ArticleHuman brain mapping2023
Probing multiple algorithms to calculate brain age: Examining reliability, relations with demographics, and predictive power.
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 27 papers.
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Who cites it
27 citing papers in PubMed.
- Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial.Dialogues in clinical neuroscience · 2024Trial
- Your brain doesn't look a day past 70! Cross-sectional associations with brain-predicted age in the cognitively-intact oldest-old.GeroScience · 2026Article
- Association of cumulative deficit frailty with brain age and Alzheimer's disease-related brain structure starting in late-middle age.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- A Systematic Evaluation of the Performance of Multiple Brain Age Algorithms in Two Cohorts of Youth.Human brain mapping · 2026Article
- The impact of balance exercise on brain age and brain morphometry: insights from MRI analysis.Aging clinical and experimental research · 2026Observational
- Applications of machine learning and natural language processing to neurocognitive outcomes in posttreatment cancer survivors: a scoping review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Article
- Prediction of brain age using structural magnetic resonance imaging: a comparison of clinical utility of publicly available software packages.EBioMedicine · 2026Article
- Ultra-fast MRI for brain-age prediction in a real-world cognitive disorders clinic.Frontiers in aging neuroscience · 2026Article
- Shared genetic architecture of brain age gap across 30 cohorts worldwide.medRxiv : the preprint server for health sciences · 2025Article
- Racial and Ethnic Disparities in Brain Age Algorithm Performance: Investigating Bias Across Six Popular Methods.medRxiv : the preprint server for health sciences · 2025Article
- Unraveling Microstructural and Macrostructural Brain Age Dynamics in Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2025Article
- Epigenetic age acceleration in peripheral blood correlates with brain-MRI age acceleration.Brain : a journal of neurology · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Repeatability and reproducibility of brain age estimates in multiple sclerosis for three publicly available models.Neuroimage. Reports · 2025Article
- Increased Brain Age Among Psychiatrically Healthy Adults Exposed to Childhood Trauma.Brain and behavior · 2025Article
- Cross-Sectional Comparison of Structural MRI Markers of Impairment in a Diverse Cohort of Older Adults.Human brain mapping · 2025Article
- Structural Magnetic Resonance Imaging Brain Age Investigation in Athletes with Persistent Postconcussion Syndrome.Neurotrauma reports · 2025Article
- Social reward and nonsocial reward processing across the adult lifespan: An interim multi-echo fMRI and diffusion dataset.Data in brief · 2024Article
- Evidence of compensatory neural hyperactivity in a subgroup of chemotherapy-treated breast cancer survivors and its association with brain aging.medRxiv : the preprint server for health sciences · 2024Article
- Examining the reliability of brain age algorithms under varying degrees of participant motion.Brain informatics · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The calculation of so-called "brain age" from structural MRIs has been an emerging biomarker in aging research. Data suggests that discrepancies between chronological age and the predicted age of the brain may be predictive of mortality and morbidity (for review, see Cole, Marioni, Harris, & Deary, 2019). However, with these promising results come technical complexities of how to calculate brain age. Various groups have deployed methods leveraging different statistical approaches, often crafting novel algorithms for assessing this biomarker derived from structural MRIs. There remain many open questions about the reliability, collinearity, and predictive power of different algorithms. Here, we complete a rigorous systematic comparison of three commonly used, previously published brain age algorithms (XGBoost, brainageR, and DeepBrainNet) to serve as a foundation for future applied research. First, using multiple datasets with repeated structural MRI scans, we calculated two metrics of reliability (intraclass correlations and Bland-Altman bias). We then considered correlations between brain age variables, chronological age, biological sex, and image quality. We also calculated the magnitude of collinearity between approaches. Finally, we used machine learning approaches to identify significant predictors across brain age algorithms related to clinical diagnoses of cognitive impairment. Using a large sample (N = 2557), we find all three commonly used brain age algorithms demonstrate excellent reliability (r > .9). We also note that brainageR and DeepBrainNet are reasonably correlated with one another, and that the XGBoost brain age is strongly related to image quality. Finally, and notably, we find that XGBoost brain age calculations were more sensitive to the detection of clinical diagnoses of cognitive impairment. We close this work with recommendations for future research studies focused on brain age.
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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.