ArticleNeuroscience letters2018
Random forest based classification of alcohol dependence patients and healthy controls using resting state MRI.
Article in Neuroscience letters, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 3 syntheses or guidelines pooled it, 79 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
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- When attention falters: Brain, breathing, and behavioral signals of lapses in interoceptive attention.Cognitive, affective & behavioral neuroscience · 2026Article
- Classification of Adolescent Drinking via Behavioral, Biological, and Environmental Features: A Machine Learning Approach with Bias Control.medRxiv : the preprint server for health sciences · 2026Article
- When attention falters: brain, breathing, and behavioral signals of lapses in interoceptive attention.bioRxiv : the preprint server for biology · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.Nature. Mental health · 2025Article
- Limited generalizability of dynamic fMRI correlates of adolescent rumination.bioRxiv : the preprint server for biology · 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
- Perspective on Using Artificial Intelligence in Alcohol Research and Treatment: Opportunities and Ethical Considerations.Alcohol research : current reviews · 2025Review
- Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Why experimental variation in neuroimaging should be embraced.Nature communications · 2024Review
- Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.Translational psychiatry · 2024Article
- Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis.Brain sciences · 2024Article
- Widespread, depth-dependent cortical microstructure alterations in pediatric focal epilepsy.Epilepsia · 2024Article
- Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- The Moderating Role of Sex in the Relation between Cue-Induced Craving and Resting-State Functional Connectivity in the Salience Network of Non-Clinically Diagnosed Drinkers.European addiction research · 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
5 authors at 1 institution in 1 country.
Funding
Abstract
Currently, classification of alcohol use disorder (AUD) is made on clinical grounds; however, robust evidence shows that chronic alcohol use leads to neurochemical and neurocircuitry adaptations. Identifications of the neuronal networks that are affected by alcohol would provide a more systematic way of diagnosis and provide novel insights into the pathophysiology of AUD. In this study, we identified network-level brain features of AUD, and further quantified resting-state within-network, and between-network connectivity features in a multivariate fashion that are classifying AUD, thus providing additional information about how each network contributes to alcoholism. Resting-state fMRI were collected from 92 individuals (46 controls and 46 AUDs). Probabilistic Independent Component Analysis (PICA) was used to extract brain functional networks and their corresponding time-course for AUD and controls. Both within-network connectivity for each network and between-network connectivity for each pair of networks were used as features. Random forest was applied for pattern classification. The results showed that within-networks features were able to identify AUD and control with 87.0% accuracy and 90.5% precision, respectively. Networks that were most informative included Executive Control Networks (ECN), and Reward Network (RN). The between-network features achieved 67.4% accuracy and 70.0% precision. The between-network connectivity between RN-Default Mode Network (DMN) and RN-ECN contribute the most to the prediction. In conclusion, within-network functional connectivity offered maximal information for AUD classification, when compared with between-network connectivity. Further, our results suggest that connectivity within the ECN and RN are informative in classifying AUD. Our findings suggest that machine-learning algorithms provide an alternative technique to quantify large-scale network differences and offer new insights into the identification of potential biomarkers for the clinical diagnosis 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.