Quantification in magnetic resonance spectroscopy based on semi-parametric approaches

Metabolic Diaschisis in Mild Traumatic Brain Injury

Neurophysiological diaschisis presents in traumatic brain injury (TBI) as functional impairment distant to the lesion site caused by axonal neuroexcitation and deafferentation. Diaschisis studies in TBI models have evaluated acute phase functional and microstructural changes. Here, in vivo biochemical changes and cerebral blood flow (CBF) dynamics following TBI are studied with magnetic resonance. Behavioral assessments, magnetic resonance spectroscopy (MRS), and CBF measurements on rats followed cortical impact TBI. Data were acquired pre-TBI and 1-3 h, 2-days, 7-days, and 14-days post-TBI. MRS was performed on the ipsilateral and contralateral sides in the cortex, striatum, and thalamus. Metabolites measured by MRS included N-acetyl aspartate (NAA), aspartate (Asp), lactate (Lac), glutathione (GSH), and glutamate (Glu). Lesion volume expanded for 2 days post-TBI and then decreased. Ipsilateral CBF dropped acutely versus baseline on both sides (-62% ipsilateral, -48% contralateral, p < 0.05) but then recovered in cortex, with similar changes in ipsilateral striatum. Metabolic changes versus baseline included increased Asp (+640% by Day 7 post-TBI, p < 0.05) and Lac (+140% on Day 2 post-TBI, p < 0.05) in ipsilateral cortex, while GSH (-67% acutely, p < 0.05) and NAA decreased (-50% on Day 2, p < 0.05). In contralateral cortex Lac decreased (-73% acutely, p < 0.05). Analysis of variance showed significance for Side (p < 0.05), Time after TBI (p < 0.05), and interactions (p < 0.005) for Asp, GSH, Lac, and NAA. Transient decreases of GSH (-30%, p < 0.05, acutely) and NAA (-23% on Day 2, p < 0.05) occurred in ipsilateral striatum with reduced GSH (-42%, p < 0.005, acutely) in the contralateral striatum. GSH was decreased in ipsilateral thalamus (-59% ipsilateral on Day 2, p < 0.05). Delayed increases of total choline were seen in the contralateral thalamus were noted as well (+21% on Day 7 post-TBI, p < 0.05). Both CBF and neurometabolite concentration changes occurred remotely from the TBI site, both ipsilaterally and contralaterally. Decreased Lac levels on the contralateral cortex following TBI may be indicative of reduced anaerobic metabolism during the acute phase. The timing and locations of the changes suggest excitatory and inhibitory signaling processes are affecting post-TBI metabolic fluctuations.

Linewidth-related bias in modelled concentration estimates from GABA-edited 1H-MRS

J-difference-edited MRS is widely used to study GABA in the human brain. Editing for low-concentration target molecules (such as GABA) typically exhibits lower signal-to-noise ratio (SNR) than conventional non-edited MRS, varying with acquisition region, volume and duration. Moreover, spectral lineshape may be influenced by age-, pathology-, or brain-region-specific effects of metabolite T2, or by task-related blood-oxygen level dependent (BOLD) changes in functional MRS contexts. Differences in both SNR and lineshape may have systematic effects on concentration estimates derived from spectral modelling. The present study characterises the impact of lineshape and SNR on GABA+ estimates from different modelling algorithms: FSL-MRS, Gannet, LCModel, Osprey, spant and Tarquin. Publicly available multi-site GABA-edited data (222 healthy subjects from 20 sites; conventional MEGA-PRESS editing; TE = 68 ms) were pre-processed with a standardised pipeline, then filtered to apply controlled levels of Lorentzian and Gaussian linebroadening and SNR reduction. Increased Lorentzian linewidth was associated with a 2-5% decrease in GABA+ estimates per Hz, observed consistently (albeit to varying degrees) across datasets and most algorithms. Weaker, often opposing effects were observed for Gaussian linebroadening. Variations are likely caused by differing baseline parametrization and lineshape constraints between models. Effects of linewidth on other metabolites (e.g., Glx and tCr) varied, suggesting that a linewidth confound may persist after scaling to an internal reference. These findings indicate a potentially significant confound for studies where linewidth may differ systematically between groups or experimental conditions, e.g. due to T2 differences between brain regions, age, or pathology, or varying T2* due to BOLD-related changes. We conclude that linewidth effects need to be rigorously considered during experimental design and data processing, for example by incorporating linewidth into statistical analysis of modelling outcomes or development of appropriate lineshape matching algorithms.

