Yang Defu, Shen Hui, Chen Minghan, Wang Shuai, Chen Jiazhou, Cai Hongmin, Chen Xueli, Wu Guorong, Zhu Wentao
IEEE Trans Med Imaging. 2025 Mar;44(3):1454-1467. doi: 10.1109/TMI.2024.3502545. Epub 2025 Mar 17.
Mounting evidence has revealed that functional brain networks are intrinsically dynamic, undergoing changes over time, even in the resting-state environment. Notably, recent studies have highlighted the existence of a small number of critical brain regions within each functional brain network that exhibit a flexible role in adapting the geometric pattern of brain connectivity over time, referred to as "temporal hub" regions. Therefore, the identification of these temporal hubs becomes pivotal for comprehending the mechanisms that underlie the dynamic evolution of brain connectivity. However, existing spatio-temporal hub identification methods rely on static network-based approaches, wherein each temporal hub region is independently inferred from individual time-segmented networks without considering their temporal consistency and consequently fails to align the evolution of hubs with the dynamic changes in brain states. To address this limitation, we propose a novel spatio-temporal hub identification method that fully leverages dynamic graph embedding to distinguish temporal hubs from peripheral nodes, in which dynamic graph embeddings are learned from both spatial and temporal dimensions. Specifically, to preserve the temporal consistency of evolving networks, we model the dynamic graph embedding as a physical model of time, where the network-to-network transition is mathematically expressed as a total variation of dynamic graph embedding with respect to time. Furthermore, a Grassmannian manifold optimization scheme is introduced to enhance graph embedding learning and capture the time-varying topology of brain networks. Experimental results on both synthetic and real fMRI data demonstrate superior temporal consistency in hub identification, surpassing conventional approaches.
越来越多的证据表明，功能性脑网络本质上是动态的，即使在静息状态环境下也会随时间发生变化。值得注意的是，最近的研究强调了每个功能性脑网络中存在少数关键脑区，这些脑区在随时间适应脑连接的几何模式方面发挥着灵活的作用，被称为“时间枢纽”区域。因此，识别这些时间枢纽对于理解脑连接动态演化的潜在机制至关重要。然而，现有的时空枢纽识别方法依赖于基于静态网络的方法，其中每个时间枢纽区域是从单独的时间分段网络中独立推断出来的，而不考虑它们的时间一致性，因此无法使枢纽的演化与脑状态的动态变化相匹配。为了解决这一局限性，我们提出了一种新颖的时空枢纽识别方法，该方法充分利用动态图嵌入来区分时间枢纽和外围节点，其中动态图嵌入是从空间和时间维度学习的。具体而言，为了保持演化网络的时间一致性，我们将动态图嵌入建模为一个时间物理模型，其中网络到网络的转变在数学上表示为动态图嵌入相对于时间的总变化。此外，引入了格拉斯曼流形优化方案来增强图嵌入学习并捕捉脑网络随时间变化的拓扑结构。在合成和真实功能磁共振成像数据上的实验结果表明，在枢纽识别方面具有优于传统方法的时间一致性。
