Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome

• URL: http://arxiv.org/abs/2211.07363v1
• Date: Thu, 27 Oct 2022 23:43:34 GMT
• Title: Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
• Authors: Yueting Li, Qingyue Wei, Ehsan Adeli, Kilian M. Pohl, and Qingyu Zhao
• Abstract summary: We propose to couple the two networks of an individual by adding inter-network edges between corresponding brain regions. 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.
• Score: 11.016035878136034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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