Brain clocks capture diversity and disparity in aging and dementia

Brain clocks capture diversity and disparity in aging and dementia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Brain clocks capture diversity and disparity in aging and dementia Agustin Ibanez, Sebastian Moguilner, Sandra Baez, Hernan Hernandez, and 71 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4150225/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2024 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Abstract Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer’s disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R²=0.37, F²=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer’s disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer’s disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging. Biological sciences/Neuroscience/Cognitive ageing Biological sciences/Neuroscience/Diseases of the nervous system/Dementia Scientific community and society/Developing world Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 26 Aug, 2024 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4150225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":317360075,"identity":"28c084d7-d448-4349-b130-caca8a627e93","order_by":0,"name":"Agustin Ibanez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYNACAwYGNgkwy4bBQIKxgbCOAwgtacRqAREQLYeBWgio5u9fY/z5Q8EdBj7p5mcPPvw5b7ddurmB4eOeWpxaJG68MZM4YPCMgU3mmLnhDJ7byTvnHGxgnPHsOG5rbpwxA/rlMNAvCWbSPBK3kw1uJDYw8xw4hlOH/I0zxh8gWtK/Sf8xOEdYi8H5HgMJiJYcM2mGhAN2UC01OLUY3mArkzhjcJiHTeZMmWTPgeQEyxmJDQdnHDiAU4vc+cObP1T8OSwnP7t9m8SPP3b25hLpDx98OFCH2/sSCWCKB8ZPbGAAR9Rh3Fr40VxgD6Xx2DIKRsEoGAUjDQAApr9ckTjph84AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-6758-5101","institution":"Trinity College Dublin","correspondingAuthor":true,"prefix":"","firstName":"Agustin","middleName":"","lastName":"Ibanez","suffix":""},{"id":317360076,"identity":"a26f145e-7151-4c3d-9d3c-a70528deddc7","order_by":1,"name":"Sebastian Moguilner","email":"","orcid":"","institution":"Harvard Medical School","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Moguilner","suffix":""},{"id":317360077,"identity":"72f78f53-9f86-483b-baa0-ff4e059295ab","order_by":2,"name":"Sandra Baez","email":"","orcid":"","institution":"Universidad de los Andes, Bogota, Colombia","correspondingAuthor":false,"prefix":"","firstName":"Sandra","middleName":"","lastName":"Baez","suffix":""},{"id":317360078,"identity":"80ea212b-a497-4ffa-8e20-9a0178555960","order_by":3,"name":"Hernan Hernandez","email":"","orcid":"https://orcid.org/0000-0002-0597-6059","institution":"BrainLat","correspondingAuthor":false,"prefix":"","firstName":"Hernan","middleName":"","lastName":"Hernandez","suffix":""},{"id":317360079,"identity":"80d6bca9-7990-4915-878e-c1d0b1ed7054","order_by":4,"name":"Joaquin Migeot","email":"","orcid":"","institution":"Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile","correspondingAuthor":false,"prefix":"","firstName":"Joaquin","middleName":"","lastName":"Migeot","suffix":""},{"id":317360080,"identity":"a7def999-6f0c-46a9-84e4-1411369e2831","order_by":5,"name":"Agustina Legaz","email":"","orcid":"","institution":"Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile","correspondingAuthor":false,"prefix":"","firstName":"Agustina","middleName":"","lastName":"Legaz","suffix":""},{"id":317360081,"identity":"c03a9886-1c3f-42de-a9c9-5aadfda032ea","order_by":6,"name":"Raul Gonzalez-Gomez","email":"","orcid":"","institution":"Latin American Brain Health Institute, Universidad Adolfo Ibañez, Santiago de Chile, Chile","correspondingAuthor":false,"prefix":"","firstName":"Raul","middleName":"","lastName":"Gonzalez-Gomez","suffix":""},{"id":317360082,"identity":"fe092a53-80ab-4a06-94f0-f70cc5076323","order_by":7,"name":"Francesca Farina","email":"","orcid":"","institution":"Global Brain Health Institute (GBHI), University of California, San Francisco, US; 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Instituto de Investigaciones Psicológicas (IIPsi), CONICET UNC, Facultad de Psicología, Universidad Nacional de Córdoba, Boulevard de la Reforma esquina","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"","lastName":"Barttfeld","suffix":""},{"id":317360134,"identity":"27065723-0d77-41e7-8d83-660e7b7e70c7","order_by":59,"name":"Ignacio Brusco","email":"","orcid":"","institution":"Centro de Neuropsiquiatría y Neurología de la Conducta (CENECON), Facultad de