ArticleNeuroImage. Clinical2019
Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users.
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 65 citations in OpenAlex.
- Relating Functional Connectivity and Alcohol Use Disorder: A Systematic Review and Derivation of Relevance Maps for Regions and Connections.Human brain mapping · 2025Pooled it
- Default Mode Network Modulation by Psychedelics: A Systematic Review.The international journal of neuropsychopharmacology · 2023Pooled it
- Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence.Psychiatry and clinical neurosciences · 2024Trial
- The effects of nalmefene on the impulsive and reflective system in alcohol use disorder: A resting-state fMRI study.Psychopharmacology · 2022Trial
- The Use of Machine Learning to Predict Offline Adolescent e-Cigarette Use: a Proof-of-Concept.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Neurodevelopment of Corticostriatal Circuits and Risk for Alcohol Use During the Transition from Adolescence to Adulthood.bioRxiv : the preprint server for biology · 2026Article
- Neurobiological subtypes in alcohol use disorder and their phenotypic and clinical profiles.Molecular psychiatry · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- The effects of polygenic risks for alcohol misuse on negative emotion processing in young adult binge drinkers.Translational psychiatry · 2025Article
- Prediction of alcohol intake patterns with olfactory and gustatory brain connectivity networks.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2025Article
- Predicting alcohol use disorder risk in firefighters using a multimodal deep learning model: a cross-sectional study.Frontiers in psychiatry · 2025Article
- Cognitive effort devaluation and the salience network: a computational model of amotivation in depression.Frontiers in psychiatry · 2025Article
- Neural correlates of executive dysfunction in alcohol use disorder: preliminary evidence fromFrontiers in psychology · 2025Article
- Triple network resting-state functional connectivity patterns of alcohol heavy drinking.Alcohol and alcoholism (Oxford, Oxfordshire) · 2024Article
- Transcranial Magnetic Stimulation as a Tool to Promote Smoking Cessation and Decrease Drug and Alcohol Use.Brain sciences · 2023Review
- Adolescent alcohol use is linked to disruptions in age-appropriate cortical thinning: an unsupervised machine learning approach.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Neural mechanisms underlying empathy during alcohol abstinence: evidence from connectome-based predictive modeling.Brain imaging and behavior · 2022Article
- Compounding Vulnerability in the Neurocircuitry of Addiction: Longitudinal Functional Connectivity Changes in Alcohol Use Disorder.Alcohol and alcoholism (Oxford, Oxfordshire) · 2022Article
- Alcohol use disorder-associated structural and functional characteristics of the insula.Journal of neuroscience research · 2022Article
- Differential association between the GLP1R gene variants and brain functional connectivity according to the severity of alcohol use.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIn the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences.
methodsAdults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24).
resultsThe resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model.
conclusionsThese findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.
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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.