Epilepsy is one of the most prevalent neurological disorders, affecting 50 million individuals worldwide, a quarter of which have medically intractable cases. The unpredictability of seizure onset can lead to severe bodily injury or even death. Thus a reliable seizure prediction method is greatly needed to protect epileptic patients from life threading incidents. Furthermore, accurate seizure prediction would allow clinicians to provide well-timed drug delivery or therapeutic brain stimulation. This ability to optimally time interventions could substantially reduce the side effects and toxicity of long term medications for medically responsive patients and provide a novel treatment for patients with intractable epilepsy. In light of this need, the American Epilepsy Society has identified seizure predication as a primary priority for epilepsy research. Neuroscientists, clinicians, mathematicians and bioengineers have developed collaborations devoted to this research field. In spite of great progress in seizure prediction research, reliable seizure prediction remains a challenging task. EEG has been employed for the last 3 decades of seizure prediction research, whilst novel alternative research tools are lacking. EEG data reflect the summation of postsynaptic potentials across a large population of cells in diffuse brain areas and thus lack temporal and spatial specificity. In this proposed study, we will for the first time employ a multiple channel, single unit recording technique to record up to 80 single cells in 5 different brain areas that have been shown to be critically involved in temporal lobe epilepsy (TLE) in rat model of TLE. The major benefits of recording single units are that it provides high temporal and spatial resolution and multiple dimensional data, which allow us to detect subtle changes in brain areas before seizure onset. We will apply this information with a battery of single unit and EEG data analytic tools to try to detect the neural activity changes that portend seizure onset. In this initial study, we will analyze the data off-line to establish a reliable seizure prediction algorithm. Once this algorithm has been established, we will apply real time, prospective seizure perdiction methods to detect seizures in real time and we will combine this prediction tool with deep brain stimulation to prevent seizure development and progression. The long term goal of this research is to translate animal experimental results to the clinic to treat epilepsy patients with this novel approach. PUBLIC HEALTH RELEVANCE: Accurate seizure prediction can prevent bodily injury for epileptic patient and enable clinicians to treat epilepsy with timely drug administration or brain stimulation. This research project will develop a novel multiple channel, single unit recording method to predict spontaneous seizures in rat model of epilepsy. The success of this project will be instrumental in improving seizure prediction in the clinic and benefit millions of patients with epilepsy. PHS 398/2590 (Rev. 09/04) Page Continuation Format Page