Medicina, Universidad de Buenos Aires (UBA), C.A.B.A., Buenos Aires, Argentina.","correspondingAuthor":false,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Brusco","suffix":""},{"id":317360135,"identity":"e41ccb89-8bf5-4f89-a69e-06bdb8eff224","order_by":60,"name":"Martín Bruno","email":"","orcid":"","institution":"Instituto de Ciencias Biomédicas (ICBM) Facultad de Ciencias Médicas, Universidad Catoóica de Cuyo, San Juan, Argentina","correspondingAuthor":false,"prefix":"","firstName":"Martín","middleName":"","lastName":"Bruno","suffix":""},{"id":317360136,"identity":"1e570db0-4077-4190-a091-866323060362","order_by":61,"name":"Ana Sosa Ortiz","email":"","orcid":"","institution":"Instituto Nacional de Neurologia y Neurocirugia MVS, Universidad Nacional Autonoma de Mexico, Mexico, Mexico","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"Sosa","lastName":"Ortiz","suffix":""},{"id":317360137,"identity":"a963d2b6-afb2-4c62-ab4f-9cd1cee262f1","order_by":62,"name":"Stefanie Pina-Escudero","email":"","orcid":"","institution":"Global Brain Health Institute (GBHI), University of California, San Francisco, US; and Trinity College Dublin, Dublin, Ireland","correspondingAuthor":false,"prefix":"","firstName":"Stefanie","middleName":"","lastName":"Pina-Escudero","suffix":""},{"id":317360138,"identity":"4fcc5075-c265-4d61-b08a-0dd73ce5b0a1","order_by":63,"name":"Leonel Takada","email":"","orcid":"https://orcid.org/0000-0002-0121-9253","institution":"Cognitive and Behavioral Neurology Unit, Hospital das Clinicas, University of São Paulo Medical School, São Paulo, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Leonel","middleName":"","lastName":"Takada","suffix":""},{"id":317360139,"identity":"f1d72acd-04c6-4c57-9da4-78f892ae6530","order_by":64,"name":"Elisa Resende","email":"","orcid":"","institution":"Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Elisa","middleName":"","lastName":"Resende","suffix":""},{"id":317360140,"identity":"91c24545-ac96-4a06-b226-c178f7caf95a","order_by":65,"name":"Katherine Possin","email":"","orcid":"","institution":"University of California, San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Possin","suffix":""},{"id":317360141,"identity":"97e29811-930d-48de-ae2a-810ae364202c","order_by":66,"name":"Maira Okada de Oliveira","email":"","orcid":"","institution":"Global Brain Health Institute (GBHI), University of California, San Francisco, US; and Trinity College Dublin, Dublin, Ireland","correspondingAuthor":false,"prefix":"","firstName":"Maira","middleName":"Okada","lastName":"de Oliveira","suffix":""},{"id":317360142,"identity":"8764b1c3-040c-4a27-b39e-58b90e7f5331","order_by":67,"name":"Alejandro Lopez-Valdes","email":"","orcid":"","institution":"Global Brain Health Institute (GBHI), University of California, San Francisco, US; and Trinity College Dublin, Dublin, Ireland","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Lopez-Valdes","suffix":""},{"id":317360143,"identity":"a44f104a-1428-4670-b580-77436e6200a9","order_by":68,"name":"Brian Lawlor","email":"","orcid":"","institution":"Trinity College of Dublin","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Lawlor","suffix":""},{"id":317360144,"identity":"ce6fec23-2558-4102-92c2-25e0d6969424","order_by":69,"name":"Ian Robertson","email":"","orcid":"","institution":"Global Brain Health Institute (GBHI), University of California, San Francisco, US; and Trinity College Dublin, Dublin, Ireland","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Robertson","suffix":""},{"id":317360145,"identity":"f960baaa-148b-4a25-837b-cc1343590d63","order_by":70,"name":"Kenneth Kosik","email":"","orcid":"https://orcid.org/0000-0003-3224-5179","institution":"University of California, Santa Barbara","correspondingAuthor":false,"prefix":"","firstName":"Kenneth","middleName":"","lastName":"Kosik","suffix":""},{"id":317360146,"identity":"1f0f5246-fb84-45fb-a71b-e64952545f7a","order_by":71,"name":"Claudia Duran-Aniotz","email":"","orcid":"","institution":"University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Duran-Aniotz","suffix":""},{"id":317360147,"identity":"e6e248c8-4c35-4ae6-9093-b6a4d82b1db9","order_by":72,"name":"Victor Valcour","email":"","orcid":"","institution":"UCSF","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"","lastName":"Valcour","suffix":""},{"id":317360148,"identity":"468b7f70-2cef-4a22-981e-6b9bdf0173ce","order_by":73,"name":"Jennifer