Brain Signal Analysis of Neurological Disorders Using Topological Graphs

Brain Signal Analysis of Neurological Disorders Using Topological Graphs | 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 Brain Signal Analysis of Neurological Disorders Using Topological Graphs Yuzhe Chen, Ercan Engin Kuruoglu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6183264/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Neurological disorders' prevalence and diverse symptoms make it crucial to study them quantitatively.However, the temporal structure and topology of brain activities are seldom studied. This paper applies graph theory and topological data analysis (TDA) to study the organization of brain signal time points in various disorders. Using the Mapper algorithm and distance correlation, we extract time-point network representations of the underlying shape and correlation among time instances of the brain signals. We then analyze the networks using graph, spectral, and topological metrics. Group comparisons and statistical tests reveal important alterations in modularity, closeness, eigenvalues, entropy, and simplicial weights curl. Autism subjects exhibit less modularized and more varied network signals, bipolar disorder shows fragmentation and reduced stability of brain activities, while schizophrenia patients demonstrate closer or repressed brain activities. These findings coincide with biomedical traits of these neurological conditions. However, the non-significant results in the COBRE and ADHD datasets underscore the limitations in region-specific, small sample size, and heterogeneous data source cases. Our study demonstrates that combining graph and topological analyses in time-point networks with appropriate statistical tests can discern altered temporal organization of brain signals with consistency and generalizability, when the abundance and quality of samples are assured. Neurological disorder Dynamic functional connectivity Graph signal processing Topological data analysis Time-point networks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 10 Sep, 2025 Reviews received at journal 09 Sep, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers invited by journal 17 Mar, 2025 Editor assigned by journal 12 Mar, 2025 Submission checks completed at journal 12 Mar, 2025 First submitted to journal 08 Mar, 2025 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. 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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-6183264","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428099852,"identity":"44157d4d-c42d-460e-a207-e5b7d6206a34","order_by":0,"name":"Yuzhe Chen","email":"","orcid":"","institution":"Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yuzhe","middleName":"","lastName":"Chen","suffix":""},{"id":428099853,"identity":"e441e293-e780-40c0-8d97-1b0c9e745524","order_by":1,"name":"Ercan Engin Kuruoglu","email":"data:image/png;base64,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","orcid":"","institution":"Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Ercan","middleName":"Engin","lastName":"Kuruoglu","suffix":""}],"badges":[],"createdAt":"2025-03-08 09:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6183264/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6183264/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78632607,"identity":"38e55510-5d67-47de-bb9f-aa163fb1d584","added_by":"auto","created_at":"2025-03-17 04:15:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4959496,"visible":true,"origin":"","legend":"","description":"","filename":"BIextendedjournalpaperwithtemplate2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6183264/v1_covered_1a84281a-5de4-4d35-8f42-135cb62c6b36.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Brain Signal Analysis of Neurological Disorders Using Topological Graphs","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":"brain-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brai","sideBox":"Learn more about [Brain Informatics](https://braininformatics.springeropen.com/about)","snPcode":"40708","submissionUrl":"https://submission.nature.com/new-submission/40708/3","title":"Brain Informatics","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neurological disorder, Dynamic functional connectivity, Graph signal processing, Topological data analysis, Time-point networks","lastPublishedDoi":"10.21203/rs.3.rs-6183264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6183264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeurological disorders' prevalence and diverse symptoms make it crucial to study them quantitatively.However, the temporal structure and topology of brain activities are seldom studied. 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