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FreeSurfer Tutorial: Applying FreeSurfer Tools to FSL fMRI Analysis (FEAT)
The purpose of these series of exercises is to give you some familiarity with integrating FreeSurfer and FSL's functional analysis. The main challenge in the integration is getting a subject's anatomical data properly registered with their functional data. Once registered, you can use FreeSurfer display tools to visualize your FSL functional activation maps on the subject's anatomical volume and on the surface. You can also convert to the common surface space for group analysis with mri_glmfit or FSL's flame or randomise.
For structural data, we will use ${SUBJECTS_DIR}/bert and ${SUBJECTS_DIR}/fsaverage. For functional data, we will use fbert1.nii.gz and fbert2.nii.gz. The functional data set consists of two runs, 85 volumes each, 64 x 64 x 35 voxels, with size 3.4375 x 3.4375 x 4.0 mm^3. TR = 3 sec. The experiment is a periodic block design with 15 sec ON blocks of simultaneous finger tapping, flashing checker board, and auditory tone. The OFF blocks are rest periods. The paradigm starts with an OFF block. The FEAT functional analysis has already been done, and the runs have been combined with GFEAT (Fixed-effects, one-sample group mean). This data was smoothed in the volume at 5mm, but when preparing for a group surface-based analysis, we recommend that you smooth on the surface prior to group analysis and not smooth in the volume at all (or less than the voxel size) You can download the tutorial data from here.
Throughout the tutorial, it will be assumed that you are in the fbert-feat directory. If you list the directory (ls), you will see:
fbert1.nii.gz -- run 1 raw functional data fbert1.design.fsf -- run 1 FEAT design fbert1.feat -- run 1 FEAT directory design fbert2.nii.gz -- run 2 raw functional data fbert2.design.fsf -- run 1 FEAT design fbert2.feat -- run 2 FEAT directory design fbert.gfeat -- runs 1 and 2 combined in FFx model run-fsfeat-tut -- script to run through all the non-interactive components
You will see some other files/directories there too, but these are the most important.
1.0 Registration
The registration process computes a matrix that maps the FEAT example_func to the subject's anatomical using FLIRT. This matrix can then be used in later steps to display functional maps on the anatomical volume and the surface.
2.0 Overlaying onto Same-Subject Anatomical
The statistical maps from Feat may be overlaid onto the subject's anatomical volume, the surface derived from the anatomical volume, or the FSL's standard volume. All these options are described in the following exercise.
3.0 Surface-based Group Functional Analysis
The statistical maps from Feat may be overlaid onto the subject's anatomical volume, the surface derived from the anatomical volume, or the FSL's standard volume. All these options are described in the following exercise.
4.0 Mapping automatic segmentations
FreeSurfer automatically generates cortical and subcortical segmentations from the subject's anatomical data. These segmentations can be mapped into the functional space for performing region of interest (ROI) analysis. Then, the segmentation for a particular structure can be extracted to create a binary mask. Go through the following exercise for details.