Computational neuroeconomic models of addiction-quantifying progression and treatment in opioid use disorder

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Project Summary/Abstract Opioid use disorder (OUD) is a debilitating chronic disease producing a growing burden on patients, providers, and the healthcare system. From 2002 to 2013, OUD rates have more than doubled and the number of individuals seeking treatment for the first time has more than quadrupled, driving unprecedented medical, scientific, and political interest in the etiology, pathophysiology, and treatment of OUD. Excellent treatments for opioid addiction exist, but their effectiveness is limited by lack of adherence to medication, treatment dropout, and relapse. There is scant knowledge about the neural and cognitive factors associated with and perhaps underlying treatment success or failure. A key goal of the present proposal is to develop reliable objective predictors of which individuals may need additional intervention and when best to intervene, i.e., when there is a risk for imminent relapse or treatment dropout. To do so, we propose to develop and test a computational neuroeconomic approach to quantifying the behavioral and neural features of addiction during OUD treatment. This computational approach to psychiatry seeks to understand circuit-level information processing in neural systems and how these mechanisms relate to normal and pathophysiological behavior. We hypothesize that quantifying individual subject choice behavior - via a longitudinally-sampled array of neuroeconomic decision tasks and models - provides information to: (1) distinguish relevant clinical populations (patients vs. controls and patient subgroups); (2) assist clinical prognosis (future treatment efficacy); (3) dynamically track ongoing clinical status (e.g. likelihood of relapse); and (4) examine the neural basis of behavioral changes in the recovery process. Specifically, we hypothesize that clinical status during treatment is characterized by the position and trajectory of individual subjects in a multidimensional space of decision parameters (quantifying impulsivity, risk tolerance, and ambiguity attitude). To test this hypothesis, we propose to longitudinally track the behavior and neural activity of patients seeking treatment for OUD. In Aim 1, we test the hypothesis that single-timepoint multidimensional decision data provides diagnostic and prognostic information, categorizing different clinical subpopulations (OUD patients vs. controls, treatment responsive vs. treatment refractory patients). In Aim 2, we test the hypothesis that dynamic multidimensional decision data tracks and predicts time-varying changes in clinical status, including the probability of future relapse. In Aim 3, we test the hypothesis that static and dynamic features of multidimensional decision data reflect corresponding features and changes in integrated value coding in specific neural circuits. Understanding how decision-related computations reflect clinical status is critical to closing the explanatory gap between biology and behavior in addiction. If successful, this approach offers both basic scientific and translational benefits: a clearer understanding of how and why behavior changes in addiction treatment, and easily-implementable tools to monitor treatment effectiveness and clinical course in individual patients.