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| For general tips on using FsFast, download this powerpoint: | For general tips on using FsFast, download this [[http://surfer.nmr.mgh.harvard.edu/pub/docs/freesurfer.fsfast.ppt|FS-FAST powerpoint]] |
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| [[http://surfer.nmr.mgh.harvard.edu/pub/docs/freesurfer.fsfast.ppt|Using FS-FAST]] | This walkthrough demonstrates how to run a functional connectivity analysis on resting state fMRI data. |
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| *STEP 1: Unpack Data into the FSFAST Hierarchy using "unpackscmdir" | *STEP 1: Unpack Data into the FSFAST Hierarchy using [[https://surfer.nmr.mgh.harvard.edu/fswiki/unpacksdcmdir|unpacksdcmdir]] |
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| [[https://surfer.nmr.mgh.harvard.edu/fswiki/unpacksdcmdir|unpacksdcmdir]] | Sample cmd: |
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| 1.QA Check after unpacking: | 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: |
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| *STEP 2: Reconstruction Anatomical data using "recon-al -all" | *STEP 2: Reconstruction Anatomical data using [[https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all|recon-all]] |
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| 1.Set SUBJECTS_DIR | Sample cmd: |
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| 2.QA Check: | 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: |
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| * D - Check hierarchy of reconstructed anatomical data [[https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all| recon-all]] | * D - Check hierarchy of reconstructed anatomical data |
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| 1.Make FSFAST basic hierarchy (only if data are not unpacked in FSFAST hierarchy) | B. Use FSFAST basic hierarch: |
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| 2.Link to FreeSurfer anatomical analysis | {{attachment:fsfast-hierarchy.jpg}} |
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| A - Make subjectname file in the session directory to link a subject's functional & structural data | 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). |
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| 3.Create a sessid file (text file with list of your sessions)in your Study DIR. | D. Create a sessid file (text file with list of your sessions)in your Study DIR (optional) |
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| 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. | *STEP 3: Pre-process your bold data using preproc-sess [[http://surfer.nmr.mgh.harvard.edu/fswiki/preproc-sess|preproc-sess]] |
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| Each paradigm file has four columns: | Sample cmd: |
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| A - Stimulus onset time (sec) | preproc-sess -s <subjid> -fwhm <#> |
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| B - Condition ID code (0, 1, 2, ...) | A. By default this will do motion correction, smoothing & brain masking |
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| C - Stimulus Duration (sec) | B. Quality Check (plot-twf-sess) |
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| D - Stimulus Weight (usually 1) | C.Examine additions to FSFAST hierarchy (in each run of bold dir): |
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| *STEP 3: Pre-process your bold data using preproc-sess [[http://surfer.nmr.mgh.harvard.edu/fswiki/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): f.nii (the raw data) fmc.nii (motion corrected-MC) fmcsm5.nii (MC & smoothed) fmc.mcdat - text file with the MC parameters (AFNI) mcextreg.bhdr - binary mask of the brain |
||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) || |
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| # Function-Structure Registration View unregistered tkregister-sess -s <subjid> -regheader) Run automatic registration spmregister-sess -s <subjid> Check automatic registration tkregister-sess -s <subjid> |
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| A - Make edits if needed using scale as the last resort Check talairach registration tkregister2 --s <subjid> --fstal --surf | NOTE: you ''may'' need to convert the file "fmcpr.mgz" to fmcpr.nii using [[http://surfer.nmr.mgh.harvard.edu/fswiki/mri_convert | 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 |
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| *STEP 5: Use mkanalysis-sess to setup an analysis for your FC data | 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" |
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| *STEP 6: Use selxavg3-sess to run the subject-level analysis | Sample cmd (mean seed region timecourse): |
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| *STEP 7: Use mri_glmfit or selxavg3-sess to run a group-level analysis | fcseed-sess -segid 1010 -o mean.L_Posteriorcingulate.dat -s <session> -fsd bold -mean Sample cmd (Principal component analysis for nuisance regressors): for white matter: . fcseed-sess -wm -o wm.dat -s <session> -fsd bold -pca for ventricles + CSF: . fcseed-sess -vcsf -o vcsf.dat -s <session> -fsd bold -pca *STEP 5: Use [[http://surfer.nmr.mgh.harvard.edu/fswiki/mkanalysis-sess|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 [[http://surfer.nmr.mgh.harvard.edu/fswiki/selxavg3-sess|selxavg3-sess]] to run the subject-level analysis outlined by the above mkanalysis-sess cmd. . selxavg3-sess -s <session> -a <analysisname> *STEP 7: Use[[http://surfer.nmr.mgh.harvard.edu/fswiki/mri_glmfit|mri_glmfit]] or[[http://surfer.nmr.mgh.harvard.edu/fswiki/selxavg3-sess|selxavg3-sess]] to run a group-level analysis |
work in progress...
About
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 basic hierarch:
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
*STEP 4: Use fcseed-sess to generate time-course information for your chosen seed region (as well as nuisance variable signal).
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"
Sample cmd (mean seed region timecourse):
fcseed-sess -segid 1010 -o mean.L_Posteriorcingulate.dat -s <session> -fsd bold -mean
Sample cmd (Principal component analysis for nuisance regressors):
for white matter:
fcseed-sess -wm -o wm.dat -s <session> -fsd bold -pca
for ventricles + CSF:
fcseed-sess -vcsf -o vcsf.dat -s <session> -fsd bold -pca
*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: Usemri_glmfit orselxavg3-sess to run a group-level analysis