Neuroscience-Informed Classification of Prevention Interventions in Substance Use Disorders: An RDoC-based Approach

Abstract

Neuroscience has contributed to uncover the mechanisms underpinning substance use disorders (SUD). The next frontier is to leverage these mechanisms as active ingredients to create more effective interventions for SUD. Recent large-scale cohort studies are generating multiple levels of neuroscience-based information with potential to inform the development and refinement of future preventive strategies. However, there is still no available well-recognized frameworks to guide the integration of these complex datasets into prevention trial protocols. The Research Domain Criteria (RDoC) provides a neuroscience-based multi-system framework that is well suited to facilitate translation of neurobiological mechanisms into behavioural domains amenable to preventative interventions. We propose a novel RDoC-based framework for prevention science that organizes and advances the integration of technologies and findings from neuroscience into the refinement of current and construction of future preventive and early interventions. This neuroscience-informed framework categorizes addiction risk factors within the dysfunction of the five major RDoC constructs (Negative Valence Systems, Positive Valence Systems, Cognitive Systems, Arousal and Regulatory Systems, and Social Processes). We adapted the framework for the existing preventive interventions and categorized their components using RDoC constructs. From a systematic review of randomized controlled trials using a person-centered drug/alcohol preventive approach for adolescents (13-18 years), we identified 98 trials on 37 preventive interventions. We categorized them within this framework based on their potential target(s). By using this neuroscience-informed framework, distinct neurocognitive trajectories which have been recognized as precursors or risk factors for SUDs, can be targeted, and more importantly, the change processes can be evaluated to inform causal hypotheses. This framework can also inform individualized assessment, intervention development and outcome measurement in preventive interventions.

Utility of Computational Approaches for Precision Psychiatry: Applications to Substance Use Disorders

“…Though this framework has been applied primarily to alcohol use disorder ( 5), more recently it has been extended to other types of substance use disorders (SUD), including opioid use disorder, cocaine use disorder, and cannabis use disorder (6,7). It has been employed predominantly in the context of assessment and treatment of SUD ( 8), but has recently been proposed for SUD prevention (9). A nontrivial practical limitation of the RDoC approach is that the multi-dimensional assessment that it requires entails administering lengthy assessment batteries, which may take up to 10 hours of testing (1).…”