TY - JOUR T1 - Comparison of algorithms for linear-combination modelling of short-echo-time magnetic resonance spectra JF - bioRxiv DO - 10.1101/2020.06.05.136796 SP - 2020.06.05.136796 AU - Helge J. Zöllner AU - Michal Považan AU - Steve C. N. Hui AU - Sofie Tapper AU - Richard A. E. Edden AU - Georg Oeltzschner Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/06/07/2020.06.05.136796.abstract N2 - Short-TE proton magnetic resonance spectroscopy is commonly used to study metabolism in the human brain. Spectral modelling is a crucial analysis step, yielding quantitative estimates of metabolite levels. Commonly used quantification methods model the data as a linear combination of metabolite basis spectra, maximizing the use of prior knowledge to constrain the model solution. Various linear-combination modelling (LCM) algorithms have been integrated into widely used commercial and open-source analysis programs.This large-scale, in-vivo, multi-vendor study compares the levels of four major metabolite complexes estimated by three LCM algorithms (Osprey, LCModel, and Tarquin). 277 short-TE spectra from a recent multi-site study were pre-processed with the Osprey software. The resulting spectra were modelled with Osprey, Tarquin and LCModel, using the same vendor-specific basis sets. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI), and glutamate+glutamine (Glx) were quantified with respect to total creatine (tCr).Mean spectra and models showed high agreement between all vendors and LCM algorithms. In contrast, the algorithms differed notably in their baseline estimates and mI models. Group means and CVs of the metabolite estimates agreed well for tNAA and tCho across vendors and algorithms. For mI and Glx, group means metabolite estimates and CVs agreed less well between algorithms, with mI systematically estimated lower by Tarquin. Across all datasets, the metabolite-mean was 10.4% for Osprey, 12.6% for LCModel, and 14.0% for Tarquin. The grand mean correlation coefficient for all pairs of LCM algorithms across all datasets and metabolites was , indicating generally moderate agreement of individual metabolite estimates between algorithms. Stronger correlations were found for tNAA and tCho than for Glx and mI. Correlations between pairs of algorithms were comparably moderate (Tarquin-vs-LCModel: ; Osprey-vs-LCModel: ; Osprey-vs-Tarquin: ). There was a significant association between local baseline power and metabolite estimates (grand mean ; up to 0.62 for LCModel analysis of Glx in Siemens datasets). Metabolite estimates with stronger associations to the local baseline power (mI and Glx) showed higher variance, suggesting that baseline estimation has a stronger influence on those metabolites than on tNAA and tCho.While estimates of major metabolite complexes broadly agree between linear-combination modelling algorithms at group level, correlations between algorithms are only weak to moderate, despite standardized pre-processing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one algorithm and much smaller sample sizes.The study is the first benchmark comparison of different LCM algorithms using large datasets. Finally, it provides a standardized framework to interrogate factors with critical impact on LCM outcomes.HighlightsMeans and CVs for tNAA and tCho are highly consistent across vendors and algorithms.Means and CVs for mI and Glx are less consistent across vendors and algorithms.Agreement between metabolite estimates from different algorithms is moderate at best.Baseline estimation contributes significantly to measurement variance.Competing Interest StatementThe authors have declared no competing interest. ER -