Department of Neurology, University of Florida, USA; Wilder Center for Epilepsy Research, University of Florida, USA.
Department of Biomedical Engineering, University of Florida, USA.
Clin Neurophysiol. 2021 Jul;132(7):1550-1563. doi: 10.1016/j.clinph.2021.03.014. Epub 2021 Apr 1.
We recently proposed a spectrum-based model of the awake intracranial electroencephalogram (iEEG) (Kalamangalam et al., 2020), based on a publicly-available normative database (Frauscher et al., 2018). The latter has been expanded to include data from non-rapid eye movement (NREM) and rapid eye movement (REM) sleep (von Ellenrieder et al., 2020), and the present work extends our methods to those data.
Normalized amplitude spectra on semi-logarithmic axes from all four arousal states (wake, N2, N3 and REM) were averaged region-wise and fitted to a multi-component Gaussian distribution. A reduced model comprising five key parameters per brain region was color-coded on to cortical surface models.
The lognormal Gaussian mixture model described the iEEG accurately from all brain regions, in all sleep-wake states. There was smooth variation in model parameters as sleep and wake states yielded to each other. Specific observations unrelated to the model were that the primary cortical areas of vision, motor function and audition, in addition to the hippocampus, did not participate in the 'awakening' of the cortex during REM sleep.
Despite the significant differences in the appearance of the time-domain EEG in wakefulness and sleep, the iEEG in all arousal states was successfully described by a parametric spectral model of low dimension.
Spectral variation in the iEEG is continuous in space (across different cortical regions) and time (stage of circadian cycle), arguing for a 'continuum' hypothesis in the generative processes of sleep and wakefulness in human brain.
我们最近基于一个公开的规范数据库（Frauscher 等人，2018）提出了一种基于频谱的清醒颅内脑电图（iEEG）模型（Kalamangalam 等人，2020）。后者已扩展到包括非快速眼动（NREM）和快速眼动（REM）睡眠的数据（von Ellenrieder 等人，2020），本工作将我们的方法扩展到这些数据。
在半对数轴上对所有四个觉醒状态（清醒、N2、N3 和 REM）的归一化幅度谱进行区域平均，并拟合多分量高斯分布。每个大脑区域包含五个关键参数的简化模型以彩色编码到皮质表面模型上。
对数正态高斯混合模型准确地描述了所有睡眠-觉醒状态下所有大脑区域的 iEEG。随着睡眠和觉醒状态的相互转化，模型参数呈现出平滑的变化。与模型无关的特定观察结果是，除了海马体之外，视觉、运动功能和听觉的主要皮质区域在 REM 睡眠期间并没有参与皮质的“唤醒”。
尽管在清醒和睡眠时的时域 EEG 外观有很大差异，但所有觉醒状态的 iEEG 都可以通过低维参数频谱模型成功描述。
iEEG 的频谱变化在空间（跨越不同的皮质区域）和时间（昼夜节律周期的阶段）上是连续的，这为人类大脑中睡眠和觉醒的产生过程提供了一个“连续体”假说。
