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 behavioral and neural mechanisms that can be targeted for future prevention and treatment development.
Opioid use disorder (OUD) is characterized by the decision to use opioids at the expense of other activities, but lab-based studies have not modeled naturalistic, dynamic decision-making. This translational rat and human project will use an innovative reinforcement-learning approach and neuroscience methods to uncover the mechanisms of maladaptive dynamic choice in OUD in order to reveal novel prevention and treatment targets.
