Longitudinal trajectories of anterior cingulate glutamate and subclinical psychotic experiences in early adolescence: the impact of bullying victimization.

Previous studies reported decreased glutamate levels in the anterior cingulate cortex (ACC) in non-treatment-resistant schizophrenia and first-episode psychosis. However, ACC glutamatergic changes in subjects at high-risk for psychosis, and the effects of commonly experienced environmental emotional/social stressors on glutamatergic function in adolescents remain unclear. In this study, adolescents recruited from the general population underwent proton magnetic resonance spectroscopy (MRS) of the pregenual ACC using a 3-Tesla scanner. We explored longitudinal data on the association of combined glutamate-glutamine (Glx) levels, measured by MRS, with subclinical psychotic experiences. Moreover, we investigated associations of bullying victimization, a risk factor for subclinical psychotic experiences, and help-seeking intentions, a coping strategy against stressors including bullying victimization, with Glx levels. Finally, path analyses were conducted to explore multivariate associations. For a contrast analysis, gamma-aminobutyric acid plus macromolecule (GABA+) levels were also analyzed. Negative associations were found between Glx levels and subclinical psychotic experiences at both Times 1 (n = 219, mean age 11.5 y) and 2 (n = 211, mean age 13.6 y), as well as for over-time changes (n = 157, mean interval 2.0 y). Moreover, effects of bullying victimization and bullying victimization × help-seeking intention interaction effects on Glx levels were found (n = 156). Specifically, bullying victimization decreased Glx levels, whereas help-seeking intention increased Glx levels only in bullied adolescents. Finally, associations among bullying victimization, help-seeking intention, Glx levels, and subclinical psychotic experiences were revealed. GABA+ analysis revealed no significant results. This is the first adolescent study to reveal longitudinal trajectories of the association between glutamatergic function and subclinical psychotic experiences and to elucidate the effect of commonly experienced environmental emotional/social stressors on glutamatergic function. Our findings may deepen the understanding of how environmental emotional/social stressors induce impaired glutamatergic neurotransmission that could be the underpinning of liability for psychotic experiences in early adolescence.

1H detection and dynamic nuclear polarization-enhanced NMR of Aß1-42 fibrils.

Several publications describing high-resolution structures of amyloid-ß (Aß) and other fibrils have demonstrated that magic-angle spinning (MAS) NMR spectroscopy is an ideal tool for studying amyloids at atomic resolution. Nonetheless, MAS NMR suffers from low sensitivity, requiring relatively large amounts of samples and extensive signal acquisition periods, which in turn limits the questions that can be addressed by atomic-level spectroscopic studies. Here, we show that these drawbacks are removed by utilizing two relatively recent additions to the repertoire of MAS NMR experiments-namely, 1H detection and dynamic nuclear polarization (DNP). We show resolved and sensitive two-dimensional (2D) and three-dimensional (3D) correlations obtained on 13C,15N-enriched, and fully protonated samples of M0Aß1-42 fibrils by high-field 1H-detected NMR at 23.4 T and 18.8 T, and 13C-detected DNP MAS NMR at 18.8 T. These spectra enable nearly complete resonance assignment of the core of M0Aß1-42 (K16-A42) using submilligram sample quantities, as well as the detection of numerous unambiguous internuclear proximities defining both the structure of the core and the arrangement of the different monomers. An estimate of the sensitivity of the two approaches indicates that the DNP experiments are currently ∼6.5 times more sensitive than 1H detection. These results suggest that 1H detection and DNP may be the spectroscopic approaches of choice for future studies of Aß and other amyloid systems.

Quantification of high-density lipoprotein particle number by proton nuclear magnetic resonance: don't believe the numbers.

