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Intact responses to non-drug rewards in long-term opioid maintenance treatment.

Marie EikemoPhilipp P LobmaierMads L PedersenNikolaj KunøeAnna Maria MatziorinisSiri LeknesMonica Sarfi
Published in: Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology (2019)
Disruption of non-drug reward processing in addiction could stem from long-term drug use, addiction-related psychosocial stress, or a combination of these. It remains unclear whether long-term opioid maintenance treatment (OMT) disrupts reward processing. Here, we measured subjective and objective reward responsiveness in 26 previously heroin-addicted mothers in >7 years stable OMT with minimal psychosocial stress and illicit drug use. The comparison group was 30 healthy age-matched mothers (COMP). Objective reward responsiveness was assessed in a two-alternative forced-choice task with skewed rewards. Results were also compared to performance from an additional 968 healthy volunteers (meta-analytic approach). We further compared subprocesses of reward-based decisions across groups using computational modelling with a Bayesian drift diffusion model of decision making. Self-reported responsiveness to non-drug rewards was high for both groups (means: OMT = 6.59, COMP = 6.67, p = 0.84, BF10 = 0.29), yielding moderate evidence against subjective anhedonia in this OMT group. Importantly, the mothers in OMT also displayed robust reward responsiveness in the behavioral task (t19 = 2.72, p = 0.013, BF10 = 3.98; d = 0.61). Monetary reward changed their task behavior to the same extent as the local comparison group (reward bias OMT = 0.12, COMP = 0.12, p = 0.96, BF10 = 0.18) and in line with data from 968 healthy controls previously tested. Computational modelling revealed that long-term OMT did not even change decision subprocesses underpinning reward behavior. We conclude that reduced sensitivity to rewards and anhedonia are not necessary consequences of prolonged opioid use.
Keyphrases
  • prefrontal cortex
  • chronic pain
  • pain management
  • mental health
  • emergency department
  • single cell
  • adverse drug
  • combination therapy
  • drug induced
  • artificial intelligence
  • stress induced
  • deep learning
  • clinical evaluation