AUTHOR=Harris Ashley D. , Amiri Houshang , Bento Mariana , Cohen Ronald , Ching Christopher R. K. , Cudalbu Christina , Dennis Emily L. , Doose Arne , Ehrlich Stefan , Kirov Ivan I. , Mekle Ralf , Oeltzschner Georg , Porges Eric , Souza Roberto , Tam Friederike I. , Taylor Brian , Thompson Paul M. , Quidé Yann , Wilde Elisabeth A. , Williamson John , Lin Alexander P. , Bartnik-Olson Brenda TITLE=Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations JOURNAL=Frontiers in Neurology VOLUME=13 YEAR=2023 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1045678 DOI=10.3389/fneur.2022.1045678 ISSN=1664-2295 ABSTRACT=

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.