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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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“The trajectory of brain aging can be predicted based on evidence from neuroimaging of decreased gray matter volume (Cole and Franke, 2017; Cole et al., 2017) and inverted U-shape curve white matter (Mwangi et al.”
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MethodConsistent Brain Ageing Synthesis
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“A number of approaches exist to estimate a “brain age” through comparison of an individual’s brain scan to a model generated from the data of hundreds or thousands of healthy participants (26).”
BackgroundMethodSelf-citationThe association between inadequate sleep and accelerated brain ageing
Ramduny, J., Bastiani, M., Huedepohl, R., Sotiropoulos, S. N., & Chechlacz, M. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.02.005 · Cited by 29
“, difference between “chronological age” calculated from the date of birth and “brain age” computed based on neuroimaging data (Cole & Franke, 2017; Cole et al., 2017; Franke & Gaser, 2019; Liem et al., 2017; Smith et al., 2019).”
MethodYeung, H. W., Stolicyn, A., Buchanan, C. R., Tucker–Drob, E. M., Bastin, M. E., Luz, S. F., McIntosh, A. M., Whalley, H. C., Cox, S. R., & Smith, K. (2022) · Human Brain Mapping · doi:10.1002/hbm.26182 · Cited by 27
“on or voxel-based morphometric (VBM) features derived from structural MRI scans (Cole et al., 2017; Hu et al., 2020; Zou et al., 2017).”
BackgroundMethodOTFPF: Optimal transport based feature pyramid fusion network for brain age estimation
Fu, Y., Huang, Y., Zhang, Z., Dong, S., Xue, L., Niu, M., Li, Y., Shi, Z., Wang, Y., Zhang, H., et al. (2022) · Information Fusion · doi:10.1016/j.inffus.2023.101931 · Cited by 29
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Self-citationSynthetic data in generalizable, learning-based neuroimaging
Gopinath, K. K., Hoopes, A., Alexander, D. C., Arnold, S. E., Balbastre, Y., Billot, B., Casamitjana, A., Cheng, Y., Chua, R. Y. Z., Edlow, B. L., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00337 · Cited by 32
“, but would open the door to applications such as age 5 ONGOING AND FUTURE WORK prediction (“BrainAge”), which is a reliable biomarker for cognitive impairment and several diseases(Cole et al., 2017).”
An Attentional-LSTM for Improved Classification of Brain Activities Evoked by Images
Zhong, S.-H., Fares, A. H., & Jiang, J. (2019) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3343031.3350886 · Cited by 29
“Cole et al. [8] used a predictive modelling approach based on CNN for predicting brain ages.”
MethodInterpretable brain age prediction using linear latent variable models of functional connectivity
Monti, R. P., Gibberd, A. J., Roy, S., Nunes, M. A., Lorenz, R., Leech, R., Ogawa, T., Kawanabe, M., & Hyvärinen, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232296 · Cited by 29
Review of Artificial Intelligence Applications and Algorithms for Brain Organoid Research
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Longitudinal brain age prediction and cognitive function after stroke
Aamodt, E. B., Alnæs, D., de Lange, A. G., Aam, S., Schellhorn, T., Saltvedt, I. T., Beyer, M. K., & Westlye, L. T. (2022) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2022.10.007 · Cited by 29
“Although linked to genetics (Kaufmann et al., 2019; Cole et al., 2017) and birth weight (Vidal-Pineiro et al.”
Brain age prediction across the human lifespan using multimodal MRI data
Guan, S., Jiang, R., Meng, C., & Biswal, B. B. (2023) · GeroScience · doi:10.1007/s11357-023-00924-0 · Cited by 32
“Although these previously mentioned ML meth-ods have been adopted to predict brain age based on sMRI or fMRI data [8, 26, 28, 29, 62, 69, 92–95], GPR increases the difficulty of adjusting for hyper-parameters, especially when the training set is small.”
InfluentialBiomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review
Xue, T., Zhu, Z., Wang, L., Li, C., Sun, L., Wang, W., & Gong, W. (2023) · Aging and Disease · doi:10.14336/ad.2023.00808-1 · Cited by 37
“The application of a deep learning algorithm makes the predicted brain age more accurate and reliable [207].”
BackgroundIn vivo biomarkers of structural and functional brain development and aging in humans
Franke, K., Bublak, P., Hoyer, D., Billiet, T., Gaser, C., Witte, O. W., & Schwab, M. G. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.002 · Cited by 29
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“Recent advances in machine learning, specifically on deep convolutional neural networks, have gradually improved brain age prediction by lowering prediction error (9).”
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Ultrafast (milliseconds), multidimensional RF pulse design with deep learning
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“extent of the topic, are diagnostics (8), pathogenesis detection (9), artefact removal (10), gadolinium dose reduction (11), FMRI analysis (12), stroke treatment efficiency (13), brain age prediction (14), image segmentation (15), and image reconstruction (16). To our knowledge, machine learning-type methods have only…”
BackgroundLin, L., Zhang, G., Wang, J., Tian, M., & Wu, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10377-8 · Cited by 27
“In a recent study, Cole and his colleagues [6] have introduced an age prediction framework based on 3D convolutional neural networks (CNNs).”
BackgroundMethodInfluentialUsing Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs
Yang, C., Pan, Y., Chou, Y., Yang, C.-J., Kao, C.-C., Huang, K.-C., Chang, J.-S., Chen, H.-C., & Kuo, K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10194431 · Cited by 28
“Recently, deep learning, an ML technique that utilizes feature-learning methods with multiple levels of representation, has also been used for bone age [21] and brain age prediction [22] with comparable accuracy.”
MethodHojjati, S. H., Abbas Babajani-feremi, A., & Initiative, T. A. D. N. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2021.769982 · Cited by 28
“…2020; Wen et al., 2020) and have gained increasing interest in diverse applications, such as classification, speech recognition, age modeling, modeling and forecasting extreme events, and even face recognition (Cole et al., 2017; Tuan Tran et al., 2017; Duc et al., 2020; Chowdhury et al., 2021).”
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“successfully to make predictions on the basis of brain structure [24] and functional connectivity 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65”
MethodA novel patch-based procedure for estimating brain age across adulthood
Beheshti, I., Gravel, P., Potvin, O., Dieumegarde, L., & Duchesne, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.025 · Cited by 27
Artificial intelligence for the study of human ageing: a systematic literature review
Bernal, M. C., Batista, E., Martínez-Ballesté, A., & Solanas, A. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05817-z · Cited by 30
“In this group, we highlight the different types of techniques to produce in-body images, such as MRI scans [27, 30, 31, 44, 45, 48, 51, 53, 54, 56, 58, 59, 61, 68, 70, 73, 75, 77, 79–81, 83, 85, 92, 94, 96], positron emission tomography (PET) scans [34, 71], and computed tomography (CT) scans [41].”
Ball, G., Adamson, C. L., Beare, R. J., & Seal, M. L. (2017) · Scientific Reports · doi:10.1038/s41598-017-18253-6 · Cited by 26
Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features
Hu, D., Wu, Z., Lin, W., Li, G., & Shen, D. G. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2897020 · Cited by 26
“regression [22], convolutional neural network [23], Gaussian process regression [24], relevance vector regression [25], and multiple linear regression with elastic-net penalty [8].”
MethodSörös, P., & Bantel, C. (2019) · Pain · doi:10.1097/j.pain.0000000000001756 · Cited by 27
“2017 and 2018.(20,21) brainageR uses SPM12 (University College London, London, United Kingdom; https://www.”
