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Unveiling Epileptic Networks: Motif Synchronization for Interictal Spike Localization.

Leonardo R Da Costa, Brunno M Campos, Marina K M Alvim

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    |December 3, 2025
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    Summary
    This summary is machine-generated.

    A new method using Motif Synchronization (MS) effectively detects interictal epileptiform discharges (IEDs) in epilepsy patients. This functional connectivity technique aids in localizing seizure origins, offering a more efficient diagnostic approach.

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    Area of Science:

    • Neuroscience
    • Computational Neuroscience
    • Medical Imaging

    Background:

    • Epilepsy impacts 50 million globally, with 30% unresponsive to anti-epilepsy drugs, highlighting the need for advanced diagnostic and localization tools.
    • Scalp electroencephalogram (EEG) visual inspection for interictal epileptiform discharges (IEDs) is time-consuming and subjective.
    • Accurate localization of epileptogenic foci is crucial for surgical intervention in refractory epilepsy.

    Purpose of the Study:

    • To introduce and validate a novel method for detecting interictal epileptiform discharges (IEDs) using Motif Synchronization (MS).
    • To assess the efficacy of MS in localizing probable epileptogenic foci by analyzing functional connectivity in EEG data.
    • To evaluate the impact of different EEG referencing schemes on IED detection and localization accuracy.

    Main Methods:

    • Developed a novel IED detection method based on Motif Synchronization (MS), a functional connectivity technique.
    • Constructed time-varying graphs (TVGs) from EEG data of 10 mesial temporal lobe epilepsy patients.
    • Analyzed TVGs and aggregated static networks to identify connectivity patterns during IEDs and assess localization accuracy with different referencing schemes.

    Main Results:

    • Motif Synchronization (MS) demonstrated increased functional connectivity during IEDs.
    • Median referencing provided the most accurate localization of epileptic regions.
    • Aggregated static networks revealed highly connected clusters corresponding to IED-generating regions in 90% of subjects.
    • Global clustering coefficients were significantly higher during IED segments.

    Conclusions:

    • Motif Synchronization (MS) is a computationally efficient and interpretable tool for detecting and localizing IEDs in epilepsy.
    • The MS method, particularly with median referencing, shows promise for improving the diagnosis and localization of epilepsy.
    • Future research should involve larger patient cohorts and integration with graph neural networks for enhanced epileptic activity classification.