State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis

State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis | 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 State-transition dynamics of resting-state functional magnetic resonance imaging data: Model comparison and test-to-retest analysis Saiful Islam, Pitambar Khanra, Johan Nakuci, Sarah F. Muldoon, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3288014/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2024 Read the published version in BMC Neuroscience → Version 1 posted 8 You are reading this latest preprint version Abstract Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals. fMRI EEG MEG microstates clustering dynamics test-retest reliability fingerprinting Full Text Additional Declarations No competing interests reported. Supplementary Files supplimentaryfinal.pdf Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2024 Read the published version in BMC Neuroscience → Version 1 posted Editorial decision: Revision requested 17 Jan, 2024 Reviewers agreed at journal 07 Sep, 2023 Reviews received at journal 05 Sep, 2023 Reviewers agreed at journal 04 Sep, 2023 Reviewers invited by journal 04 Sep, 2023 Editor assigned by journal 04 Sep, 2023 Submission checks completed at journal 01 Sep, 2023 First submitted to journal 23 Aug, 2023 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-3288014","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":230302971,"identity":"f7c328ee-0ae6-45e6-887f-6d2d181954e0","order_by":0,"name":"Saiful Islam","email":"","orcid":"","institution":"University at Buffalo, State University of New York","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saiful","middleName":"","lastName":"Islam","suffix":""},{"id":230302972,"identity":"6dd4ee6d-0431-4c4f-aa3a-4310617c76d4","order_by":1,"name":"Pitambar Khanra","email":"","orcid":"","institution":"University at Buffalo, State University of New York","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pitambar","middleName":"","lastName":"Khanra","suffix":""},{"id":230302973,"identity":"40a9d8ce-b757-49ba-8952-549fda22c401","order_by":2,"name":"Johan Nakuci","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Johan","middleName":"","lastName":"Nakuci","suffix":""},{"id":230302974,"identity":"e0b2856b-6c25-4d96-a53f-38256253728e","order_by":3,"name":"Sarah F. 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