Feasibility and implications of using subject‐specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data - Zöllner, Helge J.; Davies‐Jenkins, Christopher W.; Murali‐Manohar, Saipavitra; Gong, Tao; Hui, Steve C. N.; Song, Yulu; Chen, Weibo; Wang, Guangbin; Edden, Richard A. E.; Oeltzschner, Georg (NMR in biomedicine, 2023)
Expert consensus recommends linear‐combination modeling (LCM) of 1H MR spectra with sequence‐specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite‐nulled spectra averaged across a small cohort. The use of subject‐specific instead of cohort‐averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non‐expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort‐mean measured; subject‐specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite–age associations. 100 conventional (TE = 30 ms) and metabolite‐nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20–70 years of age), were analyzed in Osprey. Short‐TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue‐ and relaxation‐corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite–age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate‐to‐strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort‐mean measured MM. Metabolite–age associations were consistently found for two major singlet signals (total creatine (tCr])and total choline (tCho)) for all MM strategies; however, findings for metabolites that are less distinguishable from the background signals associations depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite–age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal‐to‐noise ratio MM basis function (cohort‐mean) in the short‐TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject‐specific MM information. Integration of multiple LCM analyses into a single statistical model potentially allows to identify the robustness in the detection of underlying effects (e.g., metabolite vs. age), reduces algorithm‐based bias, and estimates algorithm‐related variance. Linear‐combination modeling results of short‐TE PRESS from a 100‐subject healthy aging cohort using different strategies to model macromolecular (MM) signals (parameterized vs. measured) analyzed with Osprey. The choice of MM strategy accounts for a majority of the observed variance in the metabolite estimates for tNAA and GSH.