Neurobehavioral phenotyping of AD model mice using Motion Sequencing

摘要 阿尔茨海默病(AD)是由神经回路的进行性变化引起的，最终导致记忆 迷失、困惑、难以完成任务、退缩、情绪变化，最终导致死亡。身体上的变化 运动--如步态变慢和躲避障碍的困难--一直与AD有关， 阿尔茨海默病是AD的先兆，通常出现在认知改变明显之前的临床前阶段。这些 观察提出了一些重要的问题，即AD是如何针对支持 移动和/或动作选择；依次回答这些问题需要清楚地了解AD是如何 病理生理学影响疾病早期和晚期的行为，特别是这些变化是如何在 行为与许多在正常衰老过程中明显的运动相关变化是不同的。然而，为了 对AD小鼠模型的运动数据行为分析还没有产生明确和一致的观点 AD如何影响负责选择、合成、排序和执行进行中的神经回路 行为。这种失败至少部分地反映了用于表征老鼠模型中的行为的方法， 这取决于一组简化论者的分析，这些分析捕捉到了老鼠整体行为的有限方面 行为在给定的实验中；因此，我们不知道不同的AD模型是否共享 核心运动表型，我们也不了解阿尔茨海默病是否或如何靶向皮质-纹状体回路 创造老鼠用来与世界互动的连贯的、时时刻刻的动作模式。我们的 实验室最近开发了一种基于3D机器的新的行为表征技术 视觉和无监督机器学习技术，称为运动序列(MoSeq)。MoSeq 在没有人工监督的情况下自动地识别行为模块(例如，左转， 后方的前半部分等)自发的和自我导向的行为是由它组成的，以及 管理这些音节顺序的统计规则(“语法”)。我们之前已经演示过 背外侧纹状体(DLS)包含音节和语法的明确神经关联，并且 动态最小二乘法是将音节组合成有意义的顺应性序列的因果关系。在这里，我们建议 使用MoSeq来表征各种AD小鼠模型表达的行为表型，以执行 联合神经行为记录以探测这些观察到的表型背后的电路机制， 最后，将MoSeq开发成一个广泛适用的研究运动相关签名的平台 认知力。综上所述，这些实验有望重振行为和神经行为的研究 AD临床前模型中的关系，并揭示将AD相关基因联系在一起的关键机制 损伤、神经回路功能和持续的自然主义行为模式。

Abstract Alzheimer's disease (AD) is caused by progressive changes in neural circuits that culminate in memory loss, confusion, difficulty completing tasks, withdrawal, mood changes and ultimately death. Alterations in body movement — such as slowed gait, and difficulty in avoiding obstacles — have been associated with AD, are predictive of AD, and often appear in the pre-clinical stage, before cognitive changes are apparent. These observations raise important questions about how AD targets the cognitive and motor systems that support movement and/or action selection; addressing these questions in turn requires a clear view of how AD pathophysiology influences behavior both early and late in disease, and particularly how these changes in behavior are distinguished from the many motor-related changes apparent during normal aging. However, to date behavioral analysis of movement in AD mouse models have not yet yielded a clear and consistent view of how AD affects the neural circuits responsible for selecting, composing, sequencing and implementing ongoing behaviors. At least in part this failure reflects the methods used to characterize behavior in mouse models, which depend upon a set of reductionist assays that capture limited aspects of a mouse's overall behavioral comportment within a given experiment; as a consequence we do not know whether different AD models share core movement phenotypes, nor do we understand whether or how AD targets the cortico-striatal circuits that create the coherent, moment-to-moment patterns of action used by mice to interact with the world. Our laboratory has recently developed a novel behavioral characterization technique, based upon 3D machine vision and unsupervised machine learning techniques, called Motion Sequencing (MoSeq). MoSeq automatically and without human supervision identifies the behavioral modules (“syllables” e.g., a left turn, the first half of a rear, etc.) out of which spontaneous and self-directed behavior is composed, as well as the statistical rules governing the sequencing of these syllables (“grammar”). We have previously demonstrated that the dorsolateral striatum (DLS) contains explicit neural correlates for both syllables and grammar, and that the DLS is causally required to assemble syllables into meaningful and adaptive sequences. Here we propose to use MoSeq to characterize behavioral phenotypes expressed by a variety of AD mouse models, to perform joint neural-behavioral recordings to probe circuit mechanisms that underlie these observed phenotypes and, finally, to develop MoSeq into a broadly-applicable platform for studying the movement-related signatures of cognition. Taken together, these experiments promise to revitalize the study of behavior and neuro-behavioral relationships in pre-clinical models of AD, and to reveal key mechanisms that tie together AD-related genetic lesions, neural circuit function, and ongoing naturalistic patterns of action.