# Simultaneous Hadamard Editing of GABA and Glutathione

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2022 · $576,863

## Abstract

Summary
The neurotransmitter GABA and the redox compound glutathione can be measured in the human brain non-
invasively using Hadamard-encoded edited MRS. Such measurements currently suffer from three major
limitations: the confound of transverse relaxation (since edited MRS requires long echo times); incomplete
spectral resolution between metabolites and the macromolecular background; and inadequate/inflexible
modeling and quantification software. The overall goal of this R01 renewal period is to develop Hadamard-
encoded edited MRS of GABA and Glutathione into a robust and reproducible tool for neuroscience.
Building on the successful development of HERMES in the first funded period we will develop interleaved
multi-spectrum acquisitions to improve robustness to changes in transverse relaxation and the macromolecule
baseline and develop multi-spectrum linear combination methods within our software Osprey, including
building a demographic model of the macromolecular background spectrum. This project will culminate in a
patient-based validation study in subjects with Alzheimer’s Disease. The resulting data acquisition and analysis
tools will be made available for dissemination to the clinical neuroscience and neuroimaging communities.

## Key facts

- **NIH application ID:** 10495231
- **Project number:** 5R01EB023963-06
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Richard Anthony Edward Edden
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $576,863
- **Award type:** 5
- **Project period:** 2017-05-05 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10495231

## Citation

> US National Institutes of Health, RePORTER application 10495231, Simultaneous Hadamard Editing of GABA and Glutathione (5R01EB023963-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10495231. Licensed CC0.

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