Differences between revisions 35 and 36
Deletions are marked like this. Additions are marked like this.
Line 1: Line 1:
=== work in progress... ===
Line 3: Line 3:
Walkthrough: How to use FsFast and [[http://surfer.nmr.mgh.harvard.edu/fswiki/fcseed-sess|fcseed-sess]] for functional connectivity analysis including example commands. 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 [[http://surfer.nmr.mgh.harvard.edu/pub/docs/freesurfer.fsfast.ppt|FS-FAST powerpoint]]
Line 5: Line 5:
For general tips on using FsFast, download this [[http://surfer.nmr.mgh.harvard.edu/pub/docs/freesurfer.fsfast.ppt|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 [[https://surfer.nmr.mgh.harvard.edu/fswiki/unpacksdcmdir|unpacksdcmdir]]
*STEP 1: Unpack Data into the FSFAST Hierarchy using dcmunpack (run with -help for more documentation):
Line 13: Line 9:
unpacksdcmdir -src dicomdir/subject/ALLDICOMS -targ fcMRI_dir/subject -cfg subject_config.txt -fsfast -unpackerr 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
Line 19: Line 15:
 * 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 [[https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all|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:
 * -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
Line 61: Line 21:
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). *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).
Line 63: Line 23:
D. Create a sessid file (text file with list of your sessions) in your Study DIR (optional) C. Create a sessid file (text file with list of your sessions) in your Study DIR (optional)

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

fsfast-hierarchy.jpg

*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: Should also be possible with: Method 3:

FsFastFunctionalConnectivityWalkthrough (last edited 2024-01-16 14:11:01 by DougGreve)