Back

Chronic pain increases sensitivity to pain-induced reinstatement of ethanol seeking in male mice

Nothem, M. A.; Side, C. M.; Tran, S. C.; Brar, A.; Buck, L. A.; Barker, J. M.

2025-04-16 animal behavior and cognition
10.1101/2025.04.10.648244 bioRxiv
Show abstract

Alcohol use disorder (AUD) and chronic pain are complex and debilitating conditions that are highly comorbid. Greater than 50% of individuals with AUD have chronic pain. Clinical data suggest that people with chronic pain are more likely to report using alcohol to manage chronic pain, and that magnitude of pain is correlated with relapse probability after a period of abstinence. These data led to the hypothesis that pain can drive ethanol seeking and reinstatement in a rodent model of chronic neuropathic pain. A conditioned place preference (CPP) paradigm was used to model ethanol seeking in male C57BL6J mice with a spared nerve injury (SNI). Mice were conditioned with doses of ethanol previously found to reverse pain behavior (0.5g/kg). Mice with and without SNI showed similar magnitudes of ethanol CPP and rates of extinction. To investigate pain-induced relapse-related behavior, mice underwent reinstatement testing following painful mechanical stimulation which was delivered at either a "moderate" or "high" intensity immediately prior to return to the CPP apparatus. "Moderate" painful hindpaw stimulation reinstated ethanol seeking behavior in SNI-injured, but not sham, mice, while "high" intensity stimulation reinstated ethanol seeking in mice regardless of injury status. These data suggest that males in chronic pain are more susceptible reinstatement of ethanol seeking following a painful experience.

Published in Alcohol (predicted rank #16) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.