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脑电地形图的 BOLD 相关物揭示了快速静息态网络动态。

BOLD correlates of EEG topography reveal rapid resting-state network dynamics.

机构信息

Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland.

出版信息

Neuroimage. 2010 Oct 1;52(4):1162-70. doi: 10.1016/j.neuroimage.2010.02.052. Epub 2010 Feb 24.

Abstract

Resting-state functional connectivity studies with fMRI showed that the brain is intrinsically organized into large-scale functional networks for which the hemodynamic signature is stable for about 10s. Spatial analyses of the topography of the spontaneous EEG also show discrete epochs of stable global brain states (so-called microstates), but they remain quasi-stationary for only about 100 ms. In order to test the relationship between the rapidly fluctuating EEG-defined microstates and the slowly oscillating fMRI-defined resting states, we recorded 64-channel EEG in the scanner while subjects were at rest with their eyes closed. Conventional EEG-microstate analysis determined the typical four EEG topographies that dominated across all subjects. The convolution of the time course of these maps with the hemodynamic response function allowed to fit a linear model to the fMRI BOLD responses and revealed four distinct distributed networks. These networks were spatially correlated with four of the resting-state networks (RSNs) that were found by the conventional fMRI group-level independent component analysis (ICA). These RSNs have previously been attributed to phonological processing, visual imagery, attention reorientation, and subjective interoceptive-autonomic processing. We found no EEG-correlate of the default mode network. Thus, the four typical microstates of the spontaneous EEG seem to represent the neurophysiological correlate of four of the RSNs and show that they are fluctuating much more rapidly than fMRI alone suggests.

摘要

静息态功能磁共振成像(fMRI)研究表明,大脑在本质上是由大规模的功能网络组织而成的,其血流动力学特征在约 10 秒内是稳定的。自发脑电的空间分析也显示出离散的稳定全局脑状态(所谓的微状态),但它们仅保持准静态约 100 毫秒。为了测试快速波动的 EEG 定义的微状态与缓慢振荡的 fMRI 定义的静息状态之间的关系,我们在被试闭眼静息时在扫描仪中记录了 64 通道的 EEG。常规 EEG 微状态分析确定了主导所有被试的典型四种 EEG 拓扑结构。这些图谱的时间过程与血流动力学响应函数的卷积允许对 fMRI BOLD 响应进行线性拟合,并揭示了四个独特的分布式网络。这些网络与通过常规 fMRI 组水平独立成分分析(ICA)发现的四个静息态网络(RSN)在空间上相关。这些 RSN 以前被归因于语音处理、视觉想象、注意力重新定向和主观内脏自主处理。我们没有发现默认模式网络的 EEG 相关物。因此,自发 EEG 的四个典型微状态似乎代表了四个 RSN 的神经生理相关物,并表明它们的波动速度比 fMRI 单独显示的要快得多。

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