Temporal Cross-Graph Network for Brain Functional Activity Prediction

Prediction of brain functional activity is of great significance for neuroscience research. The brain functional activities at different regions are highly related, and their relationships can be captured with functional connectivity and structural connectivity. The existing works are challenging to integrate two connectivity information for functional activity prediction. In this paper, we propose a Temporal Cross-Graph Network (TCGN) for predicting brain functional activity, which can comprehensively exploit multi-modal spatial dependence and temporal patterns. In particular, a novel cross-graph convolution module is developed to capture the spatial features of brain structural and functional connectivity. A temporal fusion module is designed to learn the pattern of dynamic functional connectivity to guide the prediction. Specially, a multi-task loss function is proposed to incorporate functional activity and dynamic functional connectivity. Extensive experiments on the Human Connectome Project dataset demonstrate the effectiveness of the proposed framework.