| Literature DB >> 29889330 |
Philip A Spechler1,2,3, Nicholas Allgaier3, Bader Chaarani1,3, Robert Whelan4, Richard Watts5, Catherine Orr3, Matthew D Albaugh3, Nicholas D'Alberto3, Stephen T Higgins1,2,3, Kelsey E Hudson2, Scott Mackey3, Alexandra Potter3, Tobias Banaschewski6, Arun L W Bokde7, Uli Bromberg8, Christian Büchel8, Anna Cattrell9, Patricia J Conrod10, Sylvane Desrivières9, Herta Flor11,12, Vincent Frouin13, Jürgen Gallinat14, Penny Gowland15, Andreas Heinz16, Bernd Ittermann17, Jean-Luc Martinot18, Marie-Laure Paillère Martinot19,20, Frauke Nees6,11, Dimitri Papadopoulos Orfanos13, Tomáš Paus21, Luise Poustka22,23, Michael N Smolka24, Henrik Walter16, Gunter Schumann9, Robert R Althoff2,3, Hugh Garavan1,2,3.
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.Entities:
Keywords: marijuana; neuroimaging; prediction; specificity
Mesh:
Year: 2018 PMID: 29889330 PMCID: PMC7444673 DOI: 10.1111/ejn.13989
Source DB: PubMed Journal: Eur J Neurosci ISSN: 0953-816X Impact factor: 3.386