Yokoyama","email":"","orcid":"https://orcid.org/0000-0001-7274-2634","institution":"UCSF","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Yokoyama","suffix":""},{"id":317360149,"identity":"3a16414a-4652-4135-b3ff-dd98ac7c2a98","order_by":74,"name":"Bruce Miller","email":"","orcid":"","institution":"University of California San Francisco","correspondingAuthor":false,"prefix":"","firstName":"Bruce","middleName":"","lastName":"Miller","suffix":""}],"badges":[],"createdAt":"2024-03-22 13:47:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4150225/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4150225/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41591-024-03209-x","type":"published","date":"2024-08-26T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59030974,"identity":"d8361d61-0e16-4f40-9688-367c31e2fd75","added_by":"auto","created_at":"2024-06-25 14:08:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDatasets characterization and analysis pipeline.\u003c/strong\u003e Datasets included Latin American countries(LAC) and non-LAC healthy controls (HC, total N = 3509) and participants with Alzheimer’s disease (AD, total N = 828), behavioral variant frontotemporal dementia (bvFTD, total N = 463), and mild cognitive impairment (MCI, total N = 517). The functional magnetic resonance imaging dataset (fMRI, yellow lines) included 2953 participants from LAC (Argentina, Chile, Colombia, Mexico, and Peru) as well as non-LAC (the USA, China, and Japan). The electroencephalography dataset (EEG, blue lines) involved 2353 participants from Argentina, Brazil, Chile, Colombia, and Cuba (LAC) as well as Greece, Ireland, Italy, Turkey, and the UK (non-LAC). Circles represent the number of participants per group, scaled between the number of participants in the largest and smallest groups for each region to facilitate visualization. Line thickness represents the number of participants with fMRI (yellow lines) and EEG (blue lines) per country. The raw fMRI and EEG signals were preprocessed by filtering and artifact removal and the EEG signals were normalized to project them into source space. A parcellation using the automated anatomical labeling (AAL) atlas for both the fMRI and EEG signals was performed to build the nodes from which we calculated the high-order interactions using the Ω-information metric. A connectivity matrix was obtained for both modalities, which was later represented by graphs. Data augmentation was performed only in the testing dataset. The graphs were used as input for a graph convolutional deep learning network (architecture shown in the last row), with separate models for EEG and fMRI. Finally, age prediction was obtained, and the performance was measured by comparing the predicted vs. the chronological ages. This figure was partially created using Biorender under Team license.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1/02504fda34d5353ca5204b61.png"},{"id":59030976,"identity":"9d517f3b-cd02-44f8-943b-5bcec1f8a853","added_by":"auto","created_at":"2024-06-25 14:08:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":245626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003efMRI training and testing the deep learning model in different samples. (a) \u003c/strong\u003eOrdinary least squares (OLS) regression comparing chronological age vs. predicted age with the feature importance list for training and testing in the whole sample. \u003cstrong\u003e(b) \u003c/strong\u003eRegression comparing chronological age vs. predicted age with the feature importance list for training and testing in the non-LAC dataset.\u003cstrong\u003e (c) \u003c/strong\u003eRegression comparing chronological age vs. predicted age with the feature importance list for training and testing in the LAC dataset. For \u003cstrong\u003e(a)\u003c/strong\u003e, \u003cstrong\u003e(b)\u003c/strong\u003e and \u003cstrong\u003e(c), \u003c/strong\u003edata point colors indicate the kernel density estimation to provide a visual representation of the density of prediction errors across different values of chronological age. The bars show the brain region feature importance list in descending order, with ring plots and glass brain representations of the most important network-edge connections.