Behn Cecilia G Diniz, Brown Emery N, Scammell Thomas E, Kopell Nancy J
Department of Mathematics and Center for BioDynamics, Boston University, MA, USA.
J Neurophysiol. 2007 Jun;97(6):3828-40. doi: 10.1152/jn.01184.2006. Epub 2007 Apr 4.
Recent work in experimental neurophysiology has identified distinct neuronal populations in the rodent brain stem and hypothalamus that selectively promote wake and sleep. Mutual inhibition between these cell groups has suggested the conceptual model of a sleep-wake switch that controls transitions between wake and sleep while minimizing time spent in intermediate states. By combining wake- and sleep-active populations with populations governing transitions between different stages of sleep, a "sleep-wake network" of neuronal populations may be defined. To better understand the dynamics inherent in this network, we created a model sleep-wake network composed of coupled relaxation oscillation equations. Mathematical analysis of the deterministic model provides insight into the dynamics underlying state transitions and predicts mechanisms for each transition type. With the addition of noise, the simulated sleep-wake behavior generated by the model reproduces many qualitative and quantitative features of mouse sleep-wake behavior. In particular, the existence of simulated brief awakenings is a unique feature of the model. In addition to capturing the experimentally observed qualitative difference between brief and sustained wake bouts, the model suggests distinct network mechanisms for the two types of wakefulness. Because circadian and other factors alter the fine architecture of sleep-wake behavior, this model provides a novel framework to explore dynamical principles that may underlie normal and pathologic sleep-wake physiology.
近期实验神经生理学的研究已经在啮齿动物的脑干和下丘脑确定了不同的神经元群，它们分别选择性地促进清醒和睡眠。这些细胞群之间的相互抑制作用提示了一种睡眠-觉醒开关的概念模型，该模型控制着清醒和睡眠之间的转换，同时将处于中间状态的时间减到最少。通过将促进清醒和睡眠的神经元群与控制不同睡眠阶段转换的神经元群相结合，可能定义出一个神经元群的“睡眠-觉醒网络”。为了更好地理解这个网络内在的动力学机制，我们创建了一个由耦合弛豫振荡方程组成的睡眠-觉醒网络模型。对确定性模型的数学分析有助于深入了解状态转换背后的动力学机制，并预测每种转换类型的机制。加入噪声后，该模型生成的模拟睡眠-觉醒行为再现了小鼠睡眠-觉醒行为的许多定性和定量特征。特别是，模拟短暂觉醒的存在是该模型的一个独特特征。除了捕捉到实验观察到的短暂觉醒和持续觉醒之间的定性差异外，该模型还提出了两种觉醒类型的不同网络机制。由于昼夜节律和其他因素会改变睡眠-觉醒行为的精细结构，该模型提供了一个新颖的框架来探索可能构成正常和病理性睡眠-觉醒生理学基础的动力学原理。
