# Biomarkers to Predict Outcome from Responsive Brain Stimulation for Epilepsy

> **NIH NIH R61** · UNIVERSITY OF PENNSYLVANIA · 2024 · $1,191,184

## Abstract

Abstract
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 The current FDA-approved responsive neurostimulation (RNS) device offers a promising alternative to
surgery for more than 600,000 Americans with intractable epilepsy who are not candidates for resective surgery.
Unfortunately, there are no validated biomarkers to predict seizure outcomes before these devices are placed,
and approximately 1/3 of patients do not benefit from RNS long-term. There is a critical need to develop
biomarkers based upon clinical and electrophysiological data to determine the most effective therapy for patients
with medication-resistant seizures, and to bring quantitative rigor to clinical decision making. The long-term goal
of this proposal is to discover and validate a predictive biomarker signature for RNS response that can be used
in epilepsy surgery decision making and broadly adopted. To achieve this goal, our overall objective is to
develop this prognostic biomarker signature using machine learning applied to a carefully selected set of features
and models calculated from intracranial EEG (IEEG) obtained during presurgical evaluation that incorporates
qualitative clinical features. We will collaborate across centers and with industry partners via a novel federated
approach, whereby each clinical site will post data in a common format to their own, private, cloud-based data
store, which will be accessible to analysis pipelines run centrally from our cloud-based platform. Our central
hypothesis is that biomarker signatures derived from multimodal data collected during evaluation prior to device
implant can be used to predict patient response to RNS therapy. Our preliminary data, analyzing 10 RNS patients
each from UCSF, NYU and UPenn, demonstrates our ability to perform the proposed research.
 In the R61 Phase, we will test this hypothesis retrospectively in 125 patients who underwent IEEG prior
to RNS device placement at the UPenn, UCSF and NYU epilepsy centers. Our specific aims for this phase are:
1) To build a federated processing pipeline for biomarker discovery using presurgical evaluation neuroimaging,
IEEG and clinical metadata, 2) To identify a predictive biomarker signature from this data. Our federated analysis
framework will enable us to: (a) accelerate biomarker discovery across multiple sites and industry partners, (b)
satisfy patient and industry limitations on sharing proprietary data, (c) provide a practical framework for rapid
adoption across clinical centers worldwide. In the R33 phase, the biomarker signature will be validated in 100
additional patients followed longitudinally at 9 clinical sites. The proposed research is innovative because it
represents a substantive departure from the status quo by rigorously analyzing multimodal patient data to predict
response to RNS and guide decisions on device implantation. The proposed research is significant because it
has the potential to dramatically improv...

## Key facts

- **NIH application ID:** 10817807
- **Project number:** 5R61NS125568-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Kathryn Adamiak Davis
- **Activity code:** R61 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $1,191,184
- **Award type:** 5
- **Project period:** 2023-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10817807

## Citation

> US National Institutes of Health, RePORTER application 10817807, Biomarkers to Predict Outcome from Responsive Brain Stimulation for Epilepsy (5R61NS125568-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10817807. Licensed CC0.

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