Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

The impact of spectral basis set composition on estimated levels of cingulate glutamate and its associations with different personality traits.

BACKGROUND: 1H-MRS is increasingly used in basic and clinical research to explain brain function and alterations respectively. In psychosis research it is now one of the main tools to investigate imbalances in the glutamatergic system. Interestingly, however, the findings are extremely variable even within patients of similar disease states. One reason may be the variability in analysis strategies, despite suggestions for standardization. Therefore, our study aimed to investigate the extent to which the basis set configuration- which metabolites are included in the basis set used for analysis- would affect the spectral fit and estimated glutamate (Glu) concentrations in the anterior cingulate cortex (ACC), and whether any changes in levels of glutamate would be associated with psychotic-like experiences and autistic traits. METHODS: To ensure comparability, we utilized five different exemplar basis sets, used in research, and two different analysis tools, r-based spant applying the ABfit method and Osprey using the LCModel. RESULTS: Our findings revealed that the types of metabolites included in the basis set significantly affected the glutamate concentration. We observed that three basis sets led to more consistent results across different concentration types (i.e., absolute Glu in mol/kg, Glx (glutamate + glutamine), Glu/tCr), spectral fit and quality measurements. Interestingly, all three basis sets included phosphocreatine. Importantly, our findings also revealed that glutamate levels were differently associated with both schizotypal and autistic traits depending on basis set configuration and analysis tool, with the same three basis sets showing more consistent results. CONCLUSIONS: Our study highlights that scientific results may be significantly altered depending on the choices of metabolites included in the basis set, and with that emphasizes the importance of carefully selecting the configuration of the basis set to ensure accurate and consistent results, when using MR spectroscopy. Overall, our study points out the need for standardized analysis pipelines and reporting.

Age-related differences in macromolecular resonances observed in ultra-short-TE STEAM MR spectra at 7T.

PURPOSE: To understand how macromolecular content varies in the human brain with age in a large cohort of healthy subjects. METHODS: In-vivo 1H-MR spectra were acquired using ultra-short TE STEAM at 7T in the posterior cingulate cortex. Macromolecular content was studied in 147 datasets from a cohort ranging in age from 19 to 89 y. Three fitting approaches were used to evaluate the macromolecular content: (1) a macromolecular resonances model developed for this study; (2) LCModel-simulated macromolecules; and (3) a combination of measured and LCModel-simulated macromolecules. The effect of age on the macromolecular content was investigated by considering age both as a continuous variable (i.e., linear regressions) and as a categorical variable (i.e., multiple comparisons among sub-groups obtained by stratifying data according to age by decade). RESULTS: While weak age-related effects were observed for macromolecular peaks at ˜0.9 (MM09), ˜1.2 (MM12), and ˜1.4 (MM14) ppm, moderate to strong effects were observed for peaks at ˜1.7 (MM17), and ˜2.0 (MM20) ppm. Significantly higher MM17 and MM20 content started from 30 to 40 y of age, while for MM09, MM12, and MM14, significantly higher content started from 60 to 70 y of age. CONCLUSIONS: Our findings provide insights into age-related differences in macromolecular contents and strengthen the necessity of using age-matched measured macromolecules during quantification.

The effects of residual dipolar coupling on carnosine in proton muscle spectra.

