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. 2025 Jun 6:16:1515484.
doi: 10.3389/fneur.2025.1515484. eCollection 2025.

Spatio-temporal independent component classification for localization of seizure onset zone

Seyyed Mostafa Sadjadi  1   2 , Elias Ebrahimzadeh  1   2 , Alireza Fallahi  3 , Jafar Mehvari Habibabadi  4 , Mohammad-Reza Nazem-Zadeh  5   6 , Hamid Soltanian-Zadeh  1   2
Affiliations

Spatio-temporal independent component classification for localization of seizure onset zone

Seyyed Mostafa Sadjadi et al. Front Neurol. .

Abstract

Localization of the epileptic seizure onset zone (SOZ) as a step in 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 have been proposed to assist in presurgical planning with simpler procedures for data acquisition and higher speeds. 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 based on the dominant power frequency, biophysical constraints, spatial lateralization, local connectivity, temporal energy, and functional non-Gaussianity. STCC was evaluated in 13 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 that serve as SOZ functional biomarkers. Unlike 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 of such information, simpler to use in terms of data acquisition and artifact removal, and considerably easier to implement.

Keywords: epilepsy; epileptogenic zone; fMRI; functional connectivity (FC); independent component analysis (ICA); local network features; source localization.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
Diagram of the presented method algorithm.
Figure 2
Figure 2
Histogram of features among all sICs and subjects with the epileptic sICs being highlighted (Top) and the boxplot of the feature set with a normalized range among all sICs and subjects with the epileptic sICs being highlighted (Bottom).
Figure 3
Figure 3
The post-surgical MRI with overlaid localized SOZ cluster on resection area using the presented method: (A) subject 1 with behavioral arrest with oral automatisms and verbalization showing rhythmic alpha and theta activity at right temporal lobe (T4>F8) in ictal EEG; (B) subject 2 with bilateral limb automatisms showing rhythmic theta activity at both temporal lobes (T>F) but right irritative zone in ictal EEG; (C) subject 5 with left versive motion and left limb dystonia showing rhythmic alpha activity at right temporal lobe (T4>T3) in ictal EEG; (D) subject 6 with staring with oral automatisms, right versive motion, and right facial clonic activity showing rhythmic theta activity at left temporal lobe (T3>T4) in ictal EEG; (E) subject 7 with behavioral arrest, left limb automatism, and right versive motion showing rhythmic theta activity at left temporal lobe (T3>T4) in ictal EEG; (F) subject 8 with behavioral arrest with staring.
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