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人类大脑中的分隔和整合的网络属性。

Network attributes for segregation and integration in the human brain.

机构信息

Indiana University, Department of Psychological and Brain Sciences, Bloomington, IN 47405, United States.

出版信息

Curr Opin Neurobiol. 2013 Apr;23(2):162-71. doi: 10.1016/j.conb.2012.11.015. Epub 2013 Jan 4.

Abstract

Network studies of large-scale brain connectivity have begun to reveal attributes that promote the segregation and integration of neural information: communities and hubs. Network communities are sets of regions that are strongly interconnected among each other while connections between members of different communities are less dense. The clustered connectivity of network communities supports functional segregation and specialization. Network hubs link communities to one another and ensure efficient communication and information integration. This review surveys a number of recent reports on network communities and hubs, and their role in integrative processes. An emerging focus is the shifting balance between segregation and integration over time, which manifest in continuously changing patterns of functional interactions between regions, circuits and systems.

摘要

网络研究大规模脑连接开始揭示促进神经信息分离和整合的属性

社区和枢纽。网络社区是一组区域,它们之间彼此强烈相互连接,而不同社区成员之间的连接密度较低。网络社区的聚类连接支持功能分离和专业化。网络枢纽将社区彼此连接,并确保有效的通信和信息整合。这篇综述调查了一些关于网络社区和枢纽及其在整合过程中的作用的最新报告。一个新兴的焦点是随着时间的推移,分离和整合之间的平衡转移,这表现在区域、电路和系统之间的功能相互作用的不断变化的模式中。

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