# Virtual Resection to Treat Epilepsy

> **NIH NIH R56** · UNIVERSITY OF PENNSYLVANIA · 2021 · $551,628

## Abstract

More than 1/3 of the world’s 65 million people with epilepsy (~3.3 million in the U.S.) have seizures that cannot
be controlled by medications. Surgery and implanted devices are options for many, but their success depends
upon manually mapping epileptic networks, which is only possible for some patients, and poorly standardized.
When surgical targets are identified, there is currently no rigorous way to select the best surgical approach.
The overall aim of this proposal is to develop rigorous, standardized, quantitative methods to: (1) map
epileptic networks from imaging and Stereo EEG (SEEG), (2) pick the best region for resection, ablation or
neuromodulation for individual patients from their data and clinical hypotheses, and (3) to determine when focal
intervention is unlikely to succeed. These methods would have tremendous positive impact on clinical care.
Over the first four years of this grant we have made substantial progress towards these goals. Our
deliverables include: (1) robust measures derived from intracranial EEG (IEEG) that predict outcome from
epilepsy surgery; (2) personalized methods that localize epileptic networks and predict the impact of different
interventions on seizure control; (3) tools that predict the path of seizure spread from combined MRI and iEEG;
and (4) a track record of openly sharing our methods, data, results and code on our platform http: //ieeg.org.
In the next phase, we propose innovative solutions to 3 fundamental challenges in epilepsy surgery required
to translate our work into practice: (1) Guiding SEEG: We must adapt our methods to the sparser sampling
and different philosophy of stereo EEG, which maps a network of connected brain regions and tests clinical
hypotheses about where seizures initiate and propagate; (2) Assessing sampling bias and missing
information: We will develop methods to determine if electrodes sample all key regions of the epileptic
network, to ensure we do not falsely localize due to missing information; (3) Validating in a larger population
across centers: In parallel to refining the above methods, we will validate and optimize our analyses in a large
number of patients to ready this work for a prospective clinical trial. In a novel model, we have engaged a
group of major surgical epilepsy centers to openly collaborate, standardize methods, aggregate data, and
share all algorithms, computer code, data and results on http: //ieeg.org. Our central hypothesis is that
standardized, quantitative methods to guide epilepsy surgery can improve patient outcomes, lower morbidity,
reduce cost and enable uniform, higher quality care across centers.
This work is significant because it merges state of the art network neuroscience, engineering, neurology and
neurosurgery to make practical tools to improve and standardize patient care. This project leverages a thriving
collaboration between experts in neurology, computational neuroscience, neurosurgery, neuroimaging and
bioengineering at the Un...

## Key facts

- **NIH application ID:** 10355919
- **Project number:** 2R56NS099348-05A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Danielle Smith Bassett
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $551,628
- **Award type:** 2
- **Project period:** 2016-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10355919

## Citation

> US National Institutes of Health, RePORTER application 10355919, Virtual Resection to Treat Epilepsy (2R56NS099348-05A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10355919. Licensed CC0.

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