Department of Psychiatric Neurophysiology, University Hospital of Psychiatry and University of Bern, Bern, Switzerland.
PLoS One. 2010 Sep 22;5(9):e12945. doi: 10.1371/journal.pone.0012945.
fMRI Resting State Networks (RSNs) have gained importance in the present fMRI literature. Although their functional role is unquestioned and their physiological origin is nowadays widely accepted, little is known about their relationship to neuronal activity. The combined recording of EEG and fMRI allows the temporal correlation between fluctuations of the RSNs and the dynamics of EEG spectral amplitudes. So far, only relationships between several EEG frequency bands and some RSNs could be demonstrated, but no study accounted for the spatial distribution of frequency domain EEG.
METHODOLOGY/PRINCIPAL FINDINGS: In the present study we report on the topographic association of EEG spectral fluctuations and RSN dynamics using EEG covariance mapping. All RSNs displayed significant covariance maps across a broad EEG frequency range. Cluster analysis of the found covariance maps revealed the common standard EEG frequency bands. We found significant differences between covariance maps of the different RSNs and these differences depended on the frequency band.
CONCLUSIONS/SIGNIFICANCE: Our data supports the physiological and neuronal origin of the RSNs and substantiates the assumption that the standard EEG frequency bands and their topographies can be seen as electrophysiological signatures of underlying distributed neuronal networks.
功能磁共振成像静息态网络 (RSNs) 在目前的 fMRI 文献中变得越来越重要。尽管它们的功能作用毋庸置疑,其生理起源也已被广泛接受,但人们对它们与神经元活动的关系知之甚少。EEG 和 fMRI 的联合记录允许 RSNs 的波动与 EEG 频谱幅度的动力学之间进行时间相关。到目前为止,仅证明了几个 EEG 频带与一些 RSN 之间的关系,但没有研究考虑到频域 EEG 的空间分布。
方法/主要发现:在本研究中,我们使用 EEG 协方差映射报告了 EEG 频谱波动与 RSN 动力学的地形关联。所有 RSN 在广泛的 EEG 频率范围内都显示出显著的协方差图。对发现的协方差图进行聚类分析揭示了常见的标准 EEG 频带。我们发现不同 RSN 的协方差图之间存在显著差异,并且这些差异取决于频带。
结论/意义:我们的数据支持 RSN 的生理和神经元起源,并证实了标准 EEG 频带及其地形可以被视为潜在分布式神经元网络的电生理特征的假设。