Carnosine, an MR-visible dipeptide in human muscle, is well characterized by two peaks at ~8 and ~7 ppm from C2 and C4 imidazole protons. Like creatine and other metabolites, carnosine is subject to residual dipolar coupling in the anisotropic environment of muscle fibers, but the effects have not been studied extensively. Single-voxel TE 30-32 PRESS spectra from three different 3T studies were acquired from gastrocnemius medialis and soleus muscles in the human lower leg. In these studies, carnosine T2 values were measured, and spectra were obtained at three different foot angles. LCModel was used to fit the carnosine peaks with a basis set that was generated using shaped RF pulses and included a range of dipolar couplings affecting the C4 peak. A seven-parameter analytic expression was used to fit the CH2 doublets of creatine. It incorporated an optimized "effective TE" value to model the effect of shaped RF pulses. The fits confirm that the triplet C4 peak of carnosine is dipolar coupled to a pair of CH2 protons, with no need to include a contribution from a separate pool of freely rotating uncoupled carnosine. Moreover, the couplings experienced by carnosine C4 protons and creatine CH2 protons are strongly correlated (R2 = 0.88, P<0.001), exhibiting a similar 3cos2 θ - 1 dependence on the angle θ between fiber orientation and B0. T2 values for the singlet C2 peak of gastrocnemius carnosine are inversely proportional to the C4 dipolar coupling strength (R2 = 0.97, P < 0.001), which in turn is a function of foot orientation. This dependence indicates that careful positioning of the foot while acquiring lower leg muscle spectra is important to obtain reproducible carnosine concentrations. As proton magnetic resonance spectroscopy of carnosine is currently used to non-invasively estimate the muscle fiber typology, these results have important implications in sport science.

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.

Quantification of Cerebral Glucose Concentrations via Detection of the H1-α-Glucose Peak in 1 H MRS at 7 T.

BACKGROUND: Sensitive detection and quantification of cerebral glucose is desired. PURPOSE: To quantify cerebral glucose by detecting the H1-α-glucose peak at 5.23 ppm in 1 H magnetic resonance spectroscopy at 7 T. STUDY TYPE: Prospective. SUBJECTS: Twenty-eight non-fasted healthy subjects (aged 20-28 years). FIELD STRENGTH/SEQUENCE: Short echo time stimulated echo acquisition mode (short-TE STEAM) and semi-localized by adiabatic selective refocusing (semi-LASER) at 7 T. ASSESSMENT: Single voxel spectra were obtained from the posterior cingulate cortex (27-mL) using a 32-channel head coil. The H1-α-glucose peak in the spectrum with retrospective removal of the residual water peak was fitted using LCModel with a glucose basis set of only the H1-α-glucose peak. Conventional spectral analysis was performed with a glucose basis set of a full spectral pattern of glucose, also. Fitting precision was evaluated with Cramér-Rao lower bounds (CRLBs). The repeatability of glucose quantification via the semi-LASER sequence was tested. STATISTICAL TESTS: Paired or Welch's t-test were used for normally distributed values. A P value of <0.05 was considered significant. The repeatability of measures was analyzed using coefficient of variation (CV). RESULTS: Removal of the residual water peak improved the flatness and stability of baselines around the H1-α-glucose peak and reduced CRLBs for fitting the H1-α-glucose peak. The semi-LASER sequence was superior to the short-TE STEAM in the higher signal-to-noise ratio of the H1-α-glucose peak (mean ± SD 7.9 ± 2.5, P < 0.001). The conventional analysis overfitted the H1-α-glucose peak. The individual CVs of glucose quantification by detecting the H1-α-glucose peak were smaller than the corresponding CRLBs. DATA CONCLUSION: Cerebral glucose concentration is quantitated to be 1.07 mM by detecting the H1-α-glucose peak in the semi-LASER spectra. Despite requiring long scan times, detecting the H1-α-glucose peak allows true glucose quantification free from the influence of overlapping taurine and macromolecule signals. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain.

Magnetic resonance spectroscopy (MRS) is one of the few non-invasive imaging modalities capable of making neurochemical and metabolic measurements in vivo. Traditionally, the clinical utility of MRS has been narrow. The most common use has been the "single-voxel spectroscopy" variant to discern the presence of a lactate peak in the spectra in one location in the brain, typically to evaluate for ischemia in neonates. Thus, the reduction of rich spectral data to a binary variable has not classically necessitated much signal processing. However, scanners have become more powerful and MRS sequences more advanced, increasing data complexity and adding 2 to 3 spatial dimensions in addition to the spectral one. The result is a spatially- and spectrally-variant MRS image ripe for image processing innovation. Despite this potential, the logistics for robustly accessing and manipulating MRS data across different scanners, data formats, and software standards remain unclear. Thus, as research into MRS advances, there is a clear need to better characterize its image processing considerations to facilitate innovation from scientists and engineers. Building on established neuroimaging standards, we describe a framework for manipulating these images that generalizes to the voxel, spectral, and metabolite level across space and multiple imaging sites while integrating with LCModel, a widely used quantitative MRS peak-fitting platform. In doing so, we provide examples to demonstrate the advantages of such a workflow in relation to recent publications and with new data. Overall, we hope our characterizations will lower the barrier of entry to MRS processing for neuroimaging researchers.

Experimental Basis Sets of Quantification of Brain 1H-Magnetic Resonance Spectroscopy at 3.0 T.

In vivo short echo time (TE) proton magnetic resonance spectroscopy (1H-MRS) is a useful method for the quantification of human brain metabolites. The purpose of this study was to evaluate the performance of an in-house, experimentally measured basis set and compare it with the performance of a vendor-provided basis set. A 3T clinical scanner with 32-channel receive-only phased array head coil was used to generate 16 brain metabolites for the metabolite basis set. For voxel localization, point-resolved spin-echo sequence (PRESS) was used with volume of interest (VOI) positioned at the center of the phantoms. Two different basis sets were subjected to linear combination of model spectra of metabolite solutions in vitro (LCModel) analysis to evaluate the in-house acquired in vivo 1H-MR spectra from the left prefrontal cortex of 22 healthy subjects. To evaluate the performance of the two basis sets, the Cramer-Rao lower bounds (CRLBs) of each basis set were compared. The LCModel quantified the following metabolites and macromolecules: alanine (Ala), aspartate (Asp), γ-amino butyric acid (GABA), glucose (Glc), glutamine (Gln), glutamate (Glu), glutathione (GHS), Ins (myo-Inositol), lactate (Lac), N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), taurine (Tau), phosphoryl-choline + glycerol-phosphoryl-choline (tCho), N-acetylaspartate + N-acetylaspartylglutamate (tNA), creatine + phosphocreatine (tCr), Glu + Gln (Glx) and Lip13a, Lip13b, Lip09, MM09, Lip20, MM20, MM12, MM14, MM17, Lip13a + Lip13b, MM14 + Lip13a + Lip13b + MM12, MM09 + Lip09, MM20 + Lip20. Statistical analysis showed significantly different CRLBs: Asp, GABA, Gln, GSH, Ins, Lac, NAA, NAAG, Tau, tCho, tNA, Glx, MM20, MM20 + Lip20 (p < 0.001), tCr, MM12, MM17 (p < 0.01), and Lip20 (p < 0.05). The estimated ratio of cerebrospinal fluid (CSF) in the region of interest was calculated to be about 5%. Fitting performances are better, for the most part, with the in-house basis set, which is more precise than the vendor-provided basis set. In particular, Asp is expected to have reliable CRLB (<30%) at high field (e.g., 3T) in the left prefrontal cortex of human brain. The quantification of Asp was difficult, due to the inaccuracy of Asp fitting with the vendor-provided basis set.

Quantification of GABA concentration measured noninvasively in the human posterior cingulate cortex with 7 T ultra-short-TE MR spectroscopy.

PURPOSE: The increased spectral dispersion achieved at ultra-high field permits quantification of γ-aminobutyric acid (GABA) concentrations at ultra-short-TE without editing. This work investigated the influence of spectral quality and different LCModel fitting approaches on quantification of GABA. Additionally, the sensitivity with which cross-sectional and longitudinal variations in GABA concentrations can be observed was characterized. METHODS: In - vivo spectra were acquired in the posterior cingulate cortex of 10 volunteers at 7 T using a STEAM sequence. Synthetically altered spectra with different levels of GABA signals were used to investigate the reliability of GABA quantification with different LCModel fitting approaches and different realizations of SNR. The synthetically altered spectra were also used to characterize the sensitivity of GABA quantification. RESULTS: The best LCModel fitting approach used stiff spline baseline, no soft constraints, and measured macromolecules in the basis set. With lower SNR, coefficients of variation increased dramatically. Longitudinal and cross-sectional variations in GABA of 10% could be detected with 79 and 48 participants per group, respectively. However, the small cohort may bias the calculation of the coefficients of variation and of the sample size that would be needed to detect variations in GABA. CONCLUSION: Reliable quantification of normal and abnormal GABA concentrations was achieved for high quality 7 T spectra using LCModel fitting.