MethodAccurate brain age prediction using recurrent slice-based networks
Lam, P., Santhalingam, V., Suresh, P., Baboota, R., Zhu, A. H., Thomopoulos, S. I., Jahanshad, N., & Thompson, P. M. (2020) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2579630 · Cited by 27
Shirbandi, K., Khalafi, M., Mirza‐Aghazadeh‐Attari, M., Tahmasbi, M., Shahvandi, H. K., Javanmardi, P., & Rahim, F. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100710 · Cited by 29
Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning
Hwang, G., Abdulkadir, A., Erus, G., Habes, M., Pomponio, R., Shou, H., Doshi, J. J., Mamourian, E., Rashid, T., Bilgel, M., et al. (2022) · Brain Communications · doi:10.1093/braincomms/fcac117 · Cited by 31
“Whether similar results yield using other structural or functional brain measures,(66,67) or using more advanced, non-linear machine learning models can also be explored.(68,69) Moreover, additional efforts to disentangle SPARE-BA from other ageing-related comorbidities will be valuable in delineating a complete set o…”
ResultImpact of weight loss on brain age: Improved brain health following bariatric surgery
Zeighami, Y., Dadar, M., Daoust, J., Pelletier, M., Biertho, L. D., Bouvet-Bouchard, L., Fulton, S., Tchernof, A., Dagher, A., Richard, D., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119415 · Cited by 29
Brain age vector: A measure of brain aging with enhanced neurodegenerative disorder specificity
Chen, R., Yang, Y., Ye, C., Lv, H., & Ma, T. (2022) · Human Brain Mapping · doi:10.1002/hbm.26066 · Cited by 24
“Brain age modeling has recently become a popular approach to identifying individual differences in brain aging and investigating neurological disorders (Cole & Franke, 2017; Cole et al., 2017, 2018; Franke & Gaser, 2012; J (cid:2) onsson et al., 2019; Kaufmann et al., 2019).”
BackgroundMethodResultInfluentialZhou, Z., Li, H., Srinivasan, D., Abdulkadir, A., Nasrallah, I. M., Wen, J., Doshi, J. J., Erus, G., Mamourian, E., Bryan, R. N., et al. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119911 · Cited by 26
“Recent studies have demonstrated that the BAG score could potentially serve as a quantitative marker of the brain aging (Cole et al., 2017; Jonsson et al., 2019; Smith et al., 2019; Boyle et al., 2021; Dinsdale et al., 2021).”
BackgroundResultBASE: Brain Age Standardized Evaluation
Dular, L., & Špiclin, Ž. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120469 · Cited by 26
Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning
Rebsamen, M., Suter, Y., Wiest, R., Reyes, M., & Rummel, C. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00244 · Cited by 26
“A regression problem leveraging the full 3D MRI volume using a CNN was proposed by Cole et al. (27), where they successfully predicted brain age directly from raw MRI with a mean absolute error of < 5 years, i.e., much smaller than the age range of available datasets.”
MethodA unified framework for association and prediction from vertex‐wise grey‐matter structure
Couvy‐Duchesne, B., Strike, L. T., Zhang, F., Holtz, Y., Zheng, Z., Kemper, K. E., Yengo, L., Olivier Colliot, A., Wright, M. J., Wray, N. R., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.25109 · Cited by 23
“Further application of our BLUP grey-matter scores include studying correlates of brain age or predicted age difference (difference between predicted and chronological age) (Cole, 2017; Cole et al., 2017; Liem et al., 2017).”
MethodWilczok, D. (2025) · Aging · doi:10.18632/aging.206190 · Cited by 32
Vakli, P., Meszlényi, R., Auer, T., & Vidnyánszky, Z. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00010 · Cited by 25
Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning
Wang, Y., Ji, M., Jiang, S., Wang, X., Wu, J., Duan, F., Fan, J., Huang, L., Ma, S., Fang, L., et al. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0188-z · Cited by 25
“…have recently been utilized to extract high-quality physiological and pathological features and perform recognition across a wide range of scales, including cells 27,28 , histology tissue slides 29 and deep-tissue features acquired from OCT, CT and magnetic resonance imaging (MRI) 30,31 .”
