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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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“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.”
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“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
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“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).”
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“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
“…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
“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
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“Similarly, Cole et al. (2017) used Deep Learning (DL) to study BrainAGE using both pre-processed and raw T1w MRI images.”
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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 158
“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 163
MRI of healthy brain aging: A review
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Brain age from the electroencephalogram of sleep
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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
Deep learning for brain age estimation: A systematic review
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“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 145
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Improved prediction of brain age using multimodal neuroimaging data
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“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 134
“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 129
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 131
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
“…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 129
“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
“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
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“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 104
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 119
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
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
“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
“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 116
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 115
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 115
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 108
“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 101
“…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 98
“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
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 120
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 119
“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 102
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
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 99
Mishra, S., Beheshti, I., & Khanna, P. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2021.3107372 · Cited by 100
“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 106
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 102
“[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
“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 100
“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., van Loenhoud, A. C., Groot, C., Barkhof, F., van der Flier, W. M., & Ossenkoppele, R. (2021) · Neurology · doi:10.1212/wnl.0000000000012499 · Cited by 86
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 93
“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., Gianola, D., Hernández-Suárez, C., Montesinos‐López, A., Juliana, P., & Singh, R. P. (2019) · G3 Genes Genomes Genetics · doi:10.1534/g3.119.300585 · Cited by 91
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
“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 84
“…& 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 85
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 84
“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
Understanding the interplay of sleep and aging: Methodological challenges
Muehlroth, B. E., & Werkle‐Bergner, M. (2020) · Psychophysiology · doi:10.1111/psyp.13523 · Cited by 87
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
“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 85
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 85
“…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., Ball, T., & Gramfort, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119521 · Cited by 87
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 77
“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
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 85
“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
“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
“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 81
““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
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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