Measuring Mouse Somatosensory Reflexive Behaviors with High-speed Videography, Statistical Modeling, and Machine Learning.
Measuring Mouse Somatosensory Reflexive Behaviors with High-speed Videography, Statistical Modeling, and Machine Learning.
复制标题
DOI：
10.1007/978-1-0716-2039-7_21
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
客观地测量和解释动物的感官体验仍然是一项具有挑战性的任务。当使用临床前啮齿动物模型研究疼痛机制和筛选潜在的新型疼痛治疗试剂时尤其如此。如何准确、公正地确定他们的疼痛状态是该领域需要克服的障碍。在这里，我们描述了我们通过高速视频成像大大提高了测量小鼠体感反射行为的努力。我们描述了如何将反射行为的亚秒行为图与统计还原方法和监督机器学习结合起来，以创建更客观的定量小鼠“疼痛量表”。我们的目标是为读者提供如何将此处描述的一些新工具与当前使用的机械体感测定相结合的协议，同时讨论这种新方法的优点和局限性。
Objectively measuring and interpreting an animal’s sensory experience remains a challenging task. This is particularly true when using preclinical rodent models to study pain mechanisms and screen for potential new pain treatment reagents. How to determine their pain states in a precise and unbiased manner is a hurdle that the field will need to overcome. Here, we describe our efforts to measure mouse somatosensory reflexive behaviors with greatly improved precision by high-speed video imaging. We describe how coupling sub-second ethograms of reflexive behaviors with a statistical reduction method and supervised machine learning can be used to create a more objective quantitative mouse “pain scale.” Our goal is to provide the readers with a protocol of how to integrate some of the new tools described here with currently used mechanical somatosensory assays, while discussing the advantages and limitations of this new approach.