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

Abstract

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.

Keywords: high-speed imaging; machine learning; mouse pain behavior; nociceptors; optogenetics; pain scale; principle component analysis; somatosensation.