Data-driven optimization for DBS programming in temporal lobe epilepsy

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Brain stimulation therapy is a life changing treatment for patients with neurological and psychiatric disorders, including Parkinson’s disease, depression, and epilepsy. In this treatment, neurosurgeons implant electrodes inside the brain that can deliver a wide range of electrical stimulation patterns. However, the next problem is determining the optimal stimulation setting for a given patient. Given that even basic clinical stimulation devices can be configured to millions of different stimulation settings, finding the right one is challenging. This problem is exacerbated in epilepsy, where patients do not exhibit symptoms between seizures, making the process of evaluating a setting’s effectiveness even more difficult. At its core, this is an optimization problem for which many engineering solutions exist. We have developed a framework for designing optimization systems for neural modulation that can be applied to a broad spectrum of different neural modulation paradigms. In this proposal, we develop an optimization system for automatically and efficiently identifying the optimal stimulation setting to maximally suppress seizures in a rodent model of epilepsy. Our previous work has shown that a particular type of stimulation, asynchronous distributed stimulation, can reduce the frequency of seizures in the rat tetanus toxin model of temporal lobe epilepsy. However, only a limited set of stimulation patterns were evaluated. In Aim 1, we will build on this work to better characterize the differential effects of varying asynchronous distributed stimulation parameters on seizures. These experiments will serve two purposes. First, they will clarify how different subjects are affected by stimulation parameters and determine if, as in other neurological disorders treated by brain stimulation, the best stimulation setting will vary from subject to subject. The second purpose is to use the data collected to create a simulation platform for prototyping optimization systems. In Aim 2, we will use the simulation platform prototype and tune different optimization systems. After determining which optimization system performs best in our simulation platform, the optimization systems will be implemented for real-time in vivo optimization to learn the subject specific stimulation settings that best reduce seizure frequency.