Dynamic Brain Transformer with Multi-level Attention for Functional
  Brain Network Analysis
Xuan Kan Antonio Aodong Chen Gu Hejie Cui Ying Guo Carl Yang
Recent neuroimaging studies have highlighted the importance of
network-centric brain analysis, particularly with functional magnetic resonance
imaging. The emergence of Deep Neural Networks has fostered a substantial
interest in predicting clinical outcomes and categorizing individuals based on
brain networks. However, the conventional approach involving static brain
network analysis offers limited potential in capturing the dynamism of brain
function. Although recent studies have attempted to harness dynamic brain
networks, their high dimensionality and complexity present substantial
challenges. This paper proposes a novel methodology, Dynamic bRAin Transformer
(DART), which combines static and dynamic brain networks for more effective and
nuanced brain function analysis. Our model uses the static brain network as a
baseline, integrating dynamic brain networks to enhance performance against
traditional methods. We innovatively employ attention mechanisms, enhancing
model explainability and exploiting the dynamic brain network's temporal
variations. The proposed approach offers a robust solution to the low
signal-to-noise ratio of blood-oxygen-level-dependent signals, a recurring
issue in direct DNN modeling. It also provides valuable insights into which
brain circuits or dynamic networks contribute more to final predictions. As
such, DRAT shows a promising direction in neuroimaging studies, contributing to
the comprehensive understanding of brain organization and the role of neural
circuits.