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Bayesian computational markers of relapse in methamphetamine dependence.

ABSTRACT: Methamphetamine use disorder is associated with a high likelihood of relapse. Identifying robust predictors of relapse that have explanatory power is critical to develop secondary prevention based on a mechanistic understanding of relapse. Computational approaches have the potential to identify such predictive markers of psychiatric illness, with the advantage of providing a finer mechanistic explanation of the cognitive processes underlying psychiatric vulnerability. In this study, sixty-two recently sober methamphetamine-dependent individuals were recruited from a 28-day inpatient treatment program, and completed a Stop Signal Task (SST) while undergoing functional magnetic resonance imaging (fMRI). These individuals were prospectively followed for 1?year and assessed for relapse to methamphetamine use. Thirty-three percent of followed participants reported relapse. We found that neural activity associated with two types of Bayesian prediction error, i.e. the difference between actual and expected need to stop on a given trial, significantly differentiated those individuals who remained abstinent and those who relapsed. Specifically, relapsed individuals exhibited smaller neural activations to such Bayesian prediction errors relative to those individuals who remained abstinent in the left temporoparietal junction (Cohen's d?=?0.91), the left inferior frontal gyrus (Cohen's d?=?0.57), and left anterior insula (Cohen's d?=?0.63). In contrast, abstinent and relapsed participants did not differ in neural activation to non-model based task contrasts or on various self-report clinical measures. In conclusion, Bayesian cognitive models may help identify predictive biomarkers of relapse, while providing a computational explanation of belief processing and updating deficits in individuals with methamphetamine use disorder.

SUBMITTER: Harle KM

PROVIDER: S-EPMC6444286 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Bayesian computational markers of relapse in methamphetamine dependence.

Harlé Katia M KM Yu Angela J AJ Paulus Martin P MP

NeuroImage. Clinical 20190326

Methamphetamine use disorder is associated with a high likelihood of relapse. Identifying robust predictors of relapse that have explanatory power is critical to develop secondary prevention based on a mechanistic understanding of relapse. Computational approaches have the potential to identify such predictive markers of psychiatric illness, with the advantage of providing a finer mechanistic explanation of the cognitive processes underlying psychiatric vulnerability. In this study, sixty-two re ...[more]

PMID: 30928810

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Project description:BackgroundStimulant drugs are second only to cannabis as the most widely used class of illicit drug globally, accounting for 68 million past-year consumers. Dependence on amphetamines (AMPH) or methamphetamine (MA) is a growing global concern. Yet, there is no established pharmacotherapy for AMPH/MA dependence. A comprehensive assessment of the research literature on pharmacotherapy for AMPH/MA dependence may inform treatment guidelines and future research directions.MethodsWe systematically reviewed the peer-reviewed literature via the electronic databases PubMed, EMBASE, CINAHL and SCOPUS for randomised controlled trials reported in the English language examining a pharmacological treatment for AMPH/MA dependence or use disorder. We included all studies published to 19 June 2019. The selected studies were evaluated for design; methodology; inclusion and exclusion criteria; sample size; pharmacological and (if included) psychosocial interventions; length of follow-up and follow-up schedules; outcome variables and measures; results; overall conclusions and risk of bias. Outcome measures were any reported impact of treatment related to AMPH/MA use.ResultsOur search returned 43 studies that met our criteria, collectively enrolling 4065 participants and reporting on 23 individual pharmacotherapies, alone or in combination. Disparate outcomes and measures (n = 55 for the primary outcomes) across studies did not allow for meta-analyses. Some studies demonstrated mixed or weak positive signals (often in defined populations, e.g. men who have sex with men), with some variation in efficacy signals dependent on baseline frequency of AMPH/MA use. The most consistent positive findings have been demonstrated with stimulant agonist treatment (dexamphetamine and methylphenidate), naltrexone and topiramate. Less consistent benefits have been shown with the antidepressants bupropion and mirtazapine, the glutamatergic agent riluzole and the corticotropin releasing factor (CRF-1) antagonist pexacerfont; whilst in general, antidepressant medications (e.g. selective serotonin reuptake inhibitors [SSRIs], tricyclic antidepressants [TCAs]) have not been effective in reducing AMPH/MA use.ConclusionsNo pharmacotherapy yielded convincing results for the treatment of AMPH/MA dependence; mostly studies were underpowered and had low treatment completion rates. However, there were positive signals from several agents that warrant further investigation in larger scale studies; agonist therapies show promise. Common outcome measures should include change in use days. Future research must address the heterogeneity of AMPH/MA dependence (e.g. coexisting conditions, severity of disorder, differences between MA and AMPH dependence) and the role of psychosocial intervention.

Dataset Information

Bayesian computational markers of relapse in methamphetamine dependence.

Publications

Bayesian computational markers of relapse in methamphetamine dependence.

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