Structural differences in adolescent brains can predict alcohol misuse
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
- Charité – Universitätsmedizin Berlin
- Technische Universität Berlin
- Freie Universität (FU) Berlin
- Science of Intelligence
- Universität Potsdam
- Universität Heidelberg
- Trinity College Dublin
- King's College London (KCL)
- Universität Mannheim
- Université Paris-Saclay
- University of Vermont
- University of Nottingham
- Physikalisch-Technische Bundesanstalt
- École normale supérieure Paris-Saclay
- Assistance publique – Hôpitaux de Paris
- EPS Barthélémy Durand
- Humboldt-Universität zu Berlin
- Université de Bordeaux
- University of Montreal
- University of Toronto
- Georg-August-Universität Göttingen
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 ten 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.
Details
Originalsprache | Englisch |
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Aufsatznummer | e77545 |
Seitenumfang | 33 |
Fachzeitschrift | eLife |
Jahrgang | 11 |
Publikationsstatus | Elektronische Veröffentlichung vor Drucklegung - 26 Mai 2022 |
Peer-Review-Status | Ja |
Externe IDs
PubMed | 35616520 |
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ORCID | /0000-0001-5398-5569/work/150329530 |
ORCID | /0000-0002-8493-6396/work/150330253 |
Schlagworte
Ziele für nachhaltige Entwicklung
ASJC Scopus Sachgebiete
Schlagwörter
- adolescence alcohol misuse, alcohol use disorder, confound control, machine learning, magnetic resonance imaging, multivariate analysis, psychiatric research