In vivo magnetic resonance 31 P-Spectral Analysis With Neural Networks: 31P-SPAWNN.

PURPOSE: We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( 31 $$ {}^{31} $$ P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with the performance of the two approaches, are compared in this work. THEORY AND METHODS: Convolutional neural network architectures have been proposed for the analysis and quantification of 31 $$ {}^{31} $$ P-spectroscopy. The generation of training and test data using a fully parameterized model is presented herein. In vivo unlocalized free induction decay and three-dimensional 31 $$ {}^{31} $$ P-magnetic resonance spectroscopy imaging data were acquired from healthy volunteers before being quantified using either 31P-SPAWNN or traditional least-square fitting techniques. RESULTS: The presented experiment has demonstrated both the reliability and accuracy of 31P-SPAWNN for estimating metabolite concentrations and spectral parameters. Simulated test data showed improved quantification using 31P-SPAWNN compared with LCModel. In vivo data analysis revealed higher accuracy at low signal-to-noise ratio using 31P-SPAWNN, yet with equivalent precision. Processing time using 31P-SPAWNN can be further shortened up to two orders of magnitude. CONCLUSION: The accuracy, reliability, and computational speed of the method open new perspectives for integrating these applications in a clinical setting.

Quantitative measurement of peritumoral concentrations of glutamate, N-acetyl aspartate, and lactate on magnetic resonance spectroscopy predicts glioblastoma-related refractory epilepsy.

BACKGROUND: Increased extracellular glutamate is known to cause epileptic seizures in patients with glioblastoma (GBM). However, predicting whether the seizure will be refractory is difficult. The present study investigated whether evaluation of the levels of various metabolites, including glutamate, can predict the occurrence of refractory seizure in GBM by quantitative measurement of metabolite concentrations on magnetic resonance spectroscopy (MRS). METHODS: Forty patients were treated according to the same treatment protocol for primary GBM at Ehime University Hospital between April 2017 and July 2021. Of these patients, 23 underwent MRS to determine concentrations of metabolites, including glutamate, N-acetylaspartate, creatine, and lactate, in the tumor periphery by applying LC-Model. The concentration of each metabolite was expressed as a ratio to creatine concentration. Patients were divided into three groups: Type A, patients with no seizures; Type B, patients with seizures that disappeared after treatment; and Type C, patients with seizures that remained unrelieved or appeared after treatment (refractory seizures). Relationships between concentrations of metabolites and seizure types were investigated. RESULTS: In 23 GBMs, seizures were confirmed in 11 patients, including Type B in four and Type C in seven. Patients with epilepsy (Type B or C) showed significantly higher glutamate and N-acetylaspartate values than did non-epilepsy patients (Type A) (p < 0.05). No significant differences in glutamate or N-acetylaspartate levels were seen between Types B and C. Conversely, Type C showed significantly higher concentrations of lactate than did Type B (p = 0.001). Cutoff values of lactate-to-creatine, glutamate-to-creatine, and N-acetylaspartate-to-creatine ratios for refractory seizure were > 1.25, > 1.09, and > 0.88, respectively. CONCLUSIONS: Extracellular concentrations of glutamate, N-acetylaspartate, and lactate in the tumor periphery were significantly elevated in patients with GBM with refractory seizures. Measurement of these metabolites on MRS may predict refractory epilepsy in such patients and could be an indicator for continuing the use of antiepileptic drugs.

Acquisition and quantification pipeline for in vivo hyperpolarized 13 C MR spectroscopy.

