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人类大脑静息态网络的电生理特征。

Electrophysiological signatures of resting state networks in the human brain.

作者信息

Mantini D, Perrucci M G, Del Gratta C, Romani G L, Corbetta M

机构信息

Institute of Advanced Biomedical Technologies and Department of Clinical Sciences and Bio-imaging, G. D'Annunzio University Foundation, G. D'Annunzio University, Chieti 66013, Italy.

出版信息

Proc Natl Acad Sci U S A. 2007 Aug 7;104(32):13170-5. doi: 10.1073/pnas.0700668104. Epub 2007 Aug 1.

Abstract

Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.

摘要

功能神经影像学和电生理学研究已记录了静息清醒状态下内在(非刺激或任务诱发)脑活动的动态基线。该基线的特征在于功能成像信号的缓慢(<0.1 Hz)波动,这些波动在离散脑网络中进行拓扑组织,以及更快得多(1 - 80 Hz)的电振荡。为了研究血液动力学振荡与电振荡之间的关系,我们采用了一种完全数据驱动的方法,该方法结合了同步脑电图(EEG)和功能磁共振成像(fMRI)的信息。通过对fMRI数据进行独立成分分析,我们识别出六个广泛分布的静息状态网络。与每个网络相关的血氧水平依赖信号波动与δ、θ、α、β和γ节律的脑电图功率变化相关。每个功能网络都具有特定的电生理特征,该特征涉及不同脑节律的组合。此外,联合脑电图/功能磁共振成像分析对静息人脑的脑网络进行了更精细的生理细分。这一结果首次在人类中支持了生物物理学研究所暗示的大规模脑网络中几种脑节律的合并。

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Electrophysiological signatures of resting state networks in the human brain.人类大脑静息态网络的电生理特征。
Proc Natl Acad Sci U S A. 2007 Aug 7;104(32):13170-5. doi: 10.1073/pnas.0700668104. Epub 2007 Aug 1.

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