The initiation of cannabis use in adolescence is predicted by sex‐specific psychosocial and neurobiological features. (15th October 2018) - Spechler, Philip A.; Allgaier, Nicholas; Chaarani, Bader; Whelan, Robert; Watts, Richard; Orr, Catherine; Albaugh, Matthew D.; D'Alberto, Nicholas; Higgins, Stephen T.; Hudson, Kelsey E.; Mackey, Scott; Potter, Alexandra; et al.
Abstract: Cannabis use initiated during adolescence might precipitate negative consequences in adulthood. Thus, predicting adolescent cannabis use prior to any exposure will inform the aetiology of substance abuse by disentangling predictors from consequences of use. In this prediction study, data were drawn from the IMAGEN sample, a longitudinal study of adolescence. All selected participants ( n  = 1, 581) were cannabis‐naïve at age 14. Those reporting any cannabis use (out of six ordinal use levels) by age 16 were included in the outcome group ( N  = 365, males n  = 207). Cannabis‐naïve participants at age 14 and 16 were included in the comparison group ( N  = 1, 216, males n  = 538). Psychosocial, brain and genetic features were measured at age 14 prior to any exposure. Cross‐validated regularized logistic regressions for each use level by sex were used to perform feature selection and obtain prediction error statistics on independent observations. Predictors were probed for sex‐ and drug‐specificity using post‐hoc logistic regressions. Models reliably predicted use as indicated by satisfactory prediction error statistics, and contained psychosocial features common to both sexes. However, males and females exhibited distinct brain predictors that failed to predict use in the opposite sex or predict binge drinking in independent samples of same‐sex participants. Collapsed across sex, genetic variation on catecholamine and opioid receptors marginally predicted use. Using machine learning techniques applied to a large multimodal dataset, we identified a risk profile containing psychosocial and sex‐specific brain prognostic markers, which were likely to precede and influence cannabis initiation. Abstract : Machine learning techniques were used to predict the initiation of cannabis use by age 16 from a large sample of cannabis‐naïve 14‐year‐olds. Neurobiological, psychosocial and genetic features were measured prior to cannabis exposure. Results identified a sparse set of structural and functional brain features that were likely to precede use. The brain features also exhibited sex‐specific and drug‐specific effects.