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.
Metrics
-
- 1,822
- views
-
- 346
- downloads
-
- 16
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Structural differences in adolescent brains can predict alcohol misuse
eLife 11:e77545.
https://doi.org/10.7554/eLife.77545
Further reading
-
- Biochemistry and Chemical Biology
- Computational and Systems Biology
Protein–protein interactions are fundamental to understanding the molecular functions and regulation of proteins. Despite the availability of extensive databases, many interactions remain uncharacterized due to the labor-intensive nature of experimental validation. In this study, we utilized the AlphaFold2 program to predict interactions among proteins localized in the nuage, a germline-specific non-membrane organelle essential for piRNA biogenesis in Drosophila. We screened 20 nuage proteins for 1:1 interactions and predicted dimer structures. Among these, five represented novel interaction candidates. Three pairs, including Spn-E_Squ, were verified by co-immunoprecipitation. Disruption of the salt bridges at the Spn-E_Squ interface confirmed their functional importance, underscoring the predictive model’s accuracy. We extended our analysis to include interactions between three representative nuage components—Vas, Squ, and Tej—and approximately 430 oogenesis-related proteins. Co-immunoprecipitation verified interactions for three pairs: Mei-W68_Squ, CSN3_Squ, and Pka-C1_Tej. Furthermore, we screened the majority of Drosophila proteins (~12,000) for potential interaction with the Piwi protein, a central player in the piRNA pathway, identifying 164 pairs as potential binding partners. This in silico approach not only efficiently identifies potential interaction partners but also significantly bridges the gap by facilitating the integration of bioinformatics and experimental biology.
-
- Computational and Systems Biology
- Neuroscience
Accumulating evidence to make decisions is a core cognitive function. Previous studies have tended to estimate accumulation using either neural or behavioral data alone. Here, we develop a unified framework for modeling stimulus-driven behavior and multi-neuron activity simultaneously. We applied our method to choices and neural recordings from three rat brain regions—the posterior parietal cortex (PPC), the frontal orienting fields (FOF), and the anterior-dorsal striatum (ADS)—while subjects performed a pulse-based accumulation task. Each region was best described by a distinct accumulation model, which all differed from the model that best described the animal’s choices. FOF activity was consistent with an accumulator where early evidence was favored while the ADS reflected near perfect accumulation. Neural responses within an accumulation framework unveiled a distinct association between each brain region and choice. Choices were better predicted from all regions using a comprehensive, accumulation-based framework and different brain regions were found to differentially reflect choice-related accumulation signals: FOF and ADS both reflected choice but ADS showed more instances of decision vacillation. Previous studies relating neural data to behaviorally inferred accumulation dynamics have implicitly assumed that individual brain regions reflect the whole-animal level accumulator. Our results suggest that different brain regions represent accumulated evidence in dramatically different ways and that accumulation at the whole-animal level may be constructed from a variety of neural-level accumulators.
