Department of Biology, National Center for Behavioral Genomics and Volen Center for Complex Systems, Brandeis University, Waltham, MA 02454-9110.
Neuroscience Discovery, Biogen, Cambridge, MA 02142.
Proc Natl Acad Sci U S A. 2020 May 5;117(18):10024-10034. doi: 10.1073/pnas.1917573117. Epub 2020 Apr 17.
Sleep pressure and sleep depth are key regulators of wake and sleep. Current methods of measuring these parameters in have low temporal resolution and/or require disrupting sleep. Here we report analysis tools for high-resolution, noninvasive measurement of sleep pressure and depth from movement data. Probability of initiating activity, P(Wake), measures sleep depth while probability of ceasing activity, P(Doze), measures sleep pressure. In vivo and computational analyses show that P(Wake) and P(Doze) are largely independent and control the amount of total sleep. We also develop a Hidden Markov Model that allows visualization of distinct sleep/wake substates. These hidden states have a predictable relationship with P(Doze) and P(Wake), suggesting that the methods capture the same behaviors. Importantly, we demonstrate that both the Doze/Wake probabilities and the sleep/wake substates are tied to specific biological processes. These metrics provide greater mechanistic insight into behavior than measuring the amount of sleep alone.
睡眠压力和睡眠深度是调节觉醒和睡眠的关键因素。目前测量这些参数的方法时间分辨率较低，或者需要打断睡眠。在这里，我们报告了用于从运动数据中进行高分辨率、非侵入性测量睡眠压力和深度的分析工具。活动起始概率 P(Wake) 衡量睡眠深度，而活动停止概率 P(Doze) 衡量睡眠压力。体内和计算分析表明，P(Wake) 和 P(Doze) 基本独立，控制总睡眠时间的长短。我们还开发了一个隐马尔可夫模型，允许可视化不同的睡眠/觉醒子状态。这些隐藏状态与 P(Doze) 和 P(Wake) 有可预测的关系，表明这些方法捕捉到了相同的行为。重要的是，我们证明了睡眠/清醒概率和睡眠/清醒子状态都与特定的生物学过程有关。这些指标比单独测量睡眠时间更能深入了解行为的机制。
