.. # For CoSMoMVPA's license terms and conditions, see # # the COPYING file distributed with CoSMoMVPA # .. _`ex_surface_searchlight`: Surface-based fMRI searchlight ============================== Reading material ---------------- - :cite:`OWD+11`: 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: :ref:`run_surface_searchlight_skl` Solution: :ref:`run_surface_searchlight` / :pb:`surface_searchlight` .. include:: links.txt