PURPOSE OF REVIEW: Proton nuclear magnetic resonance (NMR) can rapidly assess lipoprotein concentrations and sizes in biological samples. It may be especially useful for quantifying high-density lipoprotein (HDL), which exhibits diverse particle sizes and concentrations. We provide a critical review of the strengths and limitations of NMR for quantifying HDL subclasses. RECENT FINDINGS: Recent studies using NMR have shed light on HDL's role in various disorders, ranging from residual cardiovascular risk to host susceptibility to infection. However, accurately quantifying HDL particle number, size, and concentration (HDL-P) remains a challenge. Discrepancies exist between NMR and other methods such as gel electrophoresis, ion mobility analysis and size-exclusion chromatography in estimating the abundance of HDL species and the ratio of apolipoprotein A-I (APOA1) to HDL particles. SUMMARY: NMR is a low-cost method for quantifying HDL-P that is readily applicable to clinical and translational studies. However, inconsistencies between the results of NMR quantification of HDL-P and other independent methods hinder the interpretation of NMR results. Because proton NMR apparently fails to accurately quantify the sizes and concentrations of HDL, the relevance of such studies to HDL biology poses challenges. This limits our understanding of pathophysiological implications of HDL-P as determined by NMR, particularly in determining cardiovascular disease (CVD) risk.

Multiplet-Assisted Peak Alignment for 1H NMR-Based Metabolomics.

NMR-based metabolomics aims at recovering biological information by comparing spectral data from samples of biological interest and appropriate controls. Any statistical analysis performed on the data matrix relies on the proper peak alignment to produce meaningful results. Through the last decades, several peak alignment algorithms have been proposed, as well as alternatives like spectral binning or strategies for annotation and quantification, the latter depending on reference databases. Most of the alignment algorithms, mainly based on segmentation of the spectra, present limitations for regions with peak overlap or cases of frequency order exchange. Here, we present our multiplet-assisted peak alignment algorithm, a new methodology that consists of aligning peaks by matching multiplet profiles of f1 traces from J-resolved spectra. A correspondence matrix with the linked f1 traces is built, and multivariate data analysis can be performed on it to obtain useful information from the data, overcoming the issues of peak overlap and frequency crossovers. Statistical total correlation spectroscopy can be applied on the matrix as well, toward a better identification of molecules of interest. The results can be queried on one-dimensional (1D) 1H databases or can be directly coupled to our previously published Chemical Shift Multiplet Database.

High-quality lipid suppression and B0 shimming for human brain 1H MRSI.

Magnetic Resonance Spectroscopic Imaging (MRSI) is a powerful technique that can map the metabolic profile in the brain non-invasively. Extracranial lipid contamination and insufficient B0 homogeneity however hampers robustness, and as a result has hindered widespread use of MRSI in clinical and research settings. Over the last six years we have developed highly effective extracranial lipid suppression methods with a second order gradient insert (ECLIPSE) utilizing inner volume selection (IVS) and outer volume suppression (OVS) methods. While ECLIPSE provides > 100-fold in lipid suppression with modest radio frequency (RF) power requirements and immunity to B1+ field variations, axial coverage is reduced for non-elliptical head shapes. In this work we detail the design, construction, and utility of MC-ECLIPSE, a pulsed second order gradient coil with Z2 and X2Y2 fields, combined with a 54-channel multi-coil (MC) array. The MC-ECLIPSE platform allows arbitrary region of interest (ROI) shaped OVS for full-axial slice coverage, in addition to MC-based B0 field shimming, for robust human brain proton MRSI. In vivo experiments demonstrate that MC-ECLIPSE allows axial brain coverage of 92-95 % is achieved following arbitrary ROI shaped OVS for various head shapes. The standard deviation (SD) of the residual B0 field following SH2 and MC shimming were 25 ± 9 Hz and 18 ± 8 Hz over a 5 cm slab, and 18 ± 5 Hz and 14 ± 6 Hz over a 1.5 cm slab, respectively. These results demonstrate that B0 magnetic field shimming with the MC array supersedes second order harmonic capabilities available on standard MRI systems for both restricted and large ROIs. Furthermore, MC based B0 shimming provides comparable shimming performance to an unrestricted SH5 shim set for both restricted, and 5-cm slab shim challenges. Phantom experiments demonstrate the high level of localization performance achievable with MC-ECLIPSE, with ROI edge chemical shift displacements ranging from 1-3 mm with a median value of 2 mm, and transition width metrics ranging from 1-2.5 mm throughout the ROI edge. Furthermore, MC based B0 shimming is comparable to performance following a full set of unrestricted spherical harmonic fields up to order 5. Short echo time MRSI and GABA-edited MRSI acquisitions in the human brain following MC-shimming and arbitrary ROI shaping demonstrate full-axial slice coverage and extracranial lipid artifact free spectra. MC-ECLIPSE allows full-axial coverage and robust MRSI acquisitions, while allowing interrogation of cortical tissue proximal to the skull, which has significant value in a wide range of neurological and psychiatric conditions.

