SYSTEM FOR AUTOMATED NONINVASIVE MONITORING OF MOUSE SLEEP AND BEHAVIOR

描述(申请人提供)：睡眠的基本功能尚不清楚。异常睡眠模式可表现为各种障碍-睡眠呼吸暂停、睡眠不良、REM(快速眼动睡眠)行为障碍(RBD)、嗜睡症-其中许多受遗传影响。在遗传学和药物研究中，对小鼠行为的研究越来越受到重视。然而，发现导致睡眠和相关疾病的基因需要耗时的大规模表型行为筛选，以将观察到的特征与遗传学联系起来。对小鼠的行为监测通常仅限于活动测量，如视频跟踪、跑轮和光电断束。虽然这些方法中的许多都是非侵入性的，并且具有高通量(HT)应用的潜力，但它们主要监测运动活动，而不提供关于睡眠-觉醒状态和睡眠结构的信息，这对于研究睡眠障碍是重要的。目前哺乳动物睡眠分析的金标准是脑电(EEG)和肌电(EMG)。虽然脑电可以准确地确定睡眠-觉醒状态，但它具有侵入性和资源密集性(手术、恢复等)，这限制了它的广泛应用 对啮齿动物进行大规模的遗传学研究。因此，EEG是发现促进睡眠障碍的基因的关键障碍。Signal Solutions，LLC已经开发了一种传感器笼环境，用于非侵入性的HT行为监测，许多著名的研究小组正在使用这种环境来识别与睡眠和昼夜节律相关的不同特征的基因。该系统基于对安装在笼子底部的压敏压电传感器产生的信号的分析，已经可以高精度地区分睡眠和清醒，并在动物相对不活动时跟踪呼吸努力的变化。肯塔基大学的桑德兰实验室使用Signal Solutions公司的“Piezo”系统开发了技术，并获得了初步数据，表明与呼吸努力相关的压力变化可能会区分快速眼动(REM)和非快速眼动(NREM)睡眠阶段，这一点得到了EEG/EMG同步记录的证实。本应用的具体目的是确定压电系统是否可以无创地：1.通过对压电信号特征的分类，在与EEG/EMG相当的水平上区分睡眠-觉醒状态(睡眠/觉醒，REM/NREM)和觉醒中的行为(例如，安静与活跃、高活动、进食、梳理)；2.识别队列中的异常值，并根据特定睡眠特征(在每种状态下的百分比时间、平均发作频率和持续时间、入睡REM)来区分具有已知睡眠差异的品系小鼠；以及3.发展应用和量化对感觉刺激的反应的能力，以选择性地限制睡眠和惊吓反射测量。这次调查的目的是 在压电式系统中集成和测试这些附加功能。设想的最终产品是一个传感器笼和软件接口，用于高通量监测小动物(例如KO小鼠，QTL分析)的睡眠-觉醒状态和行为，以确定导致睡眠/昼夜节律紊乱的遗传因素以及药物操作、感觉刺激或神经损伤(例如创伤性脑损伤、癫痫)的行为影响。这个系统将是 特别有利于预先筛选潜在感兴趣的表型，并保留侵入性脑电分析以供进一步证实。目前对睡眠和清醒进行分类的系统基本上和EEG/EMG一样好；REM/NREM作为第一次通过筛查将是非常有价值的。感兴趣的医学目标是睡眠/昼夜节律紊乱、睡眠呼吸暂停、肥胖/糖尿病、快速眼动/非快速眼动睡眠剥夺和压力等。潜在客户包括学术研究实验室以及对大规模行为监测(例如药物筛选)和现有用户升级感兴趣的工业实验室。

DESCRIPTION (provided by applicant): The basic functions of sleep are still unknown. Abnormal sleep patterns can manifest as a variety of disorders-sleep apnea, parasomnias, REM (rapid eye movement sleep) behavioral disorder (RBD), narcolepsy-many of which are influenced by heredity. There is an increasing focus on characterizing mouse behaviors for genetic and drug studies. However, discovering the genes responsible for sleep and related disorders requires time-consuming large-scale behavioral screening of phenotypes to correlate observed traits with genetics. Behavioral monitoring of mice is usually limited to actigraphic measurements such as video tracking, wheel-running, and photoelectric beam-breaking. Although many of these methods are noninvasive and have potential for high-throughput (HT) application, they monitor mainly locomotor activity without providing information about sleep-wake state and sleep architecture, which are important for investigating sleep disorders. The current gold standard for sleep analysis in mammals is electroencephalography (EEG) with electromyography (EMG). While EEG can be used to accurately determine sleep-wake state, it is invasive and resource-intensive (surgery, recovery, etc.), which limits its application in large scale genetic studies with rodents. EEG is therefore a critical barrier to the discovery of genes that promote sleep disorders. Signal Solutions, LLC, has developed a sensor cage environment for noninvasive, HT behavioral monitoring that is being used by many prominent research groups to identify genes responsible for different traits related to sleep and circadian rhythms. The system is based on analysis of the signal generated by a pressure- sensitive piezoelectric sensor attached to the cage floor, and can already discriminate sleep from wakefulness with high accuracy and track changes in respiratory effort when the animal is relatively inactive. The Sunderam Lab at the University of Kentucky has used Signal Solutions' "piezo" system to develop techniques and obtain preliminary data suggesting that pressure changes associated with respiratory effort may distinguish REM and non-REM (NREM) stages of sleep as verified by simultaneous EEG/EMG recordings. The specific aims of this application are to determine whether the piezo system can noninvasively: 1. Discriminate sleep-wake state (sleep/wake, REM/NREM) and behavior within wake (e.g., quiet vs. active, high activity, feeding, grooming) at a level comparable to EEG/EMG by classifying piezo signal features; 2. Identify outliers in a cohort and differentiate strains of mice with known sleep differences on the basis of specific sleep traits (percent time in each state, mean bout frequency and duration, sleep-onset REM); and 3. Develop the capability to apply and quantify responses to sensory stimulation for selective sleep restriction and startle reflex measurement. The purpose of this investigation is to integrate and test these additional capabilities in the piezo system. The envisioned end product is a sensor cage and software interface for high-throughput monitoring of sleep- wake state and behavior in small animals (e.g., KO mice, QTL analyses) to identify genetic factors responsible for sleep/circadian disorders as well as behavioral effects of pharmacological manipulation, sensory stimulation, or neural injury (e.g., traumatic brain injury, epilepsy). This system will be particularly advantageous for prescreening potentially interesting phenotypes, and reserving invasive EEG analysis for further confirmation. The current system for classifying sleep vs. wake is essentially as good as EEG/EMG; REM/NREM would be extremely valuable as a first pass screen. Medical targets of interest are sleep/circadian disorders, sleep apnea, obesity/diabetes, REM/NREM sleep deprivation, and stress, among others. Potential clients include academic research labs as well as industrial labs interested in behavioral monitoring on a large scale (e.g. drug screening), and upgrades to existing users.