# Imaging Epilepsy Sources with Biophysically Constrained Deep Neural Networks

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2024 · $585,640

## Abstract

Project Summary
 The goal of this project is to develop and validate a novel electrophysiological source imaging (ESI) approach
based on biophysically constrained deep neural networks (BioDNN), to significantly improve surgical planning in
drug resistant focal epilepsy patients. Epilepsy affects about 70 million people worldwide. For approximately 33%
of the 3.4 million Americans with epilepsy, seizures are not controlled by medications alone. Epilepsy surgery is
the most viable option for curing drug resistant focal epilepsy, only if seizure sources can be accurately localized
and safely removed. There is a clinical need to innovate technological tools for better surgical planning of focal
epilepsy. We propose in this project a novel ESI technology based on biophysically constrained deep neural
network (BioDNN) to provide accurate, robust, and objective spatio-temporal estimates of the underlying
epileptogenic zone (EZ). Of innovation is that the trained neural network, is capable of imaging brain sources
without the need to tune the model’s hyper-parameters by an operator for every new instance of data, thus
making the technique objective and easy-to-use in clinical settings. Our specific aims are: Aim 1. Establishing
and Validating the BioDNN for Imaging Epileptogenic Tissue from EEG Inter-ictal Epileptiform Discharges (IEDs)
of Focal Epilepsy Patients. We will establish, optimize and validate the proposed BioDNN for imaging EZ from
IEDs in EEG in 200 focal drug resistant epilepsy (DRE) patients, in comparison to clinical “ground truth". Aim 2.
Developing and Validating the BioDNN Model for Imaging Epileptogenic Tissue from MEG Inter-ictal Epileptiform
Discharges of Focal Epilepsy Patients. We will develop and optimize the BioDNN model for imaging EZ from
MEG IEDs and validate the MEG-BioDNN model and compare with the EEG-BioDNN model in 80 focal DRE
patients in comparison to clinical “ground truth. Aim 3. Developing and Validating the BioDNN Model for Imaging
Epileptogenic Tissue from Ictal EEG of Focal Epilepsy Patients. We will develop the BioDNN for imaging the
SOZ from scalp ictal EEG and validate it from high density ictal EEG recordings in 120 focal DRE patients, in
comparison to clinical “ground truth”. The successful completion of the proposed research will establish a novel
machine learning technology to non-invasively localize and image underlying epileptogenic tissue from interictal
and ictal electrophysiological biomarkers. The establishment of such a novel technology promises to significantly
improve the precision of intracranial EEG electrodes implantation and aid surgical planning, leading to significant
improvement in surgical outcomes, and benefiting numerous drug resistant epilepsy patients.
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## Key facts

- **NIH application ID:** 10863842
- **Project number:** 5R01NS127849-02
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** BIN HE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $585,640
- **Award type:** 5
- **Project period:** 2023-06-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10863842

## Citation

> US National Institutes of Health, RePORTER application 10863842, Imaging Epilepsy Sources with Biophysically Constrained Deep Neural Networks (5R01NS127849-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10863842. Licensed CC0.

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