Localization of Seizure Onset Zone based on Spatio-Temporal Independent Component Analysis on fMRI

Localization of Seizure Onset Zone based on Spatio-Temporal Independent Component Analysis on fMRI | 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 Localization of Seizure Onset Zone based on Spatio-Temporal Independent Component Analysis on fMRI Seyyed Mostafa Sadjadi, Elias Ebrahimzadeh, Alireza Fallahi, Jafar Mehvari Habibabadi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5760498/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 Localization of the seizure onset zone (SOZ) as a step of presurgical planning leads to higher efficiency in surgical and stimulation treatments. However, the clinical localization including structural, ictal, and invasive data acquisition and assessment is a difficult and long procedure with increasing challenges in patients with complex epileptic foci. The interictal methods are proposed to assist in presurgical planning with simpler data acquisition and higher speed. In this study, spatio-temporal component classification is presented for the localization of epileptic foci using resting-state functional magnetic resonance imaging (rs-fMRI) data. This method is based on spatio-temporal independent component analysis (ST-ICA) on rs-fMRI with a component-sorting procedure upon dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. This method aimed to utilize the rs-fMRI potential to reach a high spatial accuracy in localizing epileptic foci from interictal data while retaining the reliability of results for clinical usage. Thirteen patients with temporal lobe epilepsy (TLE) who underwent surgical resection and had seizure-free surgical outcomes after a 12-month follow-up were included in this study. All patients had pre-surgical structural MRI and rs-fMRI while post-surgical MRI images were available for ten. Based on the relationship between the localized foci and resection, the results were classified into three groups “fully concordant”, “partially concordant”, and “discordant”. These groups had the resulting cluster aligned with, in the same lobe with, and outside the lobe of the resection area, respectively. This method showed promising results highlighting valuable features as SOZ functional biomarkers. Contrary to most methods which depend on simultaneous EEG information, the occurrence of epileptic spikes, and the depth of the epileptic foci, the presented method is entirely based on fMRI data making it independent from such information and considerably easier in terms of data acquisition, artifact removal, and implement. Neurology Epilepsy Epileptogenic zone Source localization fMRI Independent Component Analysis (ICA) Functional Connectivity (FC) Local Network Features Full Text Additional Declarations The authors declare no competing interests. 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-5760498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":397468356,"identity":"c22d20a1-7a6f-4852-8104-0bdd937d3a80","order_by":0,"name":"Seyyed Mostafa Sadjadi","email":"","orcid":"","institution":"Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, 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However, the clinical localization including structural, ictal, and invasive data acquisition and assessment is a difficult and long procedure with increasing challenges in patients with complex epileptic foci. The interictal methods are proposed to assist in presurgical planning with simpler data acquisition and higher speed. In this study, spatio-temporal component classification is presented for the localization of epileptic foci using resting-state functional magnetic resonance imaging (rs-fMRI) data. This method is based on spatio-temporal independent component analysis (ST-ICA) on rs-fMRI with a component-sorting procedure upon dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. This method aimed to utilize the rs-fMRI potential to reach a high spatial accuracy in localizing epileptic foci from interictal data while retaining the reliability of results for clinical usage.\u003c/p\u003e \u003cp\u003eThirteen patients with temporal lobe epilepsy (TLE) who underwent surgical resection and had seizure-free surgical outcomes after a 12-month follow-up were included in this study. All patients had pre-surgical structural MRI and rs-fMRI while post-surgical MRI images were available for ten. Based on the relationship between the localized foci and resection, the results were classified into three groups \u0026ldquo;fully concordant\u0026rdquo;, \u0026ldquo;partially concordant\u0026rdquo;, and \u0026ldquo;discordant\u0026rdquo;. These groups had the resulting cluster aligned with, in the same lobe with, and outside the lobe of the resection area, respectively. This method showed promising results highlighting valuable features as SOZ functional biomarkers. 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