Measuring Mouse Somatosensory Reflexive Behaviors with High-speed Videography, Statistical Modeling, and Machine Learning.

Neuromethods Pub Date : 2022-01-01 Epub Date: 2022-05-27 DOI:10.1007/978-1-0716-2039-7_21
Ishmail Abdus-Saboor, Wenqin Luo
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引用次数: 1

Abstract

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.

Abstract Image

Abstract Image

用高速摄像、统计建模和机器学习测量小鼠体感反射行为。
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来研究疼痛机制和筛选潜在的新疼痛治疗试剂时,情况尤其如此。如何以精确和公正的方式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测量小鼠体感反射行为的努力,该行为的精度大大提高。我们描述了如何将反射行为的亚秒行为图与统计归约方法和监督机器学习相结合,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一个协议,说明如何将本文描述的一些新工具与当前使用的机械体感测定相结合,同时讨论了这种新方法的优点和局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
0.60
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