PURPOSE: The goal of this study was to combine a specialized acquisition method with a new quantification pipeline to accurately and efficiently probe the metabolism of hyperpolarized 13 C-labeled compounds in vivo. In this study, we tested our approach on [2-13 C]pyruvate and [1-13 C]α-ketoglutarate data in rat orthotopic brain tumor models at 3T. METHODS: We used a multiband metabolite-specific radiofrequency (RF) excitation in combination with a variable flip angle scheme to minimize substrate polarization loss and measure fast metabolic processes. We then applied spectral-temporal denoising using singular value decomposition to enhance spectral quality. This was combined with LCModel-based automatic 13 C spectral fitting and flip angle correction to separate overlapping signals and rapidly quantify the different metabolites. RESULTS: Denoising improved the metabolite signal-to-noise ratio (SNR) by approximately 5. It also improved the accuracy of metabolite quantification as evidenced by a significant reduction of the Cramer Rao lower bounds. Furthermore, the use of the automated and user-independent LCModel-based quantification approach could be performed rapidly, with the kinetic quantification of eight metabolite peaks in a 12-spectrum array achieved in less than 1 minute. CONCLUSION: The specialized acquisition method combined with denoising and a new quantification pipeline using LCModel for the first time for hyperpolarized 13 C data enhanced our ability to monitor the metabolism of [2-13 C]pyruvate and [1-13 C]α-ketoglutarate in rat orthotopic brain tumor models in vivo. This approach could be broadly applicable to other hyperpolarized agents both preclinically and in the clinical setting.

In Vitro 31P MR Chemical Shifts of In Vivo-Detectable Metabolites at 3T as a Basis Set for a Pilot Evaluation of Skeletal Muscle and Liver 31P Spectra with LCModel Software.

Most in vivo 31P MR studies are realized on 3T MR systems that provide sufficient signal intensity for prominent phosphorus metabolites. The identification of these metabolites in the in vivo spectra is performed by comparing their chemical shifts with the chemical shifts measured in vitro on high-field NMR spectrometers. To approach in vivo conditions at 3T, a set of phantoms with defined metabolite solutions were measured in a 3T whole-body MR system at 7.0 and 7.5 pH, at 37 °C. A free induction decay (FID) sequence with and without 1H decoupling was used. Chemical shifts were obtained of phosphoenolpyruvate (PEP), phosphatidylcholine (PtdC), phosphocholine (PC), phosphoethanolamine (PE), glycerophosphocholine (GPC), glycerophosphoetanolamine (GPE), uridine diphosphoglucose (UDPG), glucose-6-phosphate (G6P), glucose-1-phosphate (G1P), 2,3-diphosphoglycerate (2,3-DPG), nicotinamide adenine dinucleotide (NADH and NAD+), phosphocreatine (PCr), adenosine triphosphate (ATP), adenosine diphosphate (ADP), and inorganic phosphate (Pi). The measured chemical shifts were used to construct a basis set of 31P MR spectra for the evaluation of 31P in vivo spectra of muscle and the liver using LCModel software (linear combination model). Prior knowledge was successfully employed in the analysis of previously acquired in vivo data.

MR Spectroscopy of the Insula: Within- and between-Session Reproducibility of MEGA-PRESS Measurements of GABA+ and Other Metabolites.

The insula plays a critical role in many neuropsychological disorders. Research investigating its neurochemistry with magnetic resonance spectroscopy (MRS) has been limited compared with cortical regions. Here, we investigate the within-session and between-session reproducibility of metabolite measurements in the insula on a 3T scanner. We measure N-acetylaspartate + N-acetylaspartylglutamate (tNAA), creatine + phosphocreatine (tCr), glycerophosphocholine + phosphocholine (tCho), myo-inositol (Ins), glutamate + glutamine (Glx), and γ-aminobutyric acid (GABA) in one cohort using a j-edited MEGA-PRESS sequence. We measure tNAA, tCr, tCho, Ins, and Glx in another cohort with a standard short-TE PRESS sequence as a reference for the reproducibility metrics. All participants were scanned 4 times identically: 2 back-to-back scans each day, on 2 days. Preprocessing was done using LCModel and Gannet. Reproducibility was determined using Pearson's r, intraclass-correlation coefficients (ICC), coefficients of variation (CV%), and Bland-Altman plots. A MEGA-PRESS protocol requiring averaged results over two 6:45-min scans yielded reproducible GABA measurements (CV% = 7.15%). This averaging also yielded reproducibility metrics comparable to those from PRESS for the other metabolites. Voxel placement inconsistencies did not affect reproducibility, and no sex differences were found. The data suggest that MEGA-PRESS can reliably measure standard metabolites and GABA in the insula.

Electrical status epilepticus in sleep: The role of thalamus in etiopathogenesis.

PURPOSE: In patients diagnosed with epilepsy, decreased ratio of N-acetyl aspartate to creatine (NAA/Cr) measured in magnetic resonance spectroscopy (MRS) has been accepted as a sign of neuronal cell loss or dysfunction. In this study, we aimed to determine whether a similar neuronal cell loss is present in a group of encephalopathy with electrical status epilepticus in sleep (ESES) patients METHODS: We performed this case-control study at a tertiary pediatric neurology center with patients with ESES. Inclusion criteria for the patient group were as follows: 1) a spike-wave index of at least 50%, 2) acquired neuropsychological regression, 3) normal cranial MRI. Eventually, a total of 21 patients with ESES and 17 control subjects were enrolled in the study. MRI of all control subjects was also within normal limits. 3D Slicer program was used for the analysis of thalamic and brain volumes. LCModel spectral fitting software was used to analyze single-voxel MRS data from the right and left thalamus of the subjects. RESULTS: The mean age was 8.0 ± 1.88 years and 8.3 ± 1.70 years in ESES patients and the control subjects. After correcting for the main potential confounders (age and gender) with a linear regression model, NAA/Creatine ratio of the right thalamus was significantly lower in the ESES patient group compared to the healthy control group (p = 0.026). Likewise, the left thalamus NAA/Cr ratio was significantly lower in the ESES patient group than the healthy control group (p = 0.007). After correcting for age and gender, right thalamic volume was not statistically significantly smaller in ESES patients than in healthy controls (p = 0.337), but left thalamic volume was smaller in ESES patients than in healthy controls (p = 0.024). CONCLUSION: In ESES patients, the NAA/Creatine ratio, which is an indicator of neuronal cell loss or dysfunction in the right and left thalamus, which appears regular on MRI, was found to be significantly lower than the healthy control group. This metabolic-induced thalamic dysfunction, which was reported for the first time up to date, may play a role in ESES epileptogenesis.

Prediction of Glioma Stemlike Cell Infiltration in the Non-Contrast-Enhancing Area by Quantitative Measurement of Lactate on Magnetic Resonance Spectroscopy in Glioblastoma.

BACKGROUND: We previously reported that glioma stemlike cells (GSCs) exist in the area of the tumor periphery showing no gadolinium enhancement on magnetic resonance imaging. In the present work, we analyzed glucose metabolism to investigate whether lactate could be predictive of tumor invasiveness and of use in detection of the tumor invasion area in glioblastoma multiforme (GBM). METHODS: The expression of lactate dehydrogenase A (LDH-A) and pyruvate dehydrogenase (PDH) was investigated in 20 patients. In GSC lines, LDH-A and PDH expression also was examined in parallel to assessments of mitochondrial respiration. We then investigated the relationship between lactate/creatine ratios in the tumor periphery measured by magnetic resonance spectroscopy, using learning-compression-model algorithms and phenotypes of GBMs. RESULTS: In 20 GBMs, high-invasive GBM expressed LDH-A at significantly higher expression than did low-invasive GBM, whereas low-invasive GBM showed significantly higher expression of PDH than did high-invasive GBM. The highly invasive GSC line showed higher expression of LDH-A and lower expression of PDH compared with low-invasive GSC lines. The highly invasive GSC line also showed the lowest consumption of oxygen and the lowest production of adenosine triphosphate. Lactate levels, as measured by magnetic resonance spectroscopy, showed a significant positive correlation with LDH-A transcript levels, permitting classification of the GBMs into high-invasive and low-invasive phenotypes based on a cutoff value of 0.66 in the lactate/creatine ratio. CONCLUSIONS: In the tumor periphery area of the highly invasive GBM, aerobic glycolysis was the predominant pathway for glucose metabolism, resulting in the accumulation of lactate. The level of lactate may facilitate prediction of the tumor-infiltrating area on GBM.

