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

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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.