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

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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 reuse, in a way that can be easily implemented in clinical settings. Finally, the findings from this work will inform novel therapeutic interventions by providing precise neurocognitive targets, as well as their ideal timing, with the goal of mitigating reuse risk and improving long-term patient outcome.