Repeatability and reliability of GABA measurements with magnetic resonance spectroscopy in healthy young adults.

PURPOSE: Gamma-aminobutyric acid (GABA) abnormalities have been implicated in a range of neuropsychiatric disorders. Despite substantial interest in probing GABA in vivo, human imaging studies relying on magnetic resonance spectroscopy (MRS) have generally been hindered by technical challenges, including GABA's relatively low concentration and spectral overlap with other metabolites. Although past studies have shown moderate-to-strong test-retest repeatability and reliability of GABA within certain brain regions, many of these studies have been limited by small sample sizes. METHODS: GABA+ (macromolecular-contaminated) test-retest reliability and repeatability were assessed via a Meshcher-Garwood point resolved spectroscopy (MEGA-PRESS) MRS sequence in the rostral anterior cingulate cortex (rACC; n = 21) and dorsolateral prefrontal cortex (dlPFC; n = 20) in healthy young adults. Data were collected on a 3T scanner (Siemens Prisma, Siemens Healthcare, Erlangen, Germany) and GABA+ results were reported in reference to both total creatine (GABA+/tCr) and water (GABA+/water). RESULTS: Results showed strong test-retest repeatability (mean GABA+/tCr coefficient of variation [CV] = 4.6%; mean GABA+/water CV = 4.0%) and reliability (GABA+/tCr intraclass correlation coefficient [ICC] = 0.77; GABA+/water ICC = 0.87) in the dlPFC. The rACC showed acceptable (but comparatively lower) repeatability (mean GABA+/tCr CV = 8.0%; mean GABA+/water CV = 7.5%), yet low-moderate reliability (GABA+/tCr ICC = 0.40; GABA+/water ICC = 0.44). CONCLUSION: The present study found excellent GABA+ MRS repeatability and reliability in the dlPFC. The rACC showed inferior results, possibly because of a combination of shimming impedance and measurement error. These data suggest that MEGA-PRESS can be utilized to reliably distinguish participants based on dlPFC GABA+ levels, whereas the mixed results in the rACC merit further investigation.

Influence of fitting approaches in LCModel on MRS quantification focusing on age-specific macromolecules and the spline baseline.

Quantification of neurochemical concentrations from 1 H MR spectra is challenged by incomplete knowledge of contributing signals. Some experimental conditions hinder the acquisition of artifact-free spectra and impede the acquisition of condition-specific macromolecule (MM) spectra. This work studies differences caused by fitting solutions routinely employed to manage resonances from MM and lipids. High quality spectra (free of residual water and lipid artifacts and for which condition-specific MM spectra are available) are used to understand the influences of spline baseline flexibility and noncondition-specific MM on neurochemical quantification. Fitting with moderate spline flexibility or using noncondition-specific MM led to quantification that differed from when an appropriate, fully specified model was used. This occurred for all neurochemicals to an extent that varied in magnitude among and within approaches. The spline baseline was more tortuous when less constrained and when used in combination with noncondition-specific MM. Increasing baseline flexibility did not reproduce concentrations quantified under appropriate conditions when spectra were fitted using a MM spectrum measured from a mismatched cohort. Using the noncondition-specific MM spectrum led to quantification differences that were comparable in size with using a fitting model that had moderate freedom, and these influences were additive. Although goodness of fit was better with greater fitting flexibility, quantification differed from when fitting with a fully specified model that is appropriate for low noise data. Notable GABA and PE concentration differences occurred with lower estimates of measurement error when fitting with greater spline flexibility or noncondition-specific MM. These data support the need for improved metrics of goodness of fit. Attempting to correct for artifacts or absence of a condition-specific MM spectrum via increased spline flexibility and usage of noncondition-specific MM spectra cannot replace artifact-free data quantified with a condition-specific MM spectrum.

