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Walkthrough: How to use FsFast and fcseed-sess for functional connectivity analysis including example commands.

For general tips on using FsFast, download this FS-FAST powerpoint

This walkthrough demonstrates how to run a functional connectivity analysis on resting state fMRI data.

*STEP 1: Unpack Data into the FSFAST Hierarchy using unpacksdcmdir

Sample cmd:

unpacksdcmdir -src dicomdir/subject/ALLDICOMS -targ fcMRI_dir/subject -cfg subject_config.txt -fsfast -unpackerr

In this sample command...

• Have all fMRI dicoms linked into "ALLDICOMS" directory
• Arguement for "-targ" specifies output directory
• subject_config.txt is a configuration text file you create (format below)
• Use "-fsfast" to generate fsfast hierarchy

subject_config.txt format:

28 bold nii f.nii 29 bold nii f.nii

Col.1: scan acquisition number Col.2: output dir name will be created within "fcMRI_dir/subject" Col.3: output file format - this example is nifti format Col.4: output filename. In this example, 2 files will be created:

• fcMRI_dir/subject/028/f.nii fcMRI_dir/subject/029/f.nii

*QA Check after unpacking:

• A - Check unpacked data (time points, # of slices ..etc)
• B - Check FSFAST hierarchy in session folder

*STEP 2: Reconstruction Anatomical data using recon-all

Sample cmd:

• setenv SUBJECTS_DIR /path/to/recon_dir/ ; recon-all -s subject_dirname -all -i pathtoT1dicom_scan1.dcm -i pathtoT1dicom_scan2.dcm

In this sample command...

• set your SUBJECTS_DIR variable to your FreeSurfer subject recon directory

• set the subject's directory name with "-s" ... the arguement you provide will become the directory name within $SUBJECTS_DIR
• use "-i" to supply the dicoms to reconstruct. Use one "-i" per T1 acquisition.

A. QA Check:

• A - Check talairach transformation

• B - Check skull strip, white matter & pial surface

• C - Re-run "recon-all" if edits are made
• D - Check hierarchy of reconstructed anatomical data

B. Use FSFAST directory hierarchy:

C. 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).

D. 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

If using a full Freesurfer parcellations from aparc+aseg.mgz, continue with step 4 as described below.

If you would like to split the Freesurfer parcellation, follow the additional steps here

*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 (Principal component analysis for nuisance regressors):

for white matter:

• fcseed-config -wm -fcname wm.dat -fsd bold -pca -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 3 -nuisreg wmreg.dat 3 -nuisreg global.waveform.dat 1 -fwhm 5 -fsd bold -TR <TR> -mcextreg -polyfit 2 -nskip 4

*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