\u003cstrong\u003e (d) \u003c/strong\u003eHistogram of the prediction error when training in non-LAC dataset and testing in LAC dataset. \u003cstrong\u003e(e) \u003c/strong\u003eViolin plot of the distribution and statistical comparison of training and testing with different regions using a permutation test (5000 iterations).\u003cstrong\u003e (f) \u003c/strong\u003eViolin plot of the distribution and statistical comparison of testing the models on females and males using a permutation test (5000 iterations). LAC = Latin American countries.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1/6a2aad75a8899522359d6f3c.png"},{"id":59030972,"identity":"d205fa39-eb65-45e2-adfc-aabbcfce13a4","added_by":"auto","created_at":"2024-06-25 14:08:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":235162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEEG training and testing the deep learning model in different samples. (a) \u003c/strong\u003eOrdinary least squares (OLS) regression comparing chronological age vs. predicted age with the feature importance list for training and testing in the whole sample. \u003cstrong\u003e(b) \u003c/strong\u003eRegression comparing chronological age vs. predicted age with the feature importance list for training and testing in the non-LAC dataset.\u003cstrong\u003e (c) \u003c/strong\u003eRegression comparing chronological age vs. predicted age with the feature importance list for training and testing in the LAC dataset. For \u003cstrong\u003e(a)\u003c/strong\u003e, \u003cstrong\u003e(b)\u003c/strong\u003e and \u003cstrong\u003e(c), \u003c/strong\u003edata point colors indicate the kernel density estimation to provide a visual representation of the density of prediction errors across different values of chronological age. The bars show the brain region feature importance list in descending order, with ring plots and glass brain representations of the most important network-edge connections.\u003cstrong\u003e(d) \u003c/strong\u003eHistogram of the prediction error when training in non-LAC dataset and testing in LAC dataset. \u003cstrong\u003e(e) \u003c/strong\u003eViolin plot of the distribution and statistical comparison of training and testing with different regions using a permutation test (5000 iterations).\u003cstrong\u003e (f) \u003c/strong\u003eViolin plot of the distribution and statistical comparison of testing the models on females and males using a permutation test (5000 iterations). LAC = Latin American countries.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1/f69d3968df17550b06566067.png"},{"id":59031882,"identity":"8333e621-9faa-4260-ac7e-312b34e6d767","added_by":"auto","created_at":"2024-06-25 14:16:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65398,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGroups, sex, and macrosocial influences in BAGs. \u003c/strong\u003eViolin plots for the distribution of prediction gaps for different groups and sex effects using \u003cstrong\u003e(a) \u003c/strong\u003efMRI and \u003cstrong\u003e(b)\u003c/strong\u003e EEG datasets. The statistical comparisons were calculated using subsample permutation testing with 5000 iterations. \u003cstrong\u003e(c)\u003c/strong\u003e Associations between macrosocial and disease disparity factors with BAGs were assessed\u003cstrong\u003e \u003c/strong\u003ewith a multi-method approach comprising SHAP values, feature importance (mean decrease in impurity, MDI), and permutation importance. Plots show the mean importance values for each method, along with their 99% confidence interval, as well as the average R-squared and Cohen's f². * = Significant predictors. Shaded bars indicate significance across the three methods. LAC = Latin American countries, HC non-LAC = Healthy controls from non-LAC, HC LAC = Healthy controls from LAC, MCI = mild cognitive impairment, AD = Alzheimer’s disease, bvFTD = behavioral variant frontotemporal dementia, M = Males. F = Females, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1/c4b3d343588364f6a4fc17e6.png"},{"id":59030978,"identity":"2b6f42a2-388e-4ce7-99fa-21df646d01ef","added_by":"auto","created_at":"2024-06-25 14:08:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":197082,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSensitivity analysis. \u003c/strong\u003eViolin plots for the distribution of data quality metrics of \u003cstrong\u003e(a) \u003c/strong\u003efMRI and \u003cstrong\u003e(b) \u003c/strong\u003eEEG datasets. Both panels indicate null results between groups in terms of data quality. \u003cstrong\u003e(c)\u003c/strong\u003e Linear regression effects of scanner type, evidencing that the fMRI data quality was not significantly associated with fMRI BAGs differences. \u003cstrong\u003e(d) \u003c/strong\u003efMRI BAG differences across groups controlling for scanner differences.