Gait and whole body posture are sensitive measures of the proper functioning of numerous neural circuits, and are often perturbed in many neurological, neuromuscular, and neuropsychiatric illnesses. Rodents provide a tractable model for elucidating disease mechanisms and interventions, however, studying gait and whole body posture in rodent models requires specialized methods and remains challenging. Here, we develop a simple assay that allows adoption of the commonly used open field apparatus for gait and whole body posture analysis. We leverage modern neural networks to abstract a mouse into keypoints and extract gait and whole body coordination metrics of the animal. Gait-level analysis allows us to detect every step of the animal's movement and provides high resolution information about the animal's behavior. We quantitate gait and whole body posture with high precision and accuracy across 62 highly visually diverse strains of mice. We validate our approach using four genetic mutants with known gait deficits. In extended analysis, we demonstrate that multiple autism spectrum disorder (ASD) models show gait and posture deficits, implying this is a general feature of ASD. We conduct a large strain survey of over 1898 mice, and find that gait and whole body posture measures are highly heritable in the laboratory mouse, and fall into three classes. Furthermore, the reference mouse strain, C57BL/6J, has a distinctly different gait and posture compared to other standard laboratory and wild-derived strains. We conduct a genome wide association study (GWAS) to define the genetic architecture of mouse movement in the open field. Combined, we describe a simple, sensitive, accurate, scalable, and ethologically relevant method of mouse gait and whole body posture analysis for behavioral neurogenetics. These results provide one of the largest laboratory mouse gait-level data resources for the research community and show the utility of automated machine learning approaches for biological insights.

中文翻译：

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使用基于深度学习的姿势估计对鼠标开放场行为进行步态分析

步态和全身姿势是许多神经回路正常运作的敏感指标，通常会在许多神经系统疾病，神经肌肉疾病和神经精神疾病中受到干扰。啮齿动物为阐明疾病的机理和干预措施提供了一个易于处理的模型，但是，在啮齿动物模型中研究步态和全身姿势需要专门的方法，并且仍然具有挑战性。在这里，我们开发了一种简单的测定方法，该方法可以采用常用的开放式设备进行步态和全身姿势分析。我们利用现代神经网络将鼠标抽象为关键点，并提取动物的步态和全身协调指标。步态分析使我们能够检测到动物运动的每个步骤，并提供有关动物行为的高分辨率信息。我们对62种视觉上高度多样化的小鼠品系的步态和全身姿势进行了高精度和高准确度的定量分析。我们使用已知步态缺陷的四个遗传突变体验证了我们的方法。在扩展分析中，我们证明了多种自闭症谱系障碍（ASD）模型显示出步态和姿势缺陷，这暗示这是ASD的一个普遍特征。我们对1898余只小鼠进行了一次大型应变调查，发现在实验小鼠中，步态和全身姿势的测量高度可遗传，可分为三类。此外，与其他标准实验室菌株和野生来源菌株相比，参考小鼠菌株C57BL / 6J具有明显不同的步态和姿势。我们进行了全基因组关联研究（GWAS），以定义开放领域中小鼠运动的遗传结构。结合起来，我们描述了一个简单的，灵敏，准确，可扩展且符合人性化的小鼠步态分析方法和行为神经遗传学的全身姿势分析。这些结果为研究界提供了最大的实验室小鼠步态水平数据资源之一，并显示了自动机器学习方法对生物学见解的实用性。