State-Specific Dynamic Functional Connectivity Patterns of Cognitive Intrusions in Schizophrenia: A Convergent Multi-Approach Resting-State fMRI Study

State-Specific Dynamic Functional Connectivity Patterns of Cognitive Intrusions in Schizophrenia: A Convergent Multi-Approach Resting-State fMRI Study | 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 State-Specific Dynamic Functional Connectivity Patterns of Cognitive Intrusions in Schizophrenia: A Convergent Multi-Approach Resting-State fMRI Study Ehsan Farzadnia, Marco Simões, Vince Calhoun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9361439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract The neural basis of hallucinations and delusions remains unclear due to the heterogeneity of schizophrenia, leading to inconsistent findings across studies. This study addresses this gap by mapping edge-level connectivity to symptom severity to identify aberrant circuits. Using two symptom scores as proxies for cognitive intrusions, we highlighted a potential bridge between the two identified circuits. We decomposed the transient functional architecture of 83 medicated patients into two stable and low-dynamic states relative to a more dynamic or chaotic baseline brain state by integrating NeuroMark-constrained independent component analysis (CICA) with dynamic functional network connectivity (dFNC), followed by clustering performed on independent data partitions. To mitigate method-specific bias and assess feature robustness under strict generalization, we applied leave-one-feature-out (LOFO) ranking nested within a leave-one-out cross-validation (LOOCV) framework, evaluating feature contributions consistently across held-out subjects. Regularizing model complexity minimized misfit and identified reliable connectivity between the cognitive control (CC) and default mode network (DMN) as a robust neural predictor of symptom severity. Predictive strength was most pronounced for hallucinations (P3) in State 3 ($T = 2.73, P \approx 0.004$) and delusions (P1) in State 4 ($T = 2.06, P = 0.021$). The edge set in State 3 demonstrated a trend-level association with P3 scores under permutation testing (two-sided $P = 0.067$). At the edge level, insula–precuneus connectivity showed a significant association following FDR correction ($P = 0.036$), suggesting a localized contribution within a distributed pattern. No significant effects were observed for delusions. The observed trend may also illustrate how gate failure in the insula contributes to leakage, reinforcing the role of the precuneus in misattribution; the literature suggests that this process may lead to the emergence of hallucinatory behaviors, warranting further investigation. Health sciences/Neurology Biological sciences/Neuroscience Intrusive symptoms Schizophrenia Resting-state fMRI Independent component analysis dynamic functional connectivity states Full Text Additional Declarations No competing interests reported. Supplementary Files sup.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor invited by journal 14 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 08 Apr, 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. 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-9361439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":620263226,"identity":"a27b6359-2981-4bf3-98ed-10443f992b17","order_by":0,"name":"Ehsan Farzadnia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBAC9gYQycbAA2QZMCQQo4XnAEwLzwGYFmbitDAwSCQYQMUIaWE/+/jDj7I7MvwzH2988IDhsDyDdP8B/Fp40g0Me84945G4nVZskMBw2LBB5jB+W+wZ0hgSeNsO8zDczjGTAGpJYJBIJuAw/mcMB/8CtcjfPGP+gzgtEmmMzSBbDG7wmDEQqeUZM7PMucM8hmfSioGBlm7YJnPYAL8W/jTmj2/KDtvLHT+88eOPCmt5funGB/itQQVA89kkSNEAAWRoGQWjYBSMguENAD66P7DuH8myAAAAAElFTkSuQmCC","orcid":"","institution":"University of Coimbra","correspondingAuthor":true,"prefix":"","firstName":"Ehsan","middleName":"","lastName":"Farzadnia","suffix":""},{"id":620263229,"identity":"15f48e10-f70a-486d-a6f9-9565234f5a4c","order_by":1,"name":"Marco Simões","email":"","orcid":"","institution":"University of Coimbra","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"","lastName":"Simões","suffix":""},{"id":620263230,"identity":"0301b56b-bb82-4101-be52-7c0b7da2107f","order_by":2,"name":"Vince Calhoun","email":"","orcid":"","institution":"Center for Translational Research in Neuroimaging and Data Science","correspondingAuthor":false,"prefix":"","firstName":"Vince","middleName":"","lastName":"Calhoun","suffix":""}],"badges":[],"createdAt":"2026-04-08 23:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9361439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9361439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106726054,"identity":"d5e8b997-231c-48ec-abfa-af75f5235387","added_by":"auto","created_at":"2026-04-12 18:35:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":61784657,"visible":true,"origin":"","legend":"","description":"","filename":"articleEhsan.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9361439/v1_covered_d180fc7f-5607-47e1-ad66-e6596bdfbec3.pdf"},{"id":106616223,"identity":"4e0e3c82-9776-462e-9713-a450e36009b8","added_by":"auto","created_at":"2026-04-10 13:13:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13093,"visible":true,"origin":"","legend":"","description":"","filename":"sup.docx","url":"https://assets-eu.researchsquare.com/files/rs-9361439/v1/7dfafb15646812d19c188577.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"State-Specific Dynamic Functional Connectivity Patterns of Cognitive Intrusions in Schizophrenia: A Convergent Multi-Approach Resting-State fMRI Study","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"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":"Intrusive symptoms, Schizophrenia, Resting-state fMRI, Independent component analysis, dynamic functional connectivity states","lastPublishedDoi":"10.21203/rs.3.rs-9361439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9361439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The neural basis of hallucinations and delusions remains unclear due to the heterogeneity of schizophrenia, leading to inconsistent findings across studies. This study addresses this gap by mapping edge-level connectivity to symptom severity to identify aberrant circuits. Using two symptom scores as proxies for cognitive intrusions, we highlighted a potential bridge between the two identified circuits. We decomposed the transient functional architecture of 83 medicated patients into two stable and low-dynamic states relative to a more dynamic or chaotic baseline brain state by integrating NeuroMark-constrained independent component analysis (CICA) with dynamic functional network connectivity (dFNC), followed by clustering performed on independent data partitions. To mitigate method-specific bias and assess feature robustness under strict generalization, we applied leave-one-feature-out (LOFO) ranking nested within a leave-one-out cross-validation (LOOCV) framework, evaluating feature contributions consistently across held-out subjects. 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