Motion Sequencing for All: pipelining, distribution and training to enable broad adoption of a next-generation platform for behavioral and neurobehavioral analysis

全民运动测序：流水线、分发和培训，以实现下一代行为和神经行为分析平台的广泛采用

• 批准号: 9902565
• 负责人: Sandeep R Datta
• 金额: $ 46.67万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9902565
• 关键词: 3-Dimensional; Adoption; Animal Behavior; Animals; Behavior; Behavior assessment; Behavioral; Brain; Calcium; Cloud Computing; Code; Collaborations; Collection; Communities; Complex; Computer software; Corpus striatum structure; Coupling; Data; Data Analyses; Data Storage and Retrieval; Educational workshop; Gene Expression; Genes; Goals; Head; Imaging technology; Individual; Instruction; Knowledge; Lead; Learning; Mathematics; Measures; Methods; Modeling; Modernization; Motion; Mus; Nervous System Physiology; Nervous system structure; Neurobiology; Neurons; Neurosciences; Output; Pattern; Persons; Pharmaceutical Preparations; Population; Posture; Procedures; Pythons; Reporting; Resolution; Rodent; Stereotyping; Structure; System; Techniques; Technology; Technology Assessment; Training; Validation; Work; awake; base; behavior measurement; cell type; computer science; data format; design; experience; experimental study; flexibility; graduate student; graphical user interface; inhibitor/antagonist; innovation; insight; machine learning algorithm; machine vision; natural language; neural circuit; neurobehavioral; next generation; open source; relating to nervous system; sample fixation; skills; tool; unsupervised learning; voltage; web services

Understanding the function of the nervous system requires a sophisticated understanding of its main output, behavior. Although our ability to record from and to manipulate neurons and neural circuits has accelerated at a spectacular pace over the last decade, progress has lagged in coupling the interrogation of the nervous system to similarly high-resolution measures of behavior. As a consequence, we lack a sophisticated understanding of how the brain composes, modifies and controls action. We have recently developed a transformative behavioral characterization technology called Motion Sequencing (MoSeq), which circumvents many of the limitations imposed by typical approaches to behavioral measurement (e.g., overtraining, head-fixation, limited behavioral flexibility). This analytical system works by capturing comprehensive and continuous morphometric data about the three-dimensional (3D) posture of a mouse as it freely behaves. The 3D data are then analyzed using an unsupervised machine learning algorithm to identify patterns of motion that correspond to stereotyped and reused modules of sub-second behavior (which by analogy to natural language we refer to as behavioral “syllables”). The output of this fitting procedure is a parts list for behavior: a limited set of syllables out of which the rodent creates all of its observable action. In addition, within any given experiment MoSeq identifies the specific transition structure (or “grammar”) that places individual syllables into sequences; these sequences encode all patterns of spontaneous behavior expressed by an animal in a given experimental context. We have recently combined this behavioral assessment technology with techniques for neural recording, allowing us to assess the relationship between neural activity in behaviorally-relevant circuits and patterns of action. This combined approach allowed us, for example, to identify a code for elemental 3D pose dynamics in striatum; importantly, these observed correlations validate MoSeq as a technology that enables accurate inference of internal states from external states. However, the code that underlies MoSeq is essentially bespoke, inappropriate for distribution, and difficult for all but expert users to navigate. In addition, implementing MoSeq in its current form requires extensive prior mathematical and computational experience, limiting its use to a small set of users with specialized skills. Here we propose Aims to democratize MoSeq by (1) transforming it into an end-to-end pipeline that can be easily used by graduate-student level neuroscientists with minimal expertise, and which can be modified on an ongoing basis to accommodate improvements to MoSeq and (2) to offer hands-on training in the set-up and appropriate use of MoSeq for characterizing behavior and neural-behavioral relationships. Together these aims will create a vibrant community of MoSeq users; the creation of such a group has the potential to transform the way behavior is analyzed across neuroscience, and promises to lead to broad insights into the many and varied relationships between neural circuits and behavior.

了解神经系统的功能需要对它的主要功能有一个复杂的了解。 输出，行为。尽管我们记录和操纵神经元和神经回路的能力 在过去的十年里，以惊人的速度加速，在将审讯 神经系统的行为类似的高分辨率措施。因此，我们缺乏一个 对大脑如何组成、修改和控制动作的复杂理解。 我们最近开发了一种变革性的行为表征技术，称为运动 测序（MoSeq），其规避了由行为分析的典型方法施加的许多限制。 测量（例如，过度训练，头部固定，有限的行为灵活性）。该分析系统的工作原理是 捕获关于人的三维（3D）姿势的全面且连续的形态测量数据， 鼠标，因为它自由的行为。然后使用无监督机器学习算法分析3D数据 识别与亚秒级行为的定型和重复使用模块相对应的运动模式 （通过类比自然语言，我们称之为行为“音节”）。此拟合过程的输出 是行为的部分列表：啮齿动物创造所有可观察到的行为的有限音节集。 此外，在任何给定的实验中，MoSeq识别了特定的转换结构（或“语法”）， 将单个音节放入序列中;这些序列编码了所有自发行为的模式 在特定的实验环境中由动物表达。我们最近将这种行为 评估技术与神经记录技术，使我们能够评估之间的关系， 行为相关回路和行为模式中的神经活动。这种结合的方法使我们能够，因为 例如，识别纹状体中基本3D姿态动力学的代码;重要的是，这些观察到的 相关性验证了MoSeq作为一种技术，可以从外部状态准确推断内部状态。 states.然而，MoSeq的代码本质上是定制的，不适合发布， 除了专家用户之外，所有人都很难导航。此外，以目前的形式实施MoSeq需要 广泛的数学和计算经验，限制其使用的一小部分用户， 专业技能。在这里，我们提出了通过（1）将其转变为端到端的MoSeq民主化的目标。 管道，可以很容易地使用研究生水平的神经科学家与最小的专业知识， 可以在持续的基础上进行修改，以适应MoSeq的改进;（2）提供动手操作 培训MoSeq的设置和适当使用，以表征行为和神经行为 关系。这些目标将共同创造一个充满活力的MoSeq用户社区; 该小组有潜力改变整个神经科学的行为分析方式，并有望发挥领导作用 对神经回路和行为之间多种多样的关系的广泛见解。