Department of Psychology, University of California, Irvine, Irvine, California, United States of America.
Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America.
PLoS One. 2018 Apr 11;13(4):e0194604. doi: 10.1371/journal.pone.0194604. eCollection 2018.
The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.
睡眠阶段在一夜之间的模式（睡眠结构）受到生物、行为和临床变量的影响。然而，睡眠结构的传统测量方法，如阶段比例，无法捕捉睡眠的动态变化。在这里，我们量化了个体差异对睡眠结构动态的影响，并确定了哪些因素或因素集最能从当前阶段信息预测下一个睡眠阶段。我们使用来自非临床人群的 3202 个夜晚的大型数据集，研究了年龄、性别、体重指数、一天中的时间和睡眠时间对静态（例如阶段中的分钟数、睡眠效率）和动态睡眠结构测量（例如转换概率和阶段持续时间分布）的影响。多层次回归显示，性别对所有非快速眼动（NREM）阶段的持续时间都有影响，而年龄对睡眠后觉醒（WASO）和慢波睡眠（SWS）分钟数呈曲线关系。贝叶斯网络模型揭示了睡眠结构取决于一天中的时间、总睡眠时间、年龄和性别，但与 BMI 无关。老年人，尤其是男性，第二阶段、慢波睡眠的持续时间更短（更碎片化），他们进入这些阶段的频率也更低。此外，我们还表明，通过前 2 个阶段和年龄可以最优地预测下一个睡眠阶段及其持续时间。我们的研究结果表明，大数据和贝叶斯网络方法在量化正常睡眠的静态和动态结构方面具有潜在的优势。