\u003cstrong\u003e \u003c/strong\u003eThe statistical comparisons were calculated using subsample permutation testing with 5000 iterations. LAC = Latin American countries, HC = Healthy controls, MCI = mild cognitive impairment, AD = Alzheimer’s disease, bvFTD = behavioral variant frontotemporal dementia.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1/cacbf6f0c9aec5291c79dd8a.png"},{"id":63344741,"identity":"9b0f744a-9537-4f23-ba36-3ce901c4d9ff","added_by":"auto","created_at":"2024-08-27 07:14:48","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1730873,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptClean.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1_covered_ce54d7f7-941b-49b5-91f3-4afe6bf9344a.pdf"},{"id":59031880,"identity":"fbde7722-9fcd-4ac5-ade5-32aa43484d1a","added_by":"auto","created_at":"2024-06-25 14:16:13","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":47890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4150225/v1/d95f57740d98bda29acd1568.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Brain clocks capture diversity and disparity in aging and dementia","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4150225/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4150225/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding brain health and disease. However, the impact of multimodal diversity (geographical, socioeconomic, sociodemographic, sex, neurodegeneration) on the brain age gap (BAG) is unknown. Here, we analyzed datasets from 5,306 participants across 15 countries (7 Latin American countries -LAC, 8 non-LAC). Based on higher-order interactions in brain signals, we developed a BAG deep learning architecture for functional magnetic resonance imaging (fMRI=2,953) and electroencephalography (EEG=2,353). The datasets comprised healthy controls, and individuals with mild cognitive impairment, Alzheimer’s disease, and behavioral variant frontotemporal dementia. LAC models evidenced older brain ages (fMRI: MDE=5.60, RMSE=11.91; EEG: MDE=5.34, RMSE=9.82) compared to non-LAC, associated with frontoposterior networks. Structural socioeconomic inequality and other disparity-related factors (pollution, health disparities) were influential predictors of increased brain age gaps, especially in LAC (R²=0.37, F²=0.59, RMSE=6.9). A gradient of increasing BAG from controls to mild cognitive impairment to Alzheimer’s disease was found. In LAC, we observed larger BAGs in females in control and Alzheimer’s disease groups compared to respective males. Results were not explained by variations in signal quality, demographics, or acquisition methods. Findings provide a quantitative framework capturing the multimodal diversity of accelerated brain aging.\u003c/p\u003e","manuscriptTitle":"Brain clocks capture diversity and disparity in aging and dementia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 14:08:07","doi":"10.21203/rs.3.rs-4150225/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nm","sideBox":"Learn more about [Nature Medicine](http://www.nature.com/nm/)","snPcode":"","submissionUrl":"","title":"Nature Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"257941a8-5641-4602-8f78-28c5439ea3e2","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33561270,"name":"Biological sciences/Neuroscience/Cognitive ageing"},{"id":33561271,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Dementia"},{"id":33561272,"name":"Scientific community and society/Developing world"}],"tags":[],"updatedAt":"2024-08-27T07:14:39+00:00","versionOfRecord":{"articleIdentity":"rs-4150225","link":"https://doi.org/10.1038/s41591-024-03209-x","journal":{"identity":"nature-medicine","isVorOnly":false,"title":"Nature Medicine"},"publishedOn":"2024-08-26 04:00:00","publishedOnDateReadable":"August 26th, 2024"},"versionCreatedAt":"2024-06-25 14:08:07","video":"","vorDoi":"10.1038/s41591-024-03209-x","vorDoiUrl":"https://doi.org/10.1038/s41591-024-03209-x","workflowStages":[]},"version":"v1","identity":"rs-4150225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4150225","identity":"rs-4150225","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}