Plasticity of Aversive Salience in Opioid Use Disorder

抽象/项目总结

ABSTRACT/PROJECT SUMMARY NOT-DA-20-012 Given that deaths linked to opioid addiction are at an all-time high in the United States, there is a crucial need to understand brain mechanisms impeding versus promoting recovery, a process marked by prolonged abstinence and use of internal/external resources to resolve drug problems. The broad aims of this proposal are to understand how brain circuitry implicated in the salience of aversive internal (bodily signals) and external (stressful, loss) stimuli tracks recovery within and across individuals with opioid addiction to (1) inform future targets for intervention (e.g., brain neurofeedback); and (2) aid the development of individualized treatment to reduce drug-related deaths. Within three months of treatment, 50% of substance users relapse, taking drugs to avoid aversive bodily states linked to craving, withdrawal, and stress. Our preliminary data suggest that opioid users show greater negative affect, higher intensity of internal sensations, and faster pain reactivity than healthy controls, paired with lower salience- relevant brain responses during attention to bodily signals and anticipation of monetary losses However, it is unclear whether aversive salience-related brain circuitry improves and tracks recovery during the vulnerable first three months during treatment. The specific aims of this proposal test how salience-based brain circuitry differs between 200 treatment-seeking individuals with opioid use disorder and 50 healthy individuals at four timepoints during early opioid recovery (2 weeks and 1, 2, and 3 months), identifying what self-report, behavioral, and brain variables can be used to identify opioid users at high risk for relapse versus those who maintain abstinence. Functional magnetic resonance imaging (fMRI) paradigms involving bodily awareness, monetary win/loss, and stress/drug cues, substance use assessments, and NIDA self-report/behavioral phenotyping batteries will be collected at each timepoint. Once the proposed aims are completed, the findings from this dataset will be evaluated to determine whether these prediction metrics can be validated in additional treatment-seeking samples.