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复杂脑网络中的通信动态。

Communication dynamics in complex brain networks.

机构信息

Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405, USA.

Montreal Neurological Institute, McGill University, Montreal, Quebec H3A 2B4, Canada.

出版信息

Nat Rev Neurosci. 2017 Dec 14;19(1):17-33. doi: 10.1038/nrn.2017.149.

Abstract

Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.

摘要

神经元信号传递和通讯是大脑活动和功能的基础。网络科学方法在建模和分析网络通讯动态方面已经被证明对于模拟功能连接和预测网络状态非常有用。这篇综述调查了脑网络通讯动态的重要方面。我们首先概述了一个概念框架,将通讯动态视为结构连接和功能连接的经验领域之间的必要联系。然后,我们考虑了结构网络的不同局部和全局拓扑属性如何支持网络通讯的潜在模式,以及网络拓扑和动态模型之间的相互作用如何提供额外的见解和限制。最后,我们提出通讯动态可能作为有效连接的潜在生成模型,并且可以深入了解脑网络转换和处理信息的机制。

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