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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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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879 · cited 1×
“In choosing between sub-models, variance components are excluded from the selection process if there is no significant deterioration in model fit after the component is dropped, as assessed by the Akaike Information Criterion (Akaike, 1974).”
MethodBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · cited 2×
“The architecture contains repeated 5 blocks of a (3 3 3) convolutional layer (with stride of 1), a rectified linear unit (ReLU), a (3 3 3) convolutional layer (with stride of 1), a 3D batch-normalization layer (Ioffe and Szegedy, 2015), a ReLU and finally a (2 2 2) max-pooling layer (with stride of 2).”
BackgroundIntraclass correlations: Uses in assessing rater reliability.
Shrout, P. E., & Fleiss, J. L. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.2.420 · Cited by 24,532
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
A global optimisation method for robust affine registration of brain images
Jenkinson, M., & Smith, S. M. (2001) · Medical Image Analysis · doi:10.1016/s1361-8415(01)00036-6 · Cited by 7,018 · cited 1×
“Raw Nifti files then underwent a rigid registration (i.e. six degrees-of-freedom) to MNI152 space, (FMRIB Software Library [FSL] FLIRT, Jenkinson and Smith, 2001) to ensure consistency of orientation (Right, Posterior, Inferior [RPI]).”
Method3D Convolutional Neural Networks for Human Action Recognition
Ji, S., Xu, W., Yang, M., & Yu, K. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.59 · Cited by 6,313
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,924
Flagged first, then by citations
2 of the 72 references couldn’t be matched to a known paper.
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Items 801–869 of 869
Toward Resilient AI in Medical Imaging: Handling and Mining Multiple Sources of Data
Gravina, M., Simone, A. D., Pontillo, G., Shawa, Z., Marrone, S., Fusco, R., Granata, V., Petrillo, A., Cole, J. H., Sansone, M., et al. (2026) · SN Computer Science · doi:10.1007/s42979-026-04821-z
Self-citationWomen’s reproductive factors predict local brain aging profiles mapped using deep neural networks
Fox, R., Chaudhari, N. N., Bhattacharya, S., Imms, P. E., Eich, T. S., Mack, W. J., Gatz, M., & Andrei Irimia, A. (2026) · GeroScience · doi:10.1007/s11357-026-02180-4
Honnorat, N., Wang, D., Ho, N.-H., Martinez, D. M., Brandigampala, S. R., Heckbert, S. R., Bahrami, M., Himali, J. J., DeCarli, C. S., Beiser, A., et al. (2026) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2026.111815
“…Honnorat and Habes, 2022), but an increasing set of literature is exploring this possibility (Cole et al., 2017, 2018; Cole, 2020; Moguilner et al., 2024) with the hope of developing functional aging biomarkers that could expand the structural markers (Eavani et al., 2018; Cole et al., 2017, 2018).”
InfluentialSmith, C. W., Malhotra, A. K., Lozano, C. S., Shakil, H., Mathieu, F., Yuan, E. Y., Nathens, A. B., Colak, E., Ackery, A., & Witiw, C. D. (2026) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2026.101749
Deep survival modeling to predict future cognitive impairment in unimpaired adults
Imms, P. E., Wang, H., Bhattacharya, S., Chaudhari, N. N., Vega, O. M., Galvan, J. A. S., Chen, S., Li, R., & Andrei Irimia, A. (2026) · The Journals of Gerontology Series A · doi:10.1093/gerona/glag076
Brain Age Estimation on T2 ‐ FLAIR Scans for Application to Multiple Sclerosis
Colman, J., Pontillo, G., Goodkin, O., Foster, M. A., Mahmoudi, N., Wattjes, M. P., Brunetti, A., González‐Escamilla, G., Groppa, S., Høgestøl, E. A., et al. (2026) · Human Brain Mapping · doi:10.1002/hbm.70425
Self-citationK, S. K. L., & Sundararajan, R. (2025) · International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT) · doi:10.1109/icaiccit68829.2025.11434071
“A Convolutional Neural Network (CNN) was trained on raw and preprocessed MRI data to estimate brain age [22] reliably.”
Cross-Scanner Reliability of Brain MRI Foundation Model Embeddings: A Travelling-Heads StudyPreprint
González, R. N., Aja‐Fernández, S., Planchuelo‐Gómez, Á., & de Luis-Garcı́a, R. (2026) · medRxiv · doi:10.64898/2026.03.23.26348808
Lore, S., Julihn, C., Telfer, P., Scheibye‐Knudsen, M., & Verdin, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.26.714124
“Brain age clocks, which estimate chronological age from neurobiological data using statistical or machine-learning approaches, have emerged as a particularly informative class of biological age biomarkers 8,9 .”
