Application of Machine Vision to Determine the Influence of Sleep States and Social Interactions on Vulnerability to Drug Addiction

项目摘要/摘要 该项目的长期目标是开发一种技术，使成瘾研究人员能够测量 在大规模基因组实验中社会和睡眠行为对药物摄取行为的影响 发现调节成瘾易感性的基因。为了实现这一点，我们将开发和公开 提供了一种高度创新的技术，这将首次允许研究人员持续监测 使用低成本和易于扩展的视频设备，在一组小鼠中进行复杂的睡眠和社交行为。而当 吸毒行为与这些行为有既定的双向关系，基因 病因尚未完全阐明。这一关键差距主要是由于技术壁垒阻碍了 针对特定的睡眠状态和社会行为对大量动物进行可靠的表型分析。目前 评估不同睡眠状态的可用方法被设计用于隔离的动物，而不是 测量一组中的多个个体。尽管众所周知，社会环境对成瘾有影响 除了脆弱性，人们通过改变居住环境和测试被隔离的动物对其进行了很大程度的研究。我们 将利用人工智能技术开发一种基于神经网络的视频机器视觉方法 对这些行为的分析，旨在用于与行为相关的群体环境中，在较长的时间内 时间到了。这种非侵入性方法将是行为表型鉴定方面的重大进步，满足了需求 对于优化小鼠作为成瘾模型所需的高通量遗传学研究。我们的第一个特别节目 目的是开发一种机器视觉方法来测量快速眼动睡眠(NREM) 所有个人的睡眠和清醒行为，用于单人或集体居住的条件。我们会训练 并使用脑电和肌电记录来验证我们的机器视觉网络。演示其效用并评估 我们将比较两个不同的小鼠品系(对照小鼠C57BL/6J和A 已知这些睡眠参数不同的转基因菌株B6.129P2-NOS2 tm1Lau/J)。我们会 比较这两个品系的小鼠在两组和单独住房中的睡眠情况。我们的第二个具体目标是 扩展我们的方法以实现对群体社交动态的评估，并记录所有社交和活动行为 已经好几天了。这将使我们能够测试社交和睡眠行为的双向影响 自行服用甲基苯丙胺。我们将评估初始社会地位和睡眠的影响 并衡量药物使用后的社会互动和睡眠模式的变化 这种药是有货的。成功完成这些目标将产生一种经过验证的技术，具有以下能力 提供对小鼠睡眠和社会行为的详细测量，用于大规模遗传研究， 从而大大提高了研究人员识别与成瘾易感性相关的基因的能力。

PROJECT SUMMARY/ABSTRACT The long-term goal of this project is to develop a technology that will allow addiction researchers to measure the influence of social and sleep behaviors on drug intake behavior in large-scale genomic experiments to discover genes that regulate vulnerability to addiction. To accomplish this we will develop and make publicly available a highly innovative technology that, for the first time, will allow researchers to continuously monitor complex sleep and social behaviors in groups of mice, using low-cost and easily scaled video equipment. While drug-addiction behaviors have well-established bidirectional relationships with these behaviors, the genetic etiologies have not been fully elucidated. This critical gap is primarily due to technological barriers that prevent reliable phenotyping of large numbers of animals for specific sleep states and social behaviors. Currently available methods for assessing different sleep states are designed to be used with isolated animals and do not measure multiple individuals in a group. Although social context is known to be an influence on addiction vulnerability, it has been largely studied by changing the housing environment and testing isolated animals. We will exploit techniques of artificial intelligence to develop a neural network-based machine-vision method of video analysis of these behaviors, designed to be used in an ethologically-relevant group setting, over long periods of time. This non-invasive method will be a significant advance in behavioral phenotyping, fulfilling the demands of the high-throughput genetic studies necessary for optimizing the mouse as model of addiction. Our first specific aim is to develop a machine-vision method to measure rapid eye movement (REM) sleep, nonREM (NREM) sleep and waking behaviors of all individuals, for use in either single or group housed conditions. We will train and validate our machine-vision networks using EEG and EMG recordings. To demonstrate the utility and assess the performance of our method we will compare two different mouse lines (the control mouse C57Bl/6J and a genetically altered strain, B6.129P2-Nos2 tm1Lau/J) that are known to differ in these sleep parameters. We will compare the sleep of these two mouse strains in both group and single housing. Our second specific aim is to extend our method to enable assessment of group social dynamics and to record all social and active behaviors over multiple days. This will allow us to test the bidirectional effects of social and sleep behaviors with the consumption of self-administered methamphetamine. We will assess the effect of initial social status and sleep quality on drug consumption and also measure how the social interactions and sleep patterns are changed after the drug is available. Successful completion of these aims will yield a validated technology with the capacity to provide detailed measurements of sleep and social behaviors in mice for use in large scale genetic studies, thereby significantly enhancing researchers' ability to identify genes associated with addiction susceptibility.