Abstract
Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.
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Data availability
All derived R-indices in this study are available at Supplementary Data 12, indexed by participant ID in the iSTAGING study. Additional raw imaging and clinical data used in this study were provided by several individual studies via data-sharing agreements, which do not include permission for us to further share the data. Investigators must apply to the source data providers to access additional data and match their subject IDs to those used in this study under the current protocol (primarily for UKBB). Data from ADNI are available from the ADNI database (adni.loni.usc.edu) upon registration and compliance with the data usage agreement. Data from the UKBB are available upon request from the UKBB website (https://www.ukbiobank.ac.uk/). Data from the BLSA study are available upon request at https://www.blsa.nih.gov/how-apply. Data from the AIBL study are available upon request at https://aibl.org.au/. Data from the OASIS study are available upon request at https://www.oasis-brains.org/. Data requests for BIOCARD, PENN, WRAP, CARDIA, SHIP and WHIMS datasets should be directed to M.S.A., D.A.W., S.C.J., L.J.L., K.W. and M.A.E., respectively. As soon as access to the source studies is obtained, investigators can match our derived R-indices to the rest of the data from these studies. Further assistance in matching the R-indices can be requested from the corresponding author, C.D., at Christos.Davatzikos@pennmedicine.upenn.edu, with responses typically provided within 2 weeks. Moreover, we are actively following protocols to upload our derived measures to the UKBB and ADNI websites, making them directly accessible to investigators who obtain access to those studies. The pretrained model for deriving R-indices in this study is available at https://github.com/zhijian-yang/SurrealGAN/blob/main/pretrained_models/brain_aging_5rindices/. Researchers can derive R-indices on their own datasets by following the data processing pipeline outlined in Method 9 and the model application process in Method 4, along with the example script on the same GitHub repository. The GWAS summary statistics are publicly available at https://labs-laboratory.com/medicine.
Code availability
The software Surreal-GAN is available as a published PyPI package. Detailed information about software installation, usage and license can be found at https://pypi.org/project/SurrealGAN/0.1.1/. Custom code can be found at https://github.com/zhijian-yang/SurrealGAN. The deep-learning models are currently being integrated into NiChart (neuroimagingchart.com), enabling researchers to quickly derive R-indices for out-of-domain structural brain MRI scans.
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Acknowledgements
Data used in this study are part of the iSTAGING study (representative, C. Davatzikos), the Preclinical AD Consortium (M. S. Albert), ADNI (M. W. Weiner) and the BLSA (S. M. Resnick). The iSTAGING consortium is a multi-institutional effort funded by the National Institute on Aging (NIA) by RF1 AG054409. The BLSA neuroimaging study is funded by the Intramural Research Program, NIA, National Institutes of Health (NIH) and by HHSN271201600059C. The BIOCARD study is in part supported by NIH grant U19-AG033655. SHIP is part of the Community Medicine Research net of the University of Greifswald, which is funded by the Federal Ministry of Education and Research (grant nos. 01ZZ9603, 01ZZ0103 and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. MRI scans in SHIP-START and SHIP-TREND have been supported by a joint grant from Siemens Healthineers and the Federal State of Mecklenburg-West Pomerania. The Women’s Health Initiative was funded by the National Heart, Lung and Blood Institute of the NIH, US Department of Health and Human Services. Contracts HHSN268200464221C and N01-WH-4-4221 provided additional support. The WHIMS (M.A.E.) was funded in part by Wyeth Pharmaceuticals. The HANDLS study is supported