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Purpose: The increased spectral dispersion achieved at ultra-high field permits quantification of γ-aminobutyric acid (GABA) concentrations at ultra-short-TE without editing. This work investigated the influence of spectral quality and different LCModel fitting approaches on quantification of GABA. Additionally, the sensitivity with which cross-sectional and longitudinal variations in GABA concentrations can be observed was characterized.
Methods: In - vivo spectra were acquired in the posterior cingulate cortex of 10 volunteers at 7 T using a STEAM sequence. Synthetically altered spectra with different levels of GABA signals were used to investigate the reliability of GABA quantification with different LCModel fitting approaches and different realizations of SNR. The synthetically altered spectra were also used to characterize the sensitivity of GABA quantification.
Results: The best LCModel fitting approach used stiff spline baseline, no soft constraints, and measured macromolecules in the basis set. With lower SNR, coefficients of variation increased dramatically. Longitudinal and cross-sectional variations in GABA of 10% could be detected with 79 and 48 participants per group, respectively. However, the small cohort may bias the calculation of the coefficients of variation and of the sample size that would be needed to detect variations in GABA.
Conclusion: Reliable quantification of normal and abnormal GABA concentrations was achieved for high quality 7 T spectra using LCModel fitting.
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http://dx.doi.org/10.1002/mrm.29514 | DOI Listing |
Front Neurosci
July 2025
Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Introduction: The anterior insular cortex (AIC) integrates interoceptive, cognitive-emotional, and error-monitoring signals, and is consistently hyperactive in anxiety and depression. Converging evidence links elevated glutamate + glutamine (Glx) in fronto-insular regions to stress reactivity; however, it is unknown whether AIC Glx relates to a transdiagnostic general psychopathology factor (G-score) or to the tendency to overweight prediction errors during learning. We therefore combined functional MRS (fMRS) with reinforcement-learning modeling to test whether (i) baseline AIC Glx predicts the G-score derived from bifactor analysis of PHQ-9, GAD-7, and STAI-X1, and (ii) task-evoked Glx changes track individual differences in error sensitivity during gain- and loss-based learning.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Goethe University Frankfurt, University Hospital, Institute of Neuroradiology, Frankfurt am Main, Germany.
Background: Glutamate and glutamine are critical metabolites in gliomas, each serving distinct roles in tumor biology. Separate quantification of these metabolites using in vivo MR spectroscopy (MRS) at clinical field strengths (≤ 3T) is hindered by their molecular similarity, resulting in overlapping, hence indistinguishable, spectral peaks.
Purpose: To develop an MRS imaging (MRSI) protocol to map glutamate and glutamine separately at 3T within clinically feasible time, using J-modulation to enhance spectral differentiation, demonstrate its reliability/reproducibility, and quantify the metabolites in glioma subregions.
NMR Biomed
April 2025
Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, New York, New York, USA.
In vivo proton magnetic resonance spectroscopy (H-MRS) data often exhibit baselines or low-amplitude signal variations resulting from residual water, imperfectly suppressed lipids, low-amplitude metabolites not considered for fitting, and other features not represented in a basis set. While multitudinous approaches exist to model these baselines in H-MR spectral analysis, many continue to lack systematic validation against varied and realistic ground-truth standards. Here, we compare the accuracy (error mean) and precision (error standard deviation) of metabolite scaling estimates by linear combination modeling (LCM) spectral fitting accounting for spectral baselines via smoothed cubic splines at 50 different combinations of fixed knot interval and smoothing weight, either with or without additionally simulated Gaussian basis signals to separately model spectral macromolecules.
View Article and Find Full Text PDFMagn Reson Med
June 2025
Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.
Purpose: Accurate analysis of metabolite levels from H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise, and common artifacts further complicate the analysis, leading to instability and reports of poor agreement between different analysis approaches. One inconsistently used method to improve analysis stability is known as regularization, where poorly determined parameters are partially constrained to take a predefined value.
View Article and Find Full Text PDFJ Neurosurg
March 2025
1Departments of Neurosurgery and.