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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MethodHeritability of brain volumes in older adults: the Older Australian Twins Study
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“This is in line with previous research into the heritability of brain volumes (Batouli et al., 2014a; Batouli et al., 2014b) and cognitive function (Bouchard, 2013; Lee et al., 2010).”
Caliban's heritance and the genetics of neuronal aging
Teter, B., & Finch, C. E. (2004) · Trends in Neurosciences · doi:10.1016/j.tins.2004.08.005 · Cited by 26 · see citing works
Predicting the Age of Healthy Adults from Structural MRI by Sparse Representation
Su, L., Wang, L., & Hu, D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-36669-7_34 · Cited by 8 · see citing works
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works
“Brain age [273] Chronological age prediction from raw brain T1-MRI data, also testing the heritability of brain-predicted age”
MethodZhou, S. K., Greenspan, H. K., Davatzikos, C. A., Duncan, J. S., van Ginneken, B., Madabhushi, A., Prince, J. L., Rueckert, D., & Summers, R. M. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3054390 · Cited by 1,294 · see citing works
“In recent years, a large amount of work has been published on deep learning based prediction tasks such as brain age prediction [124], [125], [126], Alzheimer’s disease classification and trajectory modeling [127], [128], [129], [130], and schizophrenia classification [131], [132].”
BackgroundAn overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012 · see citing works
Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers
Cole, J. H., & Franke, K. (2017) · Trends in Neurosciences · doi:10.1016/j.tins.2017.10.001 · Cited by 912 · see citing works
“In particular, deep learning methods show [344_TD$DIFF]considerable promise [9].”
Self-citationHeterogeneous aging across multiple organ systems and prediction of chronic disease and mortality
Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., & Zalesky, A. (2023) · Nature Medicine · doi:10.1038/s41591-023-02296-6 · Cited by 618 · see citing works
Common brain disorders are associated with heritable patterns of apparent aging of the brain
Kaufmann, T., van der Meer, D., Doan, N. T., Schwarz, E., Lund, M. J., Agartz, I., Alnæs, D., Barch, D. M., Baur‐Streubel, R., Bertolino, A., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0471-7 · Cited by 559 · see citing works
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 514 · see citing works
Accurate brain age prediction with lightweight deep neural networks
Peng, H., Gong, W., Beckmann, C. F., Vedaldi, A., & Smith, S. M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101871 · Cited by 510 · see citing works
“For example, existing studies have observed that the brain-age delta is an effective biomarker that shows differences between different clinical groups (Kaufmann et al., 2019) and is predictive for mortality (Cole et al., 2018).”
BackgroundMethodInfluentialBrain age and other bodily ‘ages’: implications for neuropsychiatry
Cole, J. H., Marioni, R. E., Harris, S. E., & Deary, I. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0098-1 · Cited by 436 · see citing works
“Brain-predicted age is under a degree of genetic influence [19], which motivates the search for relevant risk genes for poorer brain ageing.”
BackgroundSelf-citationEstimation of brain age delta from brain imaging
Smith, S. M., Vidaurre, D., Alfaro‐Almagro, F., Nichols, T. E., & Miller, K. L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.06.017 · Cited by 432 · see citing works
Brain age prediction using deep learning uncovers associated sequence variants
Jónsson, B. A., Björnsdóttir, G., Thorgeirsson, T. E., Ellingsen, L. M., Walters, G. B., Gudbjartsson, D. F., Stefánsson, H., Stefánsson, K., & Ulfarsson, M. O. (2019) · Nature Communications · doi:10.1038/s41467-019-13163-9 · Cited by 387 · see citing works
“And predictably our h is lower than the narrow-sense heritability estimate of PAD estimated by Cole et al. 12 ( h 2 ¼ 0 : 66 [SE ¼ 0 : 09]) using a twin study sample.”
