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Self Organized Criticality as a new paradigm of sleep regulation

自组织临界作为睡眠调节的新范式

基本信息

批准号：
8887361
负责人：
Plamen Christov Ivanov
金额：
$ 42.26万
依托单位：
BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-04-05 至 2018-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8887361
关键词：
Accounting Address Advisory Committees Affect American Animal Experiments Animal Model Animals Architecture Area Arousal Atlases Behavior Biochemical Biochemical Genetics Biochemical Pathway Brain Characteristics Circadian Rhythms Clinical Complex Data Databases Diagnosis Diagnostic Disease Elements Equilibrium Exhibits Experimental Animal Model Feedback Functional disorder Homeostasis Hour Human Laws Lead Lesion Link Measures Modeling Molecular Genetics Mus Narcolepsy Neurobiology Neurons Output Pathologic Pathology Pathway interactions Pattern Pharmacological Treatment Physics Physiological Physiology Polysomnography Probability Process Prognostic Marker Rattus Regulatory Pathway Reporting Role Self Concept Signal Pathway Signal Transduction Sleep Sleep Apnea Syndromes Sleep Architecture Sleep Disorders Sleep Stages Sleeplessness Stimulus Structure System Techniques Testing Time Transcend Wild Type Mouse base biological systems clinically relevant data modeling human subject improved innovation neuronal circuitry neuroregulation neurotransmission novel novel diagnostics novel marker research study response self organization sleep regulation theories

项目摘要

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.

描述（由申请人提供）：人类和动物经常表现出从睡眠中短暂醒来（觉醒）的现象，传统上将其视为由外部刺激或病理扰动引起的随机睡眠中断。然而，我们最近的研究结果表明，唤醒表现出复杂的时间组织和尺度不变行为，其特征是其持续时间呈幂律概率分布，而睡眠阶段持续时间则表现出指数行为。这种复杂的、尺度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。迄今为止，尚未在生理系统中观察到由单一调节机制产生的尺度不变过程和指数过程的共存。这种共存类似于表现出自组织临界性（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，并且可能是通过遵循 SOC 型时间组织释放调节神经元网络中积累的兴奋来维持和调节健康睡眠所必需的。为了解决这个假设，我们建议将睡眠生理学和生物分子/遗传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）阐明导致睡眠期间唤醒的尺度不变组织的机制； (ii) 揭示病理状况如何影响 SOC 组织的觉醒和睡眠阶段转变； (iii) 获得新的、更灵敏的睡眠障碍诊断标志物。我们将分析来自（i）健康人类受试者和（ii）患有失眠、发作性睡病、睡眠呼吸暂停和其他疾病的受试者的大型数据库； (iii)来自健康野生型小鼠和大鼠。我们还将利用各种睡眠障碍实验动物模型的数据，以特定的睡眠相关神经元群和大脑区域为目标，来辨别神经生物学相互作用的哪些关键要素可能导致系统水平睡眠动态中 SOC 复杂性的出现。在睡眠动态中建立 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尺度内持续波动（唤醒）。神经元信号相互作用如何导致系统层面的 SOC 类型复杂性尚不清楚，我们将开发基于现代尺度不变网络理论的方法，以探讨神经元网络拓扑在睡眠动态生成 SOC 中的作用。