by the NIA Intramural Research Program, NIH, Project ZIA-AG000513. The HANDLS Scan substudy is supported by NIH grants 1RO1AG034161, 2P30AG028747-14S1 and 1R56AG064088-01A1. The HABS-HD project is funded by grants from the NIA: R01AG054073 and R01AG058533. HABS-HD multiple principal investigators were S. E. O’Bryant, K. Yaffe, A. Toga, R. Rissman and L. Johnson; and the HABS-HD investigators were M. Braskie, K. King, J. R Hall, M. Petersen, R. Palmer, R. Barber, Y. Shi, F. Zhang, R. Nandy, R. McColl, D. Mason, B. Christian, N. Philips, S. Large, J. Lee, B. Vardarajan, M. Rivera Mindt, A. Cheema, L. Barnes, M. Mapstone, A. Cohen, A. Kind, O. Okonkwo, R. Vintimilla, Z. Zhou, M. Donohue, R. Raman, M. Borzage, M. Mielke, B. Ances, G. Babulal, J. Llibre-Guerra, C. Hill and R. Vig. Other supporting grants include 5U01AG068057-02 and 1U24AG074855-01. S.R.H. and J.C.M. are supported by multiple grants and contracts from NIH. A.A. was supported by grants 191026 and 206795 from the Swiss National Science Foundation. R.T.S. was supported by grants R01MH123550 and R01MH112847. J.C.M. was supported by NIH ACS grant, P01AG026276. Y.F. was supported by grant R01AG066650. M.H. was supported by the National Institute of Health (NIH) grant number 1R01AG080821. This research has been conducted using the UKBB Resource under application no. 35148. Data used in the preparation of this article were in part obtained from the ADNI database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol-Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Z.Y. had full access to all the data in the study and is responsible for the integrity of the data and the accuracy of the data analysis.
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Study concept and design was by Z.Y. and C.D. Model development was by Z.Y. Data interpretation was by Z.Y., J.W., I.M.N., P.A.L., N.K., S.M.R., H.S. and C.D. Drafting of the manuscript was by Z.Y., J.W., I.M.N. and C.D. Statistical analysis was by Z.Y. Genetic analysis was by J.W. Data collection and processing was by Z.Y., J.W., G.E., S.T.G., R.M., E.M., Y.C., D.S., A.A., P.P., K.W., H.J.G., R.B., S.F., D.T., M.B., Y.A., D.Y., D.S.M., P.L., T.L.S.B., S.R.H., T.R.A., S.R.W., M.K.E., A.B.Z., L.J.L., A.S., M.A.E., C.L.M., P.M., J.F., A.W.T., S.O.B., M.M.C., S.V., S.C.J., J.C.M., M.S.A., K.Y., H.V., L.F., R.N.B., D.A.W., S.M.R. and C.D. Critical revision of the manuscript for important intellectual content was by Z.Y., J.W., G.E., S.T.G., R.M., E.M., Y.C., D.S., A.A., P.P., K.W., H.J.G., R.B., S.F., D.T., M.B., Y.A., D.Y., D.S.M., P.L., T.L.S.B., S.R.H., T.R.A., S.R.W., M.K.E., A.B.Z., L.J.L., A.S., M.A.E., C.L.M., P.M., J.F., A.W.T., S.O.B., M.M.C., S.V., S.C.J., J.C.M., M.S.A., K.Y., H.V., L.F., R.N.B., R.T.S., Y.F., M.H., P.A.L., N.K., D.A.W., S.M.R., H.S., I.M.N. and C.D.
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H.J.G. has received travel grants and speaker’s honoraria from Fresenius Medical Care, Neuraxpharm, Servier and Janssen Cilag as well as research funding from Fresenius Medical Care. R.T.S. received consulting income from Octave Bioscience and has received compensation for scientific reviewing from the American Medical Association. T.L.S.B. has received investigator-initiated research funding from the NIH, the Alzheimer’s Association, the Foundation at Barnes-Jewish Hospital, Siemens Healthineers and Avid Radiopharmaceuticals (a wholly owned subsidiary of Eli Lilly and Company). She participates as a site investigator in clinical trials sponsored by Eli Lilly and Company, Biogen, Eisai, Jaansen and Roche. She has served as a paid and unpaid consultant to Eisai, Siemens, Biogen, Janssen and Bristol-Myers Squibb. The other authors declare no competing interests.
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Yang, Z., Wen, J., Erus, G. et al. Brain aging patterns in a large and diverse cohort of 49,482 individuals. Nat Med (2024). https://doi.org/10.1038/s41591-024-03144-x
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DOI: https://doi.org/10.1038/s41591-024-03144-x
