ReviewPatterns (New York, N.Y.)2023
A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility.
Review in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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
33 citing papers in PubMed, 61 citations in OpenAlex.
- Tissue- and compartment-resolved brain age reveals vascular-metabolic and neurodegenerative aging.Research square · 2026Article
- Divergent Multimodal Age-Association Profiles Across the Human Brain.Nature communications · 2026Article
- Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study.Translational psychiatry · 2026Article
- Metabolomic signatures of brain aging: A multimodal and genetic study.Molecular psychiatry · 2026Article
- Research progress and challenges of multimodal deep learning models for prognosis of gastric cancer.Discover oncology · 2026Review
- Association of imaging-defined brain age with disease severity and adverse outcomes in CADASIL.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Aging and metabolism contribute separately to brain-body health.PLoS biology · 2026Article
- The moderating role of psychological resilience on brain aging and PTSD in a community sample of South African women.Neuroscience · 2026Article
- Differential tractography suggests a "left-to-whole" white matter degeneration pattern in aging.Brain imaging and behavior · 2026Article
- Brain Age Disparities in Psychosis Across DSM Diagnoses and B-SNIP Biotypes.Schizophrenia bulletin · 2026Article
- The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.Schizophrenia bulletin · 2026Article
- Brain Age as a Biomarker in Alzheimer's Disease: Narrative Perspectives on Imaging, Biomarkers, Machine Learning, and Intervention Potential.Brain sciences · 2025Review
- Potential vulnerability and resilience to accelerated brain aging in women exposed to stressful life events: insights from the brain age prediction model.Neurobiology of stress · 2025Article
- Disentangling Neurodegeneration with Brain Age Gap Prediction Models: A Graph Signal Processing Perspective.ArXiv · 2025Article
- Integration of Genetic Information to Improve Brain Age Gap Estimation Models in the UK Biobank.Human brain mapping · 2025Article
- Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection.PLoS biology · 2025Article
- Mapping Multimodal Risk Factors to Mental Health Outcomes.Nature. Mental health · 2025Article
- Rate of brain aging associates with future executive function in Asian children and older adults.eLife · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Temporal autocorrelation is predictive of age-An extensive MEG time-series analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain aging is a complex, multifaceted process that can be challenging to model in ways that are accurate and clinically useful. One of the most common approaches has been to apply machine learning to neuroimaging data with the goal of predicting age in a data-driven manner. Building on initial brain age studies that were derived solely from T1-weighted scans (i.e., unimodal), recent studies have incorporated features across multiple imaging modalities (i.e., "multimodal"). In this systematic review, we show that unimodal and multimodal models have distinct advantages. Multimodal models are the most accurate and sensitive to differences in chronic brain disorders. In contrast, unimodal models from functional magnetic resonance imaging were most sensitive to differences across a broad array of phenotypes. Altogether, multimodal imaging has provided us valuable insight for improving the accuracy of brain age models, but there is still much untapped potential with regard to achieving widespread clinical utility.
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What Socratic holds
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 Socratic graph.