# A Translational Determination of the Mechanisms of Maladaptive Choice in Opioid Use Disorder

> **NIH NIH R01** · UNIVERSITY OF KENTUCKY · 2021 · $648,345

## Abstract

ABSTRACT
 Opioid use disorder (OUD) is characterized by the decision to use opioids at the expense of other activities.
Lab-based efforts to address this problem have therefore included opioid choice self-administration procedures
that incorporate a non-drug alternative to model this defining feature. Studies using these procedures have
typically scheduled competing reinforcers so that the probabilities are certain. However, such deterministic
outcomes are not representative of real-world experiences in which the consequences from drug-related choices
are often unpredictable. Importantly, decision-making in a dynamic, uncertain context significantly alters the
value of choice options and requires continuous updating of option values, which engages learning processes
and related corticostriatal networks that function abnormally in OUD. Decision-making in dynamic environments
has been successfully modeled using probabilistic reinforcement-learning choice (PRLC) tasks. The integration
of these tasks with reinforcement-learning (RL) computational modeling has been used to capture moment-to-
moment changes in the mechanisms of dynamic choice, and the application of neuroscience techniques has
begun to identify the underlying neurobiology. This approach has uncovered biologically-based decision-making
abnormalities in multiple brain disorders, but has yet to be systematically applied to the experimental study of
OUD, The translation of combined RL and neuroscience approaches to OUD is logical considering the
maladaptive choice behavior that typifies the disorder, the varying reinforcement probabilities in opioid users’
natural environments, and the learning impairments that have been documented in individuals with OUD. Thus,
there are critical gaps in our understanding of the mechanisms underlying dynamic opioid use decisions, and a
strong scientific premise for applying an RL framework to fill these gaps. This project proposes rigorous PRLC
tasks, RL modeling, neurorecording/fMRI neuroimaging techniques and complementary, translational study
designs in rats and humans. The first set of cross-species experiments will demonstrate the impact of opioid
exposure and withdrawal on dynamic decision-making and reveal the neurobehavioral and neurobiological
processes underlying abnormal task performance. The second set of experiments will use a PRLC task in which
intravenous remifentanil, a prototypical opioid agonist with a favorable safety profile, is available as an alternative
to a non-drug reinforcer to determine the behavioral and neural “profiles” associated with drug choice, as well as
the increases and decreases in drug choice that occur during withdrawal and in the presence of a large
magnitude alternative reinforcer, respectively. This project will have a significant impact on the field by
establishing the experimental application of reinforcement-learning theory to the study of maladaptive dynamic
drug-use decision-making in OUD to reveal behaviora...

## Key facts

- **NIH application ID:** 10115016
- **Project number:** 5R01DA047368-03
- **Recipient organization:** UNIVERSITY OF KENTUCKY
- **Principal Investigator:** Joshua Beckmann
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $648,345
- **Award type:** 5
- **Project period:** 2019-04-15 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10115016

## Citation

> US National Institutes of Health, RePORTER application 10115016, A Translational Determination of the Mechanisms of Maladaptive Choice in Opioid Use Disorder (5R01DA047368-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10115016. Licensed CC0.

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