Yang Defu, Kim Minjeong, Zhang Yu, Wu Guorong
School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China; Department of Psychiatry, University of North Carolina at Chapel Hill, USA.
Department of Computer Science, University of North Carolina at Greensboro, USA.
Med Image Anal. 2025 Apr;101:103463. doi: 10.1016/j.media.2025.103463. Epub 2025 Jan 16.
The recent advances in neuroimaging technology allow us to understand how the human brain is wired in vivo and how functional activity is synchronized across multiple regions. Growing evidence shows that the complexity of the functional connectivity is far beyond the widely used mono-layer network. Indeed, the hierarchical processing information among distinct brain regions and across multiple channels requires using a more advanced multilayer model to understand the synchronization across the brain that underlies functional brain networks. However, the principled approach for characterizing network organization in the context of multilayer topologies is largely unexplored. In this work, we present a novel multi-variate hub identification method that takes both the intra- and inter-layer network topologies into account. Specifically, we put the spotlight on the multilayer graph embeddings that allow us to separate connector hubs (connecting across network modules) with their peripheral nodes. The removal of these hub nodes breaks down the entire multilayer brain network into a set of disconnected communities. We have evaluated our novel multilayer hub identification method in task-based and resting-state functional images. Complimenting ongoing findings using mono-layer brain networks, our multilayer network analysis provides a new understanding of brain network topology that links functional connectivities with brain states and disease progression.
神经成像技术的最新进展使我们能够了解人类大脑在体内的连接方式，以及功能活动如何在多个区域同步。越来越多的证据表明，功能连接的复杂性远远超出了广泛使用的单层网络。事实上，不同脑区之间和多个通道之间的分层处理信息需要使用更先进的多层模型来理解构成功能性脑网络基础的大脑同步。然而，在多层拓扑结构的背景下表征网络组织的原则性方法在很大程度上尚未得到探索。在这项工作中，我们提出了一种新颖的多变量中心识别方法，该方法同时考虑了层内和层间的网络拓扑结构。具体来说，我们关注多层图嵌入，它使我们能够将连接中心（跨网络模块连接）与其周边节点区分开来。去除这些中心节点会将整个多层脑网络分解为一组不相连的群落。我们在基于任务和静息状态的功能图像中评估了我们新颖的多层中心识别方法。与使用单层脑网络的现有研究结果相辅相成，我们的多层网络分析为脑网络拓扑结构提供了新的理解，将功能连接与脑状态和疾病进展联系起来。
