ArticleMolecular psychiatry2021
Neuropsychosocial markers of binge drinking in young adults.
Article in Molecular psychiatry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis.Molecular psychiatry · 2025Pooled it
- Sex-specific neural activation to stress and alcohol cues in high-risk drinkers: links between orbitofrontal circuits, alcohol craving, and future drinking.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Environmental, Health, and Psychological Factors Predict Alcohol Sipping in Childhood: A Machine Learning Analysis of the ABCD Study.JAACAP open · 2025Article
- Multivariable machine learning prediction of risky alcohol use in contemporary youth.Addiction (Abingdon, England) · 2025Article
- The effects of polygenic risks for alcohol misuse on negative emotion processing in young adult binge drinkers.Translational psychiatry · 2025Article
- Lab tests can be used to predict phosphatidylethanol-measured high-risk alcohol use among people with HIV: a proof-of-concept using machine learning.Alcohol and alcoholism (Oxford, Oxfordshire) · 2025Article
- A Systematic Review of Evidence-Based Interventions to Reduce Binge Drinking.Life (Basel, Switzerland) · 2025Review
- Risk Assessment of Maladaptive Behaviors in Adolescents: Nutrition, Screen Time, Prenatal Exposure, Childhood Adversities - Adolescent Brain Cognitive Development Study.The Journal of adolescent health : official publication of the Society for Adolescent Medicine · 2025Article
- Brain Function Outcomes of Recent and Lifetime Cannabis Use.JAMA network open · 2025Article
- Personality, Social Factors, Brain Functioning, Familial Risk, and Trajectories of Alcohol Misuse in Adolescence.JAMA network open · 2024Article
- Machine Learning-Based Prediction of Binge Drinking among Adults in the United State: Analysis of the 2022 Health Information National Trends Survey.Proceedings of the 2024 9th International Conference on Mathematics and Artificial Intelligence · 2024Article
- AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.BMC bioinformatics · 2023Article
- Loss and Frontal Striatal Reactivities Characterize Alcohol Use Severity and Rule-Breaking Behavior in Young Adult Drinkers.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2022Article
- Patterns of functional connectivity alterations induced by alcohol reflect somatostatin interneuron expression in the human cerebral cortex.Scientific reports · 2022Article
- Ketamine use disorder: preclinical, clinical, and neuroimaging evidence to support proposed mechanisms of actions.Intelligent medicine · 2022Article
- Common Laboratory Parameters Are Useful for Screening for Alcohol Use Disorder: Designing a Predictive Model Using Machine Learning.Journal of clinical medicine · 2022Article
- Perceived friendship and binge drinking in young adults: A study of the Human Connectome Project data.Drug and alcohol dependence · 2021Article
- Emotional processes in binge drinking: A systematic review and perspective.Clinical psychology review · 2021Review
- Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer.Personality neuroscience · 2021Article
- Trajectories of brain development reveal times of risk and factors promoting resilience to alcohol use during adolescence.International review of neurobiology · 2021Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Binge drinking is associated with disease and death, and developing tools to identify risky drinkers could mitigate its damage. Brain processes underlie risky drinking, so we examined whether neural and psychosocial markers could identify binge drinkers. Reward is the most widely studied neural process in addiction, but processes such as emotion, social cognition, and self-regulation are also involved. Here we examined whether neural processes apart from reward contribute to predicting risky drinking behaviors. From the Human Connectome Project, we identified 177 young adults who binged weekly and 309 nonbingers. We divided the sample into a training and a testing set and used machine-learning algorithms to classify participants based on psychosocial, neural, or both (neuropsychosocial) data. We also developed separate models for each of the seven fMRI tasks used in the study. An ensemble model developed in the training dataset was then applied to the testing dataset. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) and differences between models were assessed using DeLong's test. The three models performed better than chance in the test sample with the neuropsychosocial (AUC = 0.86) and psychosocial (AUC = 0.84) performing better than the neural model (AUC = 0.64). Two fMRI-based models predicted binge drinking status better than chance, corresponding to the social and language tasks. Models developed with psychosocial and neural variables could contribute as diagnostic tools to help classify risky drinkers. Since social and language fMRI tasks performed best among the neural discriminators (including those from gambling and emotion tasks), it suggests the involvement of a broader range of brain processes than those traditionally associated with binge drinking in young adults.
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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.