ArticleHuman brain mapping2023
Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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Who cites it
51 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
- Your brain doesn't look a day past 70! Cross-sectional associations with brain-predicted age in the cognitively-intact oldest-old.GeroScience · 2026Article
- Improving racial fairness in brain age models using style-transfer synthesis.bioRxiv : the preprint server for biology · 2026Article
- Prediction of maternal and infant outcomes from longitudinal electronic health records with a mother-child AI agent.Nature medicine · 2026Article
- Mediation Analysis Between Brain Age, Disease-Modifying Factors, and Disability and Cognitive Performance in Multiple Sclerosis.Neurology · 2026Article
- Depression and aging: insights from brain age prediction models.Psychological medicine · 2026Article
- Elevated HbA1c is associated with advanced brain age in severe obesity.medRxiv : the preprint server for health sciences · 2026Article
- Brain Age Estimation on T2-FLAIR Scans for Application to Multiple Sclerosis.Human brain mapping · 2026Article
- Longitudinal brain-age predictions comprising long-duration spaceflight missions.NPJ microgravity · 2026Article
- A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth.Human brain mapping · 2026Article
- Association of Brain Age With Physical Disability and Cognitive Impairment in People With Multiple Sclerosis of the Same Age.Neurology · 2026Article
- Brain Age Disparities in Psychosis Across DSM Diagnoses and B-SNIP Biotypes.Schizophrenia bulletin · 2026Article
- Prediction of brain age using structural magnetic resonance imaging: a comparison of clinical utility of publicly available software packages.EBioMedicine · 2026Article
- Abdominal CT-derived metabolic phenotypes are associated with the MRI-derived brain age gap in women.Frontiers in aging neuroscience · 2026Article
- Enhancing brain age estimation with structural MRI and synthesized cerebral blood volume maps.Brain communications · 2026Article
- Brain age trajectories and cognition after stroke in two longitudinal cohorts.Brain communications · 2026Article
- Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic.Frontiers in aging neuroscience · 2026Article
- A full life cycle biological clock based on routine clinical data and its impact in health and diseases.Nature medicine · 2025Article
- Brain age prediction and early neurodegeneration detection using contrastive learning on brain biomechanics: a retrospective, multicentre study.EBioMedicine · 2025Article
- Association between low-frequency oscillations in blood pressure variability and brain age derived from neuroimaging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.
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