PRESS timings for resolving 13 C4 -glutamate 1 H signal at 9.4 T: Demonstration in rat with uniformly labelled 13 C-glucose.

MRS of 13 C4 -labelled glutamate (13 C4 -Glu) during an infusion of a carbon-13 (13 C)-labelled substrate, such as uniformly labelled glucose ([U-13 C6 ]-Glc), provides a measure of Glc metabolism. The presented work provides a single-shot indirect 13 C detection technique to quantify the approximately 2.51 ppm 13 C4 -Glu satellite proton (1 H) peak at 9.4 T. The methodology is an optimized point-resolved spectroscopy (PRESS) sequence that minimizes signal contamination from the strongly coupled protons of N-acetylaspartate (NAA), which resonate at approximately 2.49 ppm. J-coupling evolution of protons was characterized numerically and verified experimentally. A (TE1 , TE2 ) combination of (20 ms, 106 ms) was found to be suitable for minimizing NAA signal in the 2.51 ppm 1 H 13 C4 -Glu spectral region, while retaining the 13 C4 -Glu 1 H satellite peak. The efficacy of the technique was verified on phantom solutions and on two rat brains in vivo during an infusion of [U-13 C6 ]-Glc. LCModel was employed for analysis of the in vivo spectra to quantify the 2.51 ppm 1 H 13 C4 -Glu signal to obtain Glu C4 fractional enrichment time courses during the infusions. Cramér-Rao lower bounds of about 8% were obtained for the 2.51 ppm 13 C4 -Glu 1 H satellite peak with the optimal TE combination.

Effects of isoflurane, sevoflurane, propofol and alfaxalone on brain metabolism in dogs assessed by proton magnetic resonance spectroscopy (1H MRS).

BACKGROUND: The purpose of this study was to determine the effects of isoflurane, sevoflurane, propofol and alfaxalone on the canine brain metabolite bioprofile, measured with single voxel short echo time proton magnetic resonance spectroscopy at 3 Tesla. Ten adult healthy Beagle dogs were assigned to receive isoflurane, sevoflurane, propofol and alfaxalone at 3 different dose rates each in a randomized cross-over study design. Doses for isoflurane, sevoflurane, propofol and alfaxalone were FE'Iso 1.7 vol%, 2.1 vol%, 2.8 vol%, FE'Sevo 2.8 vol%, 3.5 vol% and 4.7 vol%, 30, 45 and 60 mg kg- 1 h- 1 and 10, 15 and 20 mg kg- 1 h- 1 respectively. A single voxel Point Resolved Spectroscopy Sequence was performed on a 3 T MRI scanner in three brain regions (basal ganglia, parietal and occipital lobes). Spectral data were analyzed with LCModel. Concentration of total N-acetylaspartate (tNAA), choline, creatine, inositol and glutamine and glutamate complex (Glx) relative to water content was obtained. Plasma concentration of lactate, glucose, triglycerides, propofol and alfaxalone were determined. Statistics were performed using repeated measures ANOVA or Wilcoxon Sign Rank test with alpha = 5%. RESULTS: Plasma glucose increased with isoflurane, sevoflurane and alfaxalone but decreased with propofol. Plasma lactate increased with all anesthetics (isoflurane > sevoflurane > propofol > alfaxalone). Cerebral lactate could not be detected. Only minor changes in cerebral metabolite concentrations of tNAA, choline, inositol, creatine and Glx occurred with anesthetic dose changes. CONCLUSION: The metabolomic profile detected with proton magnetic resonance spectroscopy at 3 Tesla of canine brain showed only minor differences between doses and anesthetics related to tNAA, choline, creatine, inositol and Glx.