Abstract
Epilepsy affects approximately 50 million individuals worldwide, with nearly one-third suffering from drug-resistant epilepsy (DRE). For these patients, localizing the epileptogenic zone (EZ) is critical for effective surgical intervention but often requires implantation of intracranial electrodes and days to weeks in the hospital to record seizures. This study evaluates the efficacy of neural fragility, a dynamical network-based metric, as a computational biomarker for the identification of epileptogenic nodes during resting-state intracranial EEG (iEEG). Because EZ can never be truly validated in human iEEG data due to the absence of ground truth, we use in-silico data with pre-defined EZs, generated with a phenomenological network model, to assess the predictive accuracy of neural fragility in localizing seizure-generating regions. Results demonstrate a bimodal distribution of fragility scores, with a threshold-based classification accurately identifying epileptogenic nodes in 45% and 54% of simulations for two different datasets. While findings highlight the potential of neural fragility for EZ localization, variability in predictions suggests a need to determine physical and phenomenological factors driving prediction accuracies. Future work will focus on parameter optimization of dynamical network models, integration of additional network features, and validation of the model with clinically derived (iEEG) data that include surgical success results.Clinical Relevance- This research advances computational methods for epilepsy surgical planning, aiming to improve patient outcomes through more precise epileptogenic zone localization.