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.

翻译：近年来，神经影像学研究强调了以网络为中心的脑分析的重要性，特别是在功能磁共振成像领域。深度神经网络的出现极大地推动了基于脑网络预测临床结果和个体分类的研究。然而，传统的静态脑网络分析方法在捕捉脑功能动态变化方面能力有限。尽管近期研究尝试利用动态脑网络，但其高维度和复杂性带来了巨大挑战。本文提出一种新方法——动态脑Transformer（DART），将静态与动态脑网络相结合，以实现更有效、更细致的脑功能分析。我们的模型以静态脑网络为基准，集成动态脑网络以提升相较于传统方法的性能。我们创新性地采用注意力机制，增强模型可解释性并挖掘动态脑网络的时间变化特征。该方法有效解决了血氧水平依赖信号低信噪比这一直接进行深度神经网络建模时的常见问题，同时为揭示哪些脑回路或动态网络对最终预测贡献更大提供了重要见解。因此，DART为神经影像学研究指明了有前景的方向，有助于全面理解脑组织及神经回路的功能角色。