Interactions of catecholamines and GABA+ in cognitive control: Insights from EEG and 1H-MRS.

Catecholamines and amino acid transmitter systems are known to interact, the exact links and their impact on cognitive control functions have however remained unclear. Using a multi-modal imaging approach combining EEG and proton-magnetic resonance spectroscopy (1H-MRS), we investigated the effect of different degrees of pharmacological catecholaminergic enhancement onto theta band activity (TBA) as a measure of interference control during response inhibition and execution. It was central to our study to evaluate the predictive impact of in-vivo baseline GABA+ concentrations in the striatum, the anterior cingulate cortex (ACC) and the supplemental motor area (SMA) of healthy adults under varying degrees of methylphenidate (MPH) stimulation. We provide evidence for a predictive interrelation of baseline GABA+ concentrations in cognitive control relevant brain areas onto task-induced TBA during response control stimulated with MPH. Baseline GABA+ concentrations in the ACC, the striatum, and the SMA had a differential impact on predicting interference control-related TBA in response execution trials. GABA+ concentrations in the ACC appeared to be specifically important for TBA modulations when the cognitive effort needed for interference control was high - that is when no prior task experience exists, or in the absence of catecholaminergic enhancement with MPH. The study highlights the predictive role of baseline GABA+ concentrations in key brain areas influencing cognitive control and responsiveness to catecholaminergic enhancement, particularly in high-effort scenarios.

Neurochemical changes in the progression of Huntington's disease: A meta-analysis of in vivo1H-MRS studies.

Proton magnetic resonance spectroscopy (1H-MRS) allows measuring specific brain metabolic alterations in Huntington's disease (HD), and these metabolite profiles may serve as non-invasive biomarkers associated with disease progression. Despite this potential, previous findings are inconsistent. Accordingly, we performed a meta-analysis on available in vivo1H-MRS studies in premanifest (Pre-HD) and symptomatic HD stages (Symp-HD), and quantified neurometabolic changes relative to controls in 9 Pre-HD studies (227 controls and 188 mutation carriers) and 14 Symp-HD studies (326 controls and 306 patients). Our results indicated decreased N-acetylaspartate and creatine in the basal ganglia in both Pre-HD and Symp-HD. The overall level of myo-inositol was decreased in Pre-HD while increased in Symp-HD. Besides, Symp-HD patients showed more severe metabolism disruption than Pre-HD patients. Taken together, 1H-MRS is important for elucidating progressive metabolite changes from Pre-HD to clinical conversion; N-acetylaspartate and creatine in the basal ganglia are already sensitive at the preclinical stage and are promising biomarkers for tracking disease progression; overall myo-inositol is a possible characteristic metabolite for distinguishing HD stages.

Study on the Relationship Between MRI Functional Imaging and Multiple Immunohistochemical Features of Glioma: A Noninvasive and More Precise Glioma Management.

Objective: To investigate the performance of diffusion-tensor imaging (DTI) and hydrogen proton magnetic resonance spectroscopy (1H-MRS) parameters in predicting the immunohistochemistry (IHC) biomarkers of glioma. Methods: Patients with glioma confirmed by pathology from March 2015 to September 2019 were analyzed, the preoperative DTI and 1H-MRS images were collected, apparent diffusion coefficient (ADC) and fractional anisotropy (FA), in the lesion area were measured, the relative values relative ADC (rADC) and relative FA (rFA) were obtained by the ratio of them in the lesion area to the contralateral normal area. The peak of each metabolite in the lesion area of 1H-MRS image: N-acetylaspartate (NAA), choline (Cho), and creatine (Cr), and metabolite ratio: NAA/Cho, NAA/(Cho + Cr) were selected and calculated. The preoperative IHC data were collected including CD34, Ki-67, p53, S-100, syn, vimentin, NeuN, Nestin, and glial fibrillary acidic protein. Results: One predicting parameter of DTI was screened, the rADC of the Ki-67 positive group was lower than that of the negative group. Two parameters of 1H-MRS were found to have significant reference values for glioma grades, the NAA and Cr decreased as the grade of glioma increased, moreover, Ki-67 Li was negatively correlated with NAA and Cr. Conclusion: NAA and Cr have potential application value in predicting glioma grades and tumor proliferation activity. Only rADC has predictive value for Ki-67 expression among DTI parameters.

