Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome
This work addresses a fundamental problem in systems neuroscience for researchers analyzing brain connectomes, though it is incremental as it extends existing GCN methods to joint network analysis.
The paper tackled the problem of relating brain structural and functional networks by proposing a Joint-GCN that couples these networks with learnable inter-network edges, and it outperformed existing multi-modal graph learning approaches in predicting age and sex for 662 participants from the NCANDA dataset.
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.