¹ H-MRS processing parameters affect metabolite quantification: The urgent need for uniform and transparent...

Area of Science:

• Neuroimaging
• Biophysics
• Metabolomics

Background:

• Proton magnetic resonance spectroscopy (¹H-MRS) quantifies in vivo metabolite levels like lactate, GABA, and glutamate.
• Analysis choices in ¹H-MRS data processing can significantly impact metabolite quantification accuracy and precision.
• The extent to which variations in analysis pipelines affect ¹H-MRS outcomes remains unclear.

Purpose of the Study:

• To evaluate the comparability of ¹H-MRS metabolite quantification across independent research groups using identical scan data.
• To investigate the influence of differing analysis parameters and software on fitted metabolite concentrations.
• To establish the need for standardized ¹H-MRS data processing protocols.

Main Methods:

• Sixty ¹H-MRS spectra from 30 individuals were acquired using a 7-T MRI scanner.
• Four independent research groups processed the data using LCModel software with individualized parameters.
• One group reprocessed the data using NMRWizard for cross-platform comparison.

Main Results:

• High inter-group correlations were observed for key neurotransmitter metabolite ratios (e.g., NAA/Creatine) when using LCModel (mean r ≈ 0.87).
• Comparisons between LCModel and NMRWizard showed similar high correlations for metabolite ratios (mean r ≈ 0.87).
• Variations in processing parameters, basis sets, and software choice influenced metabolite quantification outcomes.

Conclusions:

• Metabolite quantification in ¹H-MRS is sensitive to analysis choices, including software and parameter settings.
• Standardized practices are crucial to ensure reproducibility and reliability of ¹H-MRS studies.
• Making basis sets publicly available is recommended to promote transparency and consistency in the scientific community.