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Recent neuroimaging studies have highlighted the importance ofnetwork-centric brain analysis, particularly with functional magnetic resonanceimaging. The emergence of Deep Neural Networks has fostered a substantialinterest in predicting clinical outcomes and categorizing individuals based onbrain networks. However, the conventional approach involving static brainnetwork analysis offers limited potential in capturing the dynamism of brainfunction. Although recent studies have attempted to harness dynamic brainnetworks, their high dimensionality and complexity present substantialchallenges. This paper proposes a novel methodology, Dynamic bRAin Transformer(DART), which combines static and dynamic brain networks for more effective andnuanced brain function analysis. Our model uses the static brain network as abaseline, integrating dynamic brain networks to enhance performance againsttraditional methods. We innovatively employ attention mechanisms, enhancingmodel explainability and exploiting the dynamic brain network's temporalvariations. The proposed approach offers a robust solution to the lowsignal-to-noise ratio of blood-oxygen-level-dependent signals, a recurringissue in direct DNN modeling. It also provides valuable insights into whichbrain circuits or dynamic networks contribute more to final predictions. Assuch, DRAT shows a promising direction in neuroimaging studies, contributing tothe comprehensive understanding of brain organization and the role of neuralcircuits.

Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis