ArticleHuman brain mapping2024
BrainAGE: Revisited and reframed machine learning workflow.
Article in Human brain mapping, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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Who cites it
19 citing papers in PubMed, 19 citations in OpenAlex.
- Impact of meditation on brain age derived from multimodal neuroimaging in experts and older adults from a randomized trial.Scientific reports · 2025Trial
- Physical Activity and BrainAGE: Exploring the Impact on Brain Health and Plasticity in Older Adults.Human brain mapping · 2025Trial
- Elevated BrainAGE precedes cognitive impairment and improves prediction of future cognitive decline.medRxiv : the preprint server for health sciences · 2026Article
- Observational
- Early Adulthood Signatures of Motherhood in Brain Aging.Human brain mapping · 2026Article
- TACR3 variant confers resilience to aging and Alzheimer's disease.medRxiv : the preprint server for health sciences · 2026Article
- Aging beyond diagnosis: the MRI brain age gap across disorders.GeroScience · 2026Review
- [Research progress on intelligent brain age prediction methods in diagnosis of Parkinson's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- NeuroFM: Toward Precision Neuroimaging with Foundation Models for Individualized Brain Health Estimation.medRxiv : the preprint server for health sciences · 2026Article
- Article
- Brain activity as a candidate biomarker for personalised caffeine treatment in premature neonates.Frontiers in pediatrics · 2026Article
- Accelerated Brain Aging, Atherogenicity, and Neurocognition in Adult Survivors of Childhood Cancer.JAMA network open · 2025Article
- Regional brain aging patterns reveal disease-specific pathways of neurodegeneration.medRxiv : the preprint server for health sciences · 2025Article
- Multiple Sclerosis Relapse Treatment During Pregnancy and Offspring Functional and Structural Neurodevelopment: A Cross-Sectional Study.Neurology · 2025Observational
- Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?Human brain mapping · 2025Article
- Caregiving is associated with lower brain age in humans.Social cognitive and affective neuroscience · 2025Article
- A Case for estradiol: younger brains in women with earlier menarche and later menopause.GigaScience · 2025Article
- Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.Tomography (Ann Arbor, Mich.) · 2024Review
- BrainAGE: Revisited and reframed machine learning workflow.Human brain mapping · 2024Article
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 2 institutions in 1 country.
Funding
Abstract
Since the introduction of the BrainAGE method, novel machine learning methods for brain age prediction have continued to emerge. The idea of estimating the chronological age from magnetic resonance images proved to be an interesting field of research due to the relative simplicity of its interpretation and its potential use as a biomarker of brain health. We revised our previous BrainAGE approach, originally utilising relevance vector regression (RVR), and substituted it with Gaussian process regression (GPR), which enables more stable processing of larger datasets, such as the UK Biobank (UKB). In addition, we extended the global BrainAGE approach to regional BrainAGE, providing spatially specific scores for five brain lobes per hemisphere. We tested the performance of the new algorithms under several different conditions and investigated their validity on the ADNI and schizophrenia samples, as well as on a synthetic dataset of neocortical thinning. The results show an improved performance of the reframed global model on the UKB sample with a mean absolute error (MAE) of less than 2 years and a significant difference in BrainAGE between healthy participants and patients with Alzheimer's disease and schizophrenia. Moreover, the workings of the algorithm show meaningful effects for a simulated neocortical atrophy dataset. The regional BrainAGE model performed well on two clinical samples, showing disease-specific patterns for different levels of impairment. The results demonstrate that the new improved algorithms provide reliable and valid brain age estimations.
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What OpenQuestion 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 OpenQuestion graph.