Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome.

ABSTRACT: The white-matter (micro-)structural architecture of the brain promotes synchrony among neuronal populations, giving rise to richly patterned functional connections. A fundamental problem for systems neuroscience is determining the best way to relate structural and functional networks quantified by diffusion tensor imaging and resting-state functional MRI. As one of the state-of-the-art approaches for network analysis, graph convolutional networks (GCN) have been separately used to analyze functional and structural networks, but have not been applied to explore inter-network relationships. In this work, we propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions, so that the joint structure-function graph can be directly analyzed

SUBMITTER: Li Y 

PROVIDER: S-EPMC9620868 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature