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Measured MM basis functions are usually derived from metabolite\u2010nulled spectra averaged across a small cohort. The use of subject\u2010specific instead of cohort\u2010averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non\u2010expert 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\u2010mean measured; subject\u2010specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite\u2013age associations. 100 conventional (TE\u2009=\u200930\u2009ms) and metabolite\u2010nulled (TI\u2009=\u2009650\u2009ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20\u201370\u2009years of age), were analyzed in Osprey. Short\u2010TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue\u2010 and relaxation\u2010corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite\u2013age 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\u2010to\u2010strong agreement between different MM strategies (r\u2009&gt;\u20090.6). The lowest relative model residuals and AIC values were found for the cohort\u2010mean measured MM. Metabolite\u2013age 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\u2013age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal\u2010to\u2010noise ratio MM basis function (cohort\u2010mean) in the short\u2010TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject\u2010specific 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\u2010based bias, and estimates algorithm\u2010related variance.<\/jats:p>","DOI":"10.1002\/nbm.4854","type":"journal-article","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T10:13:19Z","timestamp":1666433599000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Feasibility and implications of using subject\u2010specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7148-292X","authenticated-orcid":false,"given":"Helge J.","family":"Z\u00f6llner","sequence":"first","affiliation":[{"name":"Russell H. 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