# Dense Array Image compatible EEG for enhanced neonatal care

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $722,104

## Abstract

Neonatal encephalopathies are central nervous system disorders that are often accompanied by seizures.
Seizures are one of the distinctive clinical manifestations of epilepsy, hypoxia, abnormal delivery, sleep
deprivation and stress. Magnetic Resonance Imaging (MRI) plays a crucial role in the diagnosis and
understanding of neonatal seizures. However, neonatal MRI evaluation is incomplete in assessing the entire
neonate’s neurologic status, especially in regards to cortical functioning. In such circumstances, continuous
video EEG can be useful as it provides important information about changes in frequency, synchrony,
distribution and other characteristics of cerebral cortical activity. EEG is also a key modality in the
understanding of developmental disabilities from early childhood. State-of-the-art EEG or dense array EEG
(HD-EEG – 64 or more channels) has enabled the realization of EEG’s potential as a neuroimaging tool
through source localization of normal and pathological brain activity and network dynamics. However, neither
conventional EEG nor HD-EEG are imaging (MRI or CT) compatible; hence, EEG electrodes are typically
removed prior to any imaging study, with negative impacts on patient management because of extra delays
and additional costs.
The goal of this R01 project is to demonstrate the feasibility and safety of developing an imaging-compatible
HD-EEG net for cross-modal neonatal neural monitoring with artifact-free image quality. The proposed
neonatal HD-EEG net or “NeoNet” will be designed by leveraging expertise in innovative 3D printing
technology and thin film deposition at the A. A. Martinos Center, Massachusetts General Hospital. Rigorous
safety assessment of specific absorption deposition rate and temperature will be performed using Finite
Elements Method (FEM) simulations employing anatomically accurate male and female 2-week-old neonatal
whole body models, which will be released to the public. Simulations will be validated by actual temperature
measurements of induced RF heating using neonate phantoms wearing the NeoNet and compared against the
gold standard of the phantom alone and against a commercial MR-compatible net built with traditional copper
wire technology. Similarly, MRI data quality will be compared to data from the phantom-alone gold standard,
and against data from the commercial HD-EEG. CT data integrity will also be evaluated.
The proposed NeoNet will enable inexpensive, noninvasive HD-EEG and overcome current cross-modal safety
and artifact issues that have so far severely limited the effectiveness of simultaneous HD-EEG/MRI allowing
researchers and clinicians to benefit from the high spatial resolution of MRI and the high temporal resolution of
HD-EEG. Furthermore, the technology will be light weight and small in size, taking advantage of advanced
manufacturing technologies. The novel NeoNet will allow the study of brain function in healthy neonates in
natural settings, as well as the understanding of dif...

## Key facts

- **NIH application ID:** 9961576
- **Project number:** 5R01EB024343-04
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** GIORGIO BONMASSAR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $722,104
- **Award type:** 5
- **Project period:** 2017-09-15 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9961576

## Citation

> US National Institutes of Health, RePORTER application 9961576, Dense Array Image compatible EEG for enhanced neonatal care (5R01EB024343-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9961576. Licensed CC0.

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