a group-level analysis, try one of the methods below:

• Method 1:
  • create fsgd file containing all sessions of interest

  • Concatenate contrast files using mri_concat

  • Run group analysis using mri_glmfit

  Should also be possible with: Method 2:

  • Qdec

  Should also be possible with: Method 3:

  • Concatenate with isxconcat-sess

  • Run group analysis using mri_glmfit