BackgroundMethodInfluentialBao, H., Cao, J., Chen, M., Min, C., Chen, W., Chen, X., Chen, Y., Chen, Y., Chen, Y., Chen, Z., et al. (2023) · Science China Life Sciences · doi:10.1007/s11427-023-2305-0 · Cited by 389 · see citing works
Cole, J. H. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.03.014 · Cited by 379 · see citing works
Self-citationBashyam, V., Erus, G., Doshi, J. J., Habes, M., Nasrallah, I. M., Truelove‐Hill, M., Srinivasan, D., Mamourian, L., Pomponio, R., Yong Fan, A., et al. (2020) · Brain · doi:10.1093/brain/awaa160 · Cited by 372 · see citing works
Sui, J., Jiang, R., Bustillo, J., & Calhoun, V. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.02.016 · Cited by 309 · see citing works
Brain Imaging Generation with Latent Diffusion Models
Pinaya, W. H. L., Tudosiu, P.-D., Dafflon, J., Da Costa, P. F., Fernandez, V., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-18576-2_12 · Cited by 395 · see citing works
Schulz, M.-A., Yeo, B. T. T., Vogelstein, J. T., Mourao-Miranada, J., Kather, J. N., Kording, K., Richards, B., & Bzdok, D. (2020) · Nature Communications · doi:10.1038/s41467-020-18037-z · Cited by 315 · see citing works
“Second, deep learning approaches have repeatedly been found to perform worse or indistinguishably well compared to simpler baseline models when predicting demographic or behavioral phenotypes 13,40 .”
BackgroundResultFundamentals of Artificial Neural Networks and Deep Learning
Montesinos-López, O. A., Montesinos‐López, A., & Crossa, J. L. (2022) · Springer eBooks · doi:10.1007/978-3-030-89010-0_10 · Cited by 311 · see citing works
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 287 · see citing works
“raw imaging data [62, 77, 79], while reaching at least comparable performance levels as shallow architectures when these were provided with pre-designed features”
BackgroundMethodCommentary: Correction procedures in brain-age prediction
de Lange, A. G., & Cole, J. H. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102229 · Cited by 273 · see citing works
“Brain-age prediction uses machine learning to estimate an individuals apparent brain aging based on structural and functional brain characteristics derived from neuroimaging, commonly magnetic resonance imaging (MRI) [1, 2, 3, 4, 5, 6, 7].”
MethodInfluentialSelf-citationMachine learning for brain age prediction: Introduction to methods and clinical applications
Baecker, L., Garcia‐Dias, R., Vieira, S., Scarpazza, C., & Mechelli, A. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103600 · Cited by 272 · see citing works
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271 · see citing works
Artificial intelligence for aging and longevity research: Recent advances and perspectives
Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.003 · Cited by 246 · see citing works
“It serves as a biomarker for aging in adults, and for patients with conditions such as Alzheimer ’ s disease (Cole et al., 2016).”
BackgroundOptimising network modelling methods for fMRI
Pervaiz, U., Vidaurre, D., Woolrich, M. W., & Smith, S. M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116604 · Cited by 236 · see citing works
Bias-adjustment in neuroimaging-based brain age frameworks: A robust scheme
Beheshti, I., Nugent, S., Potvin, O., & Duchesne, S. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.102063 · Cited by 225 · see citing works
Abrol, A., Fu, Z., Salman, M. S., Silva, R. F., Du, Y., Plis, S., & Calhoun, V. D. (2021) · Nature Communications · doi:10.1038/s41467-020-20655-6 · Cited by 221 · see citing works
Gray Matter Age Prediction as a Biomarker for Risk of Dementia
Wang, J. Z., Knol, M. J., Tiulpin, A., Dubost, F., de Bruijne, M., Vernooij, M. W., Adams, H. H. H., Ikram, M. A., Niessen, W. J., & Roshchupkin, G. V. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902376116 · Cited by 211 · see citing works
“Previous studies looked cross sectionally (5, 6) at the association of the age gap and dementia occurrences, while in the current study, we evaluated associations in longitudinal data.”
BackgroundResultLiang, H., Zhang, F., & Niu, X. (2019) · Human Brain Mapping · doi:10.1002/hbm.24588 · Cited by 207 · see citing works
“Brain age can be predicted in individuals based on high-dimensional neuroimaging data using machine-learning techniques (Al Zoubi et al., 2018; Chung et al., 2018; Cole et al., 2017; Franke, Luders, May, Wilke, & Gaser, 2012; Liem et al., 2017).”
