# Patient-specific modeling and network perturbation to enhance the predictability of direct cortical stimulation for epilepsy

> **NIH NIH K23** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2021 · $192,275

## Abstract

PROJECT SUMMARY/ABSTRACT
 Direct cortical stimulation of the brain is used to treat epilepsy and map brain function during surgery.
Yet few patients are free of seizures with this treatment, and morbidity occurs despite ostensibly adequate
mapping. When cortical stimulation is used, it is assumed that the area nearby the electrode, within a few
centimeters, is most affected. But our prior work shows that stimulation evokes widespread effects in distant
regions of the brain. Understanding these network effects will be key in improving our use of brain stimulation.
 Using patients with electrodes implanted in the brain for epilepsy treatment, I will investigate cortical
stimulation by 1) using resting-state functional connectivity to predict evoked potential characteristics, 2) using
detailed computer models of patient brains to predict the responses of stimulation, and 3) correlating the
networks activated by stimulation with patient outcomes following NeuroPace Responsive Neurostimulator
placement.
 I am a practicing neurosurgeon and neuroscientist with a career devoted to understanding electrical
stimulation of the brain. I was trained as a computer scientist and have relied heavily on this skillset during my
PhD and post-doctoral training. For my PhD, I designed the hardware and software for a closed-loop
neurostimulator, and applied this system to epilepsy research. During my post-doc and residency in
neurosurgery, I studied the basis of electrical stimulation mapping using high-density electrocorticography.
While in industry, I worked as lead software engineer for a company designing closed-loop
stimulation/recording technology for multielectrode arrays. Now, as a functional neurosurgeon, I use
multielectrode stimulation and recording daily in my patients.
 Network imaging and computer modeling of stimulation will provide new ways to understand and
restore brain function. Such modeling goes beyond the empirical data that most researchers collect, and that
most of my prior research has focused on. To develop these models, I will work with an expert in
neuromodulation modeling, Dr. Christopher Butson, at the University of Utah. I will acquire these skills through
hands-on training, didactic coursework, and intensive mentoring. At the end of my training, my hope is to
create an independent research program to further link brain stimulation with an understanding of brain
networks, and use these insights to improve the safety and efficacy of the direct cortical stimulation I use in my
patients.

## Key facts

- **NIH application ID:** 10247063
- **Project number:** 5K23NS114178-03
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** JOHN D ROLSTON
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $192,275
- **Award type:** 5
- **Project period:** 2019-09-30 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10247063

## Citation

> US National Institutes of Health, RePORTER application 10247063, Patient-specific modeling and network perturbation to enhance the predictability of direct cortical stimulation for epilepsy (5K23NS114178-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10247063. Licensed CC0.

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