Abstract
Over 15 million epilepsy patients worldwide have drug-resistant epilepsy (DRE). Successful surgery is a standard of care treatment for DRE but can only be achieved through complete resection or disconnection of the epileptogenic zone (EZ), the brain region(s) where seizures originate. Surgical success rates vary between 20-80% because no clinically validated biological markers of the EZ exist. Localizing the EZ is a costly and time-consuming process beginning with non-invasive neuroimaging and often followed by days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity (e.g., low-voltage high frequency activity) on individual channels occurring immediately before seizures or spikes that occur on interictal iEEG (i.e., between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in EZ localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. Intracranial EEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aim to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the EZ. We hypothesize that when a patient is not clinically seizing, it is because the EZ is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, patient-specific dynamical network models (DNMs) were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics (SSMs). We validated the SSMs in a retrospective analysis of 65 patients by using the SSMs of the annotated EZ to predict surgical outcomes. The SSMs predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified regions of the brain with high SSMs that were untreated. When compared to high frequency oscillations, the most commonly proposed interictal iEEG feature for EZ localization, SSMs outperformed in predictive power (by a factor of 1.2) suggesting SSMs may be an interictal iEEG fingerprint of the EZ. Competing Interest Statement The authors have declared no competing interest.