BackgroundMethodInfluentialDeep learning-based brain age prediction in normal aging and dementia
Lee, J., Burkett, B. J., Min, H.-K. P., Senjem, M. L., Lundt, E. S., Botha, H., Graff‐Radford, J., Barnard, L. R., Gunter, J. L., Schwarz, C. G., et al. (2022) · Nature Aging · doi:10.1038/s43587-022-00219-7 · Cited by 208 · see citing works
Yu, X., Lu, H., & Wu, D. (2018) · Postharvest Biology and Technology · doi:10.1016/j.postharvbio.2018.02.013 · Cited by 203 · see citing works
Deep learning for small and big data in psychiatry
Koppe, G., Meyer-Lindenberg, A., & Durstewitz, D. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0767-z · Cited by 189 · see citing works
Smith, S. M., Elliott, L. T., Alfaro‐Almagro, F., McCarthy, P., Nichols, T. E., Douaud, G., & Miller, K. L. (2020) · eLife · doi:10.7554/elife.52677 · Cited by 194 · see citing works
Deep learning-based unlearning of dataset bias for mri harmonisation and confound removal
Dinsdale, N. K., Jenkinson, M., & Namburete, A. I. L. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117689 · Cited by 178 · see citing works
Toward a unified framework for interpreting machine-learning models in neuroimaging
Kohoutová, L., Heo, J., Cha, S., Lee, S., Moon, T., Wager, T. D., & Woo, C. (2020) · Nature Protocols · doi:10.1038/s41596-019-0289-5 · Cited by 168 · see citing works
Global-Local Transformer for Brain Age Estimation
He, S., Grant, P. E., & Ou, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3108910 · Cited by 180 · see citing works
Quantifying performance of machine learning methods for neuroimaging data
Jollans, L., Boyle, R. T., Eric Artiges, A., Banaschewski, T., Desrivières, S., Grigis, A., Martinot, J., Paus, T., Smolka, M. N., Walter, H., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.082 · Cited by 165 · see citing works
“The linear outcome variable used was participants’ age, which has been shown to have a moderate-large effect size (Cole et al., 2017).”
MethodMind the gap: Performance metric evaluation in brain‐age prediction
de Lange, A. G., Anatürk, M., Rokicki, J., Han, L. K. M., Franke, K., Alnæs, D., Ebmeier, K. P., Draganski, B., Kaufmann, T., Westlye, L. T., et al. (2022) · Human Brain Mapping · doi:10.1002/hbm.25837 · Cited by 161 · see citing works
“…2021; Cole, 2020; Cole, Franke, & Cherbuin, 2019; de Lange et al., 2019; Dunås, Wåhlin, Nyberg, & Boraxbekk, 2021; Franke et al., 2020; Smith et al., 2020), and the overall evidence supports the use of brain-predicted age as a surrogate marker for brain integrity and health (Cole et al., 2017).”
Self-citationPopulation-based neuroimaging reveals traces of childbirth in the maternal brain
de Lange, A. G., Kaufmann, T., van der Meer, D., Maglanoc, L. A., Alnæs, D., Moberget, T., Douaud, G., Andreassen, O. A., & Westlye, L. T. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1910666116 · Cited by 160 · see citing works
“To assess the robustness of these effects, the brain age analysis was rerun using predicted brain age estimates based on an independent approach and training set (16, 17).”
MethodPredicting Age From Brain EEG Signals—A Machine Learning Approach
Zoubi, O. A., Wong, C. K., Kuplicki, R., Yeh, H., Mayeli, A., Refai, H. H., Paulus, M. P., & Bodurka, J. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00184 · Cited by 156 · see citing works
“Similarly, Cole et al. (2017) used Deep Learning (DL) to study BrainAGE using both pre-processed and raw T1w MRI images.”
BackgroundMethodInfluentialLongitudinal Assessment of Multiple Sclerosis with the Brain‐Age Paradigm
Cole, J. H., Raffel, J., Friede, T., Eshaghi, A., Brownlee, W. J., Chard, D., Stefano, N. D., Enzinger, C., Pirpamer, L., Filippi, M., et al. (2020) · Annals of Neurology · doi:10.1002/ana.25746 · Cited by 154 · see citing works
“The sample size for both training and test sets is relatively large but one potential limitation is the multiple sources of training data, although previous work has shown high betweenscanner reliability.(29) Thus, application to a single individual needs to be in the context of individual scanner performance.”
