A comparison of volume-based and surface-based multi-voxel pattern analysis

For functional magnetic resonance imaging (fMRI), multi-voxel pattern analysis (MVPA) has been shown to be a sensitive method to detect areas that encode certain stimulus dimensions. By moving a searchlight through the volume of the brain, one can continuously map the information content about the experimental conditions of interest to the brain. Traditionally, the searchlight is defined as a volume sphere that does not take into account the anatomy of the cortical surface. Here we present a method that uses a cortical surface reconstruction to guide voxel selection for information mapping. This approach differs in two important aspects from a volume-based searchlight definition. First, it uses only voxels that are classified as grey matter based on an anatomical scan. Second, it uses a surface-based geodesic distance metric to define neighbourhoods of voxels, and does not select voxels across a sulcus. We study here the influence of these two factors onto classification accuracy and onto the spatial specificity of the resulting information map. In our example data set, participants pressed one of four fingers while undergoing fMRI. We used MVPA to identify regions in which local fMRI patterns can successfully discriminate which finger was moved. We show that surface-based information mapping is a more sensitive measure of local information content, and provides better spatial selectivity. This makes surface-based information mapping a useful technique for a data-driven analysis of information representation in the cerebral cortex.