  Multi-modal neuroimaging technology has greatlly facilitated the efficiency
and diagnosis accuracy, which provides complementary information in discovering
objective disease biomarkers. Conventional deep learning methods, e.g.
convolutional neural networks, overlook relationships between nodes and fail to
capture topological properties in graphs. Graph neural networks have been
proven to be of great importance in modeling brain connectome networks and
relating disease-specific patterns. However, most existing graph methods
explicitly require known graph structures, which are not available in the
sophisticated brain system. Especially in heterogeneous multi-modal brain
networks, there exists a great challenge to model interactions among brain
regions in consideration of inter-modal dependencies. In this study, we propose
a Multi-modal Dynamic Graph Convolution Network (MDGCN) for structural and
functional brain network learning. Our method benefits from modeling
inter-modal representations and relating attentive multi-model associations
into dynamic graphs with a compositional correspondence matrix. Moreover, a
bilateral graph convolution layer is proposed to aggregate multi-modal
representations in terms of multi-modal associations. Extensive experiments on
three datasets demonstrate the superiority of our proposed method in terms of
disease classification, with the accuracy of 90.4%, 85.9% and 98.3% in
predicting Mild Cognitive Impairment (MCI), Parkinson's disease (PD), and
schizophrenia (SCHZ) respectively. Furthermore, our statistical evaluations on
the correspondence matrix exhibit a high correspondence with previous evidence
of biomarkers.
