Node Degree Volatility for Seizure Onset Zone Localization

Node Degree Volatility for Seizure Onset Zone Localization | 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 Node Degree Volatility for Seizure Onset Zone Localization Igor Belykh, Kevin Slote, Kelley Smith, Marrium Shamshad, Aditi Trivedi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8061650/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 Drug-resistant epilepsy, in which seizures cannot be controlled with medication, often requires invasive treatment interventions such as surgical resection, laser ablation, or neuromodulation targeting the seizure onset zone (SOZ). However, precise biomarkers for localizing the SOZ remain lacking, limiting treatment success. Here, we introduce a novel biomarker: node degree volatility, defined as the rate of change in a node’s functional connectivity over time. We analyze 82 seizures from 80 patients, reconstructing Granger-causal networks from intracranial EEG (iEEG) recordings, where nodes represent iEEG channels and edges denote directed causal influences between them. Nodes with the highest degree volatility at seizure onset predict the clinically identified SOZ in 75% of successful surgical cases, using a stringent 5% top-quantile threshold to designate SOZ nodes. On the analyzed dataset, this biomarker outperforms established iEEG-based methods, including neural fragility and conventional network metrics such as node degree and betweenness centrality, highlighting the value of capturing local temporal dynamics in functional brain networks. Unlike many machine learning methods that can be computationally intensive and often lack transparency, our approach is direct and interpretable, offering a clear and clinically actionable path to SOZ localization. Beyond epilepsy, node degree volatility may provide a framework for identifying dynamically critical nodes in evolving biological, social, and engineering networks. Physical sciences/Mathematics and computing/Applied mathematics Health sciences/Neurology/Neurological disorders/Epilepsy Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryMaterialNodeDegreeVolatilitysubmitted.pdf Supplementary Information for 'Node Degree Volatility for Seizure Onset Zone Localization' 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. 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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-8061650","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":545235345,"identity":"f8f1bcda-a41e-4225-af56-38f2225d9dd8","order_by":0,"name":"Igor 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However, precise biomarkers for localizing the SOZ remain lacking, limiting treatment success. Here, we introduce a novel biomarker: node degree volatility, defined as the rate of change in a node’s functional connectivity over time. We analyze 82 seizures from 80 patients, reconstructing Granger-causal networks from intracranial EEG (iEEG) recordings, where nodes represent iEEG channels and edges denote directed causal influences between them. Nodes with the highest degree volatility at seizure onset predict the clinically identified SOZ in 75% of successful surgical cases, using a stringent 5% top-quantile threshold to designate SOZ nodes. On the analyzed dataset, this biomarker outperforms established iEEG-based methods, including neural fragility and conventional network metrics such as node degree and betweenness centrality, highlighting the value of capturing local temporal dynamics in functional brain networks. Unlike many machine learning methods that can be computationally intensive and often lack transparency, our approach is direct and interpretable, offering a clear and clinically actionable path to SOZ localization. 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