A Comprehensive Guide to MEGA-PRESS for GABA Measurement

Author:

Peek ALORCID,Rebbeck TJORCID,Leaver AMORCID,Puts NAORCID,Foster SLORCID,Refshauge KMORCID,Oeltzschner GORCID,

Abstract

ABSTRACTBackgroundThe aim of this guideline is to provide a series of evidence-based recommendations that allow those new to the field of MEGA-PRESS to produce high-quality data for the measurement of GABA levels using edited magnetic resonance spectroscopy with the MEGA-PRESS sequence at 3T. GABA is the main inhibitory neurotransmitter of the central nervous system and has been increasingly studied due to its relevance in many clinical disorders of the central nervous system. MEGA-PRESS is the most widely used method for quantification of GABA at 3T, but is technically challenging and operates at a low signal-to-noise ratio. Therefore, the acquisition of high-quality MRS data relies on avoiding numerous pitfalls and observing important caveats.MethodsThe guideline was developed by a working party that consisted of experts in MRS and experts in guideline development and implementation, together with key stakeholders. Strictly following a translational framework, we first identified evidence using a systematically conducted scoping literature review, then synthesised and graded the quality of evidence that formed recommendations. These recommendations were then sent to a panel of 21 world leaders in MRS for feedback and approval using a modified-Delphi process across two rounds.ResultsThe final guideline consists of 23 recommendations across six domains essential for GABA MRS acquisition (Parameters, Practicalities, Data acquisition, Confounders, Quality/reporting, Post-processing). Overall, 78% of recommendations were formed from high-quality evidence, and 91% received agreement from over 80% of the expert panel.ConclusionThese 23 expert-reviewed recommendations and accompanying extended documentation form a readily usable guideline to allow those new to the field of MEGA-PRESS to design appropriate MEGA-PRESS study protocols and generate high-quality data.

Publisher

Cold Spring Harbor Laboratory

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