DynBrainGNN: Towards Spatio-Temporal Interpretable Graph Neural Network Based on Dynamic Brain Connectome for Psychiatric Diagnosis

Mounting evidence has highlighted the involvement of altered functional connectivity (FC) within resting-state functional networks in psychiatric disorder. Considering the fact that the FCs of the brain can be viewed as a network, graph neural networks (GNNs) have recently been applied to develop useful diagnostic tools and analyze the brain connectome, providing new insights into the functional mechanisms of the psychiatric disorders. Despite promising results, existing GNN-based diagnostic models are usually unable to incorporate the dynamic properties of the FC network, which fluctuates over time. Furthermore, it is difficult to produce temporal interpretability and obtain temporally attended brain markers elucidating the underlying neural mechanisms and diagnostic decisions. These issues hinder their possible clinical applications for the diagnosis and intervention of psychiatric disorder. In this study, we propose DynBrainGNN, a novel GNN architecture to analysis dynamic brain connectome, by leveraging dynamic variational autoencoders (DVAE) and spatio-temporal attention. DynBrainGNN is capable of obtaining disease-specific dynamic brain network patterns and quantifying the temporal properties of brain. We conduct experiments on three distinct real-world psychiatric datasets, and our results indicate that DynBrainGNN achieves exceptional performance. Moreover, DynBrainGNN effectively identifies clinically meaningful brain markers that align with current neuro-scientific knowledge.