Optimized multi-voxel TE-averaged PRESS for glutamate detection in the human brain at 3T

PURPOSE

To optimize possible combinations of echo times (TE) for multi-voxel TE-averaged Point RESolved Spectroscopy (PRESS) while reducing the total number of TEs required to separate glutamate (Glu) and glutamine (Gln) within a clinically feasible scan time.

METHODS

General Approach to Magnetic resonance Mathematical Analysis (GAMMA) was used to implement 2D J-resolved PRESS technique, and the spectra of 14 individual brain metabolites were simulated at 64 different TEs. Monte Carlo simulations were used for selecting the best TE combinations to separate Glu and Gln using TE-averaged PRESS with a total number of two, three, four and five TEs. Single-voxel 1H-MRS data were acquired using 64 different TEs from a healthy volunteer on a clinical 3T MR scanner to validate the echo time combinations selected with simulations. Additionally, 2D 1H-MRSI data of eight healthy volunteers were acquired on a clinical 3T MR scanner using four different TEs that were determined by Monte Carlo simulations. Optimized TE-averaged PRESS spectra were created by averaging the spectra acquired at selected TEs. LCModel was used for spectral quantification. A Wilcoxon signed-rank test was used to detect statistically significant differences in Glu/Gln ratios between 35 ms PRESS and optimized TE-averaged PRESS data.

RESULTS

Glu could be clearly separated from Gln at 2.35 ppm, using optimized TE-averaged PRESS with only four TEs (35, 37, 40, and 42 ms) that were selected through Monte Carlo simulations. Glu/Gln ratios were significantly higher in the optimized TE-averaged PRESS data of healthy volunteers than in the 35 ms PRESS data (P = 0.008).

CONCLUSION

Optimized multi-voxel TE-averaged PRESS enabled faster and unobstructed quantification of Glu at multiple voxels in the human brain in vivo at 3T.

Advances in mass spectrometry-based multi-scale metabolomic methodologies and their applications in biological and clinical investigations

Metabolomics is a nascent field of inquiry that emerged in the late 20th century. It encompasses the comprehensive profiling of metabolites across a spectrum of organisms, ranging from bacteria and cells to tissues. The rapid evolution of analytical methods and data analysis has greatly accelerated progress in this dynamic discipline over recent decades. Sophisticated techniques such as liquid chromatograph mass spectrometry (MS), gas chromatograph MS, capillary electrophoresis MS, and nuclear magnetic resonance serve as the cornerstone of metabolomic analysis. Building upon these methods, a plethora of modifications and combinations have emerged to propel the advancement of metabolomics. Despite this progress, scrutinizing metabolism at the single-cell or single-organelle level remains an arduous task over the decades. Some of the most thrilling advancements, such as single-cell and single-organelle metabolic profiling techniques, offer profound insights into the intricate mechanisms within cells and organelles. This allows for a comprehensive study of metabolic heterogeneity and its pivotal role in multiple biological processes. The progress made in MS imaging has enabled high-resolution in situ metabolic profiling of tissue sections and even individual cells. Spatial reconstruction techniques enable the direct representation of metabolic distribution and alteration in three-dimensional space. The application of novel metabolomic techniques has led to significant breakthroughs in biological and clinical studies, including the discovery of novel metabolic pathways, determination of cell fate in differentiation, anti-aging intervention through modulating metabolism, metabolomics-based clinicopathologic analysis, and surgical decision-making based on on-site intraoperative metabolic analysis. This review presents a comprehensive overview of both conventional and innovative metabolomic techniques, highlighting their applications in groundbreaking biological and clinical studies.

