Characterization of macromolecular baseline of human brain using metabolite cycled semi-LASER at 9.4T

Macromolecule modelling for improved metabolite quantification using short echo time brain 1 H MRS at 3 T and 7 T: The PRaMM Model

Purpose

To improve reliability of metabolite quantification at both, 3 T and 7 T, we propose a novel parametrized macromolecules quantification model (PRaMM) for brain 1 H MRS, in which the ratios of macromolecule peak intensities are used as soft constraints.

Methods

Full- and metabolite-nulled spectra were acquired in three different brain regions with different ratios of grey and white matter from six healthy volunteers, at both 3 T and 7 T. Metabolite-nulled spectra were used to identify highly correlated macromolecular signal contributions and estimate the ratios of their intensities. These ratios were then used as soft constraints in the proposed PRaMM model for quantification of full spectra. The PRaMM model was validated by comparison with a single component macromolecule model and a macromolecule subtraction technique. Moreover, the influence of the PRaMM model on the repeatability and reproducibility compared to those other methods was investigated.

Results

The developed PRaMM model performed better than the two other approaches in all three investigated brain regions. Several estimates of metabolite concentration and their Cramér-Rao lower bounds were affected by the PRaMM model reproducibility, and repeatability of the achieved concentrations were tested by evaluating the method on a second repeated acquisitions dataset. While the observed effects on both metrics were not significant, the fit quality metrics were improved for the PRaMM method (p≤0.0001). Minimally detectable changes are in the range 0.5 - 1.9 mM and percent coefficients of variations are lower than 10% for almost all the clinically relevant metabolites. Furthermore, potential overparameterization was ruled out.

Conclusion

Here, the PRaMM model, a method for an improved quantification of metabolites was developed, and a method to investigate the role of the MM background and its individual components from a clinical perspective is proposed.

Application of a 1H Brain MRS Benchmark Dataset to Deep Learning for Out-of-Voxel Artifacts

Neural networks are potentially valuable for many of the challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic 1H MRS examples for training and testing neural networks. AGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemble in vivo brain data with combinations of metabolite, macromolecule, residual water signals, and noise. To demonstrate the utility, we apply AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing. Complex OOV signals were mixed into 85% of synthetic examples to train two separate CNNs for the detection and prediction of OOV signals. AGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10 normed-MSE of -1.79, an improvement of almost two orders of magnitude.

Harmonization of multi-scanner in vivo magnetic resonance spectroscopy: ENIGMA consortium task group considerations

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.

Measurement of glucose metabolism in the occipital lobe and frontal cortex after oral administration of [1-13C]glucose at 9.4 T

For the first time, labeling effects after oral intake of [1-13C]glucose are observed in the human brain with pure 1H detection at 9.4 T. Spectral time series were acquired using a short-TE 1H MRS MC-semiLASER (Metabolite Cycling semi Localization by Adiabatic SElective Refocusing) sequence in two voxels of 5.4 mL in the frontal cortex and the occipital lobe. High-quality time-courses of [4-13C]glutamate, [4-13C]glutamine, [3-13C]glutamate + glutamine, [2-13C] glutamate+glutamine and [3-13C]aspartate for individual volunteers and additionally, group-averaged time-courses of labeled and non-labeled brain glucose could be obtained. Using a one-compartment model, mean metabolic rates were calculated for each voxel position: The mean rate of the TCA-cycle (Vtca) value was determined to be 1.36 and 0.93 μmol min-1 g-1, the mean rate of glutamine synthesis (Vgln) was calculated to be 0.23 and 0.45 μmol min-1 g-1, the mean exchange rate between cytosolic amino acids and mitochondrial Krebs cycle intermediates (Vx) rate was found to be 0.57 and 1.21 μmol min-1 g-1 for the occipital lobe and the frontal cortex, respectively. These values were in agreement with previously reported data. Altogether, it can be shown that this most simple technique combining oral administration of [1-13C]Glc with pure 1H MRS acquisition is suitable to measure metabolic rates.

