权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

SYSTEM FOR AUTOMATED NONINVASIVE MONITORING OF MOUSE SLEEP AND BEHAVIOR

自动无创监测小鼠睡眠和行为的系统

基本信息

批准号：
8638993
负责人：
Bruce F O'Hara
金额：
$ 16.88万
依托单位：
SIGNAL SOLUTIONS, LLC
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-04-01 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8638993
关键词：
Affect Animal Experimentation Animals Anxiety Behavior Behavior Disorders Behavior Therapy Behavior assessment Behavioral Behavioral Genetics Behavioral Research Benchmarking Brain Characteristics Circadian Rhythm Sleep Disorders Circadian Rhythms Classification Client Cognitive Computer software Confidentiality Data Detection Diabetes Mellitus Discrimination Disease Electroencephalography Electromyography Environment Epilepsy Evaluation Event Feedback Floor Frequencies Genes Genetic Genetic Screening Goals Gold Government Grooming Health Heredity Home environment Injury Investigation Kentucky Letters Mammals Measurement Medical Mental Depression Methodology Methods Monitor Motor Activity Motor Seizures Mouse Strains Mus Narcolepsy Neurosciences Obesity Operative Surgical Procedures Outcome Pattern Performance Persons Pharmaceutical Preparations Phase Phenotype Play Polysomnography Preclinical Drug Evaluation Protocols documentation Quantitative Trait Loci REM Sleep REM Sleep Parasomnias Recovery Reflex action Research Resources Rodent Role Running Sensory Signal Transduction Sleep Sleep Apnea Syndromes Sleep Architecture Sleep Deprivation Sleep Disorders Sleep Stages Small Business Innovation Research Grant Solutions Stress System Techniques Technology Testing Time Traumatic Brain Injury Universities Wakefulness actigraphy awake base brain research cohort data acquisition feeding gene discovery high throughput analysis high throughput screening interest nerve injury nervous system disorder neuropsychiatry non rapid eye movement novel pressure programs prototype public health relevance respiratory response screening sensor sleep onset software development somatosensory success tool trait

项目摘要

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.

描述（由申请人提供）：睡眠的基本功能仍然未知。异常的睡眠模式可以表现为各种各样的疾病--睡眠呼吸暂停、异态睡眠、快速眼动睡眠（REM）行为障碍（RBD）、嗜睡症--其中许多都受到遗传的影响。有越来越多的关注表征遗传和药物研究的小鼠行为。然而，发现负责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相关联。小鼠的行为监测通常仅限于活动记录测量，如视频跟踪、轮跑和光电光束中断。虽然许多这些方法是非侵入性的，并有潜力的高通量（HT）的应用，他们主要监测运动活动，而不提供有关睡眠-觉醒状态和睡眠结构，这是重要的调查睡眠障碍的信息。目前哺乳动物睡眠分析的金标准是脑电图（EEG）和肌电图（EMG）。虽然EEG可用于准确地确定睡眠-觉醒状态，但它是侵入性的和资源密集型的（手术、恢复等），这限制了其在大范围内的应用对啮齿类动物进行大规模遗传学研究。因此，脑电图是发现促进睡眠障碍的基因的关键障碍。Signal Solutions，LLC开发了一种用于非侵入性HT行为监测的传感器笼环境，许多著名的研究小组正在使用该环境来识别负责与睡眠和昼夜节律相关的不同特征的基因。该系统基于对附在笼子地板上的压敏压电传感器产生的信号的分析，并且已经可以高精度地区分睡眠和觉醒，并在动物相对不活动时跟踪呼吸努力的变化。肯塔基州大学的Sunderam实验室使用Signal Solutions的“压电”系统开发技术并获得初步数据，表明与呼吸努力相关的压力变化可以区分REM和非REM（NREM）睡眠阶段，并通过同步EEG/EMG记录进行验证。本申请的具体目的是确定压电系统是否可以非侵入性地：1。区分睡眠-觉醒状态（睡眠/觉醒，REM/NREM）和觉醒内的行为（例如，安静与活跃、高活动、进食、梳理），通过对压电信号特征进行分类，在与EEG/EMG相当的水平上; 2.识别组群中的离群值，并基于特定的睡眠特征（每种状态的时间百分比、平均回合频率和持续时间、睡眠发作REM）区分具有已知睡眠差异的小鼠品系;以及发展应用和量化对感官刺激的反应的能力，以进行选择性睡眠限制和惊吓反射测量。这次调查的目的是在压电系统中集成和测试这些附加功能。设想的最终产品是用于高通量监测小动物（例如，KO小鼠，QTL分析）以鉴定负责睡眠/昼夜节律紊乱以及药理学操作、感觉刺激或神经损伤（例如，创伤性脑损伤、癫痫）。该系统将特别有利于预筛选潜在感兴趣的表型，并保留侵入性EEG分析用于进一步确认。目前用于分类睡眠与清醒的系统基本上与EEG/EMG一样好; REM/NREM作为第一次筛选非常有价值。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、REM/NREM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）感兴趣的工业实验室，以及对现有用户的升级。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A comparative study of sleep and diurnal patterns in house mouse (Mus musculus) and Spiny mouse (Acomys cahirinus).

家鼠（Mus musculus）和刺鼠（Acomys cahirinus）睡眠和昼夜模式的比较研究。

DOI：
10.1038/s41598-020-67859-w
发表时间：
2020
期刊：
Scientific reports
影响因子：
4.6
作者：
Wang,Chanung;Guerriero,LaurenE;Huffman,DillonM;Ajwad,AsmaaA;Brooks,TraeC;Sunderam,Sridhar;Seifert,AshleyW;O'Hara,BruceF
通讯作者：
O'Hara,BruceF

A real-time sleep scoring framework for closed-loop sleep manipulation in mice.

用于小鼠闭环睡眠操纵的实时睡眠评分框架。