# Data-driven optimization for DBS programming in temporal lobe epilepsy

> **NIH NIH R21** · EMORY UNIVERSITY · 2022 · $380,724

## Abstract

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.

## Key facts

- **NIH application ID:** 10574839
- **Project number:** 1R21NS130378-01
- **Recipient organization:** EMORY UNIVERSITY
- **Principal Investigator:** ROBERT E GROSS
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $380,724
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10574839

## Citation

> US National Institutes of Health, RePORTER application 10574839, Data-driven optimization for DBS programming in temporal lobe epilepsy (1R21NS130378-01). Retrieved via AI Analytics 2026-09-03 from https://api.ai-analytics.org/grant/nih/10574839. Licensed CC0.

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