Test-retest reproducibility of human brain multi-slice 1 H FID-MRSI data at 9.4T after optimization of lipid regularization, macromolecular model, and spline baseline stiffness

PURPOSE

This study analyzes the effects of retrospective lipid suppression, a simulated macromolecular prior knowledge and different spline baseline stiffness values on 9.4T multi-slice proton FID-MRSI data spanning the whole cerebrum of human brain and the reproducibility of respective metabolite ratio to total creatine (/tCr) maps for 10 brain metabolites.

METHODS

Measurements were performed twice on 5 volunteers using a short TR and TE FID MRSI 2D sequence at 9.4T. The effects of retrospective lipid L2-regularization, macromolecular spectrum and different LCModel baseline flexibilities on SNR, FWHM, fitting residual, Cramér-Rao lower bound, and metabolite ratio maps were investigated. Intra-subject, inter-session coefficient of variation and the test-retest reproducibility of the mean metabolite ratios (/tCr) of each slice was calculated.

RESULTS

Transversal, sagittal, and coronal slices of many metabolite ratio maps correspond to the anatomically expected concentration relations in gray and white matter for the majority of the cerebrum when using a flexible baseline in LCModel fit. Results from the second measurements of the same subjects show that slice positioning and data quality correlate significantly to the first measurement. L2-regularization provided effective suppression of lipid-artifacts, but should be avoided if no artifacts are detected.

CONCLUSION

Reproducible concentration ratio maps (/tCr) for 4 metabolites (total choline, N-acetylaspartate, glutamate, and myoinositol) spanning the majority of the cerebrum and 6 metabolites (N-acetylaspartylglutamate, γ-aminobutyric acid, glutathione, taurine, glutamine, and aspartate) covering 32 mm in the upper part of the brain were acquired at 9.4T using multi-slice FID MRSI with retrospective lipid suppression, a macromolecular spectrum and a flexible LCModel baseline.

Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting

Purpose

Definition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non‐Gaussian diffusion of brain metabolites with strongly diffusion‐weighted MR spectroscopy.

Methods

Short echo time MRS with strong diffusion‐weighting with b‐values up to 25 ms/μm² at two diffusion times was implemented on a Connectom system and applied in combination with simultaneous spectral and diffusion decay modeling. Motion‐compensation was performed with a combined method based on the simultaneously acquired water and a macromolecular signal.

Results

The motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non‐Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum.

Conclusion

The determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure.

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Simultaneous detection of metabolite concentration changes, water BOLD signal and pH changes during visual stimulation in the human brain at 9.4T

This study presents a method to directly link metabolite concentration changes and BOLD response in the human brain during visual stimulation by measuring the water and metabolite signals simultaneously. Therefore, the metabolite-cycling (MC) non-water suppressed semiLASER localization technique was optimized for functional 1H MRS in the human brain at 9.4 T. Data of 13 volunteers were acquired during a 26:40 min visual stimulation block-design paradigm. Activation-induced BOLD signal was observed in the MC water signal as well as in the NAA-CH3 and tCr-CH3 singlets. During stimulation, glutamate concentration increased 2.3 ± 2.0% to a new steady-state, while a continuous increase over the whole stimulation period could be observed in lactate with a mean increase of 35.6 ± 23.1%. These increases of Lac and Glu during brain activation confirm previous findings reported in literature. A positive correlation of the MC water BOLD signal with glutamate and lactate concentration changes was found. In addition, a pH decrease calculated from a change in the ratio of PCr to Cr was observed during brain activation, particularly at the onset of the stimulation.

Respiratory-triggered quantitative MR spectroscopy of the human cervical spinal cord at 7 T

PURPOSE

Ultra-high field 1 H MR spectroscopy (MRS) is of great interest to help characterizing human spinal cord pathologies. However, very few studies have been reported so far in this small size structure at these fields due to challenging experimental difficulties caused by static and radiofrequency field heterogeneities, as well as physiological motion. In this work, in line with the recent developments proposed to strengthen spinal cord MRS feasibility at 7 T, a respiratory-triggered acquisition approach was optimized to compensate for dynamic B 0 field heterogeneities and to provide robust cervical spinal cord MRS data.