Feasibility and implications of using subject-specific macromolecular spectra to model short echo time magnetic resonance spectroscopy data

Expert consensus recommends linear-combination modeling (LCM) of 1 H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions are usually derived from metabolite-nulled spectra averaged across a small cohort. The use of subject-specific instead of cohort-averaged measured MM basis functions has not been studied widely. Furthermore, measured MM basis functions are not widely available to non-expert users, who commonly rely on parameterized MM signals internally simulated by LCM software. To investigate the impact of the choice of MM modeling, this study, therefore, compares metabolite level estimates between different MM modeling strategies (cohort-mean measured; subject-specific measured; parameterized) in a lifespan cohort and characterizes its impact on metabolite-age associations. 100 conventional (TE = 30 ms) and metabolite-nulled (TI = 650 ms) PRESS datasets, acquired from the medial parietal lobe in a lifespan cohort (20-70 years of age), were analyzed in Osprey. Short-TE spectra were modeled in Osprey using six different strategies to consider the MM baseline. Fully tissue- and relaxation-corrected metabolite levels were compared between MM strategies. Model performance was evaluated by model residuals, the Akaike information criterion (AIC), and the impact on metabolite-age associations. The choice of MM strategy had a significant impact on the mean metabolite level estimates and no major impact on variance. Correlation analysis revealed moderate-to-strong agreement between different MM strategies (r > 0.6). The lowest relative model residuals and AIC values were found for the cohort-mean measured MM. Metabolite-age associations were consistently found for two major singlet signals (total creatine (tCr])and total choline (tCho)) for all MM strategies; however, findings for metabolites that are less distinguishable from the background signals associations depended on the MM strategy. A variance partition analysis indicated that up to 44% of the total variance was related to the choice of MM strategy. Additionally, the variance partition analysis reproduced the metabolite-age association for tCr and tCho found in the simpler correlation analysis. In summary, the inclusion of a single high signal-to-noise ratio MM basis function (cohort-mean) in the short-TE LCM leads to more lower model residuals and AIC values compared with MM strategies with more degrees of freedom (Gaussian parametrization) or subject-specific MM information. Integration of multiple LCM analyses into a single statistical model potentially allows to identify the robustness in the detection of underlying effects (e.g., metabolite vs. age), reduces algorithm-based bias, and estimates algorithm-related variance.

Subnormothermic Ex Vivo Porcine Kidney Perfusion Improves Energy Metabolism: Analysis Using 31P Magnetic Resonance Spectroscopic Imaging

The ideal preservation temperature for donation after circulatory death kidney grafts is unknown. We investigated whether subnormothermic (22 °C) ex vivo kidney machine perfusion could improve kidney metabolism and reduce ischemia-reperfusion injury.

Comparison of seven modelling algorithms for γ-aminobutyric acid-edited proton magnetic resonance spectroscopy

Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.

Glutamic Acid and Total Creatine as Predictive Markers for Epilepsy in Glioblastoma by Using Magnetic Resonance Spectroscopy Before Surgery

OBJECTIVE

Epilepsy in glioblastoma patients significantly reduces their quality of life; however, little is known about the association between predicting epilepsy and metabolites in tumors. In this study, we used 3.0-T magnetic resonance imaging and ¹H-magnetic resonance spectroscopy (MRS) to quantify metabolite concentrations in patients with varying epilepsy histories.

METHODS

Fifty-one patients with glioblastoma underwent pretreatment 3.0-T MRI/¹H-MRS scanning. Single-voxel (1.5 cm³) MRS, in an enhanced lesion, was acquired using a double-echo point-resolved spectroscopic sequence with chemical-shift selective water suppression. MRS data were quantified with linear combination model (LC-Model) software. We compared the MRS data between groups with and without epilepsy during the postoperative course (EP).

