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.

本文提出将结构网络和功能网络耦合起来，提出一种联合图卷积网络（Joint-GCN）以分析结构和功能网络之间的关系，并在预测大脑白质网络年龄和性别方面进行应用，结果表明联合 - GCN 比现有的多模态图学习方法更有效。