Toward Absolute Quantification of Soluble Proteins via Proton Nuclear Magnetic Resonance Spectroscopy: Total Protein Concentration in Blood Plasma.

For absolute protein quantification using nuclear magnetic resonance (NMR) spectroscopy, we considered proteins as homopolymers and effective amino acid (AA) residues (AAREff) as monomer units. For diverse classes of proteins, we determined the AAREff molecular weight as 111.5 ± 3.2 Da and the number of hydrogens per AA as 7.8 ± 0.2. Their ratio of 14.3 ± 0.3 (g/LP)/(mol/LH) remains constant across various protein classes and is equivalent to Kjeldahl's nitrogen-to-protein conversion constant of 5.78 ± 0.29 gN/gP. By analogy to the Kjeldahl method, we suggest that the total integral of a 1H NMR solution protein spectrum could be used for total protein quantification. We synthesized low-resolution protein spectra from the weighted sums of individual AA spectra and compared them with experimental spectra. In the methyl region, the ratio of the protein mass to the total number of protons in the synthetic spectra (corrected for the chemical shift mismatch) was ∼1 (mg/mL)/mM, which agrees with an earlier reported experimental ratio for urine (1.05 ± 0.06 (mg/mL)/mM). For human blood plasma, in the methyl region, we found empirical ratios of 1.115 ± 0.006 (mg/mL)/mM (using 96 patient samples) and 1.121 ± 0.011 (mg/mL)/mM for the NIST plasma standard. This numerical agreement points to universal conversion constants, i.e., protein mixtures with unknown compositions could be quantified without the need for calibration standards by measuring the millimolar proton concentration within the methyl region of the NMR spectrum using the same conversion constant.

Blending Samples to Increase Accuracy and Precision of 1H NMR Urine Metabolomics.

Urine is an equally attractive biofluid for metabolomics analysis, as it is a challenging matrix analytically. Accurate urine metabolite concentration estimates by Nuclear Magnetic Resonance (NMR) are hampered by pH and ionic strength differences between samples, resulting in large peak shift variability. Here we show that calculating the spectra of original samples from mixtures of samples using linear algebra reduces the shift problems and makes various error estimates possible. Since the use of two-dimensional (2D) NMR to confirm metabolite annotations is effectively impossible to employ on every sample of large sample sets, stabilization of metabolite peak positions increases the confidence in identifying metabolites, avoiding the pitfall of oranges-to-apples comparisons.

Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum.

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

The intra-individual reliability of 1 H-MRS measurement in the anterior cingulate cortex across 1 year.

Magnetic resonance spectroscopy (MRS) is the primary method that can measure the levels of metabolites in the brain in vivo. To achieve its potential in clinical usage, the reliability of the measurement requires further articulation. Although there are many studies that investigate the reliability of gamma-aminobutyric acid (GABA), comparatively few studies have investigated the reliability of other brain metabolites, such as glutamate (Glu), N-acetyl-aspartate (NAA), creatine (Cr), phosphocreatine (PCr), or myo-inositol (mI), which all play a significant role in brain development and functions. In addition, previous studies which predominately used only two measurements (two data points) failed to provide the details of the time effect (e.g., time-of-day) on MRS measurement within subjects. Therefore, in this study, MRS data located in the anterior cingulate cortex (ACC) were repeatedly recorded across 1 year leading to at least 25 sessions for each subject with the aim of exploring the variability of other metabolites by using the index coefficient of variability (CV); the smaller the CV, the more reliable the measurements. We found that the metabolites of NAA, tNAA, and tCr showed the smallest CVs (between 1.43% and 4.90%), and the metabolites of Glu, Glx, mI, and tCho showed modest CVs (between 4.26% and 7.89%). Furthermore, we found that the concentration reference of the ratio to water results in smaller CVs compared to the ratio to tCr. In addition, we did not find any time-of-day effect on the MRS measurements. Collectively, the results of this study indicate that the MRS measurement is reasonably reliable in quantifying the levels of metabolites.

