Sensitivity of biomarkers to post-acquisitional processing parameters for in vivo brain MR spectroscopy signals

We present the results of a study to determine the sensitivity of biomarkers in in vivo brain MRS signals to post-acquisitional processing algorithms and parameters. Using a comprehensive integrated suite of post-processing and inference algorithms (BIDASCA) we examine the impact of different parameter values for model-based water suppression on the identification of statistically significant wavelet-based and model-based features. We observe that the number, location, and effect-sizes of significant features vary as a function of the resonance components estimated and removed during model-based water suppression, as well as their spectral proximity to the dominant water resonance itself. Moreover, the ordering of SVD modes in different signals is not uniform, making consistent suppression of water-based resonances problematic. Less than half of all significant features remained significant when water-suppression parameters were varied, which indicates that different parameter values can lead to unique markers and effect sizes. This study demonstrates the need for an end-to-end understanding of the sensitivity of biomarkers to processes that introduce uncertainty within the data, from acquisition to post-processing algorithms.