BackgroundMethodSelf-citationAssociation of relative brain age with tobacco smoking, alcohol consumption, and genetic variants
Ning, K., Zhao, L., Matloff, W. J., Sun, F., & Toga, A. W. (2020) · Scientific Reports · doi:10.1038/s41598-019-56089-4 · Cited by 159 · see citing works
MRI of healthy brain aging: A review
MacDonald, M. E., & Pike, G. B. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4564 · Cited by 154 · see citing works
Opel, N., Thalamuthu, A., Milaneschi, Y., Grotegerd, D., Flint, C., Leenings, R., Goltermann, J., Richter, M., Hahn, T., Woditsch, G., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-020-0774-9 · Cited by 148 · see citing works
Chung, Y., Addington, J., Bearden, C. E., Cadenhead, K. S., Cornblatt, B. A., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Seidman, L. J., Tsuang, M. T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.1543 · Cited by 150 · see citing works
Brain age from the electroencephalogram of sleep
Sun, H., Paixão, L., Oliva, J. T., Goparaju, B., Carvalho, D. Z., van Leeuwen, K. G., Akeju, O., THOMAS, R. J., Cash, S. S., Bianchi, M. T., et al. (2018) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2018.10.016 · Cited by 139 · see citing works
Deep neural networks learn general and clinically relevant representations of the ageing brain
Leonardsen, E. H., Peng, H., Kaufmann, T., Agartz, I., Andreassen, O. A., Celius, E. G., Espeseth, T., Harbo, H. F., Høgestøl, E. A., de Lange, A. G., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119210 · Cited by 143 · see citing works
Deep learning for brain age estimation: A systematic review
Tanveer, M., Ganaie, M. A., Beheshti, I., Goel, T., Ahmad, N., Lai, K.-T., Huang, K., Zhang, Y., Ser, J. D., & Lin, C. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.007 · Cited by 143 · see citing works
“Another voxel-based strategy was considered in Cole et al. [41], where T1-weighted images were segmented into gray matter (GM) and white matter (WM) using SPM12 toolbox.”
MethodLearning patterns of the ageing brain in MRI using deep convolutional networks
Dinsdale, N. K., Bluemke, E., Smith, S. M., Arya, Z., Vidaurre, D., Jenkinson, M., & Namburete, A. I. L. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117401 · Cited by 138 · see citing works
Frizzell, T. O., Glashutter, M., Liu, C. C., Zeng, A., Pan, D., Hajra, S. G., D’Arcy, R. C. N., & Song, X. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101614 · Cited by 139 · see citing works
Parakh, A., Lee, H., Lee, J. H., Eisner, B. H., Sahani, D. V., & Do, S. (2019) · Radiology Artificial Intelligence · doi:10.1148/ryai.2019180066 · Cited by 135 · see citing works
Improved prediction of brain age using multimodal neuroimaging data
Niu, X., Zhang, F., Kounios, J., & Liang, H. (2019) · Human Brain Mapping · doi:10.1002/hbm.24899 · Cited by 132 · see citing works
“One potential reason might be that subjects in most of the previous studies have a larger age range (Cole et al., 2015, 2017; Mwangi et al., 2013; Schnack et al., 2016).”
BackgroundMethodResultInfluentialAge and life expectancy clocks based on machine learning analysis of mouse frailty
Schultz, M. B., Kane, A. E., Mitchell, S. J., MacArthur, M. R., Warner, E., Vogel, D. S., Mitchell, J. R., Howlett, S. E., Bonkowski, M. S., & Sinclair, D. A. (2020) · Nature Communications · doi:10.1038/s41467-020-18446-0 · Cited by 128 · see citing works
“Some previous studies have also used machine learning approaches for the development of aging biomarkers, including deep neural networks of standard blood biomarkers 51,52 and deep learning of brain imaging data 53 , with promising results 54,55 .”
MethodMultimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
de Lange, A. G., Anatürk, M., Suri, S., Kaufmann, T., Cole, J. H., Griffanti, L., Zsoldos, E., Jensen, D. E. A., Filippini, N., Singh‐Manoux, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117292 · Cited by 125 · see citing works
Self-citationAccurate brain‐age models for routine clinical MRI examinations
Wood, D. A., Kafiabadi, S., Busaidi, A. A., Guilhem, E., Montvila, A., Lynch, J., Townend, M., Agarwal, S., Mazumder, A., Barker, G. J., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118871 · Cited by 127 · see citing works
Self-citationBrain Differences Between Men and Women: Evidence From Deep Learning
Xin, J., Zhang, Y., Tang, Y., & Yang, Y. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00185 · Cited by 127 · see citing works
“…the deep convolutional neural network (CNN) uses the convolution kernels to extract the features of image and can find the characteristic spatial difference in brain images, which may promise a better result than using other conventional machine learning and statistical methods (Cole et al., 2017).”
MethodAnatomically interpretable deep learning of brain age captures domain-specific cognitive impairment
Yin, C., Imms, P. E., Cheng, M., Amgalan, A., Chowdhury, N. F., Massett, R. J., Chaudhari, N. N., Chen, X., Thompson, P. M., Bogdan, P., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2214634120 · Cited by 124 · see citing works
“1 y) (16), support vector regression synergized with a random forest classifier (MAE ' 3.”
BackgroundT1-weighted MRI-driven Brain Age Estimation in Alzheimer’s Disease and Parkinson’s Disease
Beheshti, I., Mishra, S., Sone, D., Khanna, P., & Matsuda, H. (2020) · Aging and Disease · doi:10.14336/ad.2019.0617 · Cited by 123 · see citing works
“Estimates of brain age, derived using machine learning, have previously been used in a number of contexts [14-18].”
MethodBrain-age prediction: A systematic comparison of machine learning workflows
More, S., Antonopoulos, G., Hoffstaedter, F., Caspers, J., Eickhoff, S. B., Patil, K. R., & Initiative, T. A. D. N. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119947 · Cited by 118 · see citing works
Baecker, L., Dafflon, J., da Costa, P. F., Garcia‐Dias, R., Vieira, S., Scarpazza, C., Calhoun, V. D., Sato, J. R., Mechelli, A., & Pinaya, W. H. L. (2021) · Human Brain Mapping · doi:10.1002/hbm.25368 · Cited by 108 · see citing works
“In addition, deep convolutional neural networks have shown to have a high accuracy when predicting brain age (Cole et al., 2017; Ito et al., 2018; Peng, Gong, Beckmann, Vedaldi, & Smith, 2021).”
Evaluation of non-negative matrix factorization of grey matter in age prediction
Varikuti, D. P., Genon, S., Sotiras, A., Schwender, H. R., Hoffstaedter, F., Patil, K. R., Jockwitz, C., Caspers, S., Moebus, S., Amunts, K., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.007 · Cited by 109 · see citing works
Pitfalls in brain age analyses
Butler, E. R., Chen, A. A., Ramadan, R., Le, T. T., Ruparel, K., Moore, T. M., Satterthwaite, T. D., Zhang, F., Shou, H., Gur, R. C., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25533 · Cited by 117 · see citing works
Han, L. K. M., Verhoeven, J. E., Tyrka, A. R., Penninx, B. W. J. H., Wolkowitz, O. M., Månsson, K. N. T., Lindqvist, D., Boks, M. P., Révész, D., Mellon, S. H., et al. (2019) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2019.04.004 · Cited by 104 · see citing works
Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study
Aycheh, H. M., Seong, J., Shin, J., Na, D. L., Kang, B., Seo, S. W., & Sohn, K.-A. (2018) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2018.00252 · Cited by 102 · see citing works
“The recent implementation of deep learning algorithms also benefits from the automatic feature learning property of the algorithm when the sample size is sufficient (Cole et al., 2017).”
