Exploring Spatio-temporal Interpretable Dynamic Brain Function with Transformer for Brain Disorder Diagnosis

The dynamic variation in the spatio-temporal organizational patterns of brain functional modules (BFMs) associated with brain disorders remains unclear. To solve this issue, we propose an end-to-end transformer-based framework for sufficiently learning the spatio-temporal characteristics of BFMs and exploring the interpretable variation related to brain disorders. Specifically, the proposed model incorporates a supervisory guidance spatio-temporal clustering strategy for automatically identifying the BFMs with the dynamic temporal-varying weights and a multi-channel self-attention mechanism with topology-aware projection for sufficiently exploring the temporal variation and spatio-temporal representation. The experimental results on the diagnosis of Major Depressive Disorder (MDD) and Bipolar Disorder (BD) indicate that our model achieves state-of-the-art performance. Moreover, our model is capable of identifying the spatio-temporal patterns of brain activity and providing evidence associated with brain disorders. Our code is available at https://github.com/llt1836/BISTformer .