Development of a Mouse Pain Scale Using Sub-second Behavioral Mapping and Statistical Modeling

• Published in: Cell reports (Cambridge) Vol. 28; no. 6; pp. 1623 - 1634.e4
• Main Authors: Abdus-Saboor, Ishmail, Fried, Nathan T., Lay, Mark, Burdge, Justin, Swanson, Kathryn, Fischer, Roman, Jones, Jessica, Dong, Peter, Cai, Weihua, Guo, Xinying, Tao, Yuan-Xiang, Bethea, John, Ma, Minghong, Dong, Xinzhong, Ding, Long, Luo, Wenqin
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 06.08.2019
• Subjects: Animals; Behavior, Animal; Calcium - metabolism; Female; high-speed imaging; Machine Learning; Male; Mice, Inbred C57BL; Mice, Inbred Strains; Models, Statistical; mouse pain behavior; nociceptors; optogenetics; Pain Measurement - methods; pain scale; principle component analysis; somatosensation; nociceptors; pain scale; principle component analysis; optogenetics; somatosensation; mouse pain behavior; high-speed imaging; machine learning
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2019.07.017

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. [Display omitted] •High-speed videography identifies sub-second pain-related behavioral features•Statistical modeling converts behavioral features to a single index (mouse pain scale)•Mouse pain scale classifies sensation induced by Von Frey hair stimulation•Mouse pain scale classifies sensation triggered by optogenetic activation Abdus-Saboor et al. develop a behavior-centered “mouse pain scale” using high-speed videography, statistical modeling, and machine learning. With this method, they assess the sensation induced by noxious, innocuous, and optogenetic stimuli. This method will improve the reliability of using the mouse hind paw withdrawal to measure pain.