BackgroundMethodEuropean Society of Radiology, deSouza, N. M., Achten, E., Alberich‐Bayarri, Á., Bamberg, F., Boellaard, R., Clémеnt, O., Fournier, L. S., Gallagher, F. A., Golay, X., et al. (2019) · Insights into Imaging · doi:10.1186/s13244-019-0764-0 · Cited by 108 · see citing works
“with rigorous processes for acquisition and analysis, together with deep-learning algorithms such as has been demonstrated for brain ageing [47] and treatment re-”
MethodFactors 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) · BMC Neurology · doi:10.1186/s12883-021-02331-4 · Cited by 108 · see citing works
Boyle, R. T., Jollans, L., Rueda‐Delgado, L. M., Rizzo, R., Yener, G., McMorrow, J. P., Knight, S. P., Carey, D., Robertson, I. H., Emek‐Savaş, D. D., et al. (2020) · Brain Imaging and Behavior · doi:10.1007/s11682-020-00260-3 · Cited by 104 · see citing works
Improving the repeatability of deep learning models with Monte Carlo dropout
Lemay, A., Hoebel, K., Bridge, C. P., Befano, B., de Sanjosé, S., Egemen, D., Rodríguez, A. C., SCHIFFMAN, M. H., Campbell, J. P., & Kalpathy–Cramer, J. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00709-3 · Cited by 109 · see citing works
Explainable machine learning framework to predict personalized physiological aging
Bernard, D., Doumard, E., Ader, I., Kémoun, P., Pagès, J., Galinier, A., Cussat‐Blanc, S., Furger, F., Ferrucci, L., Aligon, J., et al. (2023) · Aging Cell · doi:10.1111/acel.13872 · Cited by 101 · see citing works
“Convolutional neural networks (CNN) and recurrent neural networks (RNN) appear particularly relevant for knowledge extraction from images (Cole et al., 2017) and sequential data (Rahman & Adjeroh, 2019) for biological age estimation, respectively.”
BackgroundLevakov, G., Rosenthal, G., Shelef, I., Raviv, T. R., & Avidan, G. (2020) · Human Brain Mapping · doi:10.1002/hbm.25011 · Cited by 100 · see citing works
“…state-of-the-art results in serval neuroimaging tasks including cortical segmentation and tumor detection (Kamnitsas, Chen, & Ledig, 2015; Pereira, Pinto, Alves, & Silva, 2016) and were recently applied to age prediction from raw T1 magnetic resonance imaging (MRI) images (Cole et al., 2017).”
Backgroundde Lange, A. G., Barth, C., Kaufmann, T., Maximov, I. I., van der Meer, D., Agartz, I., & Westlye, L. T. (2020) · Human Brain Mapping · doi:10.1002/hbm.25180 · Cited by 101 · see citing works
“Brain age gap , calculated by subtracting chronological age from estimated brain age, was used as a measure of apparent brain aging (Franke & Gaser, 2019; Smith et al., 2019; Franke et al., 2010; Cole et al., 2017).”
MethodAccelerated functional brain aging in pre-clinical familial Alzheimer’s disease
Gonneaud, J., Baria, A. T., Binette, A. P., Gordon, B. A., Chhatwal, J. P., Cruchaga, C., Jucker, M., Levin, J., Salloway, S., Farlow, M. R., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25492-9 · Cited by 109 · see citing works
Data-driven modelling of neurodegenerative disease progression: thinking outside the black box
Young, A. L., Oxtoby, N. P., Garbarino, S., Fox, N. C., Barkhof, F., Schott, J. M., & Alexander, D. C. (2024) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00779-6 · Cited by 103 · see citing works
Tensor Regression NetworksPreprint
Kossaifi, J., Lipton, Z. C., Kolbeinsson, A., Khanna, A., Furlanello, T., & Anandkumar, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.08308 · Cited by 101 · see citing works
“Predicting age is an important task as the difference between true and predicted age is a biomarker that has a number of clinical applications (Cole et al., 2017; Kolenic et al., 2018; Franke et al., 2018).”
BackgroundComputational biology: deep learning
Jones, W., Alasoo, K., Fishman, D., & Parts, L. (2017) · Emerging Topics in Life Sciences · doi:10.1042/etls20160025 · Cited by 98 · see citing works
Høgestøl, E. A., Kaufmann, T., Nygaard, G. O., Beyer, M. K., Sowa, P., Nordvik, J. E., Kolskår, K. K., Richard, G., Andreassen, O. A., Harbo, H. F., et al. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00450 · Cited by 99 · see citing works
Cardiometabolic risk factors associated with brain age and accelerated brain ageing
Beck, D., de Lange, A. G., Pedersen, M. L., Alnæs, D., Maximov, I. I., Voldsbekk, I., Richard, G., Sanders, A., Ulrichsen, K. M., Dørum, E. S., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25680 · Cited by 98 · see citing works
Mishra, S., Beheshti, I., & Khanna, P. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3107372 · Cited by 98 · see citing works
“a reliable and heritable biomarker showing brain age estimation using a sample of monozygotic and dizygotic female twins [65].”
