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Structural differences in adolescent brains can predict alcohol misuse

Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼11...

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Main Authors: Rane, Roshan Prakash (Author) , de Man, Evert Ferdinand (Author) , Kim, JiHoon (Author) , Görgen, Kai (Author) , Tschorn, Mira (Author) , Rapp, Michael A (Author) , Banaschewski, Tobias (Author) , Bokde, Arun LW (Author) , Desrivieres, Sylvane (Author) , Flor, Herta (Author) , Grigis, Antoine (Author) , Garavan, Hugh (Author) , Gowland, Penny A (Author) , Brühl, Rüdiger (Author) , Martinot, Jean-Luc (Author) , Martinot, Marie-Laure Paillere (Author) , Artiges, Eric (Author) , Nees, Frauke (Author) , Papadopoulos Orfanos, Dimitri (Author) , Lemaitre, Herve (Author) , Paus, Tomas (Author) , Poustka, Luise (Author) , Fröhner, Juliane (Author) , Robinson, Lauren (Author) , Smolka, Michael N (Author) , Winterer, Jeanne (Author) , Whelan, Robert (Author) , Schumann, Gunter (Author) , Walter, Henrik (Author) , Heinz, Andreas (Author) , Ritter, Kerstin (Author)
Format: Article (Journal)
Language:English
Published: 26 May 2022
In: eLife
Year: 2022, Volume: 11, Pages: 1-33
ISSN:2050-084X
DOI:10.7554/eLife.77545
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.7554/eLife.77545
Verlag, kostenfrei, Volltext: https://elifesciences.org/articles/77545
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Author Notes:Roshan Prakash Rane, Evert Ferdinand de Man, JiHoon Kim, Kai Görgen, Mira Tschorn, Michael A Rapp, Tobias Banaschewski, Arun LW Bokde, Sylvane Desrivieres, Herta Flor, Antoine Grigis, Hugh Garavan, Penny A Gowland, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillere Martinot, Eric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Herve Lemaitre, Tomas Paus, Luise Poustka, Juliane Fröhner, Lauren Robinson, Michael N Smolka, Jeanne Winterer, Robert Whelan, Gunter Schumann, Henrik Walter, Andreas Heinz, Kerstin Ritter, IMAGEN consortium

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520 |a Alcohol misuse during adolescence (AAM) has been associated with disruptive development of adolescent brains. In this longitudinal machine learning (ML) study, we could predict AAM significantly from brain structure (T1-weighted imaging and DTI) with accuracies of 73 -78% in the IMAGEN dataset (n∼1182). Our results not only show that structural differences in brain can predict AAM, but also suggests that such differences might precede AAM behavior in the data. We predicted 10 phenotypes of AAM at age 22 using brain MRI features at ages 14, 19, and 22. Binge drinking was found to be the most predictable phenotype. The most informative brain features were located in the ventricular CSF, and in white matter tracts of the corpus callosum, internal capsule, and brain stem. In the cortex, they were spread across the occipital, frontal, and temporal lobes and in the cingulate cortex. We also experimented with four different ML models and several confound control techniques. Support Vector Machine (SVM) with rbf kernel and Gradient Boosting consistently performed better than the linear models, linear SVM and Logistic Regression. Our study also demonstrates how the choice of the predicted phenotype, ML model, and confound correction technique are all crucial decisions in an explorative ML study analyzing psychiatric disorders with small effect sizes such as AAM. 
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