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Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.

Background: Glioblastoma Multiforme (GBM) is the most common and deadly type of primary brain tumor in adults. Magnetic Resonance Spectroscopy (MRS) is a noninvasive imaging technique used to study metabolic changes in the brain tumors. Some metabolites such as Phosphocholine, Creatine, NAA/Cr, and Pcho/Cr have been proven to show a diagnostic role in GBM. The present study was conducted to analyze important metabolites using MRS multivoxel in GBM tumor. Methods: In this study, information was collected from 8 individuals diagnosed with GBM using Siemens multivoxel MRS with a magnetic field strength of 3 T. Data were obtained by Point-Resolved Spectroscopy (PRESS) protocol with TE=135 ms and TR=1570 ms. NAA, Pcho, Cr, Ala, Gln, Gly, Glu, Lac, NAAG, and Tau metabolites were extracted and evaluated statistically. Results: Given total number of normal voxels and total number of all voxels, levels of Cr, Glu, NAA, NAAG, and Gly/Tau ratio in healthy voxels were significantly higher than tumoral voxels (p=0.005, p=0.03, p

International audience; OBJECTIVE: To demonstrate that high resolution (1)H semi-LASER MRSI acquired at 7 T permits discrimination of metabolic patterns of different thalamic nuclei. MATERIALS AND METHODS: Thirteen right-handed healthy volunteers were explored at 7 T using a high-resolution 2D-semi-LASER (1)H-MRSI sequence to determine the relative levels of N-Acetyl Aspartate (NAA), choline (Cho) and creatine-phosphocreatine (Cr) in eight VOIs (volume \textless0.3 ml) centered on four different thalamic nuclei located on the Oxford thalamic connectivity atlas. Post-processing was done using the CSIAPO software. Chemical shift displacement of metabolites was evaluated on a phantom and correction factors were applied to in vivo data. RESULTS: The global assessment (ANOVA p \textless 0.05) of the neurochemical profiles (NAA, Cho and Cr levels) with thalamic nuclei and hemispheres as factors showed a significant global effect (F = 11.98, p \textless 0.0001), with significant effect of nucleus type (p \textless 0.0001) and hemisphere (p \textless 0.0001). Post hoc analyses showed differences in neurochemical profiles between the left and the right hemisphere (p \textless 0.05), and differences in neurochemical profiles between nuclei within each hemisphere (p \textless 0.05). CONCLUSION: For the first time, using high resolution 2D-PRESS semi-LASER (1)H-MRSI acquired at 7 T, we demonstrated that the neurochemical profiles were different between thalamic nuclei, and that these profiles were dependent on the brain hemisphere.

The purposes of the current study were to introduce a Mescher-Garwood (MEGA) semi-adiabatic spin-echo full-intensity localization (MEGA-sSPECIAL) sequence with macromolecule (MM) subtraction and to compare the test-retest reproducibility of gamma-aminobutyric acid (GABA) measurements at 7 T using the sSPECIAL and MEGA-sSPECIAL sequences. The MEGA-sSPECIAL editing scheme using asymmetric adiabatic and highly selective Gaussian pulses was used to compare its GABA measurement reproducibility with that of short echo-time (TE) sSPECIAL. Proton magnetic resonance spectra were acquired in the motor cortex (M1) and medial prefrontal cortex (mPFC) using the sSPECIAL (TR/TE = 4000/16 ms) and MEGA-sSPECIAL sequences (TR/TE = 4000/80 ms). The metabolites were quantified using LCModel with unsuppressed water spectra. The concentrations are reported in institutional units. The test-retest reproducibility was evaluated by scanning each subject twice. Between-session reproducibility was assessed using coefficients of variation (CVs), Pearson's r correlation coefficients, and intraclass correlation coefficients (ICCs). Intersequence agreement was evaluated using Pearson's r correlation coefficients and Bland-Altman plots. Regarding GABA measurements by sSPECIAL, the GABA concentrations were 0.92 +/- 0.31 (IU) in the M1 and 1.56 +/- 0.49 (IU) in the mPFC. This demonstrated strong between-session correlation across both regions (r = 0.81, p < 0.01; ICC = 0.82). The CVs between the two scans were 21.8% in the M1 and 10.2% in the mPFC. On the other hand, the GABA measurements by MEGA-sSPECIAL were 0.52 +/- 0.04 (IU) in the M1 and 1.04 +/- 0.24 (IU) in the mPFC. MEGA-sSPECIAL demonstrated strong between-session correlation across the two regions (r = 0.98, p < 0.001; ICC = 0.98) and lower CVs than sSPECIAL, providing 4.1% in the M1 and 5.8% in the mPFC. The MEGA-editing method showed better reproducibility of GABA measurements in both brain regions compared with the short-TE sSPECIAL method. Thus it is a more sensitive method with which to detect small changes in areas with low GABA concentrations. In GABA-rich brain regions, GABA measurements can be achieved reproducibly using both methods.