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Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies. publishedVersion

The purpose of this work is to develop and validate a new atlas-based metabolite quantification pipeline for edited magnetic resonance spectroscopic imaging (MEGA-MRSI) that enables group comparisons of brain-structure specific GABA levels. By using brain structure masks segmented from high-resolution MPRAGE images and co-registering these to MEGA-LASER 3D MRSI data, an automated regional quantification of neurochemical levels is demonstrated for the example of the thalamus. Thalamic gamma-aminobutyric acid + co-edited macromolecules (GABA+) levels from 21 healthy subjects scanned at 3T were cross-validated both against a single-voxel (SV) MEGA-PRESS acquisition in the same subjects and same scan sessions, as well as alternative MRSI processing techniques (ROI approach, four-voxel approach) using Pearson correlation analysis. In addition, reproducibility was compared across the MRSI processing techniques in test-retest data from 14 subjects. The atlas-based approach showed a significant correlation with SV MEGA-PRESS (correlation coefficient r (GABA+) = 0.63, p value

MRS provides a valuable tool for the non-invasive detection of brain γ-aminobutyric acid (GABA) in vivo. GABAergic dysfunction has been observed in the aging cerebellum. The study of cerebellar GABA changes is of considerable interest in understanding certain age-related motor disorders. However, little is known about the reproducibility of GABA MRS in an aged population. Therefore, this study aimed to explore the feasibility and reproducibility of GABA MRS in the aged cerebellum at 3.0 T and to examine the effect of differing tissue composition on GABA measurements. MRI and (1)H MRS examinations were performed on 10 healthy elderly volunteers (mean age, 75.2 ± 6.5 years) using a 3.0-T Siemens Tim Trio scanner. Among them, five subjects were scanned twice to assess the short-term reproducibility. The MEGA-PRESS (Mescher-Garwood point-resolved spectroscopy) J-editing sequence was used for GABA detection in two volumes of interest (VOIs) in the left and right cerebellar dentate. MRS data processing and quantification were performed with LCModel 6.3-0L using two separate basis sets, generated from density matrix simulations using published values for chemical shifts and J couplings. Raw metabolite levels from LCModel outputs were corrected for cerebrospinal fluid contamination and relaxation. GABA-edited spectra yielded robust and stable GABA measurements with averaged intra-individual coefficients of variation for corrected GABA+ between 4.0 ± 2.8% and 13.4 ± 6.3%, and inter-individual coefficients of variation between 12.6% and 24.2%. In addition, there was a significant correlation between GABA+ obtained with the two LCModel basis sets. Overall, our results demonstrated the feasibility and reproducibility of cerebellar GABA-edited MRS at 3.0 T in an elderly population. This information might be helpful for studies using this technique to study GABA changes in normal or diseased aging brain, e.g. for power calculations and the interpretation of longitudinal observations.

MRS provides a valuable tool for the non-invasive detection of brain γ-aminobutyric acid (GABA) in vivo. GABAergic dysfunction has been observed in the aging cerebellum. The study of cerebellar GABA changes is of considerable interest in understanding certain age-related motor disorders. However, little is known about the reproducibility of GABA MRS in an aged population. Therefore, this study aimed to explore the feasibility and reproducibility of GABA MRS in the aged cerebellum at 3.0 T and to examine the effect of differing tissue composition on GABA measurements. MRI and (1)H MRS examinations were performed on 10 healthy elderly volunteers (mean age, 75.2 ± 6.5 years) using a 3.0-T Siemens Tim Trio scanner. Among them, five subjects were scanned twice to assess the short-term reproducibility. The MEGA-PRESS (Mescher-Garwood point-resolved spectroscopy) J-editing sequence was used for GABA detection in two volumes of interest (VOIs) in the left and right cerebellar dentate. MRS data processing and quantification were performed with LCModel 6.3-0L using two separate basis sets, generated from density matrix simulations using published values for chemical shifts and J couplings. Raw metabolite levels from LCModel outputs were corrected for cerebrospinal fluid contamination and relaxation. GABA-edited spectra yielded robust and stable GABA measurements with averaged intra-individual coefficients of variation for corrected GABA+ between 4.0 ± 2.8% and 13.4 ± 6.3%, and inter-individual coefficients of variation between 12.6% and 24.2%. In addition, there was a significant correlation between GABA+ obtained with the two LCModel basis sets. Overall, our results demonstrated the feasibility and reproducibility of cerebellar GABA-edited MRS at 3.0 T in an elderly population. This information might be helpful for studies using this technique to study GABA changes in normal or diseased aging brain, e.g. for power calculations and the interpretation of longitudinal observations.