Cited by 872Dementia and Cognitive Impairment ResearchAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

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Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

Open access

Cole, James H., Poudel, Rudra P.K., Tsagkrasoulis, Dimosthenis, Caan, Matthan W.A., Steves, Claire, Spector, Tim D., Montana, Giovanni·2017·NeuroImage

DOI 10.1016/j.neuroimage.2017.07.059

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Abstract

&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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“Similar to our results, many previous studies confirmed that the whole-brain GMV (Cole et al., 2017; Varikuti et al., 2018; Wang et al., 2019) and RSFC (Wang et al.”
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“…of our results are equivalent or superior to those of others when taking the whole brain as a Accuracy of structure age prediction using the baseline acquisition of healthy subjects (95 CI: 95% conﬁdence interval). predictive model (Cole et al., 2017; Liem et al., 2017; Beheshti et al., 2022).”
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BackgroundMethod
ds-FCRN: three-dimensional dual-stream fully convolutional residual networks and transformer-based global–local feature learning for brain age prediction
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“Consequently, they have been reported to yield superior results in predicting chronological age [19].”
BackgroundResult
Transfer Learning Models on Brain Age Prediction
Ren, Y., Luo, Q., Gong, W., & Lu, W. (2019) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3364836.3364893 · Cited by 5
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BackgroundMethodInfluential
Age Estimation from MR Images via 3D Convolutional Neural Network and Densely Connect
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BackgroundMethod
History of childbirths relates to region-specific brain aging patterns in middle and older-aged womenPreprint
de Lange, A. G., Barth, C., Kaufmann, T., Anatürk, M., Suri, S., Ebmeier, K. P., & Westlye, L. T. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.08.084616 · Cited by 5
“…2017), and individual variation in “brain age” estimates has been associated with a range of clinical and biological factors (Cole and Franke, 2017; Kaufmann et al., 2019; Franke and Gaser, 2019; Cole et al., 2018, 2019; Cole, 2019; Smith et al., 2019; Cole et al., 2017; de Lange et al., 2020a,b).”
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Ju, Y., Zhao, T. T., Gao, Z., Hu, W., Luo, J., Cheng, N., Liu, C., Jiang, Y., Hong, B., Ji, T., et al. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1605291 · Cited by 4
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“Brain age uses machine learning to analyze structural MRI data and models healthy aging using a large independent “training” dataset of healthy adults, resulting in a robust biomarker based on neuroimaging measures of atrophy (5).”
BackgroundMethodSelf-citation
An individual-level weighted artificial neural network method to improve the systematic bias in BrainAGE analysis
Lu, C., Li, B., Zhang, Q., Chen, X., Pang, Y., Lu, F., Wu, Y., Li, M., He, B., & Chen, H. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac490 · Cited by 4
Regional brain aging: premature aging of the domain general system predicts aphasia severity
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“The formidable feature extraction capabilities of deep learning have led to superior performance in predicting brain age compared to that of conventional machine learning methods (Cole et al., 2017b; He et al., 2015).”
Background
Revealing the Spatial Pattern of Brain Hemodynamic Sensitivity to Healthy Aging through Sparse Dynamic Causal Model
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MRI-based whole-brain elastography and volumetric measurements to predict brain age
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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
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“Brain age prediction shares these challenges and while various methods, including DL approaches (Cole 2017; Feng et al., 2020; Kolbeinsson et al., 2020), have been explored, achieving unbiased, high-performing models remains challenging due to biases toward the group mean (Smith 2019).”
