ArticleeLife2022
Structural differences in adolescent brains can predict alcohol misuse.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 22 citations in OpenAlex.
- Evaluating clinical and neuroimaging predictors for cognitive-behavioral therapy outcome in obsessive-compulsive disorder.Scientific reports · 2026Article
- The brain age gap as a predictor of alcohol initiation in adolescence.Developmental cognitive neuroscience · 2026Article
- Brain network-based stratification of mental health disorders: design and cohort description of the STRATIFY and ESTRA studies.Molecular psychiatry · 2026Review
- The role of the orbitofrontal cortex in smoking cue-reactivity in onset phase of smoking behavior, a fMRI study in adolescents.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2026Article
- Structural connectome architecture and biological vulnerability shape cortical atrophy in cocaine use disorder.BMC medicine · 2026Article
- The Brain Age Gap as a Predictor of Alcohol Initiation in Adolescence.bioRxiv : the preprint server for biology · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Connectomics modeling of regional networks of white-matter fractional anisotropy to predict the severity of young adult drinking.Quantitative imaging in medicine and surgery · 2025Article
- Alcohol-Induced Changes in Brain Microstructure: Uncovering Novel Pathophysiological Mechanisms of AUD Using Translational DTI in Humans and Rodents.Current topics in behavioral neurosciences · 2025Review
- The CB1 receptor: linking impulsivity and substance use disorder.Frontiers in neuroscience · 2025Review
- Adolescent alcohol consumption predicted by differences in electrophysiological functional connectivity and neuroanatomy.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Structure-function coupling in white matter uncovers the hypoconnectivity in autism spectrum disorder.Molecular autism · 2024Article
- The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.Addiction biology · 2024Review
- Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking.Brain sciences · 2024Article
- Alcohol use and grey matter structure: Disentangling predispositional and causal contributions in human studies.Addiction biology · 2023Review
- Adolescent Neurodevelopment Within the Context of Impulsivity and Substance Use.Current addiction reports · 2023Article
- Machine learning-based prediction of clinical outcomes after first-ever ischemic stroke.Frontiers in neurology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
32 authors at 18 institutions in 7 countries.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.