Enabling real-world EEG applications with deep learning
Banville, H. J. (2022) · theses.fr (ABES) · doi:10.70675/12e08edaz0021z43fbzb6adz07d315fc6ee2
Sabbagh, D. (2021) · theses.fr (ABES) · doi:10.70675/b70e0626zdd29z45e9z9dc4zfc919b40c191 · Cited by 1
Collateral-Free Learning of Deep Representations : From Natural Images to Biomedical Applications
Barbano, C. A. (2023) · theses.fr (ABES) · doi:10.70675/d0edb18bzae2bz48f8zba04z48d7240e8e0d
Lee, G., Haugg, F., Bontempi, D., He, J., Bitterman, D. S., Pai, S., Guthier, C. V., Fitzgerald, K. J., Kozono, D., Kann, B. H., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2026.4872
Chaudhari, N. N., Vega, O. M., Imms, P. E., Kawamura, J., Chowdhury, N. F., Jafar, T., Andrei Irimia, A., Weiner, M. W., Aisen, P. S. S., Petersen, R. C., et al. (2026) · GeroScience · doi:10.1007/s11357-026-02112-2
Quatre, R., Baguant, A., Renard, F., Lalami, G., Julie, D., Schmerber, S., & Attyé, A. (2026) · Ear and Hearing · doi:10.1097/aud.0000000000001824
DFLBN: A Dynamic Fuzzy Learnable Brain Network for Brain Age Estimation
Ding, W., Wang, J., Zhou, T., Jiang, S., Ping, P., & Yang, J. (2026) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2026.3681478
Shiino, A., Tanigaki, K., Oki, M., & Watanabe, Y. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121927
CortiGen: An Aging-Informed Cortical Surface Generative Model for Age and Cognition
Zhang, S., Wang, H., Zhang, T., & Zhuang, M. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460725
Yang, A. C. (2026) · Journal of physiological investigation. · doi:10.4103/ejpi.ejpi-d-25-00066
Asthana, S., on behalf of the Longevity India Team, Collaborators, Bhat, A., Mahadevan, G., Kothegala, L., Negi, S., Yadav, S., Sudhakaran, A., Roy, A., et al. (2026) · Aging · doi:10.18632/aging.206373
Brain-age defines a structural signature of outcomes after vagus nerve stimulation in children
Latypov, T. H., Suresh, H., Gouveia, F. V., Mithani, K., Berger, A., Wong, S. M., Coleman, S. C., Weiss, S. K., Sham, L., Chau, V., et al. (2026) · Brain stimulation · doi:10.1016/j.brs.2026.103117
Advances in modeling memory decline: A critical overview of current animal and human paradigms
Peng, Y., Li, Y., Ma, L., Wu, T., & Chen, G. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103172
Sun, T., Zhao, X., Zheng, Q., Su, K., Zhang, C., Wang, Y., & Jiao, C.-N. (2026) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-026-01366-3
Bayesian Nonparametrics for Normative Modelling in Multiple Sclerosis via Modularised InferencePreprint
Taschler, B., Nichols, T. E., & Ganjgahi, H. (2026) · medRxiv · doi:10.64898/2026.05.10.26352835
Almas, S., Washakh, R. M. A., Waque, R. M. U., Almas, N., Bringas, M., & Valdes-Sosa, P. A. (2026) · Journal of Biosciences and Medicines · doi:10.4236/jbm.2026.145014
Wang, J., Wang, S., Zhang, L., Dong, Z., Bennett, D. A., Yue, J., & Xu, W. (2026) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2026.106817
Oura, R., ITO, K. O. K., & Aoki, T. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515633
Xu, S., Li, Z., Liu, X., Liu, S., Wang, X., Yang, P., Cai, X., Zeng, H., Liu, M., & Li, Y. (2026) · iScience · doi:10.1016/j.isci.2026.116072
Wang, Y., Zhang, S., Chen, L., Zhang, H., Cao, A., & Du, P. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1771274
Weakly-Supervised Representations of Infant Brain MRI with Age-Aware Contrastive Learning
Kapadia, S., Merrill, A., & Kapadia, S. (2025) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc68753.2025.11532977
WaveDiT: Distribution-Aware Wavelet Flow Matching for Efficient 3D Brain MRI Synthesis
Danese, D., Lombardi, A., Fasano, G., Attimonelli, M., & Noia, T. D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38189-7_59
FlowLet: Conditional 3D brain MRI synthesis using wavelet flow matching
Danese, D., Lombardi, A., Attimonelli, M., Fasano, G., & Noia, T. D. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104161 · Cited by 5
“Aprominent example is Brain Age Prediction (BAP), a data-driven framework that estimates an individual’s biological brain age from structural MRI by learning population-level associations between anatomical features and chronological age [11, 3, 39, 47].”
