Classifying Schizophrenia Subtypes via Resting- State EEG Complexity Networks

Classifying Schizophrenia Subtypes via Resting- State EEG Complexity Networks | 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 Classifying Schizophrenia Subtypes via Resting- State EEG Complexity Networks Jilin Zou, Hang Qi, Chengyan Yang, Chenyu Fan, Yun Dong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7189475/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Schizophrenia (SZ), a network disorder, features abnormal functional connectivity, but fMRI limitations hinder clinical use. While EEG offers convenience, traditional complexity measures like sample entropy (SampEn) inadequately capture spatiotemporal network dynamics and yield inconsistent SZ findings. We investigated functional network alterations in SZ subtypes—deficit (DS) and non-deficit (NDS)—using a novel EEG complexity network approach and aimed for classification against healthy controls (HC). We analyzed resting-state EEG (64 channels, 500Hz) from 19 DS patients, 19 NDS patients, and 30 HC. Data underwent preprocessing, bandpass filtering (δ:0.5-4Hz, θ:4-8Hz, α:8-13Hz, β:13-40Hz), and artifact removal (ICA). Dynamic SampEn time series were calculated per electrode. Complexity networks were constructed using Spearman correlations; topological features (global efficiency, local efficiency, strength) were analyzed. Support Vector Machine (SVM) was used for classification. Traditional SampEn differentiated groups only in δ band. The network approach revealed significant topological alterations: DS exhibited the highest local efficiency (δ, θ, α) and lowest global efficiency (δ, α), while NDS showed the lowest global efficiency (θ) and highest local efficiency (β). SVM achieved 96.3% classification accuracy, optimal in δ/θ bands. This novel EEG complexity network method effectively distinguishes HC, DS, and NDS, demonstrating strong potential for clinical application, particularly in outpatient settings. Validation with larger cohorts and task-state EEG is warranted. Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Schizophrenia Resting-state EEG Brain complexity network Topological features Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 31 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviewers invited by journal 25 Aug, 2025 Editor assigned by journal 18 Aug, 2025 Editor invited by journal 12 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 07 Aug, 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. 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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-7189475","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508575119,"identity":"26dead1a-6df5-410b-b5b4-6c6331af8798","order_by":0,"name":"Jilin Zou","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Jilin","middleName":"","lastName":"Zou","suffix":""},{"id":508575120,"identity":"b1b72ac8-746e-40db-8b92-a189c7ba1896","order_by":1,"name":"Hang Qi","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Qi","suffix":""},{"id":508575121,"identity":"ba10907f-3b5f-4df3-b5cd-3725408d1fa8","order_by":2,"name":"Chengyan Yang","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Chengyan","middleName":"","lastName":"Yang","suffix":""},{"id":508575122,"identity":"b6eb0fc9-877d-4eb7-9d1f-e99fdf531622","order_by":3,"name":"Chenyu Fan","email":"","orcid":"","institution":"Linyi University","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Fan","suffix":""},{"id":508575123,"identity":"54b6202b-0ad3-45ed-848e-e470cd583013","order_by":4,"name":"Yun Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYJACgwcVFmCGBPFaEs5IkKiFIbGNFC3y7T0GBYnzJBI3HGA+eJuHwS6PoBbGnjMGBonbQFrYkq15GJKLCWphlsiBaeExk+ZhOJDYQEgLG1jLHJAW/m/EaeEBa2kA28JGnBYJnmMFBgnHJIxnHmYztpxjkExYi3x78zaDDzU2sn3Hmx/eeFNhR1gLyDsGQMKxgRnENiBCPRAwPwAS9sSpHQWjYBSMghEJAP5cNvsIflu1AAAAAElFTkSuQmCC","orcid":"","institution":"Linyi University","correspondingAuthor":true,"prefix":"","firstName":"Yun","middleName":"","lastName":"Dong","suffix":""}],"badges":[],"createdAt":"2025-07-22 17:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7189475/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7189475/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-25921-5","type":"published","date":"2025-11-25T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97179346,"identity":"ff34dc34-f777-4cea-82cb-befccda985a4","added_by":"auto","created_at":"2025-12-01 16:14:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":771099,"visible":true,"origin":"","legend":"","description":"","filename":"paperV4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7189475/v1_covered_e627abce-ba44-4e8b-8c92-4c1438ac50a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Classifying Schizophrenia Subtypes via Resting- State EEG Complexity Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Schizophrenia, Resting-state EEG, Brain complexity network, Topological features, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7189475/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7189475/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSchizophrenia (SZ), a network disorder, features abnormal functional connectivity, but fMRI limitations hinder clinical use. While EEG offers convenience, traditional complexity measures like sample entropy (SampEn) inadequately capture spatiotemporal network dynamics and yield inconsistent SZ findings. We investigated functional network alterations in SZ subtypes\u0026mdash;deficit (DS) and non-deficit (NDS)\u0026mdash;using a novel EEG complexity network approach and aimed for classification against healthy controls (HC). We analyzed resting-state EEG (64 channels, 500Hz) from 19 DS patients, 19 NDS patients, and 30 HC. Data underwent preprocessing, bandpass filtering (δ:0.5-4Hz, θ:4-8Hz, α:8-13Hz, β:13-40Hz), and artifact removal (ICA). Dynamic SampEn time series were calculated per electrode. Complexity networks were constructed using Spearman correlations; topological features (global efficiency, local efficiency, strength) were analyzed. Support Vector Machine (SVM) was used for classification. Traditional SampEn differentiated groups only in δ band. 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