Spatio-Temporal Attention Graph Convolution Network for Functional Connectome Classification

Numerous evidence has demonstrated the pathophysiology of a number of mental disorders is intimately associated with abnormal changes of dysfunctional integration of brain network. Functional connectome (FC) exhibits a strong discriminative power for mental disorder identification. However, existing methods are insufficient for modeling both spatial correlation and temporal dynamics of FC. In this study, we propose a novel Spatio-Temporal Attention Graph Convolution Network (STAGCN) for FC classification. In spatial domain, we develop attention enhanced graph convolutional network to take advantage of brain regions' topological features. Moreover, a novel multi-head self-attention approach is proposed to capture the temporal relationships among different dynamic FC. Extensive experiments on two tasks of mental disorder diagnosis demonstrate the superior performance of the proposed STAGCN.