  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.