METHODS

A semi-LASER sequence was purposely used, and a dedicated raw data processing algorithm was developed to enhance MR spectral quality by discarding corrupted scans. To legitimate the choices done during the optimization stage, additional tests were carried out to determine the impact of breathing, voluntary motion, body mass index, and fitting algorithm. An in-house quantification tool was concomitantly designed for accurate estimation of the metabolite concentration ratios for choline, N-acetyl-aspartate (NAA), myo-inositol and glutathione. The method was tested on a cohort of 14 healthy volunteers.

RESULTS

Average water linewidth and NAA signal-to-noise ratio reached 0.04 ppm and 11.01, respectively. The group-average metabolic ratios were in good agreement with previous studies and showed intersession reproducibility variations below 30%.

CONCLUSION

The developed approach allows a rise of the acquired MRS signal quality and of the quantification robustness as compared to previous studies hence offering strengthened possibilities to probe the metabolism of degenerative and traumatic spinal cord pathologies.

In vivo Human MR Spectroscopy Using a Clinical Scanner: Development, Applications, and Future Prospects

MR spectroscopy (MRS) is a unique and useful method for noninvasively evaluating biochemical metabolism in human organs and tissues, but its clinical dissemination has been slow and often limited to specialized institutions or hospitals with experts in MRS technology. The number of 3-T clinical MR scanners is now increasing, representing a major opportunity to promote the use of clinical MRS. In this review, we summarize the theoretical background and basic knowledge required to understand the results obtained with MRS and introduce the general consensus on the clinical utility of proton MRS in routine clinical practice. In addition, we present updates to the consensus guidelines on proton MRS published by the members of a working committee of the Japan Society of Magnetic Resonance in Medicine in 2013. Recent research into multinuclear MRS equipped in clinical MR scanners is explained with an eye toward future development. This article seeks to provide an overview of the current status of clinical MRS and to promote the understanding of when it can be useful. In the coming years, MRS-mediated biochemical evaluation is expected to become available for even routine clinical practice.

Spatially resolved free-induction decay spectroscopy using a 3D ultra-short echo time multi-echo imaging sequence with systematic echo shifting and compensation of B0 field drifts

PURPOSE

Biologically interesting signals can exhibit fast transverse relaxation and frequency shifts compared to free water. For spectral assignment, a ultra-short echo time (UTE) imaging sequence was modified to provide pixel-wise free-induction decay (FID) acquisition.

METHODS

The UTE-FID approach presented relies on a multi-echo 3D spiral UTE sequence with six echoes per radiofrequency (RF) excitation (TE_min 0.05 ms, echo spacing 3 ms). A complex pixel-wise raw data set for FID spectroscopy is obtained by several multi-echo UTE measurements with systematic shifting of the readout by 0.25 or 0.5 ms, until the time domain is filled for 18 or 45 ms. B₀ drifts are compensated by mapping and according phase correction. Autoregressive extrapolation of the signal is performed before Gaussian filtering. This method was applied to a phantom containing collagen-water solutions of different concentrations. To calculate the collagen content, a 19-peak collagen model was extracted from a non-selective FID spectrum (50% collagen solution). Proton-density-collagen-fraction (PDCF) was calculated for 10 collagen solutions (2%-50%). Furthermore, an in vivo UTE-FID spectrum of adipose tissue was recorded.

RESULTS

UTE-FID signal patterns agreed well with the non-spatially selective pulse-acquire FID spectrum from a sphere filled with 50% collagen. Differentiation of collagen solution from distilled water in the PDCF map was possible from 4% collagen concentration for a UTE-FID sequence with 128 × 128 × 64 matrix (voxel size 1 × 1 × 2.85 mm³ ). The mean values of the PDCF correlate linearly with collagen concentration.

CONCLUSION

The presented UTE-FID approach allows pixel-wise raw data acquisition similar to non-spatially selective pulse-acquire spectroscopy. Spatially resolved applications for assessment of spectra of rapidly decaying signals seem feasible.

The macromolecular MR spectrum does not change with healthy aging

PURPOSE

To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex.

METHODS

102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm³ , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes.

RESULTS

MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions.

CONCLUSION

Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.

