Self Organized Criticality as a new paradigm of sleep regulation

描述(申请人提供)：人类和动物经常表现出从睡眠中短暂唤醒(唤醒)，传统上被视为由外部刺激或病理扰动引起的随机睡眠中断。然而，我们最近的发现表明，唤醒阶段表现出复杂的时间组织和尺度不变的行为，其特征是持续时间的幂函数概率分布，而睡眠阶段的持续时间表现出指数行为。这种复杂的规模不变的唤醒组织使得它们不太可能仅仅是对随机外部刺激的线性反应。到目前为止，在生理系统中还没有观察到由单一调节机制产生的标度不变过程和指数过程共存的现象。这种共存类似于呈现自组织临界性(SOC)的非平衡系统的动力学特征。因此，我们假设唤醒是睡眠调节的一个组成部分，可能是维持和调节健康睡眠所必需的，按照SOC类型的时间组织，通过在调节神经元网络中释放累积的兴奋来维持和调节健康睡眠。为了解决这一假设，我们建议将睡眠生理学和生物分子/遗传学实验的数据与统计物理学和复杂网络理论的现代概念结合起来。利用SOC的框架，我们的具体目标是：(I)阐明睡眠中唤醒的尺度不变组织的机制；(Ii)揭示病理条件如何影响唤醒和睡眠阶段转换的SOC组织；(Iii)寻找新的、更灵敏的睡眠障碍诊断标记物。我们将分析来自(I)健康人受试者和(Ii)失眠、发作性睡病、睡眠呼吸暂停和其他疾病的受试者；以及(Iii)来自健康野生型小鼠和大鼠的大型数据库。我们还将利用各种睡眠障碍的实验动物模型的数据，其中特定的睡眠相关神经元组和大脑区域为目标，以辨别神经生物相互作用的哪些关键元素可能导致系统水平上睡眠动力学中SOC复杂性的出现。在睡眠动力学中建立SOC类型的复杂性将挑战当前占主导地位的基于稳态的睡眠调节范式，因为它表明需要在广泛的时间范围内持续波动(唤醒)。神经元信号相互作用如何在系统水平上导致SOC类型的复杂性尚不清楚，我们将基于现代尺度不变网络理论开发方法，以探索神经元网络拓扑在睡眠动力学中SOC生成中的作用。

DESCRIPTION (provided by applicant): Humans and animals often exhibit brief awakenings from sleep (arousals), which are traditionally viewed as random disruptions of sleep caused by external stimuli or pathologic perturbations. However, our recent findings show that arousals exhibit complex temporal organization and scale-invariant behavior, characterized by a power-law probability distribution for their durations, while sleep stage durations exhibit exponential behavior. Such complex scale-invariant organization of the arousals makes it unlikely that they are merely a linear response to random external stimuli. The co-existence of both scale-invariant and exponential processes generated by a single regulatory mechanism has not been observed in physiological systems until now. Such co-existence resembles the dynamical features of non-equilibrium systems exhibiting self-organized criticality (SOC). Thus, we hypothesize that arousals are an integral part of sleep regulation and may be necessary to maintain and regulate healthy sleep by releasing accumulated excitations in the regulatory neuronal networks, following a SOC-type temporal organization. To address this hypothesis we propose to combine data from sleep physiology and bio-molecular/genetic experiments with modern concepts from statistical physics and the theory of complex networks. Utilizing the framework of SOC, our specific aim is: (i) to elucidate the mechanisms leading to scale-invariant organization of arousals during sleep; (ii) to uncover how pathologic conditions affect the SOC organization of arousals and sleep-stage transitions; (iii) to derive novel and more sensitive diagnostic markers of sleep disorders. We will analyze a large database from (i) healthy human subjects, and (ii) subjects with insomnia, narcolepsy, sleep apnea and other disorders; and (iii) from healthy wild type mice and rats. We will also utilize data from experimental animal models of various sleep disorders, where specific sleep-related neuronal groups and brain areas are targeted, to discern which key elements of the neurobiological interactions may be responsible for the emergence of SOC complexity in sleep dynamics at the system level. Establishing SOC-type complexity in sleep dynamics will challenge the current dominant homeostasis-based paradigm of sleep regulation, as it indicates the need of continuous fluctuations (arousals) over a broad range of time scales. How neuronal signaling interactions lead to SOC-type complexity at the system level is not known, and we will develop approaches based on the modern theory of scale-invariant networks to probe the role of the neuronal network topology in generating SOC in sleep dynamics.