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Primary progressive aphasia (PPA) is comprised of three subtypes: logopenic (lvPPA), non-fluent (nfvPPA), and semantic (svPPA). We used magnetic resonance spectroscopy (MRS) to measure tissue-corrected metabolite levels in the left inferior frontal gyrus (IFG) and right sensorimotor cortex (SMC) from 61 PPA patients. We aimed to: 1) characterize subtype differences in metabolites; and 2) test for metabolite associations with symptom severity. tCr differed by subtype across the left IFG and right SMC. tCr levels were lowest in lvPPA and highest in svPPA. tCr levels predicted lvPPA versus svPPA diagnosis. Higher IFG tCr and lower Glx correlated with greater disease severity. As tCr is involved in brain energy metabolism, svPPA pathology might involve changes in specific cellular energy processes. Perturbations to cellular energy homeostasis in language areas may contribute to symptoms. Reduced cortical excitatory capacity (i.e., lower Glx) in language regions may also contribute to symptoms. Thus, tCr may be useful for differentiating between PPA subtypes, and both tCr and Glx might have utility in understanding PPA mechanisms and tracking progression.HighlightsNo prior work has examined differences in brain metabolite levels for PPA subtypes.Total creatine (tCr) levels were lowest in lvPPA and highest in svPPA.tCr levels differentiated lvPPA from svPPA diagnosis.Higher tCr and lower Glx in the left IFG correlated with greater disease severity.Changes in cellular energy and excitatory processes may relate to PPA pathology.

The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T.The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio.Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96.Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.

Background 3,4-Methylenedioxymethamphetamine (MDMA) is a widely used recreational substance inducing acute release of serotonin. Previous studies in chronic MDMA users demonstrated selective adaptations in the serotonin system, which were assumed to be associated with cognitive deficits. However, serotonin functions are strongly entangled with glutamate as well as γ-aminobutyric acid (GABA) neurotransmission and studies in MDMA-exposed rats show long-term adaptations in glutamatergic and GABAergic signaling. Methods We used proton magnetic resonance spectroscopy (MRS) to measure the glutamate-glutamine complex (GLX) and GABA concentrations in the left striatum and medial anterior cingulate cortex (ACC) of 44 chronic but recently abstinent MDMA users and 42 MDMA-naïve healthy controls. While the Mescher-Garwood point-resolved-spectroscopy sequence (MEGA-PRESS) is best suited to quantify GABA, recent studies reported poor agreement between conventional short-echo-time PRESS and MEGA-PRESS for GLX measures. Here, we applied both sequences to assess their agreement and potential confounders underlying the diverging results. Results Chronic MDMA users showed elevated GLX levels in the striatum, but not the ACC. Regarding GABA, we found no group difference in either region, although a negative association with MDMA use frequency was observed in the striatum. Overall, GLX measures from MEGA-PRESS, with its longer echo time, appeared to be less confounded by macromolecule signal than the short-echo-time PRESS and thus provided more robust results. Conclusion Our findings suggest that MDMA use affects not only serotonin, but also striatal GLX and GABA concentrations. These insights may offer new mechanistic explanations for cognitive deficits (e.g., impaired impulse control) observed in MDMA users.

PurposeNeural networks are potentially valuable for many challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic MRS examples. To demonstrate the utility, we use AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing.MethodsAGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemblein vivobrain data with combinations of metabolite, macromolecule, and residual water signals, and noise.Complex OOV signals were mixed into 85% of synthetic examples to train two separate U-net CNNs for the detection and prediction of OOV signals.ResultsAGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10normed-MSE of –1.79, an improvement of almost two orders of magnitude.ConclusionThe AGNOSTIC benchmark dataset for MRS is introduced and various dataset features are described. As an exemplar use of AGNOSTIC, two CNNs were developed to detect and predict OOV echoes.

