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Neural responses to negative outcomes predict success in community-based substance use treatment.

ABSTRACT: BACKGROUND AND AIMS:Patterns of brain activation have demonstrated promise as prognostic indicators in substance dependent individuals (SDIs) but have not yet been explored in SDIs typical of community-based treatment settings. DESIGN:Prospective clinical outcome design, evaluating baseline functional magnetic resonance imaging data from the Balloon Analogue Risk Task (BART) as a predictor of 3-month substance use treatment outcomes. SETTING:Community-based substance use programs in Bloomington, Indiana, USA. PARTICIPANTS:Twenty-three SDIs (17 male, aged 18-43 years) in an intensive outpatient or residential treatment program; abstinent 1-4 weeks at baseline. MEASUREMENTS:Event-related brain response, BART performance and self-report scores at treatment onset, substance use outcome measure (based on days of use). FINDINGS:Using voxel-level predictive modeling and leave-one-out cross-validation, an elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) at baseline successfully predicted greater substance use during the 3-month study interval (P ? 0.006, cluster-corrected). This effect was robust to inclusion of significant non-brain-based covariates. A larger response to negative feedback in bilateral Amyg/aHipp was also associated with faster reward-seeking responses after negative feedback (r(23) = -0.544, P = 0.007; r(23) = -0.588, P = 0.003). A model including Amyg/aHipp activation, faster reward-seeking after negative feedback and significant self-report scores accounted for 45% of the variance in substance use outcomes in our sample. CONCLUSIONS:An elevated response to unexpected negative feedback in bilateral amygdala and anterior hippocampus (Amyg/aHipp) appears to predict relapse to substance use in people attending community-based treatment.

SUBMITTER: Forster SE

PROVIDER: S-EPMC5382058 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Neural responses to negative outcomes predict success in community-based substance use treatment.

Forster Sarah E SE Finn Peter R PR Brown Joshua W JW

Addiction (Abingdon, England) 20170203 5

<h4>Background and aims</h4>Patterns of brain activation have demonstrated promise as prognostic indicators in substance dependent individuals (SDIs) but have not yet been explored in SDIs typical of community-based treatment settings.<h4>Design</h4>Prospective clinical outcome design, evaluating baseline functional magnetic resonance imaging data from the Balloon Analogue Risk Task (BART) as a predictor of 3-month substance use treatment outcomes.<h4>Setting</h4>Community-based substance use pr ...[more]

PMID: 28029198

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Project description:Treatment outcomes for individuals with substance use disorders (SUDs) are variable and more individualized approaches may be needed. Cross-validated, machine-learning methods are well-suited for probing neural mechanisms of treatment outcomes. Our prior work applied one such approach, connectome-based predictive modeling (CPM), to identify dissociable and substance-specific neural networks of cocaine and opioid abstinence. In Study 1, we aimed to replicate and extend prior work by testing the predictive ability of the cocaine network in an independent sample of 43 participants from a trial of cognitive-behavioral therapy for SUD, and evaluating its ability to predict cannabis abstinence. In Study 2, CPM was applied to identify an independent cannabis abstinence network. Additional participants were identified for a combined sample of 33 with cannabis-use disorder. Participants underwent fMRI scanning before and after treatment. Additional samples of 53 individuals with co-occurring cocaine and opioid-use disorders and 38 comparison subjects were used to assess substance specificity and network strength relative to participants without SUDs. Results demonstrated a second external replication of the cocaine network predicting future cocaine abstinence, however it did not generalize to cannabis abstinence. An independent CPM identified a novel cannabis abstinence network, which was (i) anatomically distinct from the cocaine network, (ii) specific for predicting cannabis abstinence, and for which (iii) network strength was significantly stronger in treatment responders relative to control particpants. Results provide further evidence for substance specificity of neural predictors of abstinence and provide insight into neural mechanisms of successful cannabis treatment, thereby identifying novel treatment targets. Clinical trials registation: "Computer-based training in cognitive-behavioral therapy web-based (Man VS Machine)", registration number: NCT01442597 . "Maximizing the Efficacy of Cognitive Behavior Therapy and Contingency Management", registration number: NCT00350649 . "Computer-Based Training in Cognitive Behavior Therapy (CBT4CBT)", registration number: NCT01406899 .

Project description:ObjectiveClinical anxiety is prevalent, highly comorbid with other conditions, and associated with significant medical morbidity, disability, and public health burden. Excessive attentional deployment toward threat is a transdiagnostic dimension of anxiety seen at both initial and sustained stages of threat processing. However, group-level observations of these phenomena mask considerable within-group heterogeneity that has been linked to treatment outcomes, suggesting that a transdiagnostic, individual differences approach may capture critical, clinically relevant information.MethodSeventy clinically anxious individuals were randomized to receive 8 sessions of attention bias modification (ABM; n = 41 included in analysis), a computer-based mechanistic intervention that specifically targets initial stages of threat processing, or a sham control (n = 21). Participants completed a mixed block/event-related functional MRI task optimized to discriminate transient from sustained neural responses to threat.ResultsLarger transient responses across a wide range of cognitive-affective regions (e.g., ventrolateral prefrontal cortex, anterior cingulate cortex, amygdala) predicted better clinical outcomes following ABM, in both a priori anatomical regions and whole-brain analyses; sustained responses did not. A spatial pattern recognition algorithm using transient threat responses successfully discriminated the top quartile of ABM responders with 68% accuracy.ConclusionsNeural alterations occurring on the relatively transient timescale that is specifically targeted by ABM predict favorable clinical outcomes. Results inform how to expand on the initial promise of neurocognitive treatments like ABM by fine-tuning their clinical indications (e.g., through personalized mechanistic intervention relevant across diagnoses) and by increasing the range of mechanisms that can be targeted (e.g., through synergistic treatment combinations and/or novel neurocognitive training protocols designed to tackle identified predictors of nonresponse). (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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Neural responses to negative outcomes predict success in community-based substance use treatment.

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Neural responses to negative outcomes predict success in community-based substance use treatment.

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