ArticlePLoS biology2025
Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection.
Article in PLoS biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain Aging in Specific Phobia: An ENIGMA-Anxiety Mega-Analysis.Human brain mapping · 2026Pooled it
- Resting-state brain age is associated with cognitive and sensorimotor abnormalities in schizophrenia spectrum disorders: a validation & longitudinal study.Translational psychiatry · 2026Article
- Challenges and opportunities of gap score methods for studying psychopathology resilience and vulnerability.medRxiv : the preprint server for health sciences · 2026Article
- The exposome of brain aging across 34 countries.Nature medicine · 2026Article
- Resting-State Brain Age Predicts Cognitive and Sensorimotor Deficits in Schizophrenia Spectrum Disorders: A Validation & Longitudinal Study.Research square · 2026Article
- Alzheimer's Disease Brain Phenotypes are Age-dependent.bioRxiv : the preprint server for biology · 2026Article
- Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps.Brain communications · 2026Article
- Brain age gradients as intermediate phenotypes linking plasma p-tau217 to cognition in community-dwelling older adults.NPJ dementia · 2026Article
- Prediction of brain age using structural magnetic resonance imaging: a comparison of clinical utility of publicly available software packages.EBioMedicine · 2026Article
- Shared genetic architecture of brain age gap across 30 cohorts worldwide.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
This study critically reevaluates the utility of brain-age models within the context of detecting neurological and psychiatric disorders, challenging the conventional emphasis on maximizing chronological age prediction accuracy. Our analysis of T1 MRI data from 46,381 UK Biobank participants reveals that simpler machine learning models, and notably those with excessive regularization, demonstrate superior sensitivity to disease-relevant changes compared to their more complex counterparts, despite being less accurate in chronological age prediction. This counterintuitive discovery suggests that models traditionally deemed inferior might, in fact, offer a more meaningful biomarker for brain health by capturing variations pertinent to disease states. These findings challenge the assumption that accuracy-optimized brain-age models serve as useful normative models of brain aging. Optimizing for age accuracy appears misaligned with normative aims: it drives models to rely on low-variance aging features and to deemphasize higher-variance signals that are more informative about brain health and disease. Consequently, we propose a recalibration of focus towards models that, while less accurate in conventional terms, yield brain-age gaps with larger patient-control effect sizes, offering greater utility in early disease detection and understanding the multifaceted nature of brain aging.
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Registered trials
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