Approximately 1/3 of people living with epilepsy (PWE) continue to have seizures despite anti-epileptic drugs (AEDs). Recent trials using therapeutic brain stimulation show reductions in seizures, but rarely provide seizure free outcomes. Although seizures occupy a small fraction of their life, as little as 0.01%, PWE take anti-epileptic drugs (AED) daily, suffer AED related side effects, and spend their lives dreading when the next seizure will strike. The apparent randomness of seizures is associated with significant psychological consequences. We hypothesize that epilepsy can be more effectively managed, both the seizures and their psychological impact, by providing patients with accurate seizure diaries, real-time seizure forecasting, and responsive stimulation for brain state modulation. With accurate seizure forecasting patients would be empowered to manage their life activities. The optimal epilepsy management device requires: 1) Automated seizure detection 2) Accurate Automated Electronic Seizure Diaries 3) Seizure Forecasting and 4) Programmable Brain Stimulation. In this grant we develop an epilepsy management and therapy platform using Medtronic's 3rd generation device implantable device. The RC+S provides chronic nervous system sensing and analytics using embedded scientific instrumentation (e.g. - sensors, classification, and control policy implementation), and provides a unique opportunity for exploring and managing epileptic neural networks.

Public Health Relevance

In this grant we develop an epilepsy management and therapy platform using Medtronic's 3rd generation device implantable device, the RC+S. The RC+S (3rd Generation Medtronic Epilepsy Platform) is an implantable medical device that provides chronic nervous system sensing and analytics using embedded scientific instrumentation payloads (e.g. - sensors, classification, and control policy implementation), and provides a unique opportunity for exploring and managing epileptic neural networks. This is an initial step in establishing a new treatment paradigm for focal epilepsy, whereby the probability of seizure occurrence is continuously tracked for patient warning and intelligent responsive stimulation or pharmacological therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Exploratory/Developmental Cooperative Agreement Phase I (UH2)
Project #
5UH2NS095495-02
Application #
9146709
Study Section
Special Emphasis Panel (ZNS1)
Program Officer
Langhals, Nick B
Project Start
2015-09-30
Project End
2019-04-30
Budget Start
2017-07-17
Budget End
2018-04-30
Support Year
2
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Cimbalnik, Jan; Kucewicz, Michal T; Worrell, Greg (2016) Interictal high-frequency oscillations in focal human epilepsy. Curr Opin Neurol 29:175-81
Brinkmann, Benjamin H; Wagenaar, Joost; Abbot, Drew et al. (2016) Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139:1713-22