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大规模的大脑功能模块化反映在人类非快速眼动睡眠周期中的慢脑电节律中。

Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle.

机构信息

Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany.

出版信息

Neuroimage. 2013 Apr 15;70:327-39. doi: 10.1016/j.neuroimage.2012.12.073. Epub 2013 Jan 9.

Abstract

Large-scale brain functional networks (measured with functional magnetic resonance imaging, fMRI) are organized into separated but interacting modules, an architecture supporting the integration of distinct dynamical processes. In this work we study how the aforementioned modular architecture changes with the progressive loss of vigilance occurring in the descent to deep sleep and we examine the relationship between the ensuing slow electroencephalographic rhythms and large-scale network modularity as measured with fMRI. Graph theoretical methods are used to analyze functional connectivity graphs obtained from fifty-five participants at wakefulness, light and deep sleep. Network modularity (a measure of functional segregation) was found to increase during deeper sleep stages but not in light sleep. By endowing functional networks with dynamical properties, we found a direct link between increased electroencephalographic (EEG) delta power (1-4 Hz) and a breakdown of inter-modular connectivity. Both EEG slowing and increased network modularity were found to quickly decrease during awakenings from deep sleep to wakefulness, in a highly coordinated fashion. Studying the modular structure itself by means of a permutation test, we revealed different module memberships when deep sleep was compared to wakefulness. Analysis of node roles in the modular structure revealed an increase in the number of locally well-connected nodes and a decrease in the number of globally well-connected hubs, which hinders interactions between separated functional modules. Our results reveal a well-defined sequence of changes in brain modular organization occurring during the descent to sleep and establish a close parallel between modularity alterations in large-scale functional networks (accessible through whole brain fMRI recordings) and the slowing of scalp oscillations (visible on EEG). The observed re-arrangement of connectivity might play an important role in the processes underlying loss of vigilance and sensory awareness during deep sleep.

摘要

大规模脑功能网络(通过功能磁共振成像 fMRI 测量)组织成分离但相互作用的模块,这种架构支持不同动态过程的整合。在这项工作中,我们研究了在向深度睡眠下降过程中,上述模块化架构如何发生变化,以及随之而来的慢脑电节律与 fMRI 测量的大规模网络模块性之间的关系。使用图论方法分析了来自 55 名参与者在清醒、浅睡眠和深睡眠时的功能连接图。研究发现,网络模块性(功能分离的度量)在更深的睡眠阶段增加,但在浅睡眠中没有增加。通过赋予功能网络动态特性,我们发现脑电图(EEG)δ功率(1-4 Hz)增加与模块间连接中断之间存在直接联系。在从深度睡眠到清醒的唤醒过程中,EEG 减慢和网络模块性增加都被发现以高度协调的方式迅速减少。通过对排列测试进行模块结构本身的研究,我们发现与清醒状态相比,深度睡眠时模块成员不同。对模块结构中节点角色的分析揭示了局部连接良好的节点数量增加和全局连接良好的枢纽节点数量减少,这阻碍了分离功能模块之间的相互作用。我们的研究结果揭示了在向睡眠下降过程中大脑模块化组织发生的一系列明确变化,并在大规模功能网络的模块性改变(通过全脑 fMRI 记录获得)与头皮振荡(脑电图上可见)的减慢之间建立了密切的平行关系。观察到的连接重新排列可能在深度睡眠期间警觉性和感官意识丧失的过程中发挥重要作用。

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