RESULTS

The ratios of glutamate (Glu) and glutamate + glutamine (Glx) to total creatine (Glu/tCr and Glx/tCr) in the tumor were associated with epilepsy history. The receiver operating characteristic curve analysis showed that a Glu/tCr value of 1.81 was 70% sensitive and 90% specific for the prediction of EP (area under curve: 0.82). In the analysis excluding patients with preoperative epilepsy, a Glu/tCr value of 1.81 was 75% sensitive and 88% specific for the prediction (area under curve: 0.87).

CONCLUSIONS

Intratumoral metabolite concentrations measured using pretreatment 3.0-T MRI/¹H-MRS changed characteristically in the group with EP. Our study suggests that the Glu/tCr ratio in tumors has adequate reliability in predicting EP. Pretreatment MRS is a minimally invasive and simple procedure that can provide useful information on glioblastoma patients.

Feasibility of Hepatic Fat Quantification Using Proton Density Fat Fraction by Multi-Echo Chemical-Shift-Encoded MRI at 7T

Fat fraction quantification and assessment of its distribution in the hepatic tissue become more important with the growing epidemic of obesity, and the increasing prevalence of diabetes mellitus type 2 and non-alcoholic fatty liver disease. At 3Tesla, the multi-echo, chemical-shift-encoded magnetic resonance imaging (CSE-MRI)-based acquisition allows the measurement of proton density fat-fraction (PDFF) even in clinical protocols. Further improvements in SNR can be achieved by the use of phased array coils and increased static magnetic field. The purpose of the study is to evaluate the feasibility of PDFF imaging using a multi-echo CSE-MRI technique at ultra-high magnetic field (7Tesla). Thirteen volunteers (M/F) with a broad range of age, body mass index, and hepatic PDFF were measured at 3 and 7T by multi-gradient-echo MRI and single-voxel spectroscopy MRS. All measurements were performed in breath-hold (exhalation); the MRI protocols were optimized for a short measurement time, thus minimizing motion-related problems. 7T data were processed off-line using Matlab® (MRI:multi-gradient-echo) and jMRUI (MRS), respectively. For quantitative validation of the PDFF results, a similar protocol was performed at 3T, including on-line data processing provided by the system manufacturer, and correlation analyses between 7 and 3T data were performed off-line. The multi-echo CSE-MRI measurements at 7T with a phased-array coil configuration and an optimal post-processing yielded liver volume coverage ranging from 30 to 90% for high- and low-BMI subjects, respectively. PDFFs ranged between 1 and 20%. We found significant correlations between 7T MRI and -MRS measurements (R² ≅ 0.97; p < 0.005), and between MRI-PDFF at 7T and 3T fields (R² ≅ 0.94; p < 0.005) in the evaluated volumes. Based on the measurements and analyses performed, the multi-echo CSE-MRI method using a 32-channel coil at 7T showed its aptitude for MRI-based quantitation of PDFF in the investigated volumes. The results are the first step toward qMRI of the whole liver at 7T with further improvements in hardware.

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.

Ex Vivo Analysis of Kidney Graft Viability Using 31P Magnetic Resonance Imaging Spectroscopy

BACKGROUND

The lack of organs for kidney transplantation is a growing concern. Expansion in organ supply has been proposed through the use of organs after circulatory death (donation after circulatory death [DCD]). However, many DCD grafts are discarded because of long warm ischemia times, and the absence of reliable measure of kidney viability. P magnetic resonance imaging (pMRI) spectroscopy is a noninvasive method to detect high-energy phosphate metabolites, such as ATP. Thus, pMRI could predict kidney energy state, and its viability before transplantation.

METHODS

To mimic DCD, pig kidneys underwent 0, 30, or 60 min of warm ischemia, before hypothermic machine perfusion. During the ex vivo perfusion, we assessed energy metabolites using pMRI. In addition, we performed Gadolinium perfusion sequences. Each sample underwent histopathological analyzing and scoring. Energy status and kidney perfusion were correlated with kidney injury.