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrationsin vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available forin vivomeasurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances ofin vivodata. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we’ve identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2relaxation times are established based upon a meta-analyses of healthy and diseased brains.Graphical Abstract

Literature values for within-subject test-retest reproducibility of gamma-aminobutyric acid (GABA), measured with edited magnetic resonance spectroscopy (MRS), vary widely. Reasons for this variation remain unclear. Here we tested whether sequence complexity (two-experiment MEGA-PRESS versus four-experiment HERMES), editing pulse duration (14 versus 20 ms), scanner frequency drift (interleaved water referencing (IWR) turned ON versus OFF), and linear combination modeling variations (three different co-edited macromolecule models and 0.55 versus 0.4 ppm spline baseline knot spacing) affected the within-subject coefficient of variation of GABA + macromolecules (GABA+). We collected edited MRS data from the dorsal anterior cingulate cortex from 20 participants (30.8 ± 9.5 years; 10 males). Test and retest scans were separated by removing the participant from the scanner for 5-10 minutes. Each acquisition consisted of two MEGA-PRESS and two HERMES sequences with editing pulse durations of 14 and 20 ms (referred to here as: MEGA-14, MEGA-20, HERMES-14, and HERMES-20; all TE = 80 ms, 224 averages). Reproducibility did not consistently differ for MEGA-PRESS compared with HERMES or for 14 compared with 20 ms editing pulses. A composite model of the 0.9 and 3 ppm macromolecules (particularly for HERMES) and sparser (0.55 compared with 0.4 ppm) spline baseline knot spacing yielded generally better test-retest reproducibility for GABA+. Replicating our prior results, linear combination modeling in Osprey compared with simple peak fitting in Gannet resulted in substantially better test-retest reproducibility. These results highlight the importance of model selection for edited MRS studies of GABA+, particularly for clinical studies which focus on individual patient differences in GABA+ or changes following an intervention.

PurposeTo compare the respective ability of PRESS and sLASER to reveal biological relationships, using age as a validation covariate.MethodsMRS data were acquired from 102 healthy volunteers using PRESS and sLASER in centrum semiovale (CSO) and posterior cingulate cortex (PCC) regions. Acquisition parameters included TR/TE 2000/30 ms; 96 transients; 2048 datapoints sampled at 2 kHz.Spectra were analyzed using Osprey. Signal-to-noise ratio (SNR), full-width-half-maximum linewidth of tCr, and metabolite concentrations were extracted. A linear model was used to compare SNR and linewidth. Paired t-tests were used to assess differences in metabolite measurements between PRESS and sLASER. Correlations were used to evaluate the relationship between PRESS and sLASER metabolite estimates, as well as the strength of each metabolite-age relationship. Coefficients of variation were calculated to assess inter-subject variability in each metabolite measurement.ResultsSNR and linewidth were significantly higher (pConclusionThe study results suggest strong agreement between PRESS and sLASER in identifying relationships between brain metabolites and age in CSO and PCC data acquired at 3T. sLASER is technically desirable due to the reduced chemical shift displacement artifact; however, PRESS performed similarly in ‘good’ brain regions at clinical field strength.

PurposeTo investigate the age-dependence of metaboliteT1relaxation times at 3T in both gray- and white-matter-rich voxels.MethodsThis manuscript analyzes publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3T using PRESS localization. Voxels were placed in posterior cingulate cortex and centrum semiovale in 102 healthy volunteers across 5 decades of life (20s to 60s). All spectra were analyzed in Osprey v2.4.0. To estimateT1relaxation times for tNAA2.0and tCr3.0, the ratio of modeled metabolite residual amplitudes in the metabolite-nulled spectrum to the full metabolite signal was calculated using the single inversion recovery signal equation. Correlations betweenT1and subject age were evaluated.ResultsSpearman correlations revealed that estimated T1relaxation times of tNAA2.0(rs= −0.43; p < 0.001) and tCr3.0(rs= −0.23; p = 0.021) decreased significantly with age in white-matter-rich CSO, and less steeply (and not significantly) for tNAA2.0(rs= −0.15; p = 0.136) and tCr3.0(rs= −0.10; p = 0.319) in gray-matter-rich PCC.ConclusionThe analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1relaxation times for accurate quantification of metabolite levels in studies of aging.

