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瞬时时空连通性网络映射大脑功能系统中的通讯通路。

Transient networks of spatio-temporal connectivity map communication pathways in brain functional systems.

机构信息

Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne 1011, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.

Signal Processing Laboratory 2 (LTS2), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne 1015, Switzerland.

出版信息

Neuroimage. 2017 Jul 15;155:490-502. doi: 10.1016/j.neuroimage.2017.04.015. Epub 2017 Apr 12.

Abstract

The study of brain dynamics enables us to characterize the time-varying functional connectivity among distinct neural groups. However, current methods suffer from the absence of structural connectivity information. We propose to integrate infra-slow neural oscillations and anatomical-connectivity maps, as derived from functional and diffusion MRI, in a multilayer-graph framework that captures transient networks of spatio-temporal connectivity. These networks group anatomically wired and temporary synchronized brain regions and encode the propagation of functional activity on the structural connectome. In a group of 71 healthy subjects, we find that these transient networks demonstrate power-law spatial and temporal size, globally organize into well-known functional systems and describe wave-like trajectories of activation across anatomically connected regions. Within the transient networks, activity propagates through polysynaptic paths that include selective ensembles of structural connections and differ from the structural shortest paths. In the light of the communication-through-coherence principle, the identified spatio-temporal networks could encode communication channels' selection and neural assemblies, which deserves further attention. This work contributes to the understanding of brain structure-function relationships by considering the time-varying nature of resting-state interactions on the axonal scaffold, and it offers a convenient framework to study large-scale communication mechanisms and functional dynamics.

摘要

大脑动力学研究使我们能够描述不同神经群之间随时间变化的功能连接。然而,目前的方法缺乏结构连接信息。我们建议将亚慢波神经振荡和解剖连接图谱整合到一个多层图框架中,该框架可以捕捉到时空连接的瞬态网络。这些网络将解剖上有线的和临时同步的脑区分组,并对结构连接组上的功能活动传播进行编码。在一组 71 名健康受试者中,我们发现这些瞬态网络表现出幂律的空间和时间大小,全局组织成众所周知的功能系统,并描述了在解剖上连接的区域之间的激活的波动轨迹。在瞬态网络中,活动通过包括结构连接的选择性集合的多突触路径传播,并且与结构最短路径不同。根据相干性传递原理,所识别的时空网络可以编码通信通道的选择和神经集合,这值得进一步关注。这项工作通过考虑在轴突支架上静息状态相互作用的时变性质,为理解大脑结构-功能关系做出了贡献,并提供了一个方便的框架来研究大规模的通信机制和功能动力学。

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