# Universal Edited MRS at 3T

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $347,810

## Abstract

PROJECT SUMMARY
Many biochemically interesting molecules, including the inhibitory and excitatory neurotransmitters
GABA and aspartate, the redox compounds glutathione and ascorbate, and the neuromodulator N-
acetylaspartylglutamate can be measured in the human brain non-invasively using edited 1H magnetic
resonance spectroscopy (MRS). Until very recently, edited MRS has been applied as a single-metbaolite
single-voxel method – i.e. requiring a 10-minute acquisition to measure GABA, and additional
experiments to measure other metabolites. This has substantially limited the breadth of application of
edited MRS in clinical studies, and particularly studies of, for example, interactions between different
systems. The overall goal of this grant is the development of a universal acquisition and processing
pipeline for measuring levels of all these editable metabolites in the human brain from a single
experiment.
We will develop a single sequence, implemented on all three major vendor platforms, for multiplexed
editing 3T, the Gannet Toolkit for quantitative data analysis, and demonstrate the cross-platform
equivalence of the new measurement. 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:** 10152587
- **Project number:** 5R01EB016089-08
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Richard Anthony Edward Edden
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $347,810
- **Award type:** 5
- **Project period:** 2013-08-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10152587

## Citation

> US National Institutes of Health, RePORTER application 10152587, Universal Edited MRS at 3T (5R01EB016089-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10152587. Licensed CC0.

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