ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2019
Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Predicting alcohol dependence from multi-site brain structural measures.Human brain mapping · 2022Pooled it
- Integrating Anatomical Priors Into a Causal Diffusion Model.IEEE transactions on medical imaging · 2026Article
- Leveraging Machine Learning to Advance Alcohol Research: Current Applications, Challenges, and Opportunities.Alcohol research : current reviews · 2026Review
- Physiological and brain mechanisms contributing to postural tremor in aging with and without alcohol use disorder.Translational psychiatry · 2025Article
- Multi-domain predictors of grip strength differentiate individuals with and without alcohol use disorder.Addiction biology · 2024Article
- Anterior and posterior thalamic volumes differentially correlate with memory, attention, and motor processes in HIV infection and alcohol use disorder comorbidity.Brain research bulletin · 2024Article
- Experimentally Induced Reductions in Alcohol Consumption and Brain, Cognitive, and Clinical Outcomes in Older Persons With and Those Without HIV Infection (30-Day Challenge Study): Protocol for a Nonrandomized Clinical Trial.JMIR research protocols · 2024Article
- Unveiling neuroimmunology profile of immunological non-responders in HIV: a multimodal MRI approach.Frontiers in immunology · 2024Article
- Aging, HIV infection, and alcohol exert synergist effects on regional thalamic volumes resulting in functional impairment.NeuroImage. Clinical · 2024Article
- Brain function abnormalities and neuroinflammation in people living with HIV-associated anxiety disorders.Frontiers in psychiatry · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV.The Journal of infectious diseases · 2023Article
- Imaging of Brain Structural and Functional Effects in People With Human Immunodeficiency Virus.The Journal of infectious diseases · 2023Review
- Effects of unhealthy alcohol use on brain morphometry and neurocognitive function among people with HIV.Journal of neurovirology · 2022Article
- Neuroimaging and Cognitive Evidence for Combined HIV-Alcohol Effects on the Central Nervous System: A Review.Alcoholism, clinical and experimental research · 2021Article
- Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data.Journal of neurovirology · 2021Article
- Deep Learning Analysis of Cerebral Blood Flow to Identify Cognitive Impairment and Frailty in Persons Living With HIV.Journal of acquired immune deficiency syndromes (1999) · 2019Article
- Data-Driven Exploration of Brain Structure Using Statistical Machine Learning: Validity of Derived Diagnostic Patterns in Alcohol Use Disorder and Human Immunodeficiency Virus Infection.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2019Article
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5 authors at 2 institutions in 1 country.
Funding
Abstract
The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study documents complex radiologically identified, neuroanatomical effects of AUD+HIV comorbidity by identifying structural brain systems that predicted diagnosis on an individual basis. Applying novel machine learning analysis to 549 participants (199 control subjects, 222 with AUD, 68 with HIV, 60 with AUD+HIV), 298 magnetic resonance imaging brain measurements were automatically reduced to small subsets per group. Significance of each diagnostic pattern was inferred from its accuracy in predicting diagnosis and performance on six cognitive measures. While all three diagnostic patterns predicted the learning and memory score, the AUD+HIV pattern was the largest and had the highest predication accuracy (78.1%). Providing a roadmap for analyzing large, multimodal datasets, the machine learning analysis revealed imaging phenotypes that predicted diagnostic membership of magnetic resonance imaging scans of individuals with AUD, HIV, and their comorbidity.
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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.