Identification and External Validation of a Problem Cannabis Risk Network

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.

In the current study, we applied a whole-brain, data-driven, machine learning approach to identify neural features predictive of problem-level cannabis use in a nonclinical sample of college students (n = 191, 58% female) based on reward task functional connectivity data. We further examined whether the identified network 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 demonstrated identification of a problem cannabis risk network, which generalized to predict cannabis use in an independent sample of adolescents and was linked to increased addiction severity and poorer treatment outcome in a third sample of treatment-seeking adults. Furthermore, the identified network was specific for predicting cannabis versus alcohol use outcomes across all 3 datasets.

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.