Structural differences in adolescent brains can predict alcohol misuse
- Charité - Universitätsmedizin Berlin, Germany
- Technical University of Berlin, Germany
- Freie Universität Berlin, Germany
- University of Potsdam, Germany
- Heidelberg University, Germany
- Trinity College Dublin, Ireland
- King's College London, United Kingdom
- Université Paris-Saclay, France
- University of Vermont, United States
- University of Nottingham, United Kingdom
- Physikalisch-Technische Bundesanstalt, Germany
- Université Paris-Saclay, CNRS, INSERM, France
- University of Bordeaux, France
- University of Montreal, Canada
- Universitätsmedizin Göttingen, Germany
- TU Dresden, Germany
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.
Data availability
This is a computational study. All data analyses code including the modelling pipeline are openly provided publicly at https://github.com/RoshanRane/ML_for_IMAGEN for reuse and reproduction.Approval to use the IMAGEN dataset for this study was provided under the approval username / project code 'edeman'.
Article and author information
Author details
Roshan Prakash Rane
Dimitri Papadopoulos Orfanos
Funding
German Research Foundation (402170461-TRR 265)
- Roshan Prakash Rane
- JiHoon Kim
- Henrik Walter
- Andreas Heinz
- Kerstin Ritter
German Research Foundation (389563835)
- Kerstin Ritter
German Research Foundation (414984028-CRC 1404)
- Kerstin Ritter
German Research Foundation (XC 2002/1 Science of Intelligence" - project number 390523135")
- Kai Görgen
NSFC Research Fund for International Scientists (82150710554)
- Gunter Schumann
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Written and informed consent was obtained from all participants by the IMAGEN consortium and the study was approved by the institutional ethics committee of King's College London,University of Nottingham, Trinity College Dublin, University of Heidelberg, Technische Universität Dresden, Commissariat à l'Energie Atomique et aux Energies Alternatives, and University Medical Center at the University of Hamburg in accordance with the Declaration of Helsinki (doi:10. 1001/jama.2013.281053).For this specific data analysis project, approval was provided by the IMAGEN group to us under the approval username / project ID 'edeman'.For this specific data analysis project, approval was provided by the IMAGEN group under the approval username 'edeman'.
Copyright
© 2022, Rane et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Structural differences in adolescent brains can predict alcohol misuse
eLife 11:e77545.
https://doi.org/10.7554/eLife.77545
