METHODS article
Front. Behav. Neurosci.
Sec. Pathological Conditions
Automated Thermal Gradient Test for Unprovoked Assessment of Nociceptive Preference in Rodents
Provisionally accepted
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, United States
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In animal models, reflexive responses to noxious stimuli (e.g., paw withdrawal in von Frey, Hargreaves, or cold plantar tests) are largely spinal reflexes and their quantitative measures (latency or threshold) may not directly reflect clinically relevant pain perception as assessed by human quantitative sensory testing, which captures both conscious sensory and affective components of pain as a subjective experience. This study aims to develop a complementary behavioral testing strategy for rapidly and automatically detecting rodents’ thermal responses under different pain conditions without human interference. A device is engineered to create a linear thermal gradient from 4°C to 58°C along a long aluminum floor of four equal-size corridors, each having a dimension of 137 cm × 10 cm × 22 cm (L x W x H) and allowing four freely roaming rodents to be simultaneously evaluated to increase the throughput of in vivo pain testing. Animal behaviors influenced by the temperature gradient are recorded by a camera and analyzed using ANY-Maze. The duration of data collection is investigated, showing that the data collected in as short as 10 min can adequately capture thermal preferences of mice along the temperature gradient. Animal behaviors reveal differences in thermal nociception between male and female mice, capture counterintuitive changes in nociceptive thermal avoidance in the absence and presence of inflammatory pain, and show analgesic effects of morphine (10 mg/kg subcutaneously) as well as its stimulation of hyperactive locomotion. The sensitivity, reliability, and efficiency of the new thermal gradient test will not only help mechanistic investigations of various thermal sensing receptors but also enable high-throughput in vivo pain evaluation and analgesic drug screening for developing new treatments for pain management.
Keywords: Self-reported pain behaviors, Automated pain detection, Thermal gradient, thermal preference, pain assessment
Received: 19 Sep 2025; Accepted: 17 Nov 2025.
Copyright: © 2025 Deakin, Wei, Rhoades, Tillman, Tang and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yan Xu, xu2@pitt.edu
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