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Setup the necessary FreeSurfer environment variables.
tcsh users:
setenv FREESURFER_HOME <wherever_you_installed_freesurfer>
setenv SUBJECTS_DIR <wherever_you_want_to_do_the_analysis>
source ${FREESURFER_HOME}/SetUpFreeSurfer.csh
bash users:
export FREESURFER_HOME=<wherever_you_installed_freesurfer>
export SUBJECTS_DIR=<wherever_you_want_to_do_the_analysis>
. ${FREESURFER_HOME}/SetUpFreeSurfer.sh
Convert and orient the structural.
Convert your high resolution structural into compressed NIFTI format. If you are using dicom, then:
mri_convert one-dicom-file-from-series blobby.nii.gz
Make sure that its orientation is correct by using tkmedit, eg:
tkmedit -f blobby.nii.gz
Check each cardinal direction and make sure it is oriented like the small C, H and S icons (click on each to check). You will need to prove to yourself that left-right is oriented properly as that is often difficult to tell (but mri_convert usually does the right thing when dicoms are input).
Run the fully automated FreeSurfer pipeline.
recon-all -i blobby.nii.gz -subjid blobby -all
FreeSurfer options and manual intervention.
For other options in the recon-all, or for instructions on how to
do manual correction when the automated pipeline fails or is inaccurate,
see the FreeSurfer webpages (wiki and online manuals) at:
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferWiki
Analyze your data using FEAT, including registration to the subject's brain-extracted structural and standard space.
Register your functionals to anatomicals.
reg-feat2anat --feat featdir --subject blobby
where featdir is the output feat directory, and blobby is the
freesurfer name of the subject. This will create registration files
that FreeSurfer needs in featdir/reg/freesurfer. See reg-feat2anat --help for more info; in particular,
you should use the --dof option to set the functional to structural
degrees-of-freedom to be the same as what you found worked well in
FEAT.
Manually inspect the registration.
reg-feat2anat --feat featdir --subject blobby --manual
This will bring up the FreeSurfer tkregister2 interface. Hit the "compare" button to make sure that the volumes are in register. See tkregister2 --help for more info.
Overlay zmap onto the orig volume.
Set the threshold at z=1.3
tkmedit blobby orig.mgz lh.white -overlay ./featdir/stats/zstat1.nii.gz \
-overlay-reg ./featdir/reg/freesurfer/anat2exf.register.dat \
-fthresh 1.3 -fmid 2.3 -fslope 1
Resample onto the surface.
feat2surf --feat featdir
This will create featdir/reg_surf-lh-blobby/stats with surfaces value files.
View on native inflated surface.
tksurfer blobby lh inflated -overlay featdir/reg_surf-lh-blobby/stats/zstat1.nii.gz
To view the segmentation in the functional space:
tkmedit -f featdir/example_func.nii.gz \
-segmentation featdir/reg/freesurfer/aseg.nii.gz
Construct a binary mask.
For example, of the left putamen, with:
avwmaths featdir/reg/freesurfer/aseg.nii.gz \
-thr 12 -uthr 12 \
featdir/reg/freesurfer/lh.putamen.nii.gz
Repeat for cortical segmentation.
The above can also be done for the cortical segmentation (aparc) in the
same way as for the sub-cortical segmentation (aseg), using aparc2feat.
Group analysis on the surface can be performed with either FreeSurfer or FSL tools, but the FSL tools have not been extensively tested on surface-based data. Group analysis in FreeSurfer can be accomplished from feat directories in five stages:
1. Combine runs together for each subject separately:
mris_preproc --out subj1-lh.mgz --target fsaverage --hemi lh --mean \
--iv subj1.run1.feat/stats/cope1.nii.gz subj1.run1.feat/reg/freesurfer/anat2exf.register.dat \
--iv subj1.run2.feat/stats/cope1.nii.gz subj1.run2.feat/reg/freesurfer/anat2exf.register.dat \
...
Do this for each subject in the study, creating a subjN-lh.mgz for each one. The data will be sampled to the common surface space (namely, fsaverage).
2. Concatenate the subjects together:
mri_concat subj1-lh.mgz subj2-lh.mgz ... subjN-lh.mgz --o allsubjs-lh.mgz
3. Apply surface-based smoothing:
mri_surf2surf --sval allsubjs-lh.mgz --s fsaverage --fwhm 20 --tval allsubjs-lh-sm20.mgz
It is possible to convert the file allsubjs-lh-sm2.mgz to nifti format (with mri_surf2surf) in order to run randomise or other non-spatial processing at this point, if desired.
4. Create design matrix for fsgd file and run mri_glmfit:
mri_glmfit --surf fsaverage lh --y allsubjs-lh-sm20.mgz --X X.mat --glmdir mystudy-lh-sm20 --C c.mat
5. Correct for multiple comparisons:
Run permutation or monte carlo simulations (more calls to mri_glmfit plus calls to mri_surfcluster). See the FreeSurfer Wiki.