RESULTS

Using pMRI, we found that in pig kidney, ATP was rapidly generated in presence of oxygen (100 kPa), which remained stable up to 22 h. Warm ischemia (30 and 60 min) induced significant histological damages, delayed cortical and medullary Gadolinium elimination (perfusion), and reduced ATP levels, but not its precursors (AMP). Finally, ATP levels and kidney perfusion both inversely correlated with the severity of kidney histological injury.

CONCLUSIONS

ATP levels, and kidney perfusion measurements using pMRI, are biomarkers of kidney injury after warm ischemia. Future work will define the role of pMRI in predicting kidney graft and patient's survival.

Terminology and concepts for the characterization of in vivo MR spectroscopy methods and MR spectra: Background and experts' consensus recommendations

With a 40-year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past.

Inpainting as a Technique for Estimation of Missing Voxels in Brain Imaging

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI) can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels, and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). In order to evaluate the performance across varying image contrasts and spatial resolutions, we applied the same techniques to a T1-weighted MRI brain dataset, and N-acetylaspartate (NAA) spectroscopy maps from a CSI dataset. For all image types, inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered (p's < 0.001). Inpainting maintained an average NRMSE of less than 5% even with 50% missing voxels, whereas the other techniques demonstrated up to three times that value, depending on the nature of the image. For CSI maps, inpainting maintained its superiority whether the previously unusable voxels were randomly distributed, or located in regions most commonly affected by voxel loss in real-world data. Inpainting is a promising approach for recovering unusable or missing voxels in voxel-wise analyses, particularly in imaging modalities characterized by low SNR such as CSI. We hypothesize that this technique may also be applicable for datasets from other imaging modalities, such as positron emission tomography, or dynamic susceptibility contrast MRI.

Advanced single voxel 1 H magnetic resonance spectroscopy techniques in humans: Experts' consensus recommendations

Conventional proton MRS has been successfully utilized to noninvasively assess tissue biochemistry in conditions that result in large changes in metabolite levels. For more challenging applications, namely, in conditions which result in subtle metabolite changes, the limitations of vendor-provided MRS protocols are increasingly recognized, especially when used at high fields (≥3 T) where chemical shift displacement errors, B0 and B1 inhomogeneities and limitations in the transmit B1 field become prominent. To overcome the limitations of conventional MRS protocols at 3 and 7 T, the use of advanced MRS methodology, including pulse sequences and adjustment procedures, is recommended. Specifically, the semiadiabatic LASER sequence is recommended when TE values of 25-30 ms are acceptable, and the semiadiabatic SPECIAL sequence is suggested as an alternative when shorter TE values are critical. The magnetic field B0 homogeneity should be optimized and RF pulses should be calibrated for each voxel. Unsuppressed water signal should be acquired for eddy current correction and preferably also for metabolite quantification. Metabolite and water data should be saved in single shots to facilitate phase and frequency alignment and to exclude motion-corrupted shots. Final averaged spectra should be evaluated for SNR, linewidth, water suppression efficiency and the presence of unwanted coherences. Spectra that do not fit predefined quality criteria should be excluded from further analysis. Commercially available tools to acquire all data in consistent anatomical locations are recommended for voxel prescriptions, in particular in longitudinal studies. To enable the larger MRS community to take advantage of these advanced methods, a list of resources for these advanced protocols on the major clinical platforms is provided. Finally, a set of recommendations are provided for vendors to enable development of advanced MRS on standard platforms, including implementation of advanced localization sequences, tools for quality assurance on the scanner, and tools for prospective volume tracking and dynamic linear shim corrections.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

NMR metabolomics: A look ahead

NMR has been used to perform metabolic studies, metabolic profiling and metabolomics in biofluids and tissues for more than 40 years. This close connection between metabolic measurements and NMR has flourished because of NMR's many unique strengths for characterizing the chemical composition of complex mixtures. However, a number of other technologies, including mass spectrometry, have appeared in the past few years that are encroaching on NMR's dominance in metabolomics and metabolic studies. In this brief review, some of the current strengths and existing limitations of NMR-based metabolomics are highlighted. Additionally, a number of recent advances in NMR hardware, methodology and software are also described and these advancements are used to speculate about where NMR-based metabolomics is going, what needs to be done to make it more popular and how it will evolve in the next 5-10 years.

