Spectral Characterization of functional MRI data on voxel-resolution cortical graphs

TL;DR

This paper introduces a novel method for analyzing fMRI data on voxel-resolution cortical graphs, leveraging spectral graph theory to capture subtle spatial patterns related to brain function across individuals and tasks.

Contribution

It presents a new approach to study fMRI data on subject-specific cortical graphs using spectral energy metrics, revealing detailed spatial patterns linked to functional loads.

Findings

CHC graphs' Laplacian eigenvector bases effectively capture task-specific spatial patterns

Spectral metrics differentiate between functional loads and experimental conditions

Method demonstrates robustness across 100 subjects and multiple tasks

Abstract

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.