Relaxation-corrected macromolecular model enables determination of 1 H longitudinal T1 -relaxation times and concentrations of human brain metabolites at 9.4T

PURPOSE

Ultrahigh field MRS has improved characterization of the neurochemical profile. To compare results obtained at 9.4T to those from lower field strengths, it is of interest to quantify the concentrations of metabolites measured. Thus, measuring T₁ -relaxation times is necessary to correct for T₁ -weighting that occurs in acquisitions for single-voxel spectroscopy and spectroscopic imaging. A macromolecule (MM) simulation model was developed to fit MM contributions to the short TE inversion series used to measure T₁ -relaxation times.

METHODS

An inversion series with seven time points was acquired with metabolite-cycled STEAM to estimate T₁ -relaxation times of metabolites. A short TE was employed in this study to retain signals from metabolites with short T₂ -relaxation times and J-couplings. The underlying macromolecule spectrum was corrected by developing a sequence-specific, relaxation-corrected simulated MM model. Quantification of metabolite peaks was performed using internal water referencing and relaxation corrections.

RESULTS

T₁ -relaxation times for metabolites range from approximately 750 to approximately 2000 ms and approximately 1000 to approximately 2400 ms in gray matter (GM)- and white matter (WM)- rich voxels, respectively. Quantification of metabolites was compared between GM and WM voxels, as well as between results that used a simulated MM spectrum against those that used an experimentally acquired MM spectrum. Metabolite concentrations are reported in mmol/kg quantities.

CONCLUSION

T₁ -relaxation times are reported for nonsinglet resonances for the first time at 9.4T by use of a MM simulation model to account for contributions from the MM spectrum. In addition to T₁ -relaxation times, quantification results of metabolites from GM- and WM-rich voxels are reported.

Contribution of macromolecules to brain 1 H MR spectra: Experts' consensus recommendations

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Comparison of different linear-combination modeling algorithms for short-TE proton spectra

Short-TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as a linear combination of metabolite basis spectra. This large-scale multi-site study compares the levels of the four major metabolite complexes in short-TE spectra estimated by three linear-combination modeling (LCM) algorithms. 277 medial parietal lobe short-TE PRESS spectra (TE = 35 ms) from a recent 3 T multi-site study were preprocessed with the Osprey software. The resulting spectra were modeled with Osprey, Tarquin and LCModel, using the same three vendor-specific basis sets (GE, Philips and Siemens) for each algorithm. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myo-inositol (mI) and glutamate + glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and coefficient of variations of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated as lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was R 2 ¯ = 0.39, indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean R 2 ¯ = 0.10). While mean estimates of major metabolite complexes broadly agree between linear-combination modeling algorithms at group level, correlations between algorithms are only weak-to-moderate, despite standardized preprocessing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.

A novel method to measure T1 -relaxation times of macromolecules and quantification of the macromolecular resonances

PURPOSE

Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T₁ relaxation times among individual MM peaks must be considered. Characterization of T₁ -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T₁ -relaxation times of individual MM peaks is reported in this work.

METHODS

Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T₁ -relaxation times.

RESULTS

T₁ -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported.

CONCLUSION

A novel DIR method is reported in this work to calculate T₁ -relaxation times of MMs in the human brain. T₁ -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T.

In vivo characterization of downfield peaks at 9.4 T: T2 relaxation times, quantification, pH estimation, and assignments

PURPOSE

Relaxation times are a valuable asset when determining spectral assignments. In this study, apparent T₂ relaxation times ( T 2 app ) of downfield peaks are reported in the human brain at 9.4 T and are used to guide spectral assignments of some downfield metabolite peaks.

METHODS

Echo time series of downfield metabolite spectra were acquired at 9.4 T using a metabolite-cycled semi-LASER sequence. Metabolite spectral fitting was performed using LCModel V6.3-1L while fitting a pH sweep to estimate the pH of the homocarnosine (hCs) imidazole ring. T 2 app were calculated by fitting the resulting relative amplitudes of the peaks to a mono-exponential decay across the TE series. Furthermore, estimated tissue concentrations of molecules were calculated using the relaxation times and internal water as a reference.

