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Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker
Open accessAbstract
&NA; Machine learning analysis of neuroimaging data can accurately predict chronological age in healthy people. Deviations from healthy brain ageing have been associated with cognitive impairment and disease. Here we sought to further establish the credentials of ‘brain‐predicted age’ as a biomarker of individual differences in the brain ageing process, using a predictive modelling approach based on deep learning, and specifically convolutional neural networks (CNN), and applied to both pre‐processed and raw T1‐weighted MRI data. Firstly, we aimed to demonstrate the accuracy of CNN brain‐predicted age using a large dataset of healthy adults (N = 2001). Next, we sought to establish the heritability of brain‐predicted age using a sample of monozygotic and dizygotic female twins (N = 62). Thirdly, we examined the test‐retest and multi‐centre reliability of brain‐predicted age using two samples (within‐scanner N = 20; between‐scanner N = 11). CNN brain‐predicted ages were generated and compared to a Gaussian Process Regression (GPR) approach, on all datasets. Input data were grey matter (GM) or white matter (WM) volumetric maps generated by Statistical Parametric Mapping (SPM) or raw data. CNN accurately predicted chronological age using GM (correlation between brain‐predicted age and chronological age r = 0.96, mean absolute error [MAE] = 4.16 years) and raw (r = 0.94, MAE = 4.65 years) data. This was comparable to GPR brain‐predicted age using GM data (r = 0.95, MAE = 4.66 years). Brain‐predicted age was a heritable phenotype for all models and input data (h2 ≥ 0.5). Brain‐predicted age showed high test‐retest reliability (intraclass correlation coefficient [ICC] = 0.90–0.99). Multi‐centre reliability was more variable within high ICCs for GM (0.83–0.96) and poor‐moderate levels for WM and raw data (0.51–0.77). Brain‐predicted age represents an accurate, highly reliable and genetically‐influenced phenotype, that has potential to be used as a biomarker of brain ageing. Moreover, age predictions can be accurately generated on raw T1‐MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real‐time information on brain health in clinical settings. HighlightsChronological age can be accurately predicted using convolutional neural networks.Age predicted is accurate even using raw structural neuroimaging data.Brain‐predicted age can be generated in a clinically applicable timeframe.Brain‐predicted age is significantly heritable.Brain‐predicted age is highly reliable, both within and between scanners.
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Additional file 1 of Factors associated with brain ageing - a systematic review
Wrigglesworth, J., Ward, P. G. D., Harding, I. H., Nilaweera, D., Wu, Z., Woods, R. L., & Ryan, J. L. (2021) · Figshare · doi:10.6084/m9.figshare.15162203
Additional file 2 of Factors associated with brain ageing - a systematic review
Wrigglesworth, J., Ward, P. G. D., Harding, I. H., Nilaweera, D., Wu, Z., Woods, R. L., & Ryan, J. L. (2021) · Figshare · doi:10.6084/m9.figshare.15162206
Enhanced pediatric age estimation from head MRI via self-distillation hybrid-attention network
Shi, L., Huang, X., Ke, W., Brkić, H., Zhou, Y., Lu, T., Tang, X., Qiu, L., Luo, S., Zhang, X., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108748
The cause and effect of an MR image: Robustness and generalizability
Feragen, A., Petersen, E., & Ganz, M. (2025) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-443-14109-6.00016-x
Chang, T.-A., Yan, S.-Y., Wang, K.-C., & Hung, C.-W. (2026) · Electronics · doi:10.3390/electronics15010220
TissueAgeNet: Quantitative Textual Guidance for Tissue Level Brain Age Estimation
Chen, S., Wu, W., Zhang, X., Xu, J., Yu, S., Lv, Y., Xiong, T., Guo, J., Yang, C., & Xu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-09513-8_25
A Review on Deep Learning Models and Fractal Residual Learning in Detection of Different Tumors
Jakhar, S. P., Nandal, A., & Dhaka, A. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-11488-4_18
Brain age group classification based on resting state functional connectivity metrics
Singh, P., Yadav, K. S., Kumar, L., & Gandhi, T. K. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109617
“Recent advances in computational neuroscience have enabled the integration of deep learning models with functional connectivity metrics to improve the prediction of brain aging and neurodegenerative risk [20], [21].”
