Sources and Sinks in Interictal iEEG Networks: An iEEG Marker of the Epileptogenic Zone

Around 30% of epilepsy patients have seizures that cannot be controlled with medication. The most effective treatments for medically resistant epilepsy are interventions that surgically remove the epileptogenic zone (EZ), the regions of the brain that initiate seizure activity. A precise identification of the EZ is essential for surgical success but unfortunately, current success rates range from 20-80%. Localization of the EZ requires visual inspection of intracranial EEG (iEEG) recordings during seizure events. The need for seizure occurrence makes the process both costly and time-consuming and in the end, less than 1% of the data captured is used to assist in EZ localization. In this study, we aim to leverage interictal (between seizures) data to localize the EZ. We develop and test the source-sink index as an interictal iEEG marker by identifying two groups of network nodes from a patient's interictal iEEG network: those that inhibit a set of their neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, we i) estimate patient-specific dynamical network models from interictal iEEG data and ii) compute a source-sink index for every network node (iEEG channel) to identify pathological nodes that correspond to the EZ. Our results suggest that in patients with successful surgical outcomes, the source-sink index clearly separates the clinically identified EZ (CA-EZ) channels from other channels whereas in patients with failed outcomes CA-EZ channels cannot be distinguished from the rest of the network.