A Proposal of Quantum-Inspired Machine Learning for Medical Purposes: An Application Case
Pomarico, D., Fanizzi, A., Amoroso, N., Bellotti, R., Biafora, A., Bove, S., Didonna, V., Forgia, D. L., Pastena, M. I., Tamborra, P., et al. (2021) · Mathematics · doi:10.3390/math9040410 · Cited by 26
“In particular, machine learning strategies have proven their effectiveness in computer-aided detection systems in several applications, such as modeling aging processes [3–5], predicting the onset of several pathological conditions [6–11], or exploring genetic patterns [12–14].”
BackgroundGender and Age detection using Deep Learning
Saxena, A., Singh, P., & Singh, S. N. (2021) · International Conference on Cloud Computing, Data Science & Engineering (Confluence) · doi:10.1109/confluence51648.2021.9377041 · Cited by 25
Dular, L., Pernuš, F., & Špiclin, Ž. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108320 · Cited by 25
Deep Learning on Brain Cortical Thickness Data for Disease Classification
Menikdiwela, M., Nguyen, C. V., & Shaw, M. E. (2018) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2018.8615775 · Cited by 25
“Most recently, deep learning has been applied to MRI data [3].”
BackgroundWang, Y., Tu, D., Du, J., Han, X., Sun, Y., Xu, Q., Zhai, G., & Zhou, Y. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00627 · Cited by 25
Fraza, C., Rutherford, S. E., Bučková, B. R., Beckmann, C. F., & Marquand, A. F. (2025) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2025.106284 · Cited by 34
Huang, W., Li, X., Li, H., Wang, W., Chen, K., Xu, K., Zhang, J., Chen, Y., Wei, D., Shu, N., et al. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021200171 · Cited by 24
“machine learning algorithms, we also applied the same pipeline to train two other types of linear regression models (ie, least absolute shrinkage and selection operator regression and ridge regression), support vector regression model, Gaussian process regression model, and convolutional neural network model with the…”
MethodExtensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction
Lombardi, A., Amoroso, N., Diacono, D., Monaco, A., Tangaro, S., & Bellotti, R. (2020) · Brain Sciences · doi:10.3390/brainsci10060364 · Cited by 24
Metadata-conditioned generative models to synthesize anatomically-plausible 3D brain MRIs
Peng, W., Bosschieter, T. M., Ouyang, J., Paul, R. H., Sullivan, E. V., Adolf Pfefferbaum, A., Adeli, E., Zhao, Q., & Pohl, K. M. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103325 · Cited by 29
AI applications in HIV research: advances and future directions
Jin, R., & Zhang, L. (2025) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1541942 · Cited by 26
“Results indicated a significant interactive effect between BAG and detectable viral load (VL): PWH who had detectable VL exhibited an accumulation of +1.5 years in BAG per decade compared to HIV-negative controls (Petersen et al., 2022).”
InfluentialLuna, A., Bernanke, J. A., Kim, K., Aw, N., Dworkin, J. D., Cha, J., & Posner, J. E. (2021) · Human Brain Mapping · doi:10.1002/hbm.25565 · Cited by 22
“Previous studies using ML to predict brain age have often employed a single ML algorithm, such as Support Vector Regression (Franke, Luders, May, Wilke, & Gaser, 2012; Schnack et al., 2016), Relevance Vector Regression (Franke et al., 2012), or convolutional neural networks (Cole et al., 2017).”
MethodInterrelating differences in structural and functional connectivity in the older adult's brain
Stumme, J., Krämer, C., Miller, T., Schreiber, J., Caspers, S., & Jockwitz, C. (2022) · Human Brain Mapping · doi:10.1002/hbm.26030 · Cited by 21
“Predictions were found to perform best using structural brain volume data (Cole et al., 2017; Cole & Franke, 2017; Franke et al., 2010; Liem et al., 2017), while age prediction on connectivity data was found to perform significantly lower explaining about 40%–60% of the variance in age (Dosenbach…”
MethodSoch, J., Richter, A., Kizilirmak, J. M., Schütze, H., Feldhoff, H., Fischer, L., Knopf, L., Raschick, M., Schult, A., Düzel, E., et al. (2022) · eNeuro · doi:10.1523/eneuro.0212-22.2022 · Cited by 23
“They have been employed to predict chronological age from structural magnetic resonance imaging (MRI; Cole et al., 2017, 2018), to estimate brain age (Bashyam et al., 2020; Habes et al., 2021) or to distinguish health from disease (Dyrba et al., 2021; Eitel et al., 2021).”
