Regional distributions of brain glutamate and glutamine in normal subjects

Abstract

Glutamate (Glu) and glutamine (Gln) play an important role in neuronal regulation and are of value as MRS-observable diagnostic biomarkers. In this study the relative concentrations of these metabolites have been measured in multiple regions in the normal brain using a short-TE whole-brain MRSI measurement at 3T combined with a modified data analysis approach that used spatial averaging to obtain high-SNR spectra from atlas-registered anatomic regions or interest. By spectral fitting of high-SNR spectra this approach yielded reliable measurements across a wide volume of the brain. Spectral averaging also demonstrated increased SNR and improved fitting accuracy for the sum of Glu and Gln (Glx) compared to individual voxel fitting. Results in 26 healthy controls showed relatively constant Glu/Cr and Gln/Cr throughout the cerebrum, although with increased values in the anterior cingulum and paracentral lobule, and increased Gln/Cr in the superior motor area. The deep gray-matter regions of thalamus, putamen, and pallidum show lower Glu/Cr compared to cortical white-matter regions. Lobar measurements demonstrated reduced Glu/Cr and Gln/Cr in the cerebellum as compared to the cerebrum, where white-matter regions show significantly lower Glu/Cr and Gln/Cr as compared to gray-matter regions across multiple brain lobes. Regression analysis showed no significant effect of gender on Glu/Cr and Gln/Cr measurement, however, Glx/Cr ratio was found to be significantly negatively correlated to age in some lobar brain regions. In summary, this methodology provides the spectral quality necessary for reliable separation of Glu, and Gln at 3T from a single MRSI acquisition enabling generation of regional distributions of metabolites over a large volume of the brain, including cortical regions.

Spectral decomposition for resolving partial volume effects in MRSI

“…The combined effect of different noise and spatial smoothing levels represents a mean WM SNR range of 5 dB (noise level 4, no spatial smoothing) to 41 dB (noise level 0, spatial smoothing of σ = 2.0). SNR is estimated as the ratio of the height of the NAA peak to the standard deviation of the noise signal estimated between 0 to 1.2 ppm (3). SNR is expressed in decibels (dB) as 10*log 10 (SNR).…”

“…Preprocessing for in vivo MRSI data included corrections for B0 shifts (3), lipid k-space extrapolation (18), and linear registration between T1-weighted MR and whole-brain MRSI (14,19). Metabolite maps were interpolated to 64x64x32 points, for an interpolated voxel volume of 0.107 cc.…”

“…The relative metabolite concentrations estimated for WM and GM by the spectral decomposition technique (sDec) were compared to those obtained by (i) fitting of a spectrum obtained by integrating all spectra within an ROI (M-Int) (3); (ii) averaging the results from fitting individual voxels within a ROI (vAvg); and (iii) linear regression of values obtained by fitting individual spectra against tissue fractions (vReg) (15). M-Int spectra for WM and GM regions were created by averaging voxels with >80% of WM and GM tissue fraction respectively.…”

“…Clinical studies commonly rely on measurements of the relative metabolite concentrations and comparisons with normative values at the corresponding brain region or tissue. For this purpose, a previous study estimated regional distributions of brain metabolites in normal subjects at 3T using a data analysis approach that used spatial averaging of multiple MRSI voxels to obtain high-quality spectra from multiple atlas-registered anatomic regions of interest (ROIs) (3). However, regional estimation of brain metabolites is complicated by partial volume effects due to variations in tissue composition within an ROI and the relatively broad spatial response function of the MRSI acquisition.…”

“…The technique is evaluated for separation of spectra corresponding to 100% WM and GM regions using a whole-brain MRSI measurement, and for analysis of a specific brain region that is subject to partial volume contributions from surrounding tissue. Results of metabolite quantifications obtained using the spectral decomposition technique are compared to those obtained by spectral fitting of individual voxels, using mean values and linear regression against tissue fractions, and spectral fitting of regionally integrated spectra (3). These methods are compared using simulation studies and volumetric MRSI data obtained from healthy subjects.…”