Spectral Characterization of functional MRI data on voxel-resolution cortical graphs
This work addresses the need for more anatomically-informed fMRI analysis in neuroscience, though it appears incremental as it applies existing graph spectral methods to a new representation.
The researchers tackled the problem of conventional fMRI processing ignoring cortical anatomy by analyzing fMRI data on subject-specific cortical graphs at voxel resolution, finding that graph spectral energy metrics effectively captured spatial patterns across different tasks and conditions in 100 subjects from the Human Connectome Project.
The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. We study graph spectral energy metrics associated to fMRI data of 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as experimental conditions within each task.