William Stacey, PhD MD - US grants

DESCRIPTION (provided by applicant): Candidate: Dr. Stacey has the clinical and research expertise necessary to embark on his career goal as a tenure-track physician-scientist. He is Board-certified in Neurology and has completed an epilepsy fellowship. He has been engaged in multiple productive research endeavors in Bioengineering for over 15 years, earning a PhD with Dominique Durand, a pioneer in neural engineering. He has obtained competitive research grants and awards throughout his career, has several publications, and is demonstrating current productivity. His experience uniquely qualifies him to engage in the current research proposal and career plan, merging clinical knowledge of epilepsy and electroencephalography with a strong neurophysiologic and computational background. Environment: The University of Pennsylvania is a rich environment for both research and clinical endeavors, and the Department of Neurology is home to several successful K08 and K23 applicants. The lab and clinical space necessary to perform the proposed work are all within a single city block. Dr. Stacey has established strong collaborative ties to several researchers at Penn that will enable this work to succeed. A key aspect of this proposal is that Dr. Stacey already has a faculty position as an Instructor and has established a schedule with 80% protected research time for over 2 years now. This protected time will continue, following the schedule already established in the past year as Instructor and before that as a fellow. Both the Department of Neurology and Department of Bioengineering have given enthusiastic support for Dr. Stacey's career development. Dr. Stacey already has lab space in Dr. Litt's lab and is using the computing power and experimental data described in this proposal, resources which have produced publication in 2009, a second under revision at present, and additional work that has resulted in speaking invitations to international meetings. Dr. Litt has fully committed to provide the ongoing infrastructure and mentoring necessary to propel forward Dr. Stacey's research and his career as a physician-scientist. Research: A large number of people with epilepsy continue to have uncontrolled seizures despite the best available therapies. Currently available antiepileptic devices use electrical brain stimulation to arrest seizures. Although some moderate success using these therapies has been demonstrated, a better understanding of seizure generation and the role of electrical stimulation will lead to more effective second-generation devices. Recent evidence suggests that fast ripples (and other high-frequency oscillations) localize to epileptic networks and share the same pathology as epileptic tissue. I hypothesize that fast ripples, and the pathologic tissue that produces them, are integral to seizure generation and will thereby provide an ideal target for the rational development of new brain stimulation protocols. This project will use biophysically accurate computer simulations of fast ripples, iteratively based on human recordings, to design and test new stimulation protocols to control seizures. Focusing on a specific, highly localizable network phenomenon will allow for validation of the simulated results and comparison with clinically measurable parameters, critical aspects of computational modeling that have been difficult using more general simulation approaches in epilepsy research. The Specific Aims of this proposal describe a method to design and validate models of fast ripples, then use them to test different forms of stimulation. Aim 1: To develop and use a computer model of the hippocampus to determine which pathological changes in epileptic tissue: interneuronal loss, gap junctions, recurrent axons, ephaptic effects and synaptic inputs, are responsible for generating Fast Ripples. Aim 2: To validate findings of the computer predictions in Aim 1 using data from human microelectrode recordings and animal models. Aim 3: To determine which electrical stimulus paradigms are most effective in disrupting Fast Ripples in the computer model, and how the network output will change with those stimuli. Later work, beyond the scope of this proposal, will take stimuli that are successful in the computer model and deploy them in rat models of epilepsy and eventually in human stimulation trials. The likely results of these studies will be more effective second-generation antiepileptic devices that direct targeted closed-loop stimulation to discrete regions vital to seizure generation. PUBLIC HEALTH RELEVANCE: Epilepsy is a very common disease, and 25% of affected people continue to have seizures despite best available therapies. Brain stimulation devices show great potential in controlling seizures, but early results have been somewhat limited. The goal of this proposal is to develop and test new stimulation methods that can improve the efficacy of antiseizure devices, providing new treatment options for people with uncontrolled epilepsy.

? DESCRIPTION (provided by applicant): Epilepsy is one of the world's most prevalent diseases, yet the rate of uncontrolled seizures has not changed in decades. One of the reasons for this is our limited understanding of seizure mechanisms, and so one of the main goals of epilepsy research is to identify new biomarkers to help us understand the nature of the disease. Recent technological advancements now allow us to monitor brain activity with much higher resolution, which have led to the identification of promising potential biomarkers such as High Frequency Oscillations (HFOs). Unfortunately, clinicians still have not determined how to utilize this information under clinical conditions. There are three main obstacles to implementing HFOs in practice: 1) it is unclear how to acquire them in a practical way; 2) it is unclear how to ascertain which HFOs are truly related to epilepsy; and 3) it is unclear how to use the HFO data in a prospective fashion to improve clinical care. The purpose of this project is overcome each of these obstacles. The first Aim validates a universal computer algorithm that can identify HFOs automatically, then tests how to use HFO rate as method to identify where seizures will start. This method improves upon past work by improving the precision of HFO detection and determining how to avoid false predictions that would lead to unnecessary surgery. The second Aim addresses a major unsolved problem in HFO research: HFOs are seen in normal brain as well as in epilepsy. This Aim will use state-of-the- art machine learning tools to process a vast dataset of HFO collected from over 100 patients to determine how to distinguish epileptic from normal HFOs. The third Aim will analyze how HFOs change over time, a largely unexplored characteristic of HFOs that cannot be evaluated without very large datasets. These Aims together serve as the framework to establish HFOs as a clinically viable biomarker of seizures, allowing their translation into clinical epilepsy care and leading to future prospective clinical studies identifying the location and timing of seizure onset.