Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome

TL;DR

This paper introduces a joint graph convolutional network that integrates structural and functional brain networks with learnable inter-network edges, improving predictions of age and sex from neuroimaging data.

Contribution

The novel approach couples structural and functional brain networks with learnable inter-network edges within a single GCN for enhanced analysis.

Findings

Joint-GCN outperforms existing multi-modal graph learning methods.

Learnable inter-network edges reflect non-uniform structure-function coupling.

Effective in predicting age and sex from neuroimaging data.

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 by a single GCN. The weights of inter-network edges are learnable, reflecting non-uniform structure-function coupling strength across the brain. We apply our Joint-GCN to predict age and sex of 662 participants from the public dataset of the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) based on their functional and micro-structural white-matter networks. Our results support that the proposed Joint-GCN outperforms existing multi-modal graph learning approaches for analyzing structural and functional networks.