Comparison of extraction methods in vitro Plasmodium falciparum: A 1H NMR and LC-MS joined approach.

Malaria is a parasitic disease that remains a global concern and the subject of many studies. Metabolomics has emerged as an approach to better comprehend complex pathogens and discover possible drug targets, thus giving new insights that can aid in the development of antimalarial therapies. However, there is no standardized method to extract metabolites from in vitro Plasmodium falciparum intraerythrocytic parasites, the stage that causes malaria. Additionally, most methods are developed with either LC-MS or NMR analysis in mind, and have rarely been evaluated with both tools. In this work, three extraction methods frequently found in the literature were reproduced and samples were analyzed through both LC-MS and 1H NMR, and evaluated in order to reveal which is the most repeatable and consistent through an array of different tools, including chemometrics, peak detection and annotation. The most reliable method in this study proved to be a double extraction with methanol and methanol/water (80:20, v/v). Metabolomic studies in the field should move towards standardization of methodologies and the use of both LC-MS and 1H NMR in order to make data more comparable between studies and facilitate the achievement of biologically interpretable information.

Revealing metabolic and biochemical variations via 1H NMR metabolomics in streptozotocin-nicotinamide-induced diabetic rats treated with metformin.

The increasing prevalence of lean diabetes has prompted the generation of animal models that mimic metabolic disease in humans. This study aimed to determine the optimum streptozotocin-nicotinamide (STZ-NA) dosage ratio to elicit lean diabetic features in a rat model. It also used a proton nuclear magnetic resonance (1H NMR) urinary metabolomics approach to identify the metabolic effect of metformin treatment on this novel rat model. Three different STZ-NA dosage regimens (by body weight: Group A: 110 mg/kg NA and 45 mg/kg STZ; Group B: 180 mg/kg NA and 65 mg/kg STZ and Group C: 120 mg/kg NA and 60 mg/kg STZ) were administered to Sprague-Dawley rats along with oral metformin. Group A diabetic rats (A-DC) showed favorable serum biochemical analyses and a more positive response toward oral metformin administration relative to the other STZ-NA dosage ratio groups. Orthogonal partial least squares-discriminant analysis (OPLS-DA) revealed that glucose, citrate, pyruvate, hippurate, and methylnicotinamide differentiating the OPLS-DA of A-MTF rats (Group A diabetic rats treated with metformin) and A-DC model rats. Subsequent metabolic pathway analyses revealed that metformin treatment was associated with improvement in dysfunctions caused by STZ-NA induction, including carbohydrate metabolism, cofactor metabolism, and vitamin and amino acid metabolism. In conclusion, our results identify the best STZ-NA dosage ratio for a rat model to exhibit lean type 2 diabetic features with optimum sensitivity to metformin treatment. The data presented here could be informative to improve our understanding of non-obese diabetes in humans through the identification of possible activated metabolic pathways in the STZ-NA-induced diabetic rats model.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling.

PURPOSE: Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates. METHODS: We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data. RESULTS: 2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR. CONCLUSION: Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

Comparison of baseline correction algorithms for in vivo 1H-MRS.

Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short-echo-time MRS data. In short-echo-time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long-echo-time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi-LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm-QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm-QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

Improved reproducibility of γ-aminobutyric acid measurement from short-echo-time proton MR spectroscopy by linewidth-matched basis sets in LCModel.

γ-Aminobutyric acid (GABA), as the primary inhibitory neurotransmitter, is extremely important for maintaining healthy brain function, and deviations from GABA homeostasis are related to various brain diseases. Short-echo-time (short-TE) proton MR spectroscopy (1 H-MRS) has been employed to measure GABA concentration from various human brain regions at high magnetic fields. The aim of this study was to investigate the effect of spectral linewidth on GABA quantification and explore the application of an optimized basis-set preparation approach using a spectral-linewidth-matched (LM) basis set in LCModel to improve the reproducibility of GABA quantification from short-TE 1 H-MRS. In contrast to the fixed-linewidth basis-set approach, the LM basis-set preparation approach, where all metabolite basis spectra were simulated with a linewidth 4 Hz narrower than that of water, showed a smaller standard deviation of estimated GABA concentration from synthetic spectra with varying linewidths and lineshapes. The test-retest reproducibility was assessed by the mean within-subject coefficient of variation, which improved from 19.2% to 12.0% in the thalamus, from 27.9% to 14.9% in the motor cortex, and from 9.7% to 2.8% in the medial prefrontal cortex using LM basis sets at 7 T. We conclude that spectral linewidth has a large effect on GABA quantification from short-TE 1 H-MRS data and that using LM basis sets in LCModel can improve the reproducibility of GABA quantification.