Treatment outcomes for migraine and other chronic headache and pain conditions typically demonstrate modest results. A greater understanding of underlying pain mechanisms may better inform treatments and improve outcomes. Increased GABA+ has been identified in recent studies of migraine, however, it is unclear if this is present in other headache, and pain conditions. We primarily investigated GABA+ levels in the posterior cingulate gyrus (PCG) of people with migraine, whiplash-headache and low back pain compared to age- and sex-matched controls, GABA+ levels in the anterior cingulate cortex (ACC) and thalamus formed secondary aims. Using a cross-sectional design, we studied people with migraine, whiplash-headache or low back pain (n = 56) and compared them with a pool of age- and sex-matched controls (n = 22). We used spectral-edited magnetic resonance spectroscopy at 3T (MEGA-PRESS) to determine levels of GABA+ in the PCG, ACC and thalamus. PCG GABA+ levels were significantly higher in people with migraine and low back pain compared with controls (eg, migraine 4.89 IU ± 0.62 vs controls 4.62 IU ± 0.38; P = .02). Higher GABA+ levels in the PCG were not unique to migraine and could reflect a mechanism of chronic pain in general. A better understanding of pain at a neurochemical level informs the development of treatments that target aberrant brain neurochemistry to improve patient outcomes. Perspective This study provides insights into the underlying mechanisms of chronic pain. Higher levels of GABA+ in the PCG may reflect an underlying mechanism of chronic headache and pain conditions. This knowledge may help improve patient outcomes through developing treatments that specifically address this aberrant brain neurochemistry.

Expert consensus recommends linear-combination modeling (LCM) of

BackgroundMagnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.New MethodThe proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.ResultsThe automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Comparison with Existing Method(s)The workflow presented here is the first implementation of a continuous automated MRS analysis ecosystem based on NIfTI-MRS and BIDS-MRS standards. Traditional MRS workflows are non-standardized, often require manual input, and frequently noy compatible with established neuroimaging workflows. This workflow should therefore facilitate integration of MRS into large-scale and multi-center studies.ConclusionsContinuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies.

PURPOSE To investigate the editing-pulse flip angle (FA) dependence of editing efficiency and ultimately to maximize the edited signal of commonly edited MR spectroscopy (MRS) signals, such as gamma-aminobutyric acid (GABA) and lactate. METHODS Density-matrix simulations were performed for a range of spin systems to find the editing-pulse FA for maximal editing efficiency. Simulations were confirmed by phantom experiments and in vivo measurements in 10 healthy participants using a 3T Philips scanner. Four MEGA-PRESS in vivo measurements targeting GABA+ and lactate were performed, comparing the conventional editing-pulse FA (FA = 180°) to the optimal one suggested by simulations (FA = 210°). RESULTS Simulations and phantom experiments show that edited GABA and lactate signals are maximal at FA = 210°. Compared to conventional editing (FA = 180°), in vivo signals from GABA+ and lactate signals increase on average by 8.5% and 9.3%, respectively. CONCLUSION Increasing the FA of editing-pulses in the MEGA-PRESS experiment from 180° to 210° increases the edited signals from GABA+ and lactate by about 9% in vivo.

Purpose To demonstrate J-difference editing of phosphorylethanolamine (PE) with chemical shifts at 3.22 (PE3.22 ) and 3.98 (PE3.98 ) ppm, and compare the merits of two editing strategies. Methods Density-matrix simulations of MEGA-PRESS (Mescher-Garwood PRESS) for PE were performed at TEs ranging from 80 to 200 ms in steps of 2 ms, applying 20-ms editing pulses (ON/OFF) at (1) 3.98/7.5 ppm to detect PE3.22 and (2) 3.22/7.5 ppm to detect PE3.98 . Phantom experiments were performed using a PE phantom to validate simulation results. Ten subjects were scanned using a Philips 3T MRI scanner at TEs of 90 ms and 110 ms to edit PE3.22 and PE3.98 . Osprey was used for data processing, modeling, and quantification. Results Simulations show substantial TE modulation of the intensity and shape of the edited signals due to coupling evolution. Simulated and phantom integrals suggest that TEs of 110 ms and 90 ms were optimal for the edited detection of PE3.22 and PE3.98 , respectively. Phantom results indicated strong agreement with the simulated spectra and integrals. In vivo quantification of the PE3.22 /total creatine and PE3.98 /total creatine concentration ratio yielded values of 0.26 ± 0.04 (between-subject coefficient of variation [CV]: 15.4%) and 0.18 ± 0.04 (CV: 22.8%), respectively, at TE = 90 ms, and 0.24 ± 0.02 (CV: 8.2%) and 0.23 ± 0.04 (CV: 18.0%), respectively, at TE = 110 ms. Conclusion Simulations and in vivo MEGA-PRESS of PE demonstrate that both PE3.22 and PE3.98 are potential candidates for editing, but PE3.22 at TE = 110 ms yields lower variation across TEs.

