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| For structural data, we will use ${SUBJECTS_DIR}/bert and ${SUBJECTS_DIR}/fsaverage. For functional data, we will use fbert.nii.gz. The functional data set consists of 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 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 functional analysis has been precomputed for this data by running Feat. Feat was set up using the Simple Model Setup, ABAB..., A = 15 sec, B = 15 sec; and a Full analysis was performed to create fbert.feat. | For structural data, we will use ${SUBJECTS_DIR}/bert and ${SUBJECTS_DIR}/fsaverage. For functional data, we will use fbert.nii.gz. The functional data set consists of 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 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 functional analysis has been precomputed for this data by running Feat. Feat was set up using the Simple Model Setup, ABAB..., A = 15 sec, B = 15 sec; and a Full analysis was performed to create fbert.feat. 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. |
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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 fbert.nii.gz. The functional data set consists of 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 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 functional analysis has been precomputed for this data by running Feat. Feat was set up using the Simple Model Setup, ABAB..., A = 15 sec, B = 15 sec; and a Full analysis was performed to create fbert.feat. 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.
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.
[wiki:FsTutorial/RegisterFeatOntoAnatomical Exercise A. Registering FSL Feat output to the anatomical]
2.0 Overlaying
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.
[wiki:FsTutorial/OverlayFeatStatisticalMaps Exercise B. Overlaying FSL Feat statistical maps]
3.0 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.
[wiki:FsTutorial/MapSegmentationsToFunctionalSpace Exercise C. Mapping automatic segmentations to the functional space]