Liu, Z., Ding, S., Liu, H., Jing, J., Cheng, J., Zheng, H., Jiang, J., Wen, W. X., Zhang, Z., Zhu, W., et al. (2026) · Brain Health · doi:10.1002/brh2.70010
Depression and aging: insights from brain age prediction models
Mitchell, O., Connaughton, M., Kelly, J. R., Harkin, A., Roddy, D. W., & Aas, M. (2026) · Psychological Medicine · doi:10.1017/s0033291726104851
“Neuroimaging-derived brain age prediction has emerged as an informative bio-marker of brain health (Cole & Franke, 2017; Cole et al., 2017; Franke, Ziegler, Klöppel, & Gaser, 2010), with environmental, genetic, and lifestyle factors all influencing aging rates.”
SimBRAge: A Framework for Region-Level Morphometric Simulation for What-If Brain Age Analysis
Lombardi, A., Ardito, C. A., Sciascio, E. D., & Noia, T. D. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-9575-4_31
Casagrande, C. C., Deardorff, R. L., Zhai, W., Small, B. J., Bailey, J. N., Dyk, K. V., Root, J. C., Ahles, T. A., Carroll, J., Mandelblatt, J. S., et al. (2026) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djag219
Juglan, R., Ligero, M., Carrero, Z. I., Meneghetti, A. R., Lenz, T., Misera, L., Veldhuizen, G. P., Kuntke, P., Kitzler, H. H., Nebelung, S., et al. (2026) · Brain Informatics · doi:10.1186/s40708-026-00316-y · Cited by 4
“Predicting biological characteristics such as age and sex from structural brain MRI has been demonstrated with high accuracy through a range of deep learning architectures [37, 45, 65].”
InfluentialBrain Aging in Specific Phobia: An ENIGMA ‐Anxiety Mega‐Analysis
Blake, K. V., Hilbert, K., Ipser, J., Han, L. K. M., Bas‐Hoogendam, J. M., Åhs, F., Bauer, J., Beesdo‐Baum, K., Björkstrand, J., Blanco‐Hinojo, L., et al. (2026) · Human Brain Mapping · doi:10.1002/hbm.70595 · Cited by 3
“Thus, brain age models may identify unhealthy aging patterns, demonstrating potential to identify risk for psychopathology during aging (Cole et al. 2018, 2017).”
BrainYears: A functional ERP–EEG brain age clock for scalable assessment of brain agingPreprint
Verdin, E. M., Lore, S., Telfer, P., Scheibye‐Knudsen, M., & Julihn, C. (2026) · Research Square · doi:10.21203/rs.3.rs-10145234/v1
Commentary: Exercise as a brake on brain aging—Promise and unresolved questions
Abdulkarim, M. M. (2026) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2026.101156
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Зуева, М. В., Kotelin, V. I., Neroeva, N. V., Chesnokova, N. B., Zhuravleva, A. K., Balatskaya, N. V., & Ryabina, M. V. (2026) · Russian Ophthalmological Journal · doi:10.21516/2072-0076-2026-19-2-203-211
Synchrony between brain age and internalising and externalising symptoms across adolescence
Beck, D., Carrick, C., Aksnes, E. R., MacSweeney, N., Westlye, L. T., Fuhrmann, D., & Tamnes, C. K. (2026) · Communications Biology · doi:10.1038/s42003-026-10722-x
Xiao, G., Li, X., Qin, Y., Zhao, W., Li, X., Qian, Y., Tian, J., Chen, X., Li, W., & Wang, L. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1795744
“The brain age model was implemented via R environment 4.3.2 and the brainageR framework (version 2.1) 2 , which was pretrained on a large-scale reference dataset of healthy individuals ( n = 3,377; age range: 18–92 years) using Gaussian Processes Regression (Cole et al., 2017).”
