# Computational psychiatry investigation of the role of unrealistic optimism in opioid use disorder and relapse

> **NIH NIH R01** · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · 2024 · $59,660

## Abstract

Project Summary/Abstract
The current opioid epidemic is a major health crisis that has contributed to decreased life expectancy in the U.S.
A main cause of morbidly and mortality is opioid reuse and relapse in chronic cases. Understanding the
neurocognitive mechanisms and factors underlying reuse vulnerability is thus a pressing need. Leveraging a
novel combination of neurocognitive tools and a multi-session longitudinal design, our recent work in opioid use
disorder (OUD) has begun to delineate a precise decision making mechanism for opioid reuse by showing that
treatment-engaged patients are at higher risk for reuse when they exhibit increased tolerance of unknown
probabilistic outcomes (ambiguity tolerance) in a financial choice task (Konova et al., 2019 JAMA Psychiatry).
But why patients become more tolerant of ambiguous uncertainty in periods preceding reuse remains unknown.
One potential explanation consistent with decision theory is that, in these periods, they become overoptimistic
about ambiguous outcomes, which leads them to overestimate the probability of good outcomes (or
underestimate bad outcomes) when faced with a decision to reuse, and therefore more likely to do so. Here, we
propose a multi-level, convergent test of this framework by using well-defined, quantitative measures of this
presumed “optimism bias”, alongside quantitative measures of uncertainty tolerance, which we propose to collect
with concurrent high-resolution fMRI recordings, and yoked to longitudinal clinical assessments. In Aim 1, we
aim to establish the relationship between uncertainty tolerance and optimism bias in patients with OUD and
matched controls by studying these behaviors across a set of choice and estimation tasks (the latter designed
to capture optimism about simple financial and more complex outcomes, tapping into drug-choice-relevant
domains such as health outcomes). We also examine for potential moderation by various psychopathological
dimensions in a large, unselected population of online (MTurk) subjects. In Aim 2, we collect fMRI data during
the same choice and estimation tasks to delineate the mechanism by which optimistic neural representations of
uncertainty might drive behavioral tolerance of this uncertainty, and reuse, in OUD. In Aim 3, we use a multi-
session longitudinal design to understand the interaction between optimism bias and uncertainty tolerance as
they relate to opioid reuse, session-to-session, allowing us to elucidate the specific timescale and nature of this
interaction. With this project we aim to provide an answer to why patients become more uncertainty tolerant in
periods preceding reuse and, in doing so, hope to uncover an upstream mechanism (centered on optimism bias)
of this vulnerability, including its neural implementation. In addition to this conceptual advance, this work will
provide a novel set of cognitive tools to precisely and objectively measure these processes with potential to
predict poor outcomes such as ...

## Key facts

- **NIH application ID:** 10937680
- **Project number:** 3R01DA053282-04S1
- **Recipient organization:** RUTGERS BIOMEDICAL AND HEALTH SCIENCES
- **Principal Investigator:** Anna Borisova Konova
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $59,660
- **Award type:** 3
- **Project period:** 2021-03-01 → 2024-11-13

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10937680

## Citation

> US National Institutes of Health, RePORTER application 10937680, Computational psychiatry investigation of the role of unrealistic optimism in opioid use disorder and relapse (3R01DA053282-04S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10937680. Licensed CC0.

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