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The power of automated high-resolution behavior analysis revealed by its application to mouse models of Huntingtons and prion diseases

机译:通过将其应用于亨廷顿氏病和病毒疾病的小鼠模型揭示了自动高分辨率行为分析的强大功能

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摘要

Automated analysis of mouse behavior will be vital for elucidating the genetic determinants of behavior, for comprehensive analysis of human disease models, and for assessing the efficacy of various therapeutic strategies and their unexpected side effects. We describe a video-based behavior-recognition technology to analyze home-cage behaviors and demonstrate its power by discovering previously unrecognized features of two already extensively characterized mouse models of neurodegenerative disease. The severe motor abnormalities in Huntington's disease mice manifested in our analysis by decreased hanging, jumping, stretching, and rearing. Surprisingly, behaviors such as resting and grooming were also affected. Unexpectedly, mice with infectious prion disease showed profound increases in activity at disease onset: rearing increased 2.5-fold, walking 10-fold and jumping 30-fold. Strikingly, distinct behaviors were altered specifically during day or night hours. We devised a systems approach for multiple-parameter phenotypic characterization and applied it to defining disease onset robustly and at early time points.
机译:对小鼠行为的自动分析对于阐明行为的遗传决定因素,对人类疾病模型的综合分析以及评估各种治疗策略的功效及其意外副作用至关重要。我们描述了一种基于视频的行为识别技术,用于分析家庭笼子的行为并通过发现两个神经变性疾病小鼠模型的先前无法识别的特征来证明其功能。在我们的分析中,亨廷顿舞蹈病小鼠的严重运动异常表现为悬挂,跳跃,伸展和饲养减少。令人惊讶的是,诸如休息和修饰等行为也受到影响。出乎意料的是,感染性病毒病的小鼠在疾病发作时表现出极大的活动性:饲养增加2.5倍,行走10倍,跳跃30倍。令人惊讶的是,不同的行为在白天或晚上特别发生了变化。我们设计了一种用于多参数表型表征的系统方法,并将其应用于强有力地定义疾病的发作和在早期时间点。

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