TY  - JOUR
PY  - 2018//
TI  - The initiation of cannabis use in adolescence is predicted by sex-specific psychosocial and neurobiological features
JO  - European journal of neuroscience
A1  - Spechler, Philip A.
A1  - Allgaier, Nicholas
A1  - Chaarani, Bader
A1  - Whelan, Robert
A1  - Watts, Richard
A1  - Orr, Catherine
A1  - Albaugh, Matthew D.
A1  - D'Alberto, Nicholas
A1  - Higgins, Stephen T.
A1  - Hudson, Kelsey E.
A1  - Mackey, Scott
A1  - Potter, Alexandra
A1  - Banaschewski, Tobias
A1  - Bokde, Arun L. W.
A1  - Bromberg, Uli
A1  - Büchel, Christian
A1  - Cattrell, Anna
A1  - Conrod, Patricia J.
A1  - Desrivieres, Sylvane
A1  - Flor, Herta
A1  - Frouin, Vincent
A1  - Gallinat, Jürgen
A1  - Gowland, Penny
A1  - Heinz, Andreas
A1  - Ittermann, Bernd
A1  - Martinot, Jean-Luc
A1  - Martinot, Marie-Laure Paillère
A1  - Nees, Frauke
A1  - Orfanos, Dimitri Papadopoulos
A1  - Paus, Tomas
A1  - Smolka, Michael N.
A1  - Walter, Henrik
A1  - Schumann, Gunter
A1  - Althoff, Robert R.
A1  - Garavan, Hugh
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - Cannabis use initiated during adolescence might precipitate negative consequences in adulthood. Thus, predicting adolescent cannabis use prior to any exposure will inform the etiology 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 6 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. This article is protected by copyright. All rights reserved.<br><br>This article is protected by copyright. All rights reserved.<p />  <p>Language: en</p>
LA  - en
SN  - 0953-816X
UR  - http://dx.doi.org/10.1111/ejn.13989
ID  - ref1
ER  -