|
Size: 4865
Comment:
|
Size: 6644
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| === work in progress... === | |
| Line 3: | Line 2: |
| 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]] and the [[http://surfer.nmr.mgh.harvard.edu/fswiki/FsFastTutorial|FS-FAST tutorial]]. |
| Line 5: | Line 4: |
| For general tips on using FsFast, download this [[http://surfer.nmr.mgh.harvard.edu/pub/docs/freesurfer.fsfast.ppt|FS-FAST powerpoint]] | *STEP 1: Unpack Data into the FSFAST Hierarchy using dcmunpack (run with -help for more documentation): |
| Line 7: | Line 6: |
| This walkthrough is based on | Sample cmd: |
| Line 9: | Line 8: |
| *STEP 1: Unpack Data into the FSFAST Hierarchy using [[https://surfer.nmr.mgh.harvard.edu/fswiki/unpacksdcmdir|unpacksdcmdir]] | dcmunpack -src dicomdir -targ sessionid -fsfast -run 3 bold nii.gz f.nii.gz -run 4 bold nii.gz f.nii.gz |
| Line 11: | Line 10: |
| Ex: unpacksdcmdir -src dicomdir/subject/ALLDICOMS -targ fcMRI_dir/subject -cfg subject_config.txt -fsfast -unpackerr |
In this sample command... |
| Line 14: | Line 12: |
| In this example 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 1.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]] 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... *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. 2.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 1.Make FSFAST basic hierarchy (only if data are not unpacked in FSFAST hierarchy) |
* Have all fMRI dicoms for this subject in the dicomdir folder or subfolders under this folder * Arguement for "-targ" specifies output directory here called "sessionid". This should be unique to the subject (and visit if longitudinal). This is called the session folder. * -run 3 bold nii.gz f.nii.gz will unpack run 3 fmri to sessionid/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 * The parent folder of the sessionid folder is called the "Project Folder" or "Project Directory". All the commands below should be run from the Project Folder. |
| Line 59: | Line 21: |
| 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: * A - Stimulus onset time (sec) * B - Condition ID code (0, 1, 2, ...) * C - Stimulus Duration (sec) * D - Stimulus Weight (usually 1) |
*STEP 2: Link to FreeSurfer anatomical analysis. This is done by creating a text file called sessionid/subjectname with the name of the FreeSurfer anatomical folder as created with recon-all and found in $SUBJECTS_DIR. |
| Line 76: | Line 25: |
| # Preprocessing of fMRI Data | Sample cmd: |
| Line 78: | Line 27: |
| preproc-sess -s <subjid> -fwhm <#> | preproc-sess -s sessionid -fwhm 10 -surface fsaverage lhrh |
| Line 80: | Line 29: |
| 1.By default this will do motion correction, smoothing & brain masking | By default this will do motion correction, masking, registration to the anatomical, sampling to the surface, and surface smoothing. The sampling is done onto the surface of the lh and rh hemispheres of fsaverage. Note that eventhough the time series data are sampled onto fsaverage, the FC seeds are derived from the indvidual anatomy as shown below. |
| Line 82: | Line 31: |
| 2.Quality Check (plot-twf-sess) 3.Examine additions to FSFAST hierarchy (in each run of bold dir): | *STEP 4: |
| Line 84: | Line 33: |
| . 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) |
There are two methods of deriving a seed region: |
| Line 90: | Line 35: |
| # Function-Structure Registration View unregistered: | 1) To use a full-size Freesurfer parcellation from aparc+aseg.mgz, continue with STEP 5 on this page. |
| Line 92: | Line 37: |
| . [[http://surfer.nmr.mgh.harvard.edu/fswiki/tkregister-sess|tkregister-sess]] -s <subjid> -regheader) | 2) To split the full Freesurfer parcellation into multiple seeds ("split parcellation"), follow the [[FsFastFunctionalConnectivityWalkthroughSplittingSeeds |additional steps here]] - and resume with step 5 on this page... |
| Line 94: | Line 39: |
| Run automatic registration: | *STEP 5: Use fcseed-config to configure the parameters you wish to pass to your connectivity analysis. |
| Line 96: | Line 41: |
| . [[http://surfer.nmr.mgh.harvard.edu/fswiki/spmregister-sess|spmregister-sess]] -s <subjid> | Sample command: {{{fcseed-config -segid 1010 -fcname mean.L_Posteriorcingulate.dat -fsd bold -mean -cfg mean.L_Posteriorcingulate.config}}} |
| Line 98: | Line 44: |
