Seed-based functional connectivity
analysis
Technical Overview

Pre-processing

First-Level Analysis

Higher-Level (Group) Analysis
Pre-processing: directory structure





Follow standard procedures for unpacking data
(unpacksdcmdir command) and running basic
recon procedures for all subjects
Session does not equal subjectID (i.e. data from
one subject may be spread over different
sessions)
Each subject's functional scans must be in bold
subdirectory (fsd)
Runs containing raw time-series data must be zero
padded (i.e. 003, 005)
Add “subjectname” file (the content of the file will
be subject name that was passed to recon-all i.e.
CVRAM_001_recon)
Pre-processing: preproc-sess
Preproc-sess command runs the following processing steps by default:

Motion Correction

Smoothing

Brain-mask creation
Generates the following additions to FS-FAST hierarchy: f.nii (raw data), fmc.nii
(motion-corrected), fmcsm5.nii (motion-corrected & smoothed), fmc.mcdat (text file
with MC params (AFNI), brain.mgz (binary brain mask)
Sample Usage:

preproc-sess -s <subjid> -fwhm <#> -per-run
Pre-processing: fcseed-sess
Fcseed-sess command computes seeds (regressors) that can be used for functional
connectivity analysis or for use as nuisance regressors.
Use fcseed-sess to generate time-course information for your chosen seed region (as
well as nuisance variable signal)
Sample usage:
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" :
fcseed-sess -segid 1010 -o mean.L_Posteriorcingulate.dat -s <session> -fsd
bold -mean
Principal component analysis for nuisance regressors:
For white matter:
ventricles + CSF:
fcseed-sess -wm -o wm.dat -s <session> -fsd bold -pca
fcseed-sess -vcsf -o vcsf.dat -s <session> -fsd bold -pca
Source: http://surfer.nmr.mgh.harvard.edu/fswiki/fcseed-sess
For
First-level Analysis: mkanalysis-sess
mkanalysis-sess sets up an analysis for your functional connectivity data.
An analysis is a concept within the FS-Fast processing stream framework. It is a
placeholder for all the within-session processing options (motion correction, spatial
smoothing, event-type definition, etc). As such, the parameters apply to ALL sessions
(i.e. it does not need to be run separately for each session), and does not depend
upon the data in any one session.
NOTE: This program only configures the analysis; it does not analyze the data
Sample Usage:

mkanalysis-sess -a <analysis_name> -surface fsaverage <hemi> notask -taskreg mean.L_Posteriorcingulate.dat 1 -nuisreg vcsf.dat 3
-nuisreg wm.dat 3 -nuisreg global.waveform.dat 1 -fwhm 5 -fsd bold
-TR <TR> -mcextreg -polyfit 2 -nskip
source: http://surfer.nmr.mgh.harvard.edu/fswiki/mkanalysis-sess
First-level Analysis: selxavg3-sess
Selxavg3-sess computes the average signal intensity maps for each condition for
each individual subject. This program separately analyzes the data in each
session, then computes the average signal intensity maps for each condition. This
average data can be further processed on an individual basis and/or can be used
for group analyses.
Sample usage:
selxavg3-sess -s <subjid> -a <analysis_name>
This will create a sub-directory with the same name as your analysis_name under the
bold directory in subjects directory. This folder has all of the results for this
analysis, including: beta.nii (regression coefficients), rvar.nii (residual error
variance), mask.nii (copy of bold/masks/brain.nii), meanfunc.nii (mean functional
image), fsnr.nii (functional SNR map), X.mat (design matrix), fwhm.dat ( file with
smoothness estimate), and contrast folders.
Source: http://surfer.nmr.mgh.harvard.edu/fswiki/selxavg3-sess
Group Level Analysis: isxconcat-sess
Isxconcat-sess resamples a group of FS-FAST first-level analyses into a common
space, either volume- or surface-based, and concatenates all the sessions
together into one multi-frame file suitable for use with mri_glmfit. It will go through
each session and find the contrast_file in the analysis_name analysis. It will use
the register.dat for that session to resample to MNI305/fsaverage space.
Sample usage:
isxconcat-sess -sf <subject_list.txt> -analysis <analysis_name> -c
<contrast_file> -o group-analysis -hemi <lh> or <rh>
Group Level Analysis: mri_glmfit
The final step is to use mri_glmtfit command to perform general linear model
(GLM)analysis in the volume or the surface.
Sample usage:
mri_glmfit --y <input_file> --fsgd <fsgd_file> --surf fsaverage <hemi> --osgm
--glmdir <output_directory> --fwhm <#> --pca