Cerebral quantification of N-acetyl aspartate, aspartate, and glutamate levels in local structures of the human brain using J-editing of 1H magnetic resonance spectra in vivo

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Importance of Linear Combination Modeling for Quantification of Glutathione and γ-Aminobutyric Acid Levels Using Hadamard-Edited Magnetic Resonance Spectroscopy

Background

J-difference-edited 1H-MR spectra require modeling to quantify signals of low-concentration metabolites. Two main approaches are used for this spectral modeling: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data.

Purpose

The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach would improve quantification of Hadamard-edited data compared with simple peak fitting.

Methods

A test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE = 80 ms for HERMES and TE = 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variation (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate.

Results

The reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0-9.9%. For MEGA-PRESS GSH data, reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3 and 8.8%. GABA + CVs from HERMES were 16.7 and 15.2%, respectively for the two models.

Conclusion

LCM with simulated basis functions substantially improved the reproducibility of GSH quantification for HERMES data.

3T MEGA-PRESS study of N-acetyl aspartyl glutamate and N-acetyl aspartate in activated visual cortex

OBJECTIVE

To measure N-acetyl aspartyl glutamate (NAAG) and N-acetyl aspartate (NAA) concentrations in visual cortex activated by a continuous stimulation in a 3 T field.

METHODS

NAAG and NAA spectra were obtained with MEGA-PRESS pulse sequence (TE/TR = 140/2000 ms; δ_ON^NAAG/δ_OFF^NAAG = 4.61/4.15 ppm; δ_ON^NAA/δ_OFF^NAA = 4.84/4.38 ppm) in 14 healthy volunteers at rest and upon stimulation by a radial checkerboard flickering at a frequency of 8 Hz. Spectra of all subjects were frequency and phase aligned and then averaged. Additionally, to obtain the time-dependency data, spectra were divided into time sections of 64 s each. The intensities of NAA, NAAG and lactate + macromolecular (Lac + MM) signals were defined by integration of the real part of spectra. The heights of the central resonance of NAAG and NAA signals were measured.

RESULTS

The NAAG and NAA concentrations, measured with 2.5% and 0.5% error, respectively, were unaffected by visual activation. A significant increase in the Lac + MM signal by ~ 12% is clearly observed. No stimulation-induced time dependency was found for NAAG or NAA, while the increase in Lac + MM was gradual. The concentration values in visual cortex are in good agreement with the 7 T MRS measurements: [NAAG] = 1.55 mM, [NAA] = 11.95 mM.

CONCLUSION

The MEGA-PRESS pulse sequence together with the spectral preprocessing techniques allowed to demonstrate that the concentrations of NAAG and NAA in the visual cortex remain constant during continuous visual stimulation within the margin of error. An increase in the lactate signal intensity signifies the activation of the anaerobic glycolysis in activated visual cortex.

Separate N-acetyl aspartyl glutamate, N-acetyl aspartate, aspartate, and glutamate quantification after pediatric mild traumatic brain injury in the acute phase

PURPOSE

To separately measure N-acetyl aspartul glutamate (NAAG), N-acetyl aspartate (NAA), aspartate (Asp), and glutamate (Glu) concentrations in white matter (WM) using J-editing techniques in patients with mild traumatic brain injury (mTBI) in the acute phase.

METHODS

Twenty-four patients with closed concussive head injury and 29 healthy volunteers were enrolled in the current study. For extended ¹ H MRS examination, patients and controls were equally divided into two subgroups. In subgroup 1 (12 patients/15 controls), NAAG and NAA concentrations were measured in WM separately with MEGA-PRESS (echo time/repetition time [TE/TR] = 140/2000 ms; δ ON NAA / δ OFF NAA = 4.84/4.38 ppm, δ ON NAAG / δ OFF NAAG = 4.61/4.15 ppm). In subgroup 2 (12 patients/14 controls), Asp and Glu concentrations were acquired with MEGA-PRESS (TE/TR = 90/2000 ms; δ ON Asp / δ OFF Asp = 3.89/5.21 ppm) and TE-averaged PRESS (TE from 35 ms to 185 ms with 2.5-ms increments; TR = 2000 ms) pulse sequences, respectively.

RESULTS

tNAA and NAAG concentrations were found to be reduced, while NAA concentrations were unchanged, after mild mTBI. Reduced Asp and elevated myo-inositol (mI) concentrations were also found.

CONCLUSION

The main finding of the study is that the tNAA signal reduction in WM after mTBI is associated with a decrease in the NAAG concentration rather than a decrease in the NAA concentration, as was thought previously. This finding highlights the importance of separating these signals, at least for WM studies, to avoid misinterpretation of the results. NAAG plays an important role in selectively activating mGluR3 receptors, thus providing neuroprotective and neuroreparative functions immediately after mTBI. NAAG shows potential for the development of new therapeutic strategies for patients with injuries of varying severity.