Rodents are often used for studying chronic pain mechanisms and developing new pain therapeutics, but objectively determining the animals pain state is a major challenge. To improve the precision of using reflexive withdrawal behaviors for interpreting the mouse pain state, we adopted high-speed videography to capture sub-second movement features of mice upon hind paw stimulation. We identified several parameters that are significantly different between behaviors evoked by innocuous and noxious stimuli, and combined them to map the mouse pain state through statistical modeling and machine learning. To test the utility of this approach, we determined the pain state triggered by von Frey hairs (VFHs) and optogenetic activation of two nociceptor populations. Our method reliably assesses the pain-like probability for each mouse paw withdrawal reflex under all scenarios, highlighting the precision of using this high resolution behavior-centered composite methodology to determine the mouse pain state from reflexive withdrawal assays.
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