Surface-based fMRI searchlight¶
[OWDD11]: One of the early papers using a surface-based searchlight, with comparison between volumetric and surface-based searchlight.
surfing toolbox: https://github.com/nno/surfing
AFNI Matlab toolbox: https://sscc.nimh.nih.gov/pub/dist/tgz/afni_matlab.tgz (or part of the full AFNI distribution in the
src/matlabdirectory, see https://github.com/afni/afni)
In this exercise, data is analyzed from one participant who pressed buttons with either the index or middle finger in blocks. We try to infer where in the brain
Surface-models were reconstructed using FreeSurfer’s
recon-all; further processing was done using AFNI and the script
prep_afni_surf.py that is part of PyMVPA. Surfaces representing the left and right hemispheres were merged as described in the FAQ.
For this exercise use the digit dataset.
Part 1 (cortical thickness)¶
Load the anatomical surface models for the outer (pial) and inner (white) surfaces that separate the grey matter from non-gray matter. Compute, for each node on the surface, the cortical thickness, and then plot the thickness on a 3D surface model.
Part 2 (Classification analysis)¶
Load the anatomical surface models and the functional data. Define a surface-based neighborhood with approximately 100 voxels per searchlight center. Then run the searchlight with a classifier to distinguish between the different digit presses and visualize the results.