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Identifying multilayer network hub by graph representation learning

delete2025-04-01
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杨德富 封面图
杨德富 (Defu Yang) *
M
Minjeong Kim
Y
Yu Zhang
DOI：10.1016/j.media.2025.103463delete
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摘要

摘要

En 中文
近期神经成像技术的进步使我们能够理解人类大脑在体是如何连接的，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超广泛使用的单层网络。实际上，不同脑区之间及跨多个通道的层次化处理信息需要采用更先进的分层模型来理解支持功能脑网络的大脑同步。然而，在多层拓扑背景下表征网络组织的原则性方法在很大程度上尚未探索。在本工作中，我们提出了一种新型多变量枢纽识别方法，同时考虑了层内和层间网络拓扑。具体而言，我们将重点放在多层图嵌入上，使其能够区分连接枢纽（跨网络模块连接）及其周边节点。移除这些枢纽节点会使整个多层脑网络分解为一组断开连接的社区。我们已在任务依赖和静息态功能影像中评估了本新型多层枢纽识别方法。与使用单层脑网络的研究发现相补充，我们的多层网络分析为理解脑网络拓扑提供了新的视角，将功能连接与脑状态和疾病进展联系起来。
Keyword:
Multilayer network
Graph embedding
Representation learning
Brain network
Hub identification

期刊

Medical Image Analysis 封面图
Medical Image Analysis
IF:
11.8
论文数:
3.8K
被引数:
2.4W

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