Quantitative MRI-Based Modal-Informed Transformer Network for Brain Age Prediction
Kurian, D., & Paul, J. S. (2025) · IEEE Recent Advances in Intelligent Computational Systems · doi:10.1109/raics66191.2025.11332588
“However, single-modality approaches provide limited insight into the underlying mechanisms of aging [7], [8].”
Ghaderi, S., Jouzdani, A. F., Pourbagher‐Shahri, A. M., & Mohammadi, S. (2026) · Brain and Behavior · doi:10.1002/brb3.71202
“Foundational work using deep learning on raw MRI data has established that brain-predicted age is a highly reliable and heritable phenotype, capable of serving as a robust biomarker for individual differences in the brain aging process (Cole et al. 2017 ).”
A Lightweight 2D Projection CNN Framework for MRI-Based Brain Age Prediction
Chang, T.-A., & Syu, R.-C. (2026) · IEEE Access · doi:10.1109/access.2026.3656276
“Cole et al. [8] pioneered the use of deep learning in this domain with a five-layer 3D CNN, achieving an MAE of 4.16 years across ages 18–90.”
InfluentialCui, W., Pang, H., Yang, Y., Shang, M., HongDong, L. I., & Du, L. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356568
Comorbid HIV and Cocaine Use Exacerbate Accelerated Brain AgingPreprint
Gu, H., Salmeron, B. J., Wang, D., Lai, H. C., Kuang, N., Zheng, H., Lai, S., & Yang, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704435
Convolutional Neural Network and Human Age Detection : A Pilot Study
Ogar, J. A., Liman, M. M., & Barka, C. A. (2026) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2612134 · Cited by 1
Brain Ageing Prediction Using Isolation Forest Technique and Residual Neural Network (ResNet)
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Geoly, A. D., Coetzee, J. P., Buchanan, D. M., Struckmann, W., Kim, B., Sridhar, M., Azeez, A., Lissemore, J. I., Cherian, K. N., Faerman, A., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115121 · Cited by 1
“28 With millions of civilians and military veterans suffering daily from TBI, identifying rapid-acting interventions is critically important.”
Subbarayudu, J., Michael, G., & Kumari, V. S. (2025) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaihei65991.2025.11379193
“With the help of CNNs in deep learning, brain age estimation has made big steps ahead, reaching much better and more consistent results [2].”
Metadata
- Authors
- Cole, J. H., Poudel, R. P. K., Tsagkrasoulis, D., Caan, M. W. A., Steves, C., Spector, T. D., & Montana, G.
- Affiliations
- Imperial College London (GB)
- King's College London (GB)
- Amsterdam UMC Location University of Amsterdam (NL)
- Corresponding author
- Montana, Giovanni
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-12
- Volume
- 163
- Pages
- 115-124
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2579617530
- Semantic Scholar
- 5d71c918db224e21982d9eb5ab2359735466959b
- Cited by
- 872
- References
- 72
- Citations by year
- 2026: 91 · 2025: 116 · 2024: 99 · 2023: 106 · 2022: 113 · 2021: 122 · 2020: 92 · 2019: 87 · 2018: 39 · 2017: 6
- FWCI
- 49.85
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by-nc-nd)
- Pure Amsterdam UMC (submittedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by-nc-nd)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Wellcome Trust
- Medical Research Council
- European Commision’s Seventh Framework Program (FP7/2007-2013, 259749)
- National Institute for Health Research (NIHR)
- BioResource, Clinical Research Facility
- Biomedical Research Centre
- St Thomas' NHS Foundation Trust
- NIHR Biomedical Research Centre
- Imperial College London from the Medical Research Council (MR/L022141/1)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- brain age prediction, brain aging, biomarkers, heritability, monozygotic and dizygotic twins, test-retest reliability, convolutional neural networks, gray matter volume, white matter volume, Gaussian process regression, healthy adults, cognitive impairment risk, neuroimaging
- MeSH terms
- Machine Learning, Adolescent, Adult, Aged, Aged, 80 and over, Aging, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Phenotype, Neural Networks, Computer, Young Adult
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