| Check automatic registration: | This example will use the FreeSurfer cortical segmentation for the left posterior cingulate (segID: 1010) as defined for this individual. For seed regions, we recommend generating the mean signal timecourse by using "-mean". Note that this does not perform any analysis, it just creates a text file with the configuration. You can include more -segid flags to include more regions (though it will create only one seed time course). NOTE: Once a config file is created it may be used for multiple sessions. |
| Line 100: | Line 46: |
| . [[http://surfer.nmr.mgh.harvard.edu/fswiki/tkregister-sess|tkregister-sess]] -s <subjid> | *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). |
| Line 102: | Line 48: |
| A - Make edits if needed using scale as the last resort Check talairach registration: | {{{fcseed-sess -s sessionid -cfg L_Posteriorcingulate.config}}} |
| Line 104: | Line 50: |
| . [[http://surfer.nmr.mgh.harvard.edu/fswiki/tkregister2|tkregister2]] --s <subjid> --fstal --surf | This creates a file called L_Posteriorcingulate.dat in each resting state run. This will have a single time course in it |
| Line 106: | Line 52: |
| *STEP 4: Use fcseed-sess to generate time-course information for your chosen seed region (as well as nuisance variable signal). | *STEP 6: Create nuisance variables |
| Line 108: | Line 54: |
| *STEP 5: Use [[http://surfer.nmr.mgh.harvard.edu/fswiki/mkanalysis-sess|mkanalysis-sess]] to setup an analysis for your FC data | for white matter: . fcseed-config -wm -fcname wm.dat -fsd bold -pca -cfg wm.config . fcseed-sess -s sessionid -cfg wm.config |
| Line 110: | Line 58: |
| *STEP 6: Use [[http://surfer.nmr.mgh.harvard.edu/fswiki/selxavg3-sess|selxavg3-sess]] to run the subject-level analysis | for ventricles + CSF: . fcseed-config -vcsf -fcname vcsf.dat -fsd bold -pca -cfg vcsf.config . fcseed-sess -s sessionid -cfg vcsf.config |
| Line 112: | Line 62: |
| *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 | These commands will create wm.dat and vcsf.dat in for each resting state run. These are text files with multiple columns. Each column is a principle component. You will choose the number of components to use below. *STEP 6: Use [[http://surfer.nmr.mgh.harvard.edu/fswiki/mkanalysis-sess|mkanalysis-sess]] to configure an analysis for your FC data. Like the fcseed-config above, this is done once regardless of how many sessionds you have. {{{mkanalysis-sess -analysis fc.lpccseed.surf.lh -surface fsaverage lh -fwhm 5 -notask -taskreg L_Posteriorcingulate.dat 1 -nuisreg vcsf.dat 5 -nuisreg wm.dat 5 -mcreg -polyfit 5 -nskip 4 -fsd bold -TR <TR> }}} This command does not perform any analysis; it just creates an analysis configuration called "fc.lpccseed.surf.lh". There will be a folder of this name with a text file called analysis.info. The analysis will be run on the left hemisphere of fsaverage smoothed by 5 mm (this was already performed in preprocessing above). The waveform in the file called mean.L_Posteriorcingulate.dat will be used as the "task regressor"; this is just the FC seed. There are five sets of nuisance variables: (1) the CSF from which the top 5 principle components will be used, (2) the white matter from which the top 5 principle components will be used, (3) motion correction parameters (-mcreg), (4) 5th order polynomial, and (5) the first 4 time points will be discarded. If you with to perform global signal regression, add "-nuisreg global.waveform.dat 1". Specify the TR with the -TR flag (eg, TR 2 for 2 seconds). *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 just as you would with a task-based analysis {{{selxavg3-sess -s sessionid -a fc.lpccseed.surf.lh}}} This performs the voxel-wise time series analysis for the given session. This results in several files being generated in the session folder. These include # 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 7: Group-level analysiss As with task-based analysis, you will run isxconcat-sess to create a "stack" of maps from each subject. If you want to use the correlation coefficient instead of the ces, specify -map pcc. Specify the contrast as L_Posteriorcingulate (or just use -all-contrasts). After that, you can perform the standard group FS group analysis. |
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 and the FS-FAST tutorial.
