Journal
EUROPEAN JOURNAL OF NEUROSCIENCE
Volume 50, Issue 3, Pages 2346-2356Publisher
WILEY
DOI: 10.1111/ejn.13989
Keywords
marijuana; neuroimaging; prediction; specificity
Categories
Funding
- National Institutes of Health Center of Biomedical Research Excellence award from the National Institute of General Medical Sciences [P20GM103644]
- European Union [LSHM-CT-2007-037286]
- FP7 project IMAGEMEND [602450]
- FP7 project MATRICS [603016]
- Innovative Medicine Initiative Project EU-AIMS [115300-2]
- Medical Research Council Programme Grant Developmental pathways into adolescent substance abuse [93558]
- Swedish funding agency FORMAS
- Wellcome Trust (Behavioural and Clinical Neuroscience Institute, University of Cambridge)
- National Institute for Health Research (NIHR) Biomedical Research Centre at SouthLondon and Maudsley NHS Foundation Trust and King's College London
- Bundesministerium fur Bildung und Forschung (BMBF) [01GS08152, 01EV0711, eMED SysAlc 01ZX1311A]
- Bundesministerium fur Bildung und Forschung (BMBF
- Forschungsnetz AERIA)
- Deutsche Forschungsgemeinschaft (DFG) [SP 383/5-1, SM 80/7-1, SFB 940/1, FOR 1617]
- French MILDT (Mission Interministe rielle de Lutte contre la Drogue et la Toxicomanie)
- CENIR (Centre de NeuroImagerie de Recherche) within the ICM institute
- National Institute of Mental Health [MH082116]
- NIH Consortium grant [U54 EB020403]
- cross-NIH alliance
- Medical Research Council
- MRC [G0901858] Funding Source: UKRI
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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-naive 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-naive 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.
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