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Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

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Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
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Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
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Related Experiment Video

Updated: Jan 7, 2026

Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy
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Noninvasive Seizure Onset Zone Localization Using Janashia-Lagvilava Algorithm-Based Spectral Factorization in

Sofia Kasradze1,2, Giorgi Lomidze3, Lasha Ephremidze4

  • 1Institute of Neurology and Neuropsychology, 83/11 Vazha-Pshavela Ave., 0186 Tbilisi, Georgia.

Brain Sciences
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

Janashia-Lagvilava algorithm (JLA)-based spectral factorization in nonparametric Granger causality accurately identified seizure onset zones (SOZs) from scalp EEG. This noninvasive method aligns with positive surgical outcomes, offering potential for improved epilepsy diagnosis and treatment.

Keywords:
Granger causalityJanashia–Lagvilava algorithmdrug-resistant epilepsymatrix spectral factorizationnoninvasive SOZ localizationseizure onset zone

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Signal Processing

Background:

  • Precise identification of seizure onset zones (SOZs) is crucial for epilepsy surgery and interventional therapies.
  • Intracranial electroencephalography (EEG) is the current standard for SOZ identification, but invasive procedures carry risks.
  • Previous studies utilized Granger causality (GC) on high-frequency oscillations for SOZ detection, with parametric and Wilson algorithm (WL)-based methods showing promise.

Purpose of the Study:

  • To evaluate the effectiveness of the Janashia-Lagvilava algorithm (JLA)-based matrix factorization in nonparametric Granger causality (GC) for noninvasively identifying SOZs from ictal scalp EEG recordings.
  • To assess the JLA approach's ability to pinpoint seizure onset regions and propagation pathways in patients with drug-resistant epilepsy.

Main Methods:

  • Analyzed scalp EEG recordings from six epilepsy patients undergoing presurgical evaluation.
  • Isolated regions of interest (ROIs) and computed the cross-power spectral density matrix using the multitaper method.
  • Factorized the spectral density matrix using the JLA to obtain transfer functions and noise covariance matrices for GC estimation.
  • Estimated GC values at various prediction time steps to confirm suspected SOZs and propagation pathways.

Main Results:

  • JLA-based spectral factorization within the GC framework successfully identified SOZs and their propagation patterns from scalp EEG.
  • The identified SOZs and pathways from JLA-GC analysis correlated with positive surgical outcomes (Engel Class I) in all six patients.
  • Demonstrated alignment between noninvasively identified seizure dynamics and successful surgical intervention.

Conclusions:

  • JLA-based spectral factorization in nonparametric GC is a potent noninvasive tool for accurate SOZ localization in epilepsy.
  • This method supports diagnosis and treatment planning for drug-resistant epilepsy.
  • The JLA approach has broader implications for understanding information flow in neuroimaging and computational neuroscience.