Sex differences in the effect of acute administration of nicotine on MRS-measured metabolic profile of the rat brain

Women are less able to stop smoking than men. Elucidation of sex differences in the tobacco addiction could facilitate personalized treatment. Specialized brain reward systems are controlling the behavior through reinforcement using specific neuromediators. Using non-invasive magnetic resonance spectroscopy (MRS) to ascertain addiction/harm biomarkers could lead to better management of public health through advancements in regulatory and translational research. Proton MRS was used to monitor changes of specific neurometabolites in hippocampus (HC), nucleus accumbens (NAC), and anterior cingulate cortex (ACC) of rats of both sexes after single intraperitoneal injection of nicotine. At the baseline, male rats showed higher level of GABA, taurine, N-acetyl aspartate, and creatine in HC, and taurine in NAC. Also, there were stronger correlations between neurometabolites in females than in males at the baseline. Nicotine administration changed taurine, GABA, myo-inositol, choline, and N-acetyl aspartate in HC, and taurine in NAC. Significant interactions between time, treatment, and sex were detected for taurine and choline in HC. The number of inter-metabolite correlations increased significantly in ACC and decreased in NAC and HC in females after nicotine administration, while in males it was unchanged. There are distinct sex differences in neurometabolic profiles at the baseline and after acute nicotine administration. Nicotine changes inter-metabolite correlations in females more than in males.

Big GABA: Edited MR spectroscopy at 24 research sites

Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study's protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.

In vivo functional neurochemistry of human cortical cholinergic function during visuospatial attention

Cortical acetylcholine is involved in key cognitive processes such as visuospatial attention. Dysfunction in the cholinergic system has been described in a number of neuropsychiatric disorders. Levels of brain acetylcholine can be pharmacologically manipulated, but it is not possible to directly measure it in vivo in humans. However, key parts of its biochemical cascade in neural tissue, such as choline, can be measured using magnetic resonance spectroscopy (MRS). There is evidence that levels of choline may be an indirect but proportional measure of acetylcholine availability in brain tissue. In this study, we measured relative choline levels in the parietal cortex using functional (event-related) MRS (fMRS) during performance of a visuospatial attention task, with a modelling approach verified using simulated data. We describe a task-driven interaction effect on choline concentration, specifically driven by contralateral attention shifts. Our results suggest that choline MRS has the potential to serve as a proxy of brain acetylcholine function in humans.

Quantification of 1 H-MRS signals based on sparse metabolite profiles in the time-frequency domain

MRS is an analytical approach used for both quantitative and qualitative analysis of human body metabolites. The accurate and robust quantification capability of proton MRS (¹ H-MRS) enables the accurate estimation of living tissue metabolite concentrations. However, such methods can be efficiently employed for quantification of metabolite concentrations only if the overlapping nature of metabolites, existing static field inhomogeneity and low signal-to-noise ratio (SNR) are taken into consideration. Representation of ¹ H-MRS signals in the time-frequency domain enables us to handle the baseline and noise better. This is possible because the MRS signal of each metabolite is sparsely represented, with only a few peaks, in the frequency domain, but still along with specific time-domain features such as distinct decay constant associated with T₂ relaxation rate. The baseline, however, has a smooth behavior in the frequency domain. In this study, we proposed a quantification method using continuous wavelet transformation of ¹ H-MRS signals in combination with sparse representation of features in the time-frequency domain. Estimation of the sparse representations of MR spectra is performed according to the dictionaries constructed from metabolite profiles. Results on simulated and phantom data show that the proposed method is able to quantify the concentration of metabolites in ¹ H-MRS signals with high accuracy and robustness. This is achieved for both low SNR (5 dB) and low signal-to-baseline ratio (-5 dB) regimes.

