# Computational Tools for Improving Stereo-EEG Implantation and Resection Surgery

> **NIH NIH F31** · DUKE UNIVERSITY · 2022 · $38,880

## Abstract

More than 30% of the 3.4 million Americans with epilepsy do not benefit from drug therapies. Surgical
removal of the brain tissue where seizures originate (the epileptogenic zone) is an alternative that can eliminate
seizures in these patients with 1 year seizure freedom rates of 61%. However, successful resection requires
localization of the epileptogenic zone, which is challenging because the epileptogenic zone is indistinguishable
from healthy tissue on clinical images (e.g. MRI). Stereo-EEG (sEEG) is a minimally invasive recording technique
where 100-200 electrode contacts are inserted through small transcranial burr holes into widespread regions of
the brain hundreds of microns from active neurons, resulting in substantially higher signal fidelity than
conventional EEG. Despite the enormous potential of sEEG, outcomes of seizure resection surgeries have not
improved substantially over the past 20 years. This is due, in part, to an incomplete understanding of an optimal
sEEG implantation strategy and a lack of algorithms to exploit the spatiotemporal resolution of sEEG for
epileptogenic zone localization. The goal of this proposal is to develop and deploy clinically useful computational
tools to improve epileptogenic zone localization using sEEG.
 The first aim is to develop a set of computational tools that visualize the brain tissue that can be recorded
by a set of sEEG electrodes and an optimization algorithm that optimizes the electrode trajectories to minimize
the number of implanted electrodes while maximizing cortical coverage. I will develop realistic computational
head models using patient specific head finite element modeling. I will couple the head models to an established
estimate of the neural source strength to estimate and visualize the tissue that can be recorded by a set of sEEG
electrodes. I will then couple the head models and source strength estimate to a Monte Carlo Tree Search
algorithm to determine the minimum set of electrode trajectories necessary to map a region of interest. The
outcome will be a pair of computational tools that visualize the recordable brain tissue and optimize electrode
implantation trajectories.
 The second aim is to develop a spatiotemporal source reconstruction algorithm to map neural recordings
into the brain. I will develop a Bayesian source reconstruction algorithm that estimates the time courses and
spatial extent of neural activity. I will use the source reconstruction algorithm on recordings of epileptiform activity
to delineate zone of the brain that are associated with the epileptogenic zone. The outcome will be a Bayesian
source reconstruction tool that that epileptologists can use aid surgical resection decision making.
 Successful completion of this work is expected to improve epileptogenic zone localization and result in
higher seizure freedom rates in patients with epilepsy.

## Key facts

- **NIH application ID:** 10462231
- **Project number:** 1F31NS124094-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Brandon Joon-Sun Thio
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $38,880
- **Award type:** 1
- **Project period:** 2022-08-15 → 2024-08-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10462231

## Citation

> US National Institutes of Health, RePORTER application 10462231, Computational Tools for Improving Stereo-EEG Implantation and Resection Surgery (1F31NS124094-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10462231. Licensed CC0.

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