Identification and external validation of a problem cannabis risk network.

Abstract

Background: Cannabis use is common, particularly during emerging adulthood when brain development is ongoing, and its use is associated with harmful outcomes for a subset of people. An improved understanding of the neural mechanisms underlying risk for problem-level use is critical to facilitate the development of more effective prevention and treatment approaches.

Methods: The current study applied a whole-brain, data-driven, machine-learning approach to identify neural features predictive of problem-level cannabis use in a non-clinical sample of college students (n=191, 58% female) based on reward task functional connectivity data. We further examined whether the network identified would generalize to predict cannabis use in an independent sample of European adolescents/emerging adults (n=1320, 53% female), whether it would predict clinical characteristics among adults seeking treatment for cannabis use disorder (n=33, 9% female), and whether it was specific for predicting cannabis versus alcohol use outcomes across datasets.

Results: Results demonstrated (i) identification of a problem cannabis risk network, which (ii) generalized to predict cannabis use in an independent sample of adolescents, and (iii) linked to increased addiction severity and poorer treatment outcome in a third sample of treatment-seeking adults; further, (iv) the identified network was specific for predicting cannabis versus alcohol use outcomes across all three datasets.

Conclusions: Findings provide insight into neural mechanisms of risk for problem-level cannabis use among adolescents/emerging adults. Future work is needed to assess whether targeting this network can improve prevention and treatment outcomes.