General Linear Modeling For Magnetic Resonance Spectroscopy

NIH RePORTER · NIH · R21 · $245,254 · view on reporter.nih.gov ↗

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
10818478
Project number
5R21EB033516-03
Recipient
JOHNS HOPKINS UNIVERSITY
Principal Investigator
Georg Oeltzschner
Activity code
R21
Funding institute
NIH
Fiscal year
2024
Award amount
$245,254
Award type
5
Project period
2022-07-05 → 2026-03-31