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.

本文提出了一种新方法，Dynamic bRAin Transformer（DART），通过结合静态和动态脑网络进行更有效和细致的脑功能分析，使用静态脑网络作为基线，整合动态脑网络以提高性能，并采用注意机制提高模型的可解释性和利用动态脑网络的时间变化，该方法对血氧水平依赖信号的低信噪比问题提供了一个强大的解决方案，同时为最终预测做出有价值的洞察，因此，DRAT 在神经影像研究中展示出了一个有希望的方向，有助于全面理解脑机构和神经回路的作用。