RESULTS

T 2 app of downfield metabolites are reported within a range from 16 to 32 ms except for homocarnosine with T 2 app of 50 ms. Correcting T 2 app for exchange rates ( T 2 c o r r ) resulted in relaxation times between 20 and 33 ms. The estimated pH values based on hCs imidazole range from 7.07 to 7.12 between subjects. Furthermore, analyzing the linewidths of the downfield peaks and their T 2 app contribution led to possible peak assignments.

CONCLUSION

T 2 app relaxation times were longer for the assigned metabolite peaks compared to the unassigned peaks. Tissue pH estimation in vivo with proton MRS and simultaneous quantification of amide protons at 8.30 ± 0.15 ppm is likely possible. Based on concentration, linewidth, and exchange rates measurements, tentative peak assignments are discussed for adenosine triphosphate (ATP), N-acetylaspartylglutamate (NAAG), and urea.

Effects of different macromolecular models on reproducibility of FID-MRSI at 7T

PURPOSE

A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID-MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility.

METHODS

Clinically feasible, high-resolution FID-MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test-retest reproducibility of MRSI scans was assessed voxel-wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model.

RESULTS

The full MM model provided the most reproducible quantification of total NAA, total Cho, myo-inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to -0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites.

CONCLUSION

Clinically feasible FID-MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered.

Macromolecule-suppressed GABA measurements correlate more strongly with behavior than macromolecule-contaminated GABA+ measurements

The inhibitory neurotransmitter γ-aminobutyric acid (GABA) is known to be fundamental to the neuronal processes underlying visual orientation and vibrotactile frequency and amplitude discrimination. Previous studies have demonstrated that performance on visual and vibrotactile psychophysics tasks is associated with in vivo measurements of "GABA+" levels - a measure of GABA substantially contaminated by a macromolecular (MM) signal. Here, we establish that these prior findings are indeed driven by the GABA fraction of that signal. Edited magnetic resonance spectroscopy (MRS) was used to measure GABA with and without MM suppression in the sensorimotor (SM1) and occipital cortices in 14 healthy male adults. Volunteers also underwent psychophysical experiments to assess their performance on visual orientation discrimination and vibrotactile amplitude and frequency discrimination. We show that MM-suppressed GABA levels correlate more strongly with individual differences in vibrotactile (in the case of SM1 GABA; amplitude: r = -0.63, p = 0.03; frequency: r = -0.62, p = 0.02) and visual orientation (in the case of occipital GABA; r = -0.59, p = 0.05) discrimination thresholds than GABA levels contaminated by MM (vibrotactile amplitude: r = -0.36, p = 0.30; vibrotactile frequency: r = -0.53, p = 0.09; visual orientation: r = 0.21, p = 0.55). These findings further support the view that measurements of endogenous GABA acquired with edited MRS can usefully probe neurochemical-behavioral relationships in humans. Moreover, the more specific measurement of GABA used in this study provides increased statistical power to observe these regionally specific relationships.

Metabolite-cycled density-weighted concentric rings k-space trajectory (DW-CRT) enables high-resolution 1 H magnetic resonance spectroscopic imaging at 3-Tesla

Magnetic resonance spectroscopic imaging (MRSI) is a promising technique in both experimental and clinical settings. However, to date, MRSI has been hampered by prohibitively long acquisition times and artifacts caused by subject motion and hardware-related frequency drift. In the present study, we demonstrate that density weighted concentric ring trajectory (DW-CRT) k-space sampling in combination with semi-LASER excitation and metabolite-cycling enables high-resolution MRSI data to be rapidly acquired at 3 Tesla. Single-slice full-intensity MRSI data (short echo time (TE) semi-LASER TE = 32 ms) were acquired from 6 healthy volunteers with an in-plane resolution of 5 × 5 mm in 13 min 30 sec using this approach. Using LCModel analysis, we found that the acquired spectra allowed for the mapping of total N-acetylaspartate (median Cramer-Rao Lower Bound [CRLB] = 3%), glutamate+glutamine (8%), and glutathione (13%). In addition, we demonstrate potential clinical utility of this technique by optimizing the TE to detect 2-hydroxyglutarate (long TE semi-LASER, TE = 110 ms), to produce relevant high-resolution metabolite maps of grade III IDH-mutant oligodendroglioma in a single patient. This study demonstrates the potential utility of MRSI in the clinical setting at 3 Tesla.