School of Physics, University of Sydney, Sydney, Australia.
Cooperative Research Centre for Alertness, Safety, and Productivity, Melbourne, Australia.
J Biol Rhythms. 2018 Apr;33(2):203-218. doi: 10.1177/0748730418758454.
A model of arousal dynamics is applied to predict objective performance and subjective sleepiness measures, including lapses and reaction time on a visual Performance Vigilance Test (vPVT), performance on a mathematical addition task (ADD), and the Karolinska Sleepiness Scale (KSS). The arousal dynamics model is comprised of a physiologically based flip-flop switch between the wake- and sleep-active neuronal populations and a dynamic circadian oscillator, thus allowing prediction of sleep propensity. Published group-level experimental constant routine (CR) and forced desynchrony (FD) data are used to calibrate the model to predict performance and sleepiness. Only the studies using dim light (<15 lux) during alertness measurements and controlling for sleep and entrainment before the start of the protocol are selected for modeling. This is done to avoid the direct alerting effects of light and effects of prior sleep debt and circadian misalignment on the data. The results show that linear combination of circadian and homeostatic drives is sufficient to predict dynamics of a variety of sleepiness and performance measures during CR and FD protocols, with sleep-wake cycles ranging from 20 to 42.85 h and a 2:1 wake-to-sleep ratio. New metrics relating model outputs to performance and sleepiness data are developed and tested against group average outcomes from 7 (vPVT lapses), 5 (ADD), and 8 (KSS) experimental protocols, showing good quantitative and qualitative agreement with the data (root mean squared error of 0.38, 0.19, and 0.35, respectively). The weights of the homeostatic and circadian effects are found to be different between the measures, with KSS having stronger homeostatic influence compared with the objective measures of performance. Using FD data in addition to CR data allows us to challenge the model in conditions of both acute sleep deprivation and structured circadian misalignment, ensuring that the role of the circadian and homeostatic drives in performance is properly captured.
一个觉醒动力学模型被应用于预测客观表现和主观困倦测量，包括视觉警觉性测试（vPVT）的失误和反应时间、数学加法任务（ADD）的表现以及卡罗林斯卡困倦量表（KSS）的得分。该觉醒动力学模型由一个在觉醒和睡眠活跃神经元群体之间基于生理的翻转开关和一个动态的昼夜节律振荡器组成，从而允许预测睡眠倾向。发表的组水平实验恒常程序（CR）和强制去同步（FD）数据被用于校准模型以预测表现和困倦。仅选择在警觉测量期间使用低光照（<15 勒克斯）且在协议开始前控制睡眠和同步的研究进行建模。这是为了避免光的直接警觉效应以及先前的睡眠债务和昼夜节律失准对数据的影响。结果表明，昼夜节律和内稳态驱动的线性组合足以预测 CR 和 FD 协议期间各种困倦和表现测量的动力学，睡眠-觉醒周期范围从 20 到 42.85 小时，并且有 2:1 的觉醒到睡眠比。开发了新的指标，将模型输出与表现和困倦数据相关联，并与来自 7（vPVT 失误）、5（ADD）和 8（KSS）实验协议的组平均结果进行比较，结果显示与数据具有良好的定量和定性一致性（均方根误差分别为 0.38、0.19 和 0.35）。发现这些措施之间的内稳态和昼夜节律效应的权重不同，与客观表现测量相比，KSS 具有更强的内稳态影响。除了 CR 数据外，还使用 FD 数据，这使得我们能够在急性睡眠剥夺和结构化昼夜节律失准的条件下对模型提出挑战，确保昼夜节律和内稳态驱动在表现中的作用得到正确捕捉。