Influential
Quantification of brain age using high-resolution 7 tesla MR imaging and implications for patients with epilepsy
Verma, G., Jacob, Y., Jha, M. K., Morris, L. S., Delman, B. N., Marcuse, L. V., Fields, M. C., & Balchandani, P. (2022) · Epilepsy & Behavior Reports · doi:10.1016/j.ebr.2022.100530 · Cited by 4
Harmonization of multi-site functional connectivity measures in tangent space improves brain age prediction
Zhou, Z., Srinivasan, D., Li, H., Abdulkadir, A., Shou, H., Davatzikos, C. A., & Yong Fan, A. (2022) · SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging · doi:10.1117/12.2611557 · Cited by 4
“Brain age prediction based on neuroimaging data has demonstrated promising performance for quantifying brain development, aging, and health status [1-6].”
Background
A novel network architecture combining central-peripheral deviation with image-based convolutional neural networks for diffusion tensor imaging studies
Park, S., Yu, J., Woo, H. H., & Park, C. G. (2022) · Journal of Applied Statistics · doi:10.1080/02664763.2022.2108386 · Cited by 4
“It has been known that the deep learning technique has contributed to predicting a brain age using raw magnetic resonance imaging (MRI) [8,11,28,53].”
Background
Naturalistic Sleep Patterns are Linked to Global Structural Brain Aging in Adolescence
Soehner, A. M., Hayes, R. A., Franzen, P. L., Goldstein, T. R., Hasler, B. P., Buysse, D. J., Siegle, G. J., Dahl, R. E., Forbes, E. E., Ladouceur, C. D., et al. (2022) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2022.08.022 · Cited by 5
Deep Learning Techniques for Pattern Recognition in EEG Audio Signal-Processing-Based Eye-Closed and Eye-Open Cases
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Active Acquisition for multimodal neuroimaging
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“Recent developments in deep learning offer promise, where for example a structural MR image can undergo an analogue of a complete pre-processing pipeline in a matter of seconds (Cole et al., 2017).”
BackgroundSelf-citation
Notice of Violation of IEEE Publication Principles: Deep Learning Assisted Image Interactive Framework for Brain Image Segmentation
Han, Y., & Zhang, Z. (2020) · IEEE Access · doi:10.1109/access.2020.3003624 · Cited by 4
“The ﬁgure shows that the image-speciﬁc ﬁnish increases the efﬁciency of the segmentation [35], [36].”
Background
Retracted
Brain Devlopment Age Prediction Using Convolutnal Neural Network on Pediatrics Brain Ct Mages
Morita, R., Ando, S., Fujita, D., Nii, M., Ando, K., Ishikura, R., & Kobashi, S. (2021) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc54886.2021.9737254 · Cited by 4
“Several studies have reported methods for estimating brain age using CNNs [11][12][13][14][15].”
Method
[A review on brain age prediction in brain ageing].
Lin, L., Wang, J., Fu, Z., Wu, X., & Wu, S. (2019) · PubMed Central · doi:10.7507/1001-5515.201804030 · Cited by 4
A Tool for Super-Resolving Multimodal Clinical MRIPreprint
Brudfors, M., Balbastre, Y., Nachev, P., & Ashburner, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.01140 · Cited by 4
“It is now well known that these features can be very accurately predicted from brain images (Cole et al., 2017; He et al., 2018; Monté-Rubio et al., 2018; Smith et al., 2019).”
Background
Neuroimaging in Epilepsy
Brinkmann, B. H., So, E. L., Watson, R. E., & Kotsenas, A. L. (2021) · Epilepsy · doi:10.1002/9781119431893.ch7 · Cited by 4
Brain age predicted using graph convolutional neural network explains developmental trajectory in preterm neonatesPreprint
Liu, M., Kim, S. Y., Duffy, B. A., Yuan, S., Cole, J. H., Toga, A. W., Jahanshad, N., Barkovich, A. J., Xu, D., & Kim, H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.15.444320 · Cited by 4
“To date, brain age prediction approaches have used machine learning methods fed with volume images directly (Cole et al. , 2017) or structural connectome metrics (Brown et al. , 2017).”
BackgroundMethodSelf-citation
Using deep learning to predict brain age from brain magnetic resonance images and cognitive tests reveals that anatomical and functional brain aging are phenotypically and genetically distinctPreprint
Goallec, A. L., Diai, S., Collin, S., Vincent, T., & Patel, C. J. (2021) · medRxiv · doi:10.1101/2021.06.22.21259280 · Cited by 4
