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以边缘为中心的网络神经科学揭示重叠系统级架构
作者:小柯机器人 发布时间:2020/10/22 13:41:02

美国印第安纳大学Richard F. Betzel研究组发现,人类大脑皮层的以边缘为中心的功能网络表示揭示了重叠的系统级架构。2020年10月19日的《自然-神经科学》杂志发表了这项成果。

在这项研究中,他们开发了一个以边缘为中心的网络模型,该模型生成结构“边缘时间序列”和“边缘功能连接”(eFC)。使用网络分析,他们发现,静止时,eFC在数据集之间是一致的,并且可以在多个扫描会话中的同会话内重现。他们证明聚类eFC产生的边缘群体自然将大脑分成重叠的集群,在感觉运动和注意力网络中的区域表现出最大程度的重叠。

他们表明,eFC通过感官输入的变化被系统地调控。在未来的工作中,以边缘为中心的方法可用于识别疾病的新型生物标记,表征个体变异并绘制高度解析的神经回路的结构。

据介绍,网络神经科学依赖于以节点为中心的网络模型,在该模型中,细胞、种群和区域通过解剖或功能连接相互链接。这种模型无法解释边缘之间的相互作用。

附:英文原文

Title: Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture

Author: Joshua Faskowitz, Farnaz Zamani Esfahlani, Youngheun Jo, Olaf Sporns, Richard F. Betzel

Issue&Volume: 2020-10-19

Abstract: 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.

DOI: 10.1038/s41593-020-00719-y

Source: https://www.nature.com/articles/s41593-020-00719-y

期刊信息

Nature Neuroscience:《自然—神经科学》,创刊于1998年。隶属于施普林格·自然出版集团,最新if:21.126
官方网址:https://www.nature.com/neuro/
投稿链接:https://mts-nn.nature.com/cgi-bin/main.plex