BackgroundMethodRichard, G., Kolskår, K. K., Sanders, A., Kaufmann, T., Petersen, A., Doan, N. T., Sánchez, J. M., Alnæs, D., Ulrichsen, K. M., Dørum, E. S., et al. (2018) · PeerJ · doi:10.7717/peerj.5908 · Cited by 102 · see citing works
Brain Age Estimation From MRI Using Cascade Networks With Ranking Loss
Cheng, J., Liu, Z., Guan, H., Wu, Z., Zhu, H., Jiang, J., Wen, W., Tao, D., & Liu, T. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3085948 · Cited by 97 · see citing works
“[11], [12] trained a VGG network [28] and obtained promising brain age estimation results with the Mean Absolute Error (MAE) of 4.”
BackgroundMethodResultInfluentialLi, H., Satterthwaite, T. D., & Yong Fan, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363532 · Cited by 95 · see citing works
“In the past years, computational neuroanatomy analytic tools and machine learning techniques have been adopted to predict the brain age based on structural/functional neuroimaging data [1-4].”
BackgroundMethodFeng, X., Lipton, Z. C., Yang, J., Small, S. A., & Provenzano, F. A. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.02.009 · Cited by 96 · see citing works
“More modern deep learning based methods are especially well suited to this task provided enough training data, and have been previously applied by [16] who demonstrated favorable performance.”
Measuring Resilience and Resistance in Aging and Alzheimer Disease Using Residual Methods
Bocancea, D. I., van Loenhoud, A. C., Groot, C., Barkhof, F., van der Flier, W. M., & Ossenkoppele, R. (2021) · Neurology · doi:10.1212/wnl.0000000000012499 · Cited by 94 · see citing works
Jiang, H., Lu, N., Chen, K., Li, Y., Li, K., Zhang, J., & Guo, X. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2019.01346 · Cited by 89 · see citing works
“Compared to conventional machining learning methods, deep learning methods can automatically learn abstract and complex features from brain imaging data using progressive non-linear transformations and have already been widely and successfully used in neuroimaging studies (25–29).”
BackgroundMethodInfluentialMontesinos-López, O. A., Martín‐Vallejo, J., Crossa, J. L., Daniel Gianola, B., Hernández-Suárez, C., Montesinos‐López, A., Juliana, P., & Singh, R. P. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.300585 · Cited by 88 · see citing works
Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network
Al-Khuzaie, F. E. K., Bayat, O., & Duru, A. D. (2021) · Applied Bionics and Biomechanics · doi:10.1155/2021/6690539 · Cited by 89 · see citing works
“In addition to the above classifications, the methods of deep learning classification of magnetic resonance imaging (MRI) images have been used also by other fields of classification, like stroke diagnosis [16], age prediction [17], classification of attention deficit hyperactivity disorder (ADHD) [18], discrimination…”
MethodRetractedThe maternal brain: Region‐specific patterns of brain aging are traceable decades after childbirth
de Lange, A. G., Barth, C., Kaufmann, T., Anatürk, M., Suri, S., Ebmeier, K. P., & Westlye, L. T. (2020) · Human Brain Mapping · doi:10.1002/hbm.25152 · Cited by 86 · see citing works
“…& Franke, 2017), and individual variation in “brain age” estimates has been associated with a range of clinical and biological factors (Cole, 2020; Cole et al., 2017, 2018; Cole & Franke, 2017; Cole, Marioni, Harris, & Deary, 2019; de Lange, Anatürk, et al., 2020; de Lange, Barth, et al., 2020;…”
BackgroundAging biomarkers and the brain
Higgins‐Chen, A., Thrush, K., & Levine, M. E. (2021) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2021.01.003 · Cited by 86 · see citing works
Shi, W., Yan, G., Li, Y., Li, H., Liu, T., Sun, C., Wang, G., Zhang, Y., Zou, Y., & Wu, D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117316 · Cited by 83 · see citing works
“Recently, deep learning techniques have been applied in brain age estimation, offering superior performance over traditional methods (Chen et al., 2020; Cole et al., 2017; Jonsson et al., 2019; Levakov et al., 2020; Wang et al., 2019).”
