ArticleScientific reports2018
Alcohol use effects on adolescent brain development revealed by simultaneously removing confounding factors, identifying morphometric patterns, and classifying individuals.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 16 citations in OpenAlex.
- Using deep learning to identify brain networks mediating cognitive and motor impairments in alcohol use disorder.Translational psychiatry · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.Artificial intelligence in medicine · 2025Article
- Neurocognitive Latent Space Regularization for Multi-Label Diagnosis from MRI.Predictive Intelligence in Medicine. PRIME (Workshop) · 2025Article
- A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.bioRxiv : the preprint server for biology · 2024Article
- Identifying high school risk factors that forecast heavy drinking onset in understudied young adults.Developmental cognitive neuroscience · 2024Article
- Multi-label, multi-domain learning identifies compounding effects of HIV and cognitive impairment.Medical image analysis · 2022Article
- Alcohol and the Adolescent Brain: What We've Learned and Where the Data Are Taking Us.Alcohol research : current reviews · 2022Review
- Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.IEEE journal of biomedical and health informatics · 2021Article
- Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.Personality neuroscience · 2021Article
- Training confounder-free deep learning models for medical applications.Nature communications · 2020Article
- Confounder-Aware Visualization of ConvNets.Machine learning in medical imaging. MLMI (Workshop) · 2019Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
Abstract
Group analysis of brain magnetic resonance imaging (MRI) metrics frequently employs generalized additive models (GAM) to remove contributions of confounding factors before identifying cohort specific characteristics. For example, the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) used such an approach to identify effects of alcohol misuse on the developing brain. Here, we hypothesized that considering confounding factors before group analysis removes information relevant for distinguishing adolescents with drinking history from those without. To test this hypothesis, we introduce a machine-learning model that identifies cohort-specific, neuromorphometric patterns by simultaneously training a GAM and generic classifier on macrostructural MRI and microstructural diffusion tensor imaging (DTI) metrics and compare it to more traditional group analysis and machine-learning approaches. Using a baseline NCANDA MR dataset (N = 705), the proposed machine learning approach identified a pattern of eight brain regions unique to adolescents who misuse alcohol. Classifying high-drinking adolescents was more accurate with that pattern than using regions identified with alternative approaches. The findings of the joint model approach thus were (1) impartial to confounding factors; (2) relevant to drinking behaviors; and (3) in concurrence with the alcohol literature.
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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.