“We found that the nine most predictive scalar features are all related to speed of answering questions and include, in order: (1) time to answer, (2) mean time to correctly identify matches, (3) duration to complete alphanumeric path (trail #2), (4) time to complete test, (5) duration to complete numeric path (trail #…”
Background
Cognitive prediction using regional connectivities and network biomarkers in Alzheimer’s disease
Sheng, J., Wang, J., Zhang, Q., Huang, R., Lu, Y., & Li, T. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.08.059 · Cited by 3
Assessing demographic bias in brain age prediction models using multiple deep learning paradigms
Gravina, M., Pontillo, G., Shawa, Z., Cole, J. H., & Sansone, C. (2025) · Pattern Recognition Letters · doi:10.1016/j.patrec.2025.11.029 · Cited by 3
Self-citation
Deep neural networks and genome-wide associations reveal the polygenic architecture of local brain aging
Kim, N. J., Mishra, A., Chowdhury, N. F., Anderson, S. D., Vega, O. M., Chaudhari, N. N., Buetow, K. H., Thompson, P. M., & Andrei Irimia, A. (2025) · GeroScience · doi:10.1007/s11357-025-02046-1 · Cited by 3
AI-assisted design and application of wound dressings
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Accelerated Brain Aging, Atherogenicity, and Neurocognition in Adult Survivors of Childhood Cancer
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Circulating inflammatory proteins predict dementia risk, and are linked to structural brain changes and modifiable risk factors
Abdolkarimi, D., Liu, Y., Gilchrist, L., Calhas, S., Waters, S., Marshall, C. R., & Proitsi, P. (2026) · Alzheimer s Research & Therapy · doi:10.1186/s13195-025-01951-z · Cited by 4
“Brain Age was computed using a Gaussian process regression model trained on the raw T1-weighted images collected during the first imaging session, and Brain Age delta was calculated as the difference between Brain Age and chronological age [19].”
Robust brain age estimation from structural MRI with contrastive learning
Barbano, C. A., Dufumier, B., Duchesnay, É., Grangetto, M., & Gori, P. (2026) · Pattern Recognition Letters · doi:10.1016/j.patrec.2026.02.032 · Cited by 3
The Brain‐Age Gap in Pediatric Dystonia: Neuroanatomical Deviations Inform Deep Brain Stimulation Outcomes
Latypov, T. H., Berger, A., Mohareb, M., Mithani, K., Breitbart, S. C., Vogt, L. M., Dinger, T. F., Meijer, I. A., Weil, A. G., Fasano, A. A., et al. (2026) · Movement Disorders · doi:10.1002/mds.70262 · Cited by 3
“8,9 Brain-age gap and local deviation metrics could be used as a reference for interpreting structural changes across diverse pathologies.”
Cognitive and Brain Reserve as Modifiers of Early Alzheimer Disease–Related Cognitive Vulnerability
Sewell, K. R., Solis‐Urra, P., Huang, H., Karikari, T. K., Grove, G., Kramer, A. F., McAuley, E., Burns, J. M., Hillman, C. H., Vidoni, E. D., et al. (2026) · Neurology · doi:10.1212/wnl.0000000000214833 · Cited by 3
“22 normalized brain volumetric maps from 3,377 healthy adults (aged 18 – 92 years) across 7 publicly available datasets, employing a Gaussian Process Regression model.”
The Evolving Landscape of Clinical Aging Clocks: From Epigenetic to Multi‐Omics Integration
Liu, L., Lai, Y., Tian, C., Huang, Y., Hao, J., Zhao, Y., Chen, D., Wu, T., Zhou, D., Zheng, X., et al. (2026) · Aging Cell · doi:10.1111/acel.70579 · Cited by 3
Brain Age Estimation based on Brain MRI by an Ensemble of Deep Networks
Jahanshiri, Z., Abadeh, M. S., & Sajedi, H. (2021) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom51814.2021.9377399 · Cited by 3
“The ECNN is a fast and accurate method compared to other CNN models on the Brain Age Normative Control (BANK) dataset [12].”
Method
Does Pre-training on Brain-Related Tasks Results in Better Deep-Learning-Based Brain Age Biomarkers?
Pacheco, B. M., de Oliveira, V. H. R., Antunes, A. B. F., Pedro, S. D. D. S., Silva, D., & Initiative, A. D. N. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45389-2_13 · Cited by 3
“Deep learning models have shown great potential in accurately predicting brain age from magnetic resonance imaging (MRI) data [7,4,27,29,17].”
BackgroundMethod
Signed Curvature Graph Representation Learning of Brain Networks for Brain Age Estimation
Li, J., Lyu, Z., Yu, H., Fu, S., Li, K., Li, Y., & Guo, X. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3434473 · Cited by 3
