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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 is based on

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

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

In this example command...

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:

1.QA Check after unpacking:

*STEP 2: Reconstruction Anatomical data using recon-all

Ex:

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

In this example command...

2.QA Check:

1.Make FSFAST basic hierarchy (only if data are not unpacked in FSFAST hierarchy)

fsfast-hierarchy.jpg

2.Link to FreeSurfer anatomical analysis

A - Make subjectname’ file in the session directory to link a subject's functional & structural data

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

4.Create a Stimulus Schedule (Paradigm file) in bold folder (A "paradigm" file is a record of which stimulus was presented when & for how long.

Each paradigm file has four columns:

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

# Preprocessing of fMRI Data

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

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

2.Quality Check (plot-twf-sess) 3.Examine additions to FSFAST hierarchy (in each run of bold dir):

# Function-Structure Registration View unregistered:

Run automatic registration:

Check automatic registration:

A - Make edits if needed using scale as the last resort Check talairach registration:

*STEP 4: Use fcseed-sess to generate time-course information for your chosen seed region (as well as nuisance variable signal).

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

*STEP 6: Use selxavg3-sess to run the subject-level analysis

*STEP 7: Use mri_glmfit or selxavg3-sess to run a group-level analysis