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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Electrophysiological signatures of resting state networks in the human brain
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Electrophysiological signatures of resting state networks in the human brain

机译：人脑中静止状态网络的电生理特征

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摘要

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 electro-encephalography (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 f ractionation 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.
机译：功能性神经影像学和电生理学研究已记录了静息清醒过程中内在（而非刺激或任务诱发的）大脑活动的动态基线。此基线的特征是在离散的大脑网络中按拓扑结构组织的功能成像信号的缓慢（<0.1 Hz）波动，以及电振荡更快（1-80 Hz）。为了研究血液动力学和电振荡之间的关系，我们采用了一种完全由数据驱动的方法，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，我们确定了六个分布广泛的静止状态网络。与每个网络相关的依赖于血液氧合水平的信号波动与δ，θ，α，β和γ节奏的脑电功率变化相关。每个功能网络都具有特定的电生理特征，涉及不同的脑节律。此外，联合脑电图/功能磁共振成像分析提供了人类静息的大脑网络更好的生理功能。正如生物物理研究所表明的那样，这一结果在人类中首次支持了大规模脑网络中几种脑节律的合并。

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