Deans

Elaine Wallace, D.O., M.S., M.S., M.S. – College of Osteopathic Medicine Thomas Temple, M.D. – Translational Research and Economic Development Lisa Deziel, Pharm.D., Ph.D., BCPS, FASHP – College of Pharmacy

Award Date

1-1-2018

Abstract

Epilepsy is the most common chronic neurological disease, affecting approximately 1% of the world's population. About 30% of these individuals are not able to achieve good control of their symptoms with medication. This has a severe negative impact on their quality of life, since they do not know when seizures will occur. Over the past 30 years, significant research has focused on using electrophysiological signals from brain activity, including both traditional and intracranial electroencephalography (EEG), to find a reliable method for predicting seizures. Unfortunately, while remarkable progress has been made in the field of neurology overall, reliable seizure prediction algorithms have not yet materialized. If such methods could be developed, they would significantly improve the quality of life by either warning individuals of impending seizures, so they could take steps to minimize their risk of injury, or by triggering a mechanism to avert the impending seizure from occurring using either local electrical stimulation or drug release in the affected region of the brain. In the proposed research, we will use a simulated network of neurons, along with actual intracranial EEG patient data, to help refine the process of seizure prediction. This data will be a unique aspect of our project, since it has much higher spatial resolution than tradition EEG and is not readily accessible to the research community. By controlling the parameters of the computer simulation, we can assess the conditions that lead to seizure onset. Our model connects the nerve cells using a small-world network topology, which has been demonstrated to be a feature of the human brain. A small-world network is one in which most pairs of nodes are not neighbors, but can be reached with only a small number of steps. We will use this new computational platform for testing conditions contributing to the transition from a non-seizure to a seizure state. We propose to use this platform to test a variety of conditions to develop a simple, reliable method of seizure prediction that can be used clinically in epilepsy patients.

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