神经科学
GSM演进的增强数据速率
生物神经网络
系统神经科学
功能连接
感觉系统
节点(物理)
网络体系结构
聚类分析
计算机科学
人工智能
生物
计算机网络
中枢神经系统
少突胶质细胞
髓鞘
结构工程
工程类
作者
Joshua Faskowitz,Farnaz Zamani Esfahlani,Youngheun Jo,Olaf Sporns,Richard F. Betzel
标识
DOI:10.1038/s41593-020-00719-y
摘要
Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs ‘edge time series’ and ‘edge functional connectivity’ (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits. The authors present an edge-centric model of brain connectivity. Edge networks are stable across datasets, and their structure can be modulated by sensory input. When clustered, edge networks yield pervasively overlapping functional modules.
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