Computational Neuroeconomic Decision-Making Trajectories as Predictors of Clinical Outcomes for Opioid Use Disorder

Silvia Lopez-Guzman

Research output: Contribution to journalMeeting Abstractpeer-review

1 Scopus citations


Decision-making is a principal target of computational investigation of both normal and pathological behavior. It is notably altered in addiction, where impulsive and risky behaviors are common and associated with changes in neural circuits related to reinforcement learning, reward valuation, and choice selection. While it is clear that decision-making behavior differentiates addicted individuals from controls, a more clinically-relevant question is whether it is informative in predicting deleterious outcomes such as relapse and treatment dropout. Several studies have pointed to personality and sociodemographic variables as potential prognosis predictors for these outcomes, but few have also incorporated individual decision-making task measures and taken into account their dynamics in time. To address this issue, we measured changes through time-in-treatment using three established neuroeconomic assays, offering an algorithmic framework for studying impulsivity, tolerance for known risks, and tolerance for unknown risks, in a cohort of patients with opioid use disorder (OUD) followed for up to seven months of medication for OUD (MOUD).
Original languageEnglish
Pages (from-to)39-40
Issue numberSUPPL 1
StatePublished - Dec 2019

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