Interpretable Dynamic Brain Network Analysis with Functional and Structural Priors.

The dynamic functional brain network (DFBN) inherently captures topological changes in brain connectivity pattern during activity, attracting increasing attention for detecting brain disorders. However, most current DFBN analysis methods rely on data-driven modeling and ignore crucial prior knowledge of brain structure and function, resulting in weak interpretability of models. Furthermore, effectively extracting dynamic topological features from DFBN is still a challenging issue, due to its intricate spatio-temporal features coupling. In this paper, we propose an interpretable spatio-temporal tensor graph convolutional network for DFBN analysis. Firstly, by incorporating functional and structural priors into the construction of DBFN, we develop a hierarchical DBFN representation with brain region clustering that effectively captures the spatio-temporal topology among subnetworks. Secondly, we design a tensor graph convolutional network with both intra-graph propagation and inter-graph propagation to simultaneously extract the spatio-temporal features from the hierarchical DFBN. Additionally, we derive a functional subnetwork constraint to enhance the consistency within subnetworks and the differences between subnetworks, which guides the learned features to better reflect the topology prior of the brain network. Finally, self-attention is employed to fuse the learned dynamic topological features of different subnetworks for classification. Experimental results on epilepsy, ADNI and ABIDE datasets demonstrate that our method achieves competitive diagnostic performance and offers network-level interpretability for brain disease diagnosis.