eLife (May 2022)
Structural differences in adolescent brains can predict alcohol misuse
- 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
Affiliations
- Roshan Prakash Rane
- ORCiD
- Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
- Evert Ferdinand de Man
- Faculty IV – Electrical Engineering and Computer Science, Technische Universität Berlin, Berlin, Germany
- JiHoon Kim
- ORCiD
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
- Kai Görgen
- ORCiD
- Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany; Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
- Mira Tschorn
- Social and Preventive Medicine, Department of Sports and Health Sciences, Intra-faculty unit “Cognitive Sciences”, Faculty of Human Science, and Faculty of Health Sciences Brandenburg, Research Area Services Research and e-Health, University of Potsdam, Potsdam, Germany
- Michael A Rapp
- Social and Preventive Medicine, Department of Sports and Health Sciences, Intra-faculty unit “Cognitive Sciences”, Faculty of Human Science, and Faculty of Health Sciences Brandenburg, Research Area Services Research and e-Health, University of Potsdam, Potsdam, Germany
- Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Arun LW Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Sylvane Desrivieres
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology Neuroscience SGDP Centre, King’s College London, London, United Kingdom
- Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany; Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
- Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, Paris, France
- Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, United States
- Penny A Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
- Rüdiger Brühl
- ORCiD
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
- Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 ”Trajectoires développementales en psychiatrie” Universite Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France
- Marie-Laure Paillere Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 ”Trajectoires développementales en psychiatrie” Universite Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France; AP-HP Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 ”Trajectoires développementales en psychiatrie” Universite Paris-Saclay, Ecole Normale Supérieure Paris-Saclay, CNRS, Centre Borelli, Gif-sur-Yvette, France; Psychiatry Department, EPS Barthélémy Durand, Etampes, France
- Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany; PONS Research Group, Dept of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin, Germany
- Dimitri Papadopoulos Orfanos
- ORCiD
- NeuroSpin, CEA, Université Paris-Saclay, Paris, France
- Herve Lemaitre
- NeuroSpin, CEA, Université Paris-Saclay, Paris, France; Institut des Maladies Neurodégénératives, UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France
- Tomas Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Canada; Departments of Psychiatry and Psychology, University of Toronto, Toronto, Canada
- Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, Göttingen, Germany
- Juliane Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- Lauren Robinson
- Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Michael N Smolka
- ORCiD
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- Jeanne Winterer
- Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany; Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
- Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Gunter Schumann
- PONS Research Group, Dept of Psychiatry and Psychotherapy, Campus Charite Mitte, Humboldt University, Berlin, Germany
- Henrik Walter
- Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
- Andreas Heinz
- Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
- Kerstin Ritter
- Charité – Universitätsmedizin Berlin (corporate member of Freie Universiät at Berlin, Humboldt-Universiät at zu Berlin, and Berlin Institute of Health), Department of Psychiatry and Psychotherapy, Bernstein Center for Computational Neuroscience, Berlin, Germany
- IMAGEN consortium
- DOI
- https://doi.org/10.7554/eLife.77545
- Journal volume & issue
-
Vol. 11
Abstract
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.
Keywords
- adolescence alcohol misuse
- machine learning
- data science for psychiatry
- alcohol use disorder
- magnetic resonance imaging
- confound control
