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

要了解神经系统的功能，需要对它的主要功能有深入的了解。 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，在结合审讯 神经系统对同样高分辨率的行为进行测量。因此，我们缺少一个 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名为Motion的变革性行为表征技术 排序(MoSeq)，它绕过了行为行为的典型方法施加的许多限制 测量(例如，过度训练、头部固定、行为灵活性有限)。这个分析系统的工作原理是 捕获有关人体三维(3D)姿势的全面且连续的形态测量数据 老鼠，因为它自由地行为。然后使用无监督机器学习算法分析3D数据 识别与亚秒行为的定型和重复使用的模块相对应的运动模式 (类似于自然语言，我们称之为行为“音节”)。这一拟合过程的输出 是行为的部件列表：一组有限的音节，啮齿类动物根据这些音节来创建它所有的可观察到的动作。 此外，在任何给定的实验中，MoSeq都会识别特定的转换结构(或语法 将单个音节放入序列中；这些序列编码所有自发行为的模式 在特定的实验环境中由动物表达的。我们最近结合了这一行为 评估技术与神经记录技术，允许我们评估之间的关系 行为相关回路和动作模式中的神经活动。这种结合的方法使我们能够 例如，识别纹状体中基本3D姿势动力学的代码；重要的是，观察到 相关性验证了MoSeq是一种能够从外部准确推断内部状态的技术 各州。然而，MoSeq基础上的代码本质上是定制的，不适合分发，并且 除了专家用户以外，所有人都很难导航。此外，以当前形式实现MoSeq需要 丰富的以前的数学和计算经验，将其使用限制为一小部分用户 专业技能。在这里，我们提出旨在通过(1)将MoSeq转变为端到端的 具有最低专业知识的研究生级别的神经学家可以轻松使用的管道，以及 可持续修改以适应对MoSeq的改进和(2)提供实际操作 训练MoSeq的建立和适当使用，以表征行为和神经行为 两性关系。这些目标加在一起将创建一个充满活力的MoSeq用户社区；创建这样一个 群体有可能改变神经科学对行为的分析方式，并有望引领 对神经回路和行为之间的多种不同关系的广泛洞察。

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.