GABA‐edited MEGA‐PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling?

Purpose: The J‐difference edited γ‐aminobutyric acid (GABA) signal is contaminated by other co‐edited signals—the largest of which originates from co‐edited macromolecules (MMs)—and is consequently often reported as "GABA+." MM signals are broader and less well‐characterized than the metabolites, and are commonly approximated using a Gaussian model parameterization. Experimentally measured MM signals are a consensus‐recommended alternative to parameterized modeling; however, they are relatively under‐studied in the context of edited MRS. Methods: To address this limitation in the literature, we have acquired GABA‐edited MEGA‐PRESS data with pre‐inversion to null metabolite signals in 13 healthy controls. An experimental MM basis function was derived from the mean across subjects. We further derived a new parameterization of the MM signals from the experimental data, using multiple Gaussians to accurately represent their observed asymmetry. The previous single‐Gaussian parameterization, mean experimental MM spectrum and new multi‐Gaussian parameterization were compared in a three‐way analysis of a public MEGA‐PRESS dataset of 61 healthy participants. Results: Both the experimental MMs and the multi‐Gaussian parameterization exhibited reduced fit residuals compared to the single‐Gaussian approach (p = 0.034 and p = 0.031, respectively), suggesting they better represent the underlying data than the single‐Gaussian parameterization. Furthermore, both experimentally derived models estimated larger MM fractional contribution to the GABA+ signal for the experimental MMs (58%) and multi‐Gaussian parameterization (58%), compared to the single‐Gaussian approach (50%). Conclusions: Our results indicate that single‐Gaussian parameterization of edited MM signals is insufficient and that both experimentally derived GABA+ spectra and their parameterized replicas improve the modeling of GABA+ spectra. [ABSTRACT FROM AUTHOR]