Neurobehavioral phenotyping of AD model mice using Motion Sequencing

使用运动测序对 AD 模型小鼠进行神经行为表型分析

• 批准号: 10281230
• 负责人: Sandeep R Datta
• 金额: $ 193.19万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10281230
• 关键词: 3-Dimensional; Address; Affect; Age; Alzheimer' s Disease; Alzheimer' s disease model; Animal Model; Behavior; Behavioral; Biological Assay; Biological Markers; Cessation of life; Characteristics; Code; Cognition; Cognitive; Confusion; Corpus striatum structure; Data; Disease; Disease Progression; Emotional; Evolution; Exhibits; Failure; Financial cost; Functional disorder; Gait; Genes; Genetic; Histopathology; Human; Impaired cognition; Intervention; Joints; Laboratories; Lead; Left; Lesion; Longevity; Machine Learning; Memory Loss; Methods; Moods; Motion; Motor; Mouse Strains; Movement; Mus; Mutate; Mutation; Neurofibrillary Tangles; Pathology; Pattern; Phenotype; Population; Pre-Clinical Model; Predictive Value; Prevalence; Risk; Sampling; Self-Direction; Sensory; Societies; Supervision; Support System; Techniques; Testing; Time; Withdrawal; base; behavior influence; behavioral phenotyping; behavioral study; cognitive change; cognitive performance; disease phenotype; experimental study; loss of function; machine vision; mouse model; movement analysis; neural circuit; neural correlate; neurobehavioral; normal aging; novel; object recognition; pre-clinical; relating to nervous system; unsupervised learning

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.

摘要 阿尔茨海默病（AD）是由神经回路的渐进性变化引起的，这种变化在记忆中达到顶峰 失落、困惑、难以完成任务、退缩、情绪变化，最终死亡。身体改变 运动--如步态减慢和难以避开障碍物--与AD有关， 这是AD的预测，并且通常出现在临床前阶段，在认知变化明显之前。这些 观察结果提出了关于AD如何靶向支持AD的认知和运动系统的重要问题。 运动和/或动作选择;解决这些问题反过来需要清楚地了解AD如何 病理生理学影响疾病早期和晚期的行为，特别是这些变化如何在 行为与正常衰老过程中明显的许多运动相关变化不同。但要 AD小鼠模型中运动的行为分析尚未产生明确和一致的观点， AD如何影响负责选择、组成、排序和实施持续进行的神经回路 行为。这种失败至少在一定程度上反映了用于表征小鼠模型行为的方法， 它依赖于一组还原主义的分析，这些分析捕捉了小鼠整体行为的有限方面， 行为在给定的实验;因此，我们不知道是否不同的AD模型共享 核心运动表型，我们也不了解AD是否或如何针对皮质-纹状体回路， 创造出老鼠用来与世界互动的连贯的、时时刻刻的行为模式。我们 一个实验室最近开发了一种基于3D机器的新型行为表征技术 视觉和无监督机器学习技术，称为运动序列（MoSeq）。MoSeq 自动地且无需人的监督来识别行为模块（“音节”例如，一个左转， 后部的前半部分等）其中自发和自我导向的行为是组成，以及 控制这些音节顺序的统计规则（“语法”）。之前我们已经证实 背外侧纹状体（DLS）包含与音节和语法相关的明确神经， DLS被因果地要求将音节组装成有意义的和自适应的序列。在这里我们建议 使用MoSeq来表征由各种AD小鼠模型表达的行为表型， 联合神经行为记录来探测这些观察到的表型的电路机制， 最后，将MoSeq发展成为一个广泛适用的平台，用于研究与运动相关的特征， 认知.总之，这些实验有望振兴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相关遗传因素联系在一起的关键机制。 病变，神经回路功能，以及正在进行的自然主义行为模式。