The present study characterized associations among brain metabolite levels, applying bivariate and multivariate (i.e., factor analysis) statistical methods to tCr-referenced estimates of the major PRESS 1H-MRS metabolites (i.e., tNAA/tCr, tCho/tCr, mI/tCr, Glx/tCr), acquired from medial parietal lobe in a large (n=299), well-characterized international cohort of healthy volunteers (Povazan et al., 2020). Results supported the hypothesis that 1H-MRS-measured metabolite estimates are moderately intercorrelated (Mr = 0.42, SDr = 0.11, ps < 0.001), with more than half (i.e., 57%) of the total variability in metabolite estimates common to (i.e., shared by) all metabolites. Older age was significantly associated with lower levels of common metabolite variance (CMV; β = -0.09, p = 0.048), despite not being associated with levels of any individual metabolite. Holding CMV levels constant, females had significantly lower levels of total choline (i.e., unique metabolite variance or UMV; β = -0.19, p < 0.001), mirroring significant bivariate correlations between sex and total choline reported previously. If replicated, these results would suggest that applied 1H-MRS researchers should shift their analytical framework from examining bivariate associations between individual metabolites and specialty-dependent (e.g., clinical, research) variables of interest (e.g., using t-tests) to examining multi-variable (i.e., covariate) associations between multiple metabolites and specialty-dependent variables of interest (e.g., using multiple regression). Without this shift, clear interpretation of associations of 1H-MRS metabolites with specialty-dependent variables of interest may not be possible.

Hepatic encephalopathy (HE) is a common neurological manifestation of liver cirrhosis. Clinical symptoms range from subtle attention deficits and motor disturbance to stupor and hepatic coma in the most severe cases. HE pathophysiology is characterized by an increase of ammonia in the brain due to impaired clearance in the cirrhotic liver. This results in disturbed glutamate-glutamine homeostasis as ammonia is increasingly metabolized by glutamine synthetase. Ammonia accumulation furthermore causes increased oxidative stress and disrupts neurotransmitter balance, including the GABAergic and glutamatergic systems. Clinical symptoms in the motor domain suggest that the cerebello-thalamo-cortical system plays a key role in HE. The aim of this study is to investigate metabolic abnormalities in the cerebello-thalamo-cortical system of HE patients using GABA-edited MRS. The study also investigates links between metabolite levels, disease severity, critical flicker frequency (CFF), motor performance scores, and blood ammonia levels.GABA-edited MRS was performed in 35 participants (16 controls, 19 patients (3 minimal HE, 16 HE)) on a clinical 3T MRI system. MRS voxels were placed in the right cerebellum, left thalamus, and left motor cortex. GABA+ levels were estimated from the GABA-edited difference spectra using Gaussian fitting with the Gannet software. Levels of other metabolites of interest (glutamine, glutamate, myo-inositol, glutathione, total choline, total NAA, and total creatine) were assessed using linear-combination modeling in LCModel. Creatine- and water-referenced levels were reported to minimize biases of both reference standards. Group differences in metabolite levels and associations with clinical metrics were tested. Modeling uncertainty estimates of metabolite levels (Cramer-Rao Lower Bounds) were included as statistical weighting factors.GABA+ levels were significantly increased in the cerebellum of patients with HE. GABA+ levels in the motor cortex were significantly decreased in HE patients, and correlated with the CFF (r = 0.73; p < .05) and motor performance scores (r = −0.65; p < .05). Well-established HE-typical metabolite patterns (increased glutamine, decreased myo-inositol and total choline) were confirmed in all three regions. These alterations were closely linked to clinical metrics. Increased glutathione levels were found in the thalamus and motor cortex. Explorative analysis indicated increased aspartate levels in all three regions and decreased scyllo-inositol levels in the motor cortex.In summary, our findings provide further evidence for alterations in the GABAergic system in the cerebellum and motor cortex in HE. These changes were accompanied by characteristic patterns of osmolytes and oxidative stress markers in the cerebello-thalamo-cortical system. These metabolic disturbances are a likely contributor to HE motor symptoms in HE.Graphical AbstractIn patients with hepatic encephalopathy, GABA+ levels in the cerebello-thalamo-cortical loop are significantly increased in the cerebellum and significantly decreased in the motor cortex. GABA+ levels in the motor cortex strongly correlate with critical flicker frequency (CFF) and motor performance score (pegboard test tPEG), but not blood ammonia levels (NH3).HighlightsMotor deficits in HE may originate from the cerebello-thalamo-cortical systemAltered GABAergic neurotransmission plays a critical role in the pathophysiology of HEJ-difference GABA-edited MRS can be used to study in vivo GABA+ levelsCerebellar and motor cortical GABA+ levels were significantly altered in HEGABA+ levels in the motor cortex strongly correlated with clinical metrics

