Surface-based fMRI searchlight

Reading material

• [OWDD11]: One of the early papers using a surface-based searchlight, with comparison between volumetric and surface-based searchlight.

Required toolboxes

• 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/matlab directory, see https://github.com/afni/afni)

Background

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.

Exercise

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.

Hint: run surface searchlight skl

Solution: run surface searchlight / Matlab output: run_surface_searchlight