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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >EEG microstate sequences in healthy humans at rest reveal scale-free dynamics
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EEG microstate sequences in healthy humans at rest reveal scale-free dynamics

机译：健康人静息时的脑电微状态序列显示无标度动力学

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Recent findings identified electroencephalography (EEG) microstates as the electrophysiological correlates of f MRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly chang-ing EEG microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas microstates can be considered the "atoms of thoughts," the shortest constituting elements of cog-nition, they carry a dynamic signature that is reminiscent at charac-teristic timescales up to multiple seconds. The scale-free dynamics of the microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.
机译：最近的发现将脑电图（EEG）微状态确定为f MRI静止状态网络的电生理相关性。微状态定义为短时间内（100 ms），在此期间EEG头皮的地形保持准稳定。也就是说，整体地形是固定的，但强度可能会变化并且极性会反转。微状态代表了全球功能性大脑网络内的亚秒相干激活。出人意料的是，这些快速变化的脑电微状态与构成强大的时间平滑滤波器的血液动力学响应函数卷积后，与功能磁共振成像静息状态网络中的活动显着相关。我们在这里假设微状态序列应该显示无标度，自相似的动力学来解释这种显着的影响，因此微状态时间序列在很长的时间范围内显示出依存关系。为此，我们部署了基于小波的分形分析，该分析可确定无标度行为。我们发现强大的统计证据表明，微状态序列在覆盖256毫秒至16秒范围的六个二进阶尺度上是无尺度的。当对局部微状态标签进行混洗时，保持了远程依赖性的程度，但是在使微状态持续时间相等时，白噪声变得难以区分，这表明时间动态是其关键特征。这些结果促进了对大脑规模的神经元网络模型（例如全局工作空间模型）的时间动力学的理解。微观状态可以被认为是“思想的原子”，是认识的最短的构成要素，而微观状态则具有动态的特征，这种特征在长达数秒的特征时间尺度上令人联想到。微观状态的无标度动力学可能是大脑功能网络快速重组和适应的基础。

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