Implementation Of Novel Deep Learning Techniques For MRI-Based Brain Disease Classification and Age Estimation
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Regional Brain Aging Disparity Index: Region-Specific Brain Aging State Index for Neurodegenerative Diseases and Chronic Disease Specificity
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Aging dimensions and markers as relative predictors of mortality in a longitudinal epidemiological sample
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“Four variables were available during Time 2 that were not available at Time 1: two measures of balance (Romberg sway path area and path length), hearing acuity, and a measure of brain age [14].”
A Joint Model for Predicting Structural and Functional Brain Health in Elderly Individuals
Varatharajah, Y., Saboo, K., Iyer, R. K., Przybelski, S. A., Schwarz, C. G., Petersen, R. C., Jack, C. R., & Vemuri, P. (2019) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm47256.2019.8983291 · Cited by 3
“SMRI-based models of brain age and cognitive performance have been studied extensively in the literature [1], [5].”
BackgroundMethod
Distributional Transformation Improves Decoding Accuracy When Predicting Chronological Age From Structural MRI
Soch, J. (2020) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2020.604268 · Cited by 3
“While early attempts at predicting human age from functional MRI (9) have used decoding algorithms such as support vector machines (SVM), more recent ML-based decoding from structural MRI has focused on deep learning (10), specifically using convolutional neural networks (CNN) to predict chronological age (4, 8, 11),…”
BackgroundMethodInfluential
Parallel Pathway Convolutional Neural Network with Low-rank Fusion for Brain Age Prediction
Shen, X., Huang, J., Sun, Y., Li, M., Pan, B., & Ding, W. (2021) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi52967.2021.9540107 · Cited by 3
Assessing the Performance of Automated Prediction and Ranking of Patient Age from Chest X-rays Against Clinicians
MacPherson, M., Muthuswamy, K., Amlani, A., Hutchinson, C. E., Goh, V., & Montana, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16449-1_25 · Cited by 3
Self-citation
Deep Learning Assessment for Mining Important Medical Image Features of Various Modalities
Apostolopoulos, I. D., Papathanasiou, N. D., Papandrianos, N. I., Papageorgiou, E. I., & Panayiotakis, G. (2022) · Diagnostics · doi:10.3390/diagnostics12102333 · Cited by 3
Quantification of Pediatric Brain Development with X-ray CT Images using 3D-CNN
Morita, R., Ando, S., Fujita, D., Ishikawa, S., Onoue, K., Ando, K., Ishikura, R., & Kobashi, S. (2022) · Joint International Conference on Soft Computing and Intelligent Systems and International Symposium on Advanced Intelligent Systems (SCIS&ISIS) · doi:10.1109/scisisis55246.2022.10002151 · Cited by 3
“In reference [3], 3D-CNN based age prediction was performed from 3D adult MRI image data.”
Method
Neuroimaging and machine learning for brain age estimation
Kocar, T. D., Denkinger, M. D., & Kassubek, J. (2023) · Aging · doi:10.18632/aging.204694 · Cited by 3
Brain Disease Classification along with Age Estimation from MRI
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A deep neural network estimation of brain age is sensitive to cognitive impairment and decline
Yang, Y., Sathe, A., Schilling, K. G., Shashikumar, N., Moore, E. E., Dumitrescu, L. C., Pechman, K. R., Landman, B. A., Gifford, K. A., Hohman, T. J., et al. (2023) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811286421_0012 · Cited by 3

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
ORCID: Cole, James H.
Caan, Matthan W.A.
Steves, Claire
Spector, Tim D.
Montana, Giovanni
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2017-12
Volume: 163
Pages: 115-124
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2017.07.059
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
doi.org/10.15223/policy-017 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
doi.org/10.15223/policy-012 (stm-asf)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-004 (stm-asf)
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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Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker

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