Indirect 1H-[13C] MRS of the human brain at 7 T using a 13C-birdcage coil and eight transmit-receive 1H-dipole antennas with a 32-channel 1H-receive array.

The neuronal tricarboxylic acid and glutamate/glutamine (Glu/Gln) cycles play important roles in brain function. These processes can be measured in vivo using dynamic 1H-[13C] MRS during administration of 13C-labeled glucose. Proton-observed carbon-edited (POCE) MRS enhances the signal-to-noise ratio (SNR) compared with direct 13C-MRS. Ultra-high field further boosts the SNR and increases spectral dispersion; however, even at 7 T, Glu and Gln 1H-resonances may overlap. Further gain can be obtained with selective POCE (selPOCE). Our aim was to create a setup for indirect dynamic 1H-[13C] MRS in the human brain at 7 T. A home-built non-shielded transmit-receive 13C-birdcage head coil with eight transmit-receive 1H-dipole antennas was used together with a 32-channel 1H-receive array. Electromagnetic simulations were carried out to ensure that acquisitions remained within local and global head SAR limits. POCE-MRS was performed using slice-selective excitation with semi-localization by adiabatic selective refocusing (sLASER) and stimulated echo acquisition mode (STEAM) localization, and selPOCE-MRS using STEAM. Sequences were tested in a phantom containing non-enriched Glu and Gln, and in three healthy volunteers during uniformly labeled 13C-glucose infusions. In one subject the voxel position was alternated between bi-frontal and bi-occipital placement within one session. [4-13C]Glu-H4 and [4-13C]Gln-H4 signals could be separately detected using both STEAM-POCE and STEAM-selPOCE in the phantom. In vivo, [4,5-13C]Glx could be detected using both sLASER-POCE and STEAM-POCE, with similar sensitivities, but [4,5-13C]Glu and [4,5-13C]Gln signals could not be completely resolved. STEAM-POCE was alternately performed bi-frontal and bi-occipital within a single session without repositioning of the subject, yielding similar results. With STEAM-selPOCE, [4,5-13C]Glu and [4,5-13C]Gln could be clearly separated. We have shown that with our setup indirect dynamic 1H-[13C] MRS at 7 T is feasible in different locations in the brain within one session, and by using STEAM-selPOCE it is possible to separate Glu from Gln in vivo while obtaining high quality spectra.

High-resolution 1H-MRSI at 9.4 T by integrating relaxation enhancement and subspace imaging.

Achieving high-resolution and high signal-to-noise ratio (SNR) in vivo metabolic imaging via fast magnetic resonance spectroscopic imaging (MRSI) has been a longstanding challenge. This study combines the methods of relaxation enhancement (RE) and subspace imaging for the first time, enabling high-resolution and high-SNR in vivo MRSI of rodent brains at 9.4 T. Specifically, an RE-based chemical shift imaging sequence, which combines a frequency-selective pulse to excite only the metabolite frequencies with minimum perturbation of the water spins and a pair of adiabatic pulses to spatially localize the slice of interest, is designed and evaluated in vivo. This strategy effectively shortens the apparent T1 of metabolites, thereby increasing the SNR during relatively short repetition time ((TR) compared with acquisitions with only spatially selective wideband excitations, and does not require water suppression. The SNR was further enhanced via a state-of-the-art subspace reconstruction method. A novel subspace learning strategy tailored for 9.4 T and RE acquisitions is developed. In vivo, high-resolution (e.g., voxel size of 0.6 × 0.6 × 1.5 mm3) MRSI of both healthy mouse brains and a glioma-bearing mouse brain in 12.5 min has been demonstrated.

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification.

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.