Source-sink connectivity: a novel interictal EEG marker for seizure localization

Area of Science:

• Neuroscience
• Computational Neuroscience
• Medical Technology

Background:

• Drug-resistant epilepsy affects over 15 million worldwide.
• Surgical success hinges on precise epileptogenic zone localization, yet current methods are time-consuming and lack validated biomarkers.
• Existing intracranial EEG (iEEG) analysis often overlooks interictal data, missing opportunities for improved diagnostics.

Purpose of the Study:

• To develop and validate a novel network-based marker using interictal iEEG data for accurate epileptogenic zone identification.
• To investigate the hypothesis that the epileptogenic zone is inhibited by other brain regions during non-seizure periods.
• To improve surgical outcome prediction in drug-resistant epilepsy.

Main Methods:

• Developed a network-based algorithm to analyze interictal iEEG data, identifying 'source' and 'sink' nodes based on inhibitory network dynamics.
• Estimated patient-specific dynamical network models from iEEG data to quantify source-sink metrics.
• Validated the algorithm retrospectively in 65 epilepsy patients and compared its predictive power against clinician assessments and high-frequency oscillations.

Main Results:

• The source-sink metrics achieved 73% accuracy in identifying epileptogenic regions.
• Clinician agreement with the algorithm was high (93%) in seizure-free patients.
• Source-sink metrics predicted surgical outcomes with 79% accuracy, significantly outperforming clinician predictions (43%).
• The algorithm identified untreated brain regions associated with failed surgical outcomes.
• Source-sink metrics demonstrated 1.2 times greater predictive power than high-frequency oscillations for epileptogenic zone localization.

Conclusions:

• Network-based analysis of interictal iEEG data provides a powerful, non-invasive tool for epileptogenic zone localization.
• The developed source-sink metrics serve as a potential interictal iEEG fingerprint for the epileptogenic zone.
• This approach can significantly enhance the prediction of surgical success and guide treatment strategies for drug-resistant epilepsy.