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

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $426,245

## Abstract

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 a...

## Key facts

- **NIH application ID:** 9751824
- **Project number:** 5R01DA043676-04
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** PAUL W GLIMCHER
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $426,245
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9751824

## Citation

> US National Institutes of Health, RePORTER application 9751824, Computational neuroeconomic models of addiction-quantifying progression and treatment in opioid use disorder (5R01DA043676-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9751824. Licensed CC0.

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