MethodResultMultimodal Sparse Classifier for Adolescent Brain Age Prediction
Kassani, P. H., Gossmann, A., & Wang, Y.-P. (2019) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2019.2925710 · Cited by 18
Accelerated brain ageing and disability in multiple sclerosisPreprint
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. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/584888 · Cited by 24
Self-citationAssociations of plasma proteomics and age-related outcomes with brain age in a diverse cohort
Casanova, R., Walker, K. A., Justice, J. N., Anderson, A., Duggan, M. R., Cordon, J., Barnard, R., Lu, L., Hsu, F., Sedaghat, S., et al. (2024) · GeroScience · doi:10.1007/s11357-024-01112-4 · Cited by 25
“On the other hand, the range of ages of individuals whose MRIs were used for training of the machine learning algorithms was narrower with respect to other published models [43, 71] which it is unlikely to produce accurate estimates of brain age in young individuals.”
MethodDisentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning
Pontillo, G., Prados, F., Colman, J., Kanber, B., Abdel‐Mannan, O., Al‐Araji, S., Bellenberg, B., Bianchi, A., Bisecco, A., Brownlee, W. J., et al. (2024) · Neurology · doi:10.1212/wnl.0000000000209976 · Cited by 25
Self-citationPérez-Pelegrí, M., Monmeneu, J. V., López-Lereu, M. P., Pérez-Pelegrí, L., Maceira, A. M., Bodı́, V., & Moratal, D. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106275 · Cited by 22
Gautherot, M., Kuchcinski, G., Bordier, C., Sillaire, A. R., Delbeuck, X., Leroy, M., Leclerc, X., Pruvo, J., Pasquier, F., & Lopes, R. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.729635 · Cited by 22
Different effects of cardiometabolic syndrome on brain age in relation to gender and ethnicity
Kang, S. H., Liu, M., Park, G., Kim, S. Y., Lee, H., Matloff, W. J., Zhao, L., Yoo, H., Kim, J. P., Jang, H., et al. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01215-8 · Cited by 24
Tensor Dropout for Robust Learning
Kolbeinsson, A., Kossaifi, J., Panagakis, Y., Bulat, A., Anandkumar, A., Tzoulaki, I., & Matthews, P. M. (2021) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2021.3064182 · Cited by 23
“To put this into context, the current state-of-art for convolutional neural networks on age prediction from brain MRI on most datasets is an MAE of around 3.6 years [58].”
BackgroundInfluentialMacDonald, M. E., Williams, R. J., Rajashekar, D., Stafford, R. B., Hanganu, A., Sun, H., Berman, A. J. L., McCreary, C. R., Frayne, R., Forkert, N. D., et al. (2020) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2020.06.019 · Cited by 21
Structural inequality and temporal brain dynamics across diverse samples
Baez, S., Hernandez, H., Moguilner, S., Cuadros, J., Santamaria‐Garcia, H., Medel, V., Migeot, J., Cruzat, J., Valdes‐Sosa, P. A., Lopera, F., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.70032 · Cited by 21
“For space and clarity, following previous approaches, 127,133,134 we reported the top three regression models for each set of outcomes, based on the highest R 2 values.”
MethodScaling Neuroscience Research Using Federated Learning
Stripelis, D., Ambite, J. L., Lam, P., & Thompson, P. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433925 · Cited by 29
“Recent work [10, 11] has shown that deep learning methods can accurately predict an individual’s brain age.”
BackgroundMethod
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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