Lost Balance: Synergy-Redundancy Dysfunction in Alzheimer’s Disease

Lost Balance: Synergy-Redundancy Dysfunction in Alzheimer’s Disease | 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 Lost Balance: Synergy-Redundancy Dysfunction in Alzheimer’s Disease Shuiling Shi, Wenqi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8444204/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Alzheimer’s disease (AD) is the most common neurodegenerative disorder, yet reliable early biomarkers are still lacking, which greatly hinders timely intervention for AD. This study employed an integrated information decomposition framework to systematically investigate the dynamic evolution of brain synergy and redundancy information in different AD stages and their associations with cognitive function, aiming to explore potential novel biomarkers for AD early diagnosis. The results show that, compared with the cognitively normal (CN) group, the cognitively abnormal (CA) group exhibited a distinct pattern of enhanced synergy and reduced redundancy. This pattern difference is primarily distributed within the default mode network (DMN), and is significant even in early-stages of AD, indicating that synergy-redundancy information is a promising biomarker for AD early diagnosis. Despite the inclusion of cognitively impaired individuals, functional decoding analyses still revealed that synergy mainly supported higher-level cognitive and social functions, whereas redundancy was more related to sensorimotor and basic cognitive functions. Additionally, compared to the CN group, the CA group exhibited stronger positive synergy correlations in brain regions related to working memory and cognitive control, possibly reflecting compensatory mechanisms, while negative synergy correlations were observed in pain-processing regions, potentially linked to impaired sensory perception. Stronger positive redundancy correlations in motor-related regions but negative correlations in reward-related regions, may suggest AD-related selective reorganization of information processing. Finally, a machine learning model constructed based on synergy–redundancy information demonstrated superior performance in AD stage classification, providing new insights for AD early diagnosis and further validating the potential of synergy-redundancy information as early biomarkers. Information interactions neurodynamics synergy-redundancy dysfunction integrated information decomposition AD early detection AD biomarker Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers invited by journal 27 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Editor invited by journal 06 Jan, 2026 Submission checks completed at journal 06 Jan, 2026 First submitted to journal 06 Jan, 2026 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. 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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-8444204","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":581395042,"identity":"12704190-e22b-4f81-83e9-14a931f82806","order_by":0,"name":"Shuiling Shi","email":"","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuiling","middleName":"","lastName":"Shi","suffix":""},{"id":581395043,"identity":"47b59e97-f8be-4bbb-86eb-ded579064cb4","order_by":1,"name":"Wenqi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3RsarCMBSA4SOBdIl2jRT1FQIH4lJ8l1Lo7iLdriB0KrrqWxR8gQMBXSK+giI4F1zcvC13u0hbN4f8W0I+ckgAXK4vjDOPzFNJ5ou/jd6yjQw8Ed0gDb1hXq2oCxn7gAg28ZXtSjiDJJhnJkBr8VFCOCqI3S/NpHcIdplBfcq1JEiwID5VbbfIfmZifRa6GsxEBQkuW4iuyc9+I7AkeHUiiMImTPVzVQ1GXYiIrts0ZNIeFtKqGHeG60Yy2RyJyvor83hfpulstD6u7o3kX/VTsQ/Ou1wul+t9vz3IRkxgPI4cAAAAAElFTkSuQmCC","orcid":"","institution":"Kunming University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Wenqi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-12-24 16:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8444204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8444204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101390607,"identity":"86a65f60-ad7f-4363-b3f9-8a16d0bcc8e9","added_by":"auto","created_at":"2026-01-29 08:20:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6184420,"visible":true,"origin":"","legend":"","description":"","filename":"BMC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8444204/v1_covered_4604639d-9040-41a4-bc34-efa77e069bb0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lost Balance: Synergy-Redundancy Dysfunction in Alzheimer’s Disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Information interactions, neurodynamics, synergy-redundancy dysfunction, integrated information decomposition, AD early detection, AD biomarker","lastPublishedDoi":"10.21203/rs.3.rs-8444204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8444204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Alzheimer’s disease (AD) is the most common neurodegenerative disorder, yet reliable early biomarkers are still lacking, which greatly hinders timely intervention for AD. This study employed an integrated information decomposition framework to systematically investigate the dynamic evolution of brain synergy and redundancy information in different AD stages and their associations with cognitive function, aiming to explore potential novel biomarkers for AD early diagnosis. The results show that, compared with the cognitively normal (CN) group, the cognitively abnormal (CA) group exhibited a distinct pattern of enhanced synergy and reduced redundancy. This pattern difference is primarily distributed within the default mode network (DMN), and is significant even in early-stages of AD, indicating that synergy-redundancy information is a promising biomarker for AD early diagnosis. Despite the inclusion of cognitively impaired individuals, functional decoding analyses still revealed that synergy mainly supported higher-level cognitive and social functions, whereas redundancy was more related to sensorimotor and basic cognitive functions. Additionally, compared to the CN group, the CA group exhibited stronger positive synergy correlations in brain regions related to working memory and cognitive control, possibly reflecting compensatory mechanisms, while negative synergy correlations were observed in pain-processing regions, potentially linked to impaired sensory perception. Stronger positive redundancy correlations in motor-related regions but negative correlations in reward-related regions, may suggest AD-related selective reorganization of information processing. 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