# Electrophysiological Source Imaging of Partial Epilepsy

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2022 · $554,563

## Abstract

Project Summary
The long-term goal of the research program is to develop and establish a novel electrophysiological source
imaging technology to localize and image epileptogenic brain tissues aiding pre-surgical planning in focal (partial)
epilepsy. Epilepsy is a common neurological disease impacting more than 3.4 million patients in the US and 70
million globally. The standard clinical routine heavily relies on using intracranial EEG (iEEG) implanted into the
brain to determine seizure onset zone, to aid in the localization of epileptogenic zone (EZ), despite the limited
coverage of iEEG electrodes and invasive nature of the multiple-day procedure. There is a clinical need to
develop a noninvasive neuroimaging approach based on electrophysiological recordings that can reliably image
and delineate the EZ from relevant epilepsy biomarkers. In this research project, we propose to establish a novel
unsupervised machine learning framework that will be able to process unmarked, continuous, and long-term
EEG recordings of focal epilepsy patients to detect high frequency oscillations (HFOs), automatically identify the
pathological HFOs (pHFOs, i.e., HFOs riding on spikes), and localize and image epileptogenic brain activity from
the identified pHFOs. The proposed techniques will be rigorously validated in over 120 focal epilepsy patients
against clinical findings from iEEG recordings and surgical resection outcomes. Our specific aims are: Aim 1.
Development and evaluation of a novel unsupervised machine learning technique to identify pathological HFOs
from continuous EEG recordings. This aim will establish a novel unsupervised machine learning approach for
automatically identifying pHFOs originated from epileptogenic activity, by incorporating data-driven feature
learning methods. Aim 2. Development and validation of a novel brain tensor decomposition imaging approach
for HFO source imaging. We will develop a novel source imaging framework imaging epileptic sources in
temporal, spectral, and spatial domains. This aim will establish a novel data-driven source imaging approach for
accurate mapping and localization of EZ from scalp recorded pHFOs. Aim 3: Validation of scalp-detected pHFOs
by iEEG-identified pHFOs, with clinical findings of EZ in focal epilepsy patients. We will test the hypothesis that
the scalp-EEG identified pHFO events reflect the essential features of iEEG-identified pHFOs, and that both are
indicative of the EZ determined from clinical iEEG and confirmed by surgical resection outcome. This aim will
establish the relationship between scalp-identified pHFOs with iEEG-identified pHFOs and the underlying
epileptogenic networks. The successful completion of the proposed research will establish a novel unsupervised
machine learning technology to detect and identify pathological HFOs from continuous and long-term scalp EEG
recordings, and localize and image the underlying epileptogenic zone noninvasively and accurately. The
establishment of ...

## Key facts

- **NIH application ID:** 10439197
- **Project number:** 2R01NS096761-06
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** BIN HE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $554,563
- **Award type:** 2
- **Project period:** 2016-06-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10439197

## Citation

> US National Institutes of Health, RePORTER application 10439197, Electrophysiological Source Imaging of Partial Epilepsy (2R01NS096761-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10439197. Licensed CC0.

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