# General Linear Modeling For Magnetic Resonance Spectroscopy

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2022 · $220,343

## Abstract

Project Summary
Advanced multi-spectrum magnetic resonance spectroscopy (MRS) methods allow the non-invasive
measurement of the concentration of neurochemicals, but also of other biophysical properties. The currently
available one-dimensional modeling tools cannot adequately model such data because they are incapable of
incorporating prior knowledge about the relationships between sub-spectra into a single multi-dimensional model.
Additional parameters that can be encoded in the acquisition, but not adequately accommodated within the
quantification model include metabolite relaxation times, metabolite diffusion tensors, and physiological
metabolic response to external stimulation.
This project addresses the gap in currently available modeling tools for MRS by introducing a generalized linear
combination modeling framework for MRS. This avoids the overfitting that arises from serial application of current
one-dimensional models, dramatically increasing model parsimony. All code developed will be made available
to the community open-source, and the modeling framework will be made available in the cloud via a web user
interface.

## Key facts

- **NIH application ID:** 10509724
- **Project number:** 1R21EB033516-01
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Georg Oeltzschner
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $220,343
- **Award type:** 1
- **Project period:** 2022-07-05 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10509724

## Citation

> US National Institutes of Health, RePORTER application 10509724, General Linear Modeling For Magnetic Resonance Spectroscopy (1R21EB033516-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10509724. Licensed CC0.

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