Topological Phase Transitions in Functional Brain Networks: A Robust, Explainable Precursor to Epileptic Seizures

Topological Phase Transitions in Functional Brain Networks: A Robust, Explainable Precursor to Epileptic Seizures | 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 Topological Phase Transitions in Functional Brain Networks: A Robust, Explainable Precursor to Epileptic Seizures Madjid Eshaghi Gordji, Mohamadali Berahman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8200744/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Epileptic seizure forecasting has evolved from linear univariate measures to complex Deep Learning (DL) models. However, the black-box nature of DL limits clinical trust, and inter-patient variability remains a significant hurdle. Methods: We propose a transparent, network-based biomarker—the Canonical Dimension (Φ)—derived from the zeroth-order persistent homology of functional connectivity networks. Instead of using real EEG recordings, we generate fully synthetic multichannel signals and construct simulated functional networks that reproduce key statistical properties and recording conditions inspired by the CHB-MIT (pediatric) and TUH (adult) cohorts. Unlike threshold-based graph metrics, Φ integrates topological features across all filtration scales. We evaluate Φ across two simulated cohorts (24 pediatric-inspired and 45 adult-inspired virtual subjects) with heterogeneous seizure profiles and noise conditions. Results: Across all simulations, Φ consistently identified pre-ictal transitions with an average lead time of 32 seconds (AUROC = 0.94). Sensitivity analysis showed robustness against signal-to-noise ratios as low as 5dB. Statistical significance was confirmed via Benjamini–Hochberg FDR correction (padj < 0.001). Conclusion: Our findings indicate that simulated seizure onsets are preceded by a topological collapse—a generic earlywarning mechanism analogous to critical slowing down in dynamical systems. The Canonical Dimension Φ provides a computationally efficient (12ms/epoch) and fully explainable alternative to opaque neural networks, offering a principled target for future validation on real EEG datasets. Biological sciences/Computational biology and bioinformatics Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8200744","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":557563230,"identity":"844cc883-c1b8-46c2-900e-cec58d722a65","order_by":0,"name":"Madjid Eshaghi 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However, the black-box nature of DL limits clinical trust, and inter-patient variability remains a significant hurdle. \u003cstrong\u003eMethods:\u003c/strong\u003e We propose a transparent, network-based biomarker—the Canonical Dimension (Φ)—derived from the zeroth-order persistent homology of functional connectivity networks. Instead of using real EEG recordings, we generate fully synthetic multichannel signals and construct simulated functional networks that reproduce key statistical properties and recording conditions inspired by the CHB-MIT (pediatric) and TUH (adult) cohorts. Unlike threshold-based graph metrics, Φ integrates topological features across all filtration scales. 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The Canonical Dimension Φ provides a computationally efficient (12ms/epoch) and fully explainable alternative to opaque neural networks, offering a principled target for future validation on real EEG datasets.\u003c/p\u003e","manuscriptTitle":"Topological Phase Transitions in Functional Brain Networks: A Robust, Explainable Precursor to Epileptic Seizures","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-11 04:26:44","doi":"10.21203/rs.3.rs-8200744/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6468488-a2bb-4182-8f48-73a938f982fc","owner":[],"postedDate":"December 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59342465,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":59342466,"name":"Health sciences/Neurology"},{"id":59342467,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-02-13T06:25:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-11 04:26:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8200744","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8200744","identity":"rs-8200744","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}