# Model Development for Prediction of Surgical Outcome in Temporal Lobe Epilepsy Patients: Incorporation of the Correlation between Post-Surgical Reorganization Phenotypes and Pre-Surgical Data

> **NIH NIH R01** · THOMAS JEFFERSON UNIVERSITY · 2021 · $442,761

## Abstract

Project Summary
For epileptic patients who undergo brain resection or ablation interventions, it is the postoperative brain that will
dictate seizure status, whether controlled or relapsed. Yet, it is data from the preoperative brain that drives the
postoperative prediction process – a critical process for both patient and doctor, and one that is only clinically
meaningful when seizure outcomes are predicted presurgically to optimize surgical-decision making.
Accordingly, we propose to develop a multi-step model that will establish more accurate predictors of post-
surgical seizure outcome in temporal lobe epilepsy (TLE) emphasizing post-surgical status, for it is the areas of
the brain spared during surgery that form the neural substrates generating postoperative seizures. A second
perspective motivating our project is the need to identify those changes in functional and structural brain network
organization that support adaptive versus maladaptive seizure outcomes following brain surgery. These are the
network changes (e.g., the new seizure generators) that dispose and place a potential surgical candidate on a
specific outcome trajectory. Therefore, identifying the phenotypes of brain reorganization and change, and
incorporating their status into presurgical predictive models of outcome will likely prove crucial to enhancing our
ability to predict postoperative neuroplastic responses. While existing outcome prediction models in TLE have
focused on clinical variables (e.g., lesional status), we choose instead to focus on structural and functional
measures of network reorganization (communication dynamics, regional interactions, structural control). This
stems from our belief that capturing network changes throughout the whole postsurgical brain offers a better
practical method for identifying and predicting the latent seizure foci (epileptogenesis) that will emerge after
surgery. Through machine learning techniques we will deliver an algorithm to be used with new, potential surgical
patients, an algorithm that utilizes solely presurgical data, but incorporates our innovative prediction about
postsurgical brain organization. Accordingly, our approach provides both a methodologic and conceptual
(reorganization phenotypes) advance. The scientific premise leading to our hypotheses is that the failure in the
literature to account for the impact of unresected/ablated brain regions, and the brain reorganizations these
areas compel, has seriously impeded the predictive power of previous outcome models.

## Key facts

- **NIH application ID:** 10134456
- **Project number:** 5R01NS112816-03
- **Recipient organization:** THOMAS JEFFERSON UNIVERSITY
- **Principal Investigator:** Joseph I. Tracy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $442,761
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10134456

## Citation

> US National Institutes of Health, RePORTER application 10134456, Model Development for Prediction of Surgical Outcome in Temporal Lobe Epilepsy Patients: Incorporation of the Correlation between Post-Surgical Reorganization Phenotypes and Pre-Surgical Data (5R01NS112816-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10134456. Licensed CC0.

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