Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
Sleep. 2018 Jul 1;41(7). doi: 10.1093/sleep/zsy079.
Sleep-wake history, wake behaviors, lighting conditions, and circadian time influence sleep, but neither their relative contribution nor the underlying mechanisms are fully understood. The dynamics of electroencephalogram (EEG) slow-wave activity (SWA) during sleep can be described using the two-process model, whereby the parameters of homeostatic Process S are estimated using empirical EEG SWA (0.5-4 Hz) in nonrapid eye movement sleep (NREMS), and the 24 hr distribution of vigilance states. We hypothesized that the influence of extrinsic factors on sleep homeostasis, such as the time of day or wake behavior, would manifest in systematic deviations between empirical SWA and model predictions. To test this hypothesis, we performed parameter estimation and tested model predictions using NREMS SWA derived from continuous EEG recordings from the frontal and occipital cortex in mice. The animals showed prolonged wake periods, followed by consolidated sleep, both during the dark and light phases, and wakefulness primarily consisted of voluntary wheel running, learning a new motor skill or novel object exploration. Simulated SWA matched empirical levels well across conditions, and neither waking experience nor time of day had a significant influence on the fit between data and simulation. However, we consistently observed that Process S declined during sleep significantly faster in the frontal than in the occipital area of the neocortex. The striking resilience of the model to specific wake behaviors, lighting conditions, and time of day suggests that intrinsic factors underpinning the dynamics of Process S are robust to extrinsic influences, despite their major role in shaping the overall amount and distribution of vigilance states across 24 hr.
睡眠-觉醒史、觉醒行为、光照条件和昼夜节律时间都会影响睡眠,但这些因素的相对贡献及其潜在机制尚不完全清楚。睡眠期间脑电图(EEG)慢波活动(SWA)的动力学可以用双过程模型来描述,其中通过非快速眼动睡眠(NREMS)中的经验 EEG SWA(0.5-4 Hz)和警觉状态的 24 小时分布来估计内稳态过程 S 的参数。我们假设,外在因素对睡眠内稳态的影响,如一天中的时间或觉醒行为,会表现为经验 SWA 和模型预测之间存在系统偏差。为了验证这一假设,我们使用来自小鼠额皮质和枕皮质的连续 EEG 记录进行了参数估计,并测试了模型预测。动物在黑暗和光照阶段都会表现出长时间的觉醒期,随后是巩固的睡眠期,而觉醒期主要由自愿轮跑、学习新的运动技能或探索新的物体组成。模拟 SWA 在所有条件下与经验水平都很好地匹配,且觉醒经历和一天中的时间都对数据和模拟之间的拟合没有显著影响。然而,我们一致观察到,在新皮质的额区,过程 S 在睡眠期间的下降速度明显快于枕区。模型对特定觉醒行为、光照条件和一天中的时间具有很强的适应性,这表明尽管它们在塑造 24 小时内警觉状态的总体数量和分布方面起着主要作用,但支撑过程 S 动力学的内在因素对外部影响具有很强的弹性。