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Comparison of methods for spectral alignment and signal modelling of GABA-edited MR spectroscopy data.

Accepted version
Peer-reviewed

Type

Article

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Authors

Mikkelsen, Mark 
Edden, Richard AE 

Abstract

Many methods exist for aligning and quantifying magnetic resonance spectroscopy (MRS) data to measure in vivo γ-aminobutyric acid (GABA). Research comparing the performance of these methods is scarce partly due to the lack of ground-truth measurements. The concentration of GABA is approximately two times higher in grey matter than in white matter. Here we use the proportion of grey matter within the MRS voxel as a proxy for ground-truth GABA concentration to compare the performance of four spectral alignment methods (i.e., retrospective frequency and phase drift correction) and six GABA signal modelling methods. We analyse a diverse dataset of 432 MEGA-PRESS scans targeting multiple brain regions and find that alignment to the creatine (Cr) signal produces GABA+ estimates that account for approximately twice as much of the variance in grey matter as the next best performing alignment method. Further, Cr alignment was the most robust, producing the fewest outliers. By contrast, all signal modelling methods, except for the single-Lorentzian model, performed similarly well. Our results suggest that variability in performance is primarily caused by differences in the zero-order phase estimated by each alignment method, rather than frequency, resulting from first-order phase offsets within subspectra. These results provide support for Cr alignment as the optimal method of processing MEGA-PRESS to quantify GABA. However, more broadly, they demonstrate a method of benchmarking quantification of in vivo metabolite concentration from other MRS sequences.

Description

Keywords

Data Analysis, Databases, Factual, Gray Matter, Humans, Magnetic Resonance Spectroscopy, Models, Neurological, gamma-Aminobutyric Acid

Journal Title

Neuroimage

Conference Name

Journal ISSN

1053-8119
1095-9572

Volume Title

232

Publisher

Elsevier BV

Rights

All rights reserved
Sponsorship
Leverhulme Trust (ECF-2017-573)
Isaac Newton Trust (17.08(o))
The work was supported by the Leverhulme Trust (ECF-2017-573), the Isaac Newton Trust (17.08(o)). MM receives salary support from NIH grant K99 EB028828. RAEE receives salary support from NIH P41 EB015909, R01 EB016089 and R01 EB023963.