UL1 Pilot Award - Determination of the Neurobehavioral Effects of Opioid Cues on Value-Based Decision-Making in People with Opioid Use Disorder and Physical Dependence on Fentanyl

Disordered opioid use is characterized by maladaptive decisions. Gaining a better understanding of the neurobehavioral mechanisms underlying these decisions will improve prevention and treatment for OUD. Decision theory research has demonstrated a role for reinforcement learning (RL), and that decisions can often be explained by individuals selecting the option with the highest reward value (value-based decision-making; VBDM). Reward value is determined by objective and subjective variables, with choice behavior being an observable index of relative reward value. Decision-making occurs in contexts in which reward attributes and individuals change, requiring that predicted option reward values be updated continually. Reward value updating occurs via reward prediction errors, which are encoded by a dopamine signal originating in the midbrain and processed through multiple interacting brain circuits to guide future choices. We have combined dynamic choice tasks, RL modeling and fMRI to examine VBDM as a function of drug use disorder (OUD, cocaine [CUD] or cannabis, and controls), which revealed a common characteristic: SUD groups made fewer “rich” decisions and were less likely to use option value to guide decisions. Drug cues promote relapse and shift choice behavior, but the mechanisms of cue-induced preferences are not well understood. This project proposes to employ opioid- and neutral-cued choice tasks, RL modeling and fMRI to determine how opioid cues influence decisions in pwOUD (n=16). These discoveries will contribute new scientific knowledge, including novel intervention (e.g., neuromodulation) targets, and will provide critical pilot data to support an extramural application to determine the effects of opioid withdrawal on opioid-cued choices.