# Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture

> **NIH NIH R01** · MEDICAL UNIVERSITY OF SOUTH CAROLINA · 2020 · $646,134

## Abstract

Abstract
Temporal lobe epilepsy (TLE) is one of the most common forms of pharmacologically resistant epilepsy. The
resection or ablation of medial temporal structures can be curative for many patients. Unfortunately,
approximately one third of patients who undergo TLE surgery continue to have disabling seizures post-
procedurally. The reasons for suboptimal outcomes are not well understood and therefore constitute a very
important knowledge gap in epilepsy care. A better understanding of this difference in surgical response
phenotype could be used to improve surgical planning, treatment, outcome prediction and counseling.
Promising preliminary studies suggest that TLE surgical outcomes can be inferred by neuroimaging
computational tools assessing the cumulative degree of abnormalities in the topological organization of
structural networks involving limbic and extra-limbic regions. Nonetheless, network abnormalities are not
routinely or systematically used and quantified in the pre-surgical evaluation of epilepsies, and their
assessment requires refinement and further validation. The purpose of this proposal is to perform a
prospective study to test the hypothesis that the degree of limbic and extra-limbic network abnormalities in
TLE, systematically assessed using a connectome approach based on optimized diffusion MRI (dMRI), can be
used to predict and better understand epilepsy surgery outcomes. This hypothesis builds on the well-defined
basic science and neurobiological premises that epilepsy is associated with pathological alterations in
networks that are related to seizure onset and seizure propagation. Importantly, network abnormalities are not
visible on routine MRI, but their detection using connectomes constitutes a modern approach to quantifying the
location and magnitude of “lesional epilepsy,” where broad computational network abnormalities imply worse
outcomes. We will prospectively gather clinical and imaging data at six epilepsy centers using the NIH epilepsy
common data elements. This project will be fundamentally based on standard of care data, thus minimizing the
burden of extra data collection and ensuring feasibility. Furthermore, this project will be embedded in the
ENIGMA-Epilepsy framework, which is a collaborative platform for clinical and neuromaging multi-center
research. Specific Aim 1 will define the accuracy, reproducibility, and predictive values of the pre-surgical dMRI
tractography connectome model towards surgical results in TLE. We will perform hypothesis-driven tests of
specific limbic and extra-limbic networks in relationship with clinical data and surgical outcomes. Specific Aim 2
will test if the neuroimaging-clinical outcome model can be further improved with advanced diffusion methods
(multi-shell diffusional kurtosis imaging), resting state functional MRI networks, or a multimodal approach. We
believe that this research will have an important impact on our understanding of the mechanisms related to
TLE trea...

## Key facts

- **NIH application ID:** 9994414
- **Project number:** 5R01NS110347-02
- **Recipient organization:** MEDICAL UNIVERSITY OF SOUTH CAROLINA
- **Principal Investigator:** Leonardo F Bonilha
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $646,134
- **Award type:** 5
- **Project period:** 2019-08-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9994414

## Citation

> US National Institutes of Health, RePORTER application 9994414, Predicting Epilepsy Surgery Outcomes Using Neural Network Architecture (5R01NS110347-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9994414. Licensed CC0.

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