*STEP 1: Unpack Data into the FSFAST Hierarchy using dcmunpack (run with -help for more documentation):
Sample cmd:
dcmunpack -src dicomdir -targ sessionid -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 for this subject in the dicomdir folder or subfolders under this folder
- Arguement for "-targ" specifies output directory here called "sessionid". This should be unique to the subject (and visit if longitudinal). This is called the session folder.
- -run 3 bold nii.gz f.nii.gz will unpack run 3 fmri to sessionid/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
- The parent folder of the sessionid folder is called the "Project Folder" or "Project Directory". All the commands below should be run from the Project Folder.
*STEP 2: Link to FreeSurfer anatomical analysis. This is done by creating a text file called sessionid/subjectname with the name of the FreeSurfer anatomical folder as created with recon-all and found in $SUBJECTS_DIR.
*STEP 3: Pre-process your bold data using preproc-sess preproc-sess
Sample cmd:
preproc-sess -s sessionid -fwhm 10 -surface fsaverage lhrh
By default this will do motion correction, masking, registration to the anatomical, sampling to the surface, and surface smoothing. The sampling is done onto the surface of the lh and rh hemispheres of fsaverage. Note that eventhough the time series data are sampled onto fsaverage, the FC seeds are derived from the indvidual anatomy as shown below.
*STEP 4:
There are two methods of deriving a seed region:
1) To use a full-size Freesurfer parcellation from aparc+aseg.mgz, continue with STEP 5 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 5: Use fcseed-config to configure 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) as defined for this individual. For seed regions, we recommend generating the mean signal timecourse by using "-mean". Note that this does not perform any analysis, it just creates a text file with the configuration. You can include more -segid flags to include more regions (though it will create only one seed time course). NOTE: Once a config file is created it may be used for multiple sessions.
*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).
fcseed-sess -s sessionid -cfg L_Posteriorcingulate.config
This creates a file called L_Posteriorcingulate.dat in each resting state run. This will have a single time course in it
*STEP 6: Create nuisance variables
for white matter:
- fcseed-config -wm -fcname wm.dat -fsd bold -pca -cfg wm.config
- fcseed-sess -s sessionid -cfg wm.config
for ventricles + CSF:
- fcseed-config -vcsf -fcname vcsf.dat -fsd bold -pca -cfg vcsf.config
- fcseed-sess -s sessionid -cfg vcsf.config
These commands will create wm.dat and vcsf.dat in for each resting state run. These are text files with multiple columns. Each column is a principle component. You will choose the number of components to use below.
*STEP 6: Use mkanalysis-sess to configure an analysis for your FC data. Like the fcseed-config above, this is done once regardless of how many sessionds you have.
mkanalysis-sess -analysis fc.lpccseed.surf.lh -surface fsaverage lh -fwhm 5 -notask -taskreg L_Posteriorcingulate.dat 1 -nuisreg vcsf.dat 5 -nuisreg wm.dat 5 -mcreg -polyfit 5 -nskip 4 -fsd bold -TR <TR>
This command does not perform any analysis; it just creates an analysis configuration called "fc.lpccseed.surf.lh". There will be a folder of this name with a text file called analysis.info. The analysis will be run on the left hemisphere of fsaverage smoothed by 5 mm (this was already performed in preprocessing above). The waveform in the file called mean.L_Posteriorcingulate.dat will be used as the "task regressor"; this is just the FC seed. There are five sets of nuisance variables: (1) the CSF from which the top 5 principle components will be used, (2) the white matter from which the top 5 principle components will be used, (3) motion correction parameters (-mcreg), (4) 5th order polynomial, and (5) the first 4 time points will be discarded. If you with to perform global signal regression, add "-nuisreg global.waveform.dat 1". Specify the TR with the -TR flag (eg, TR 2 for 2 seconds).
*STEP 6: Use selxavg3-sess to run the subject-level analysis outlined by the above mkanalysis-sess cmd just as you would with a task-based analysis
selxavg3-sess -s sessionid -a fc.lpccseed.surf.lh
This performs the voxel-wise time series analysis for the given session. This results in several files being generated in the session folder. These include
- # 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 7: Group-level analysiss
As with task-based analysis, you will run isxconcat-sess to create a "stack" of maps from each subject. If you want to use the correlation coefficient instead of the ces, specify -map pcc. Specify the contrast as L_Posteriorcingulate (or just use -all-contrasts). After that, you can perform the standard group FS group analysis.