Amini, M., Pedram, M. M., Moradi, A., & Ouchani, M. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/5514839 · Cited by 84 · see citing works
Understanding the interplay of sleep and aging: Methodological challenges
Muehlroth, B. E., & Werkle‐Bergner, M. (2020) · Psychophysiology · doi:10.1111/psyp.13523 · Cited by 80 · see citing works
Montesinos-López, O. A., Montesinos‐López, A., Tuberosa, R., Maccaferri, M., Sciara, G., Ammar, K., & Crossa, J. L. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01311 · Cited by 82 · see citing works
“In biological sciences there are also many successful applications of deep learning (Alipanahi et al., 2015; Cole et al., 2017; Pan and Shen, 2017; Tavanaei et al., 2017), while in the context of GS, this methodology is applied to continuous (univariate and multivariate), binary and ordinal…”
MethodHe, S., Pereira, D., Perez, J. D., Gollub, R. L., Murphy, S. N., Prabhu, S. P., Pienaar, R., Robertson, R. L., Grant, P. E., & Ou, Y. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102091 · Cited by 82 · see citing works
Explainable Deep Learning for Personalized Age Prediction With Brain Morphology
Lombardi, A., Diacono, D., Amoroso, N., Monaco, A., Tavares, J. M. R. S., Bellotti, R., & Tangaro, S. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.674055 · Cited by 82 · see citing works
“…2020); on the other hand, more complex models such as convolutional neural networks directly exploiting raw image as input have proven to be particularly effective in brain age prediction even in broad age ranges (Cole et al., 2017, 2019; Feng et al., 2020; Levakov et al., 2020; Peng et al., 2021).”
BackgroundA reusable benchmark of brain-age prediction from M/EEG resting-state signals
Engemann, D. A., Mellot, A., Höchenberger, R., Banville, H. J., Sabbagh, D., Gemein, L. A. W., Tonio Ball, A., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119521 · Cited by 74 · see citing works
Local Brain-Age: A U-Net Model
Popescu, S. G., Glocker, B., Sharp, D. J., & Cole, J. H. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.761954 · Cited by 78 · see citing works
“However, the between-scanner reliability was moderate, similar to our previous work using deep learning to predict brain age (Cole et al., 2017).”
MethodResultSelf-citationAge Prediction Based on Brain MRI Image: A Survey
Sajedi, H., & Pardakhti, N. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1401-7 · Cited by 77 · see citing works
Dörfel, R. P., Arenas‐Gomez, J. M., Fisher, P. M., Ganz, M., Knudsen, G. M., Svensson, J. E., & Plavén‐Sigray, P. (2023) · Human Brain Mapping · doi:10.1002/hbm.26502 · Cited by 77 · see citing works
“We”
BackgroundMethodResultInfluentialLongitudinal self-supervised learning
Zhao, Q., Liu, Z., Adeli, E., & Pohl, K. M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102051 · Cited by 71 · see citing works
“To predict the brain age from structural MRIs, recent publications ( Cole et al., 2016; Elliott et al., 2019; Kaufmann et al., 2019; Smith et al., 2020; Zhao et al., 2019 ) proposed to train supervised learning models on a cohort of healthy subjects with the prediction target being their…”
Secure neuroimaging analysis using federated learning with homomorphic encryption
Stripelis, D., Saleem, H., Ghai, T., Dhinagar, N. J., Gupta, U., Anastasiou, C., Steeg, G. V., Ravi, S., Naveed, M., Thompson, P. M., et al. (2021) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2606256 · Cited by 72 · see citing works
“8 In our study, we perform the BrainAge prediction task using a 2D Convolutional Neural Network (CNN), which was shown 21 to yield better predictive performance compared to its 2D-Slice-RNN 19 and 3D-CNN 17,20 counterparts.”
MethodKim, S. Y., Yeh, P., Ollinger, J. M., Morris, H. D., Hood, M. N., Ho, V. B., & Choi, K. H. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02569-1 · Cited by 77 · see citing works
““Brain age” can be estimated by comparing an individual’s brain scan to a model generated from a large dataset of healthy participants [169, 170].”
Background
- 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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