Processing tracking in jMRUI software for magnetic resonance spectra quantitation reproducibility assurance

Background

Proton magnetic resonance spectroscopy is a non-invasive measurement technique which provides information about concentrations of up to 20 metabolites participating in intracellular biochemical processes. In order to obtain any metabolic information from measured spectra a processing should be done in specialized software, like jMRUI. The processing is interactive and complex and often requires many trials before obtaining a correct result. This paper proposes a jMRUI enhancement for efficient and unambiguous history tracking and file identification.

Results

A database storing all processing steps, parameters and files used in processing was developed for jMRUI. The solution was developed in Java, authors used a SQL database for robust storage of parameters and SHA-256 hash code for unambiguous file identification. The developed system was integrated directly in jMRUI and it will be publically available. A graphical user interface was implemented in order to make the user experience more comfortable. The database operation is invisible from the point of view of the common user, all tracking operations are performed in the background.

Conclusions

The implemented jMRUI database is a tool that can significantly help the user to track the processing history performed on data in jMRUI. The created tool is oriented to be user-friendly, robust and easy to use. The database GUI allows the user to browse the whole processing history of a selected file and learn e.g. what processing lead to the results, where the original data are stored, to obtain the list of all processing actions performed on spectra.

Refined modelling of the short-T2 signal component and ensuing detection of glutamate and glutamine in short-TE, localised, (1) H MR spectra of human glioma measured at 3 T

Short-TE (1) H MRS has great potential for brain cancer diagnostics. A major difficulty in the analysis of the spectra is the contribution from short-T2 signal components, mainly coming from mobile lipids. This complicates the accurate estimation of the spectral parameters of the resonance lines from metabolites, so that a qualitative to semi-quantitative interpretation of the spectra dominates in practice. One solution to overcome this difficulty is to measure and estimate the short-T2 signal component and to subtract it from the total signal, thus leaving only the metabolite signals. The technique works well when applied to spectra obtained from healthy individuals, but requires some optimisation during data acquisition. In the clinical setting, time constraints hardly allow this. Here, we propose an iterative estimation of the short-T2 signal component, acquired in a single acquisition after measurement of the full spectrum. The method is based on QUEST (quantitation based on quantum estimation) and allows the refinement of the estimate of the short-T2 signal component after measurement. Thus, acquisition protocols used on healthy volunteers can also be used on patients without further optimisation. The aim is to improve metabolite detection and, ultimately, to enable the estimation of the glutamine and glutamate signals distinctly. These two metabolites are of great interest in the characterisation of brain cancer, gliomas in particular. When applied to spectra from healthy volunteers, the new algorithm yields similar results to QUEST and direct subtraction of the short-T2 signal component. With patients, up to 12 metabolites and, at least, seven can be quantified in each individual brain tumour spectrum, depending on the metabolic state of the tumour. The refinement of the short-T2 signal component significantly improves the fitting procedure and produces a separate short-T2 signal component that can be used for the analysis of mobile lipid resonances. Thus, in brain tumour spectra, distinct estimates of signals from glutamate and glutamine are possible. Copyright © 2016 John Wiley & Sons, Ltd.

Cholinergic Neurotransmission in the Posterior Insular Cortex Is Altered in Preclinical Models of Neuropathic Pain: Key Role of Muscarinic M2 Receptors in Donepezil-Induced Antinociception

