Abstract: Electrical Stimulation and Dynamical Network Modeling to Improve Therapies for Epilepsy

Medically-refractory epilepsy is a devastating neurological disease that is defined by recurrent and unprovoked seizures that are insufficiently controlled by anti-epileptic medications. If the seizures are originating from a specific region of the brain, surgical removal or stimulation of the epileptogenic region can be an effective therapy for these patients. The accurate localization of the seizure onset zone (SOZ) is critical for surgical success, but localizing the SOZ is difficult and surgical success rates vary from 34-70%. Single-pulse electrical stimulation (SPES) has become increasingly utilized as a tool to localize epileptogenic networks because features of the evoked potentials are differentiable between SOZ and non-SOZ regions. In this talk, I will describe a study that aims to improve seizure onset localization and expedite the intracranial monitoring process by employing dynamical network models that investigate resonance phenomena in the patient’s epileptogenic network with recordings obtained during SPES. We hypothesize that a dynamical quantification of the connectivity networks derived from the evoked responses induced by SPES could be used to accurately localize the SOZ and guide clinicians in eliciting typical seizures with electrical stimulation when stimulating at resonant frequencies. Further, we tested whether virtual stimulation in our model, a purely simulated stimulation, can highlight SOZ regions as well as physical electrical stimulation. I will give an overview of these dynamical network techniques and describe their potential impact in the clinical treatment of medically-refractory epilepsy.