Development of a Mouse Pain Scale Using Sub-second Behavioral Mapping and Statistical Modeling
Development of a Mouse Pain Scale Using Sub-second Behavioral Mapping and Statistical Modeling
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DOI：
10.1016/j.celrep.2019.07.017
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发表时间：
2019-08-06
期刊：
影响因子：
8.8
通讯作者：
Luo, Wenqin
中科院分区：
文献类型：
--
作者：
Abdus-Saboor, Ishmail;Fried, Nathan T.;Luo, Wenqin
啮齿动物是疼痛研究的主要模型系统，但确定其疼痛状态具有挑战性。为了开发一种客观的方法来评估小鼠的疼痛感觉，我们采用高速摄像技术来捕捉伤害性和无害性刺激后爪刺激的亚秒级行为特征，并确定了几个区分参数，这些参数指示伤害性感受的情感和反射方面。使用统计建模和机器学习，我们将这些参数整合到一个单一的指数中，并创建了一个“小鼠疼痛量表”，它允许我们以分级的方式评估每次戒断的疼痛感觉。我们证明了这种方法的实用性，通过确定由三种不同的冯弗雷毛发和两种不同的伤害感受器群体的光遗传学激活触发的感觉。我们基于行为的“疼痛量表”方法将有助于提高使用退缩反射测定法评估小鼠疼痛感觉的严谨性和可重复性。
Rodents are the main model systems for pain research, but determining their pain state is challenging. To develop an objective method to assess pain sensation in mice, we adopt high-speed videography to capture sub-second behavioral features following hind paw stimulation with both noxious and innocuous stimuli and identify several differentiating parameters indicating the affective and reflexive aspects of nociception. Using statistical modeling and machine learning, we integrate these parameters into a single index and create a "mouse pain scale,'' which allows us to assess pain sensation in a graded manner for each withdrawal. We demonstrate the utility of this method by determining sensations triggered by three different von Frey hairs and optogenetic activation of two different nociceptor populations. Our behavior-based "pain scale'' approach will help improve the rigor and reproducibility of using withdrawal reflex assays to assess pain sensation in mice.