Abstract
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 have been 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. 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 macromolecular 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 (tCr, tCho) for all MM strategies, however, findings for highly J-coupled metabolites it was 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-SNR MM basis function (cohort-mean) leads to more robust metabolite estimation (lower model residuals and AIC values) compared to 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 improves the robustness in the detection of underlying effects (e.g. metabolite vs age), reduces algorithm-based bias, and estimates algorithm-related variance.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
We have included an additional discussion paragraph to emphasize data homogeneity to be one criterion to successfully use the cohort-mean MM spectra for modelling and how to deal with this aspect in heterogeneous cohorts. We have further included a new analysis to assess differences in the between-MM-strategy correlations and to investigate the stability of the metabolite-age associations. We followed the minor comments to further improve the accessibility of the results and figures.
Abbreviations
- LCM
- linear-combination modelling
- MM
- macromolecular
- AIC
- Akaike information criterion
- mMM
- measured MM
- pMM
- parameterized MM
- PCC
- posterior cingulate cortex
- FWHM
- full-width half-maximum
- HSVD
- Hankel singular value decomposition
- tMM
- total macromolecules
- LME
- linear mixed-effect model
- ICC
- Inter-class correlation coefficients
- spbl
- spline baseline