Neuropathic pain is one of the most debilitating pain conditions, yet no therapeutic strategy has been really effective for its treatment. Hence, a better understanding of its pathophysiological mechanisms is necessary to identify new pharmacological targets. Here, we report important metabolic variations in brain areas involved in pain processing in a rat model of oxaliplatin-induced neuropathy using HRMAS (1)H-NMR spectroscopy. An increased concentration of choline has been evidenced in the posterior insular cortex (pIC) of neuropathic animal, which was significantly correlated with animals' pain thresholds. The screening of 34 genes mRNA involved in the pIC cholinergic system showed an increased expression of the high-affinity choline transporter and especially the muscarinic M2 receptors, which was confirmed by Western blot analysis in oxaliplatin-treated rats and the spared nerve injury model (SNI). Furthermore, pharmacological activation of M2 receptors in the pIC using oxotremorine completely reversed oxaliplatin-induced mechanical allodynia. Consistently, systemic treatment with donepezil, a centrally active acetylcholinesterase inhibitor, prevented and reversed oxaliplatin-induced cold and mechanical allodynia as well as social interaction impairment. Intracerebral microdialysis revealed a lower level of acetylcholine in the pIC of oxaliplatin-treated rats, which was significantly increased by donepezil. Finally, the analgesic effect of donepezil was markedly reduced by a microinjection of the M2 antagonist, methoctramine, within the pIC, in both oxaliplatin-treated rats and spared nerve injury rats. These findings highlight the crucial role of cortical cholinergic neurotransmission as a critical mechanism of neuropathic pain, and suggest that targeting insular M2 receptors using central cholinomimetics could be used for neuropathic pain treatment.

SIGNIFICANCE STATEMENT

Our study describes a decrease in cholinergic neurotransmission in the posterior insular cortex in neuropathic pain condition and the involvement of M2 receptors. Targeting these cortical muscarinic M2 receptors using central cholinomimetics could be an effective therapy for neuropathic pain treatment.

Separation of small metabolites and lipids in spectra from biopsies by diffusion-weighted HR-MAS NMR: a feasibility study

High Resolution Magic Angle Spinning (HR-MAS) NMR allows metabolic characterization of biopsies. HR-MAS spectra from tissues of most organs show strong lipid contributions that are overlapping metabolite regions, which hamper metabolite estimation. Metabolite quantification and analysis would benefit from a separation of lipids and small metabolites. Generally, a relaxation filter is used to reduce lipid contributions. However, the strong relaxation filter required to eliminate most of the lipids also reduces the signals for small metabolites. The aim of our study was therefore to investigate different diffusion editing techniques in order to employ diffusion differences for separating lipid and small metabolite contributions in the spectra from different organs for unbiased metabonomic analysis. Thus, 1D and 2D diffusion measurements were performed, and pure lipid spectra that were obtained at strong diffusion weighting (DW) were subtracted from those obtained at low DW, which include both small metabolites and lipids. This subtraction yielded almost lipid free small metabolite spectra from muscle tissue. Further improved separation was obtained by combining a 1D diffusion sequence with a T2-filter, with the subtraction method eliminating residual lipids from the spectra. Similar results obtained for biopsies of different organs suggest that this method is applicable in various tissue types. The elimination of lipids from HR-MAS spectra and the resulting less biased assessment of small metabolites have potential to remove ambiguities in the interpretation of metabonomic results. This is demonstrated in a reproducibility study on biopsies from human muscle.

Assessing Susceptibility to Epilepsy in Three Rat Strains Using Brain Metabolic Profiling Based on HRMAS NMR Spectroscopy and Chemometrics

The possibility that a metabolomic approach can inform about the pathophysiology of a given form of epilepsy was addressed. Using chemometric analyses of HRMAS NMR data, we compared several brain structures in three rat strains with different susceptibilities to absence epilepsy: Genetic Absence Epilepsy Rats from Strasbourg (GAERS), Non Epileptic Control rats (NEC), and Wistar rats. Two ages were investigated: 14 days postnatal (P14) before the onset of seizures and 5 month old adults with fully developed seizures (Adults). The relative concentrations of 19 metabolites were assessed using (1)H HRMAS NMR experiments. Univariate and multivariate analyses including multiblock models were used to identify the most discriminant metabolites. A strain-dependent evolution of glutamate, glutamine, scyllo-inositol, alanine, and glutathione was highlighted during cerebral maturation. In Adults, data from somatosensory and motor cortices allowed discrimination between GAERS and NEC rats with higher levels of scyllo-inositol, taurine, and phosphoethanolamine in NEC. This epileptic metabolic phenotype was in accordance with current pathophysiological hypothesis of absence epilepsy (i.e., seizure-generating and control networks) and putative resistance of NEC rats and was observed before seizure onset. This methodology could be very efficient in a clinical context.