Table 5.

Recommendations for spectral analysis.

Name of method	Recommendation
Linear combination model fitting	- Generally recommended due to its proven effectiveness, versatility and relative ease of use. - Ensure accuracy of the basis set:   a) for experimental basis sets derived from phantoms, the phantom temperature and pH, and the phantom acquisition parameters (pulse sequence, field strength, TE, etc.) should match the in vivo acquisition;   b) for simulated basis sets, the simulation parameters should match the in vivo acquisition (pulse sequence, field strength, echo time, and optionally the RF pulse shapes and durations).   c) simulations should use reliable estimates of chemical shifts and coupling constants of each metabolite spin system117–119. - Always visually inspect the quality of the fit. A good fit should have small fit residuals which mostly appears like noise. - Compute the Cramér-Rao minimum variance bounds (CRMVB), which are estimators of the minimum uncertainties in the estimated parameters (assuming that the model is complete and accurate. In particular the estimated errors would not apply if baseline estimation or phasing is done separately from actual modeling). - Metabolite measures of individual subjects should not be excluded based on high relative uncertainties (% CRMVB). Instead, individual subjects may be excluded on the basis of high absolute CRMVB values. - If the average %CRMVB for a metabolite is consistently high (>30%) across all subjects, consider excluding that metabolite from the reported results across the entire subject cohort. - Estimated baseline should be smooth, without fine structure or sharp peaks. - The number of protons per metabolite spin system is automatically encoded within the simulated or acquired basis set. Therefore, when using linear combination model fitting, the number of protons does NOT need to be considered in quantification (see quantification section).
Handling MM and baseline contributions	- MM fitting and baseline correction are generally required, but MM components can be omitted for long echo-time data (TE ≥ 150 ms at 3 T; TE ≥ 100 ms at 7 T; TE ≥ 100 ms at 9.4 T in rodent brain). - MM resonances should be removed or accounted for by including them as components in the analysis model. - Ideally, MM models should be based on an acquired MM spectrum. - Even with nuisance peak removal and MM modelling, an additional baseline correction should be performed. Use either time domain methods that assume rapid decay of baseline components, or frequency domain methods that assume a spline baseline.
Peak fitting and peak integration	- Recommended only in cases where   a) the spectrum is sparse (contains relatively few peaks), and   b) MM and baseline contribution are minimal, or have been removed in preprocessing. - Spectrum must be properly phased prior to peak integration. - Peak fitting requires that spectral peaks can be approximated by simple line shape functions. Peak integration does not have this requirement. - When using peak fitting or peak integration the number of protons per metabolite peak does need to be considered in quantification (see quantification section). - In case of peak fitting, always visually inspect the quality of the fit. A good fit should have small fit residuals and low uncertainties on peak area estimates.