ORIGINAL RESEARCH article
Front. Neurol.
Sec. Applied Neuroimaging
Volume 16 - 2025 | doi: 10.3389/fneur.2025.1515484
Spatio-Temporal Independent Component Classification for Localizing Localization of Seizure Onset Zone
Provisionally accepted
Seyyed Mostafa Sadjadi1*
Elias Ebrahimzadeh1*
Alireza Fallahi1Jafar Mehvari Habibabadi2
Mohammad-Reza Nazem-Zadeh3
Hamid Soltanian-Zadeh1- 1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- 2Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Iran, Isfahan, Iran
- 3Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Alborz, Iran
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Localization of the epileptic seizure onset zone (SOZ) as a step of presurgical planning leads to higher efficiency in surgical and stimulation treatments. However, the clinical localization procedure is a difficult, long procedure with increasing challenges in patients with complex epileptic foci. The interictal methods are proposed to assist in presurgical planning with simpler procedures in data acquisition and higher speed. In this study, spatio-temporal component classification (STCC) 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 data with a component-sorting procedure upon dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. STCC was evaluated in thirteen patients with temporal lobe epilepsy (TLE) who underwent surgical resection and had seizure-free surgical outcomes after a 12-month follow-up. The results showed promising accuracy highlighting valuable features as SOZ functional biomarkers. Contrary to most presented 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, simpler to use in terms of data acquisition and artifact removal, and considerably easier to implement.
Keywords: Epilepsy, epileptogenic zone, source localization, fMRI, independent component analysis (ICA), functional connectivity (FC), Local network features
Received: 24 Oct 2024; Accepted: 17 Apr 2025.
Copyright: © 2025 Sadjadi, Ebrahimzadeh, Fallahi, Habibabadi, Nazem-Zadeh and Soltanian-Zadeh. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Seyyed Mostafa Sadjadi, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
Elias Ebrahimzadeh, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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