Table 2:

Current recommendations and open issues in the field to improve the general knowledge about MM

Recommendations
1.	If the objective of a study is to determine metabolite concentrations and/or metabolite ratios, then the MM contribution has to be removed or included in the basis set used for quantification, especially for TE’s below 80 ms.
2.	We recommend to use the unified nomenclature of the MM components (Table 1), which can be easily expanded to new peaks.
3.	The MM spectrum is different from a spectral baseline. These terms should not be used interchangeably.
4.	Since the shape, amplitudes and relaxation times of different MM contributions vary with B0, we recommend to measure the MM spectra for metabolite quantification specifically for each B0 field strength and each acquisition sequence used.
5.	We recommend to acquire MM spectra in vivo using adiabatic RF pulse(s) and with the highest possible spatial and spectral resolution (i.e. high quality B0 shimming), high SNR, efficient water suppression and no baseline distortions, and to avoid subcutaneous lipid contamination.
6.	We recommend to identify properly and eliminate residual peaks of metabolites from any MM spectrum measured in vivo (Section 3.1). Some residual peaks of metabolites are always present independent of the sequence used to measure them. AMARES or similar approaches with the ability to consider prior knowledge for the residual metabolite peaks, are recommended for post-processing of the MM spectrum.
7.	Both single and double inversion methods of MM measurement are recommended since they provide good MM signal recovery, while the double inversion methods provide improved metabolite suppression (e.g. for mapping in the presence of B1+ - inhomogeneities) at the expense of stronger T1-weighting (e.g. reduced MM signal recovery).
8.	If the goal of the study is to estimate individual MM peaks, care has to be taken to avoid over-parameterization of the fitting, for instance via including some soft constraints on the relative amplitudes, frequencies and linewidth of the different MM components. One possible indication for overfitting is the presence of strong correlation between a MM component and an overlapping metabolite peak.
9.	In human brain, the MM content and pattern change in older adult subjects (≥ 60 years), therefore age-specific MM spectra are required for metabolite estimation (i.e. a spectrum per 5 years after the age of 60).
10.	It should be kept in mind that the MM content of some individual MM peaks varies across brain regions in humans. This should be particularly considered for low abundant metabolites, such as GABA+ in spectral editing, where the contribution of MM to GABA+ seem to be higher in WM than in GM.
11.	The MM content and spectral pattern does not seem to differ in hippocampus, cortex and striatum in healthy rodents (rats and mice). Thus, fitting metabolite concentrations assuming the constant shape of the MM spectrum can be a practical approach. Therefore, we recommend to use one single MM spectrum and to provide a clear description of this MM spectrum when publishing the data.
12.	The MM and ML content and pattern change in disease in both humans and rodents. We recommend characterizing the MM and ML contribution in each specific disease by measuring the MM or MM+ML spectrum in vivo.
13.	The choice of the approach to handle the MM highly depends on the circumstances:1) a single MM basis spectrum when metabolite concentrations are of primary interest; 2) individual MM components if MM measures are desired as disease biomarkers.
14.	To avoid duplicated effort, we recommend sharing of the various MM models through a list of parameters or data points.
15.	We recommend that each publication contains a clear description of how MM were handled during the metabolite quantification step and also a brief description of the parameters used for acquisition, voxel position and size, quality of the shimming reported as water linewidth, the names of metabolite residuals eliminated and the type of post-processing used.
	Open issues in the field of MM
1.	Identification of the biological background of individual MM peaks, especially those yet unknown MM peaks observed at ultra-high B0. Improvement of the assignment of the individual MM peaks to particular amino acids and further investigate the origin of the MM signal with respect to contributions from structured versus unstructured cytosolic proteins, membrane bound proteins and large protein complexes in order to better understand any spatial/tissue differences. Investigation of the contribution of other types of macromolecules, such as sugars or DNA/RNA. Performing additional ex vivo validation studies.
2.	Measurement of T1 and T2 relaxation times of different MM peaks (M1.81 to M4.20) at different B0 in humans and rodents.
3.	Analyze the possible contribution of signals of metabolites with short T1 relaxation times, such as GSH, in the measured metabolite-nulled MM spectrum48.
4.	Performing additional studies on how MM vary with TE using different types of acquisition sequences (i.e. LASER, STEAM, PRESS, SPECIAL) and determine a threshold of TE above which the MM contribution is insignificant and does not need to be considered in the quantification step
5.	Improvement of IR or DIR methods for MM measurement in vivo. Development of alternative methods for MM mapping, which will not be based on IR-nulling or spectral quantification.
6.	Identification of possible soft constrains and systematic errors in fitting individual MM peaks.
7.	Confirmation of the observed regional differences in MM spectra in humans. Measurement of MM in additional brain regions in rodents (i.e. cerebellum, thalamus) to confirm the lack of brain regional changes in MM in rodent brain.
8.	Performing additional studies on quantification of individual MM peaks in normal versus diseased brain tissue. Rapid changes (in hours/days) in the 0.9–1.8 ppm range are observed in response to (systemic) hypoxia, and slow changes in relative amplitudes are observed on the timescale of several weeks. Prediction of patient outcome seems possible, and further systematic investigations are needed. Continuation of the studies on ML, MM+ML and MM only changes in pathologies and performing a precise identification of the origins of these MM peaks and the mechanisms behind. Determination of the most suitable approach on how to handle the MM contributions in pathologies.
9.	Evaluate whether the age-associated MM pattern and content differences observed in the occipital cortex and posterior cingulate cortex are more widely spread throughout the brain. Evaluate whether changes with age occur in GM, WM or in both tissue types. Identify the causes of these differences. Assess the effect of age-associated differences in the MM spectral pattern on quantification of non-edited and edited spectra. Acquire data from neo-natal through to young adult subjects to fully characterize the age-dependence of the MM contribution to human/rodent brain spectra.