Neuro-VisAge: a visual and statistical strategy to investigate age effect on brain activity

Neuro-VisAge: a visual and statistical strategy to investigate age effect on brain activity | 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 Research Article Neuro-VisAge: a visual and statistical strategy to investigate age effect on brain activity Igor Rodrigues, Juciara Silva, Emerson Carvalho, Sabrina Silveira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6614290/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The last decade was marked by a spike in the use of Machine Learning (ML) andfunctional magnetic resonance image for neurological disorders diagnoses. How-ever, there is a limitation considering age bias when designing their experiments,which can impact the final result. Here, we investigate the effects of age bias ona sample of typical neurological subjects, looking for patterns in brain activity.We also suggest that age groups be used in the ML training and classificationfor future works. Our results show that for the five brain regions investigated(Frontal Gyrus, Cingulum Bundle, Putamen, Angular Gyrus, and Heschl Gyrus),using a 10-year span would increase the reliability of ML experiments aiming todiagnose neural disorders by reducing the age bias effect on the models. Brain Aging Age Bias fMRI Functional Magnetic Resonance Image Brain Activity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-6614290","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474375012,"identity":"d9d5275f-2efb-4e62-96bf-66f1bd486357","order_by":0,"name":"Igor Rodrigues","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACNgSZwHAARPEzMD5gYGwgRYtkA7MBXi1IGhMgbIMDBLTw8S8+9oGhjCGfnz334cGvbTb2xjeS2SQYd9zDbb7Es+QZDOcYLGf2PDc4LNuWlrgNrOVMMR4tZ4wZGNsYDAxupDEclmw7nGB2I/+wAWNbAtFa/tsbz0hmxq+Fvweh5eDHtgOMGySSGR/gt4UtmSHhnISBZM8zhsMM55ITZ5x5zPgg8QxuLfL9hw8zfCizMeBnT2P++KPMzp6/PZnhwMcduLUwSCSAYkQCzGbmgYni0QBMHwcQbMYf+FSOglEwCkbBiAUAV+RPgQ8TAkwAAAAASUVORK5CYII=","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":true,"prefix":"","firstName":"Igor","middleName":"","lastName":"Rodrigues","suffix":""},{"id":474375013,"identity":"2095d640-e15d-4c9b-b02f-53243cac8141","order_by":1,"name":"Juciara Silva","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Juciara","middleName":"","lastName":"Silva","suffix":""},{"id":474375015,"identity":"02692829-1546-48ab-8cdb-5ef9ae0ea669","order_by":2,"name":"Emerson Carvalho","email":"","orcid":"","institution":"Instituto Federal do Sul de Minas","correspondingAuthor":false,"prefix":"","firstName":"Emerson","middleName":"","lastName":"Carvalho","suffix":""},{"id":474375018,"identity":"b0976b80-5122-42dc-8f75-e968486e83f8","order_by":3,"name":"Sabrina Silveira","email":"","orcid":"","institution":"Universidade Federal de Viçosa","correspondingAuthor":false,"prefix":"","firstName":"Sabrina","middleName":"","lastName":"Silveira","suffix":""}],"badges":[],"createdAt":"2025-05-07 17:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6614290/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6614290/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90323253,"identity":"80d8509c-b862-47cd-8f82-0496b305cedf","added_by":"auto","created_at":"2025-09-01 11:17:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1000452,"visible":true,"origin":"","legend":"","description":"","filename":"NeuroVisAgeavisualandstatisticalstrategytoinvestigateageeffectonbrainactivityBMCBioinformatics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6614290/v1_covered_43be3df7-b594-4c78-94ca-db6751c4deef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neuro-VisAge: a visual and statistical strategy to investigate age effect on brain activity","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Brain Aging, Age Bias, fMRI, Functional Magnetic Resonance Image, Brain Activity","lastPublishedDoi":"10.21203/rs.3.rs-6614290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6614290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The last decade was marked by a spike in the use of Machine Learning (ML) andfunctional magnetic resonance image for neurological disorders diagnoses. 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