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UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases
Marco Capó, Silvia Vitali, Georgios Athanasiou, Nicole Cusimano, Daniel García, Garth S. Cruickshank, Bipin Patel. NeuroImage, 2025-01-30
Topic: Risk, prevention and care in population studies
In this study, we present a comprehensive pipeline to train and compare a broad spectrum of machine learning and deep learning brain clocks, integrating diverse preprocessing strategies and correction terms. Our analysis also includes established methodologies which have shown success in prior UK Biobank-related studies. For our analysis we used T1-weighted MRI scans and processed de novo all images via FastSurfer, transforming them into a conformed space for deep learning and extracting image-derived phenotypes for our machine learning approaches. We rigorously evaluated these approaches both as robust age predictors for healthy individuals and as potential biomarkers for various neurodegenerative conditions, leveraging data from the UK Biobank, ADNI, and NACC datasets. To this end we designed a statistical framework to assess age prediction performance, the robustness of the prediction across cohort variability (database, machine type and ethnicity) and its potential as a biomarker for neurodegenerative conditions. Results demonstrate that highly accurate brain age models, typically utilising penalised linear machine learning models adjusted with Zhang's methodology, with mean absolute errors under 1 year in external validation, can be achieved while maintaining consistent prediction performance across different age brackets and subgroups (e.g., ethnicity and MRI machine/manufacturer). Additionally, these models show strong potential as biomarkers for neurodegenerative conditions, such as dementia, where brain age prediction achieved an AUROC of up to 0.90 in distinguishing healthy individuals from those with dementia.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Capó, M., Vitali, S., Athanasiou, G., Cusimano, N., García, D., Cruickshank, G. S., et al. (2025). UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases. NeuroImage. https://doi.org/10.1016/j.neuroimage.2025.121064
Vancouver
Capó M, Vitali S, Athanasiou G, Cusimano N, García D, Cruickshank GS, et al. UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases. NeuroImage. 2025. doi:10.1016/j.neuroimage.2025.121064. PMID: 39892529.
BibTeX
@article{capo2025-39892529,
author = {Capó, Marco and Vitali, Silvia and Athanasiou, Georgios and Cusimano, Nicole and García, Daniel and Cruickshank, Garth S. and Patel, Bipin},
title = {{UK Biobank MRI data can power the development of generalizable brain clocks: A study of standard ML/DL methodologies and performance analysis on external databases}},
journal = {NeuroImage},
year = {2025},
doi = {10.1016/j.neuroimage.2025.121064},
pmid = {39892529},
url = {https://doi.org/10.1016/j.neuroimage.2025.121064}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 7 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Results demonstrate that highly accurate brain age models, typically utilising penalised linear machine learning models adjusted with Zhang's methodology, with mean absolute errors under 1 year in external validation, can be achieved while maintaining consistent prediction performance across different age brackets and subgroups (e.g., ethnicity and MRI machine/manufacturer).
Why it matters
This study developed and compared machine learning and deep learning brain age models using MRI data, finding that accurate models can be achieved and may serve as biomarkers for neurodegenerative conditions. It is a methodological study and does not test any treatment, cure, or prevention for Alzheimer's disease.
Study details
- Evidence strengthModerate
How this study scored 41/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on senior author's track record, so that was left out: it earned 39 of the 96 points we could judge it on, which is 41 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | 4 / 4 | NeuroImage: journal h-index 523 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 4 / 5 | 2025 — within 3 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 4 times (OpenAlex, 2026-10-10) |
| Citations per year | 1 / 5 | 1.3 citations a year over 3 year(s) since 2025 |
| Impact for its field and year | 2 / 6 | Cited 2.5× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 1 / 4 | Cited 4 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | — / 4 | OpenAlex's profile for last-listed author Bipin Patel holds only 5 work(s), too few to judge a track record — left out of this score |
| Institution's track record | 1 / 3 | University of Birmingham: institution h-index 1,266 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 39892529) |
| Free to read | 2 / 2 | Free to read |
| Points | 39 / 96 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 39 of the 96 points we could judge it on is 41 out of 100. |
| Total | 41 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2025.121064
- PubMed: 39892529
- Original source →
- Free full text: NeuroImage →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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Referenced by this study and also in the alzhub library, so you can read them here.
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