Universidad de la República, Departamento de Fisiología de Facultad de Medicina, 11200 Montevideo, Uruguay.
Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina; Universidad Autónoma de Entre Ríos (UADER), Entre Ríos, Argentina; Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL), Santa Fe, Argentina.
Neuroscience. 2022 Jul 1;494:1-11. doi: 10.1016/j.neuroscience.2022.04.025. Epub 2022 May 6.
Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.
最近，人们使用新的复杂度度量方法对睡眠-觉醒状态进行了分析，这为 EEG 按频带进行的经典分析提供了补充。这种新方法一致表明，在慢波睡眠期间，脑电图的复杂度会降低，但目前尚不清楚皮质振荡如何塑造这些复杂度变化。在这项工作中，我们分析了脑信号的频率内容如何影响自由活动大鼠的复杂度估计。我们发现，低频谱 - 包括 Delta、Theta 和 Sigma 频段 - 驱动着睡眠-觉醒状态下的复杂度变化。这是因为低频振荡源于神经元群体模式，正如我们通过从微观、中观和宏观记录中恢复睡眠-觉醒周期期间的复杂度变化所表明的那样。此外，我们发现较低的频率揭示了整个新皮层的同步模式，例如 REM 睡眠期间发生的感觉运动解耦。总的来说，我们的研究表明，脑电图的低频对于在皮质尺度上塑造睡眠-觉醒状态的复杂性至关重要。