InfluentialYusef, Y., Avetisov, K. S., Matchekhina, L. V., Melnitskaia, A. A., Strazhesko, I. D., Altemirova, K. K., & Khalatyan, A. S. (2026) · Ophthalmology Reports · doi:10.17816/ov695820
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Suk, K. (2026) · Experimental Gerontology · doi:10.1016/j.exger.2026.113260
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Pae, C., Kim, M., Hwang, I., Moon, J. H., & Choi, K. S. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1893253
Nourelden, A. Z., Bao, F., Bansal, P., & Memon, A. B. (2026) · Neuro-Ophthalmology · doi:10.1080/01658107.2026.2702541
He, R., Zhu, D., Wang, Y., Yue, S., Zhao, L., Fu, Y., Wu, D., Hu, B., & Zheng, W. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.114542
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Rajesh, S., Sharma, D., Venugopal, R., Sasidharan, A., Malipeddi, S., Chowdhury, P., & Ravindra, P. N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.04.742735
“Since then, a substantial literature has developed around predicting age from neuroimaging data (Cole et al., 2017; Dosenbach et al., 2010; Franke et al., 2010, 2012; Jonsson et al., 2019).”
Wan, L., Kang, C., Harrison, R., Solis‐Urra, P., Sewell, K. R., Oberlin, L. E., Jain, S., Huang, H., Grove, G., Kamboh, M. I., et al. (2026) · Alzheimer s & Dementia · doi:10.1002/alz.71743
“27 The pipeline first initiates voxel-level preprocessing using the SPM12”
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Kespechara, K., Fongsri, T., Yaisoongnern, S., & Mingkhwan, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-35387-0_35
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Pilli, R., & Goel, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-44-330291-6.00013-5
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Bhattacharya, M., Singh, A., Kurtz, A. P., Iwamoto, F. M., Prasanna, P., & Singh, G. (2026) · Research Square · doi:10.21203/rs.3.rs-10655143/v1
“Although emerging MRI-based foundation models have shown utility in neurodevelopmental and neurodegenerative applications 20,21 , neuro-oncology applications remain largely unexplored.”
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Lisazo, C., Casamitjana, A., Oliver, A., & Lladó, X. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103534
Payas, A., Çildir, D. A., Eker, Ö. O., Ökçesiz, İ., Çiçek, F., Öztaş, E., Uçar, S., Kocaman, H., Yıldırım, H., & Uçar, İ. (2026) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2026.112337
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Sénant, Q., Guichet, C., Grivel, N., Baciu, M. V., & Mermillod, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.14.751405
“However, this application would require evidence that individual brain-age gaps predict clinically meaningful cognitive or neurological outcomes (Azzam et al., 2025; Baecker et al., 2021; Cole et al., 2017), which require validation in additional cohorts.”
Zhang, Y., Li, H., & Yong Fan, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38172-9_31
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Chen, T., Chen, M., Zhang, J., Cao, C., Su, L., Liu, T., & Zhu, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38239-9_27
Okada, N., Seitz‐Holland, J., Ikegame, T., Omileke, F., Kiyota, M., Shoji, E., Mori, S., Morita, S., Koshiyama, D., Satomura, Y., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-8177826/v1
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Luo, Y., Cole, J. R., & Lu, H. (2026) · Ageing Research Reviews · doi:10.1016/j.arr.2026.103420
Self-citationAltas, B., Georgiou, P., & Onisiforou, A. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag568
“For example, in brain-age prediction, a hypernetwork receiving age, sex, and genotype can generate convolutional filters tailored to age-and genotype-specific aging patterns, enabling a single CNN backbone to specialize across patient subgroups [ 48, 49].”
Application of interpretable deep learning in brain age prediction: a comprehensive review
Wang, Z., Gao, H., Lin, L., Li, Y., & Zhu, X. (2026) · Reviews in the Neurosciences · doi:10.1515/revneuro-2026-0186
“…age, which serves as a static temporal reference, brain age provides an individualized snapshot of the brain ’ s structural and functional integrity, re fl ecting cumulative neurobiological alterations accrued over the life-span (Cole and Franke 2017; Cole et al. 2017; Franke et al. 2010).”
Advanced brain age and longitudinal cognitive decline in healthy older adults
Kristinsson, S., Newman-Norlund, R., Bonilha, L., Bakos, L., Rorden, C., Sigurdsson, S., Guðnason, V., & Fridriksson, J. (2026) · GeroScience · doi:10.1007/s11357-026-02598-w
Assaf, A., Alsaify, B. A., AlJarrah, O. Y., Li, S., Schmidt-Trucksaess, A., Kilani, A., Kotliar, K., & Lanzl, I. M. (2026) · Scientific Reports · doi:10.1038/s41598-026-75953-8
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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