Out-of-voxel (OOV) signals are common spurious echo artifacts in MRS. These signals often manifest in the spectrum as very strong "ripples," which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing optimization through coherence order pathway selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B

Expert consensus recommends linear-combination modeling (LCM) of 1H MR spectra with sequence-specific simulated metabolite basis function and experimentally derived macromolecular (MM) basis functions. Measured MM basis functions have been 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. 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 macromolecular 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 (tCr, tCho) for all MM strategies, however, findings for highly J-coupled metabolites it was 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-SNR MM basis function (cohort-mean) leads to more robust metabolite estimation (lower model residuals and AIC values) compared to 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 improves the robustness in the detection of underlying effects (e.g. metabolite vs age), reduces algorithm-based bias, and estimates algorithm-related variance.

Out-of-voxel (OOV) signals are common spurious echo artifacts in magnetic resonance spectroscopy (MRS). These signals often manifest in the spectrum as very strong ‘ripples’ which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing Optimization Through Coherence Order Pathway Selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B0 gradients can refocus unintended CTPs, it is possible that OOVs are less prevalent when only linear first-order shimming is applied. Here we compare the size of unwanted OOV signals in Hadamard-edited (HERMES) data acquired with either a local gradient scheme (which we refer to here as “Shared”) or DOTCOPS, and with first or second-order shimming. We collected data from 15 healthy volunteers in two brain regions (voxel size 30×26×26mm3) from which it is challenging to acquire MRS data: medial prefrontal cortex (MPFC) and left temporal cortex (LTC). Characteristic OOV echoes were seen in both GABA- and GSH-edited spectra for both brain regions, gradient schemes, and shimming approaches. A linear mixed-effect (LME) model revealed a statistically significant difference in the average residual based on gradient scheme in both GABA-(p < 0.001) and GSH-edited (p < 0.001) spectra; that is, the DOTCOPS gradient scheme resulted in smaller OOV artifacts compared with the Shared scheme. There were no significant differences in OOV artifacts associated with shimming method. Thus, these results suggest that the DOTCOPS gradient scheme for J-difference-edited PRESS acquisitions yields spectra with smaller OOV echo artifacts than the Shared gradient scheme implemented in a widely disseminated editing sequence.

BACKGROUND: Magnetic resonance spectroscopy (MRS) methods have quantified changes in levels of neurotransmitters and other neurometabolites in patients with major depression across the lifespan. 7T field strengths and greater have not been a major focus of study in patients with late-life depression (LLD). METHODS: Nine LLD patients who met DSM-IV criteria for a current major depressive episode and nine non-depressed, healthy, age-matched controls underwent clinical and neuropsychological assessment and single-voxel 7T (1)H-MRS at baseline and after 10–12 weeks of antidepressant treatment (Citalopram; patients only). Spectra were acquired from two brain regions implicated in both depressive symptoms and neuropsychological deficits in LLD, the anterior (ACC) and posterior cingulate (PCC). Levels of γ-aminobutyric acid (GABA), glutamate (Glu), glutathione (GSH), N-acetylaspartylglutamate (NAAG), N-acetylaspartate (NAA), and myo-inositol (mI) were quantified relative to total creatine (tCr) using linear-combination modeling. RESULTS: Baseline Glu/tCr levels were not significantly different between groups. Decreased Glu/tCr levels levels after Citalopram treatment was observed in a subset of LLD patients, although the group difference was not statistically significant. Exploratory analyses showed that LLD patients had lower NAA levels in the PCC relative to controls. Higher levels of ml in the LLD patients relative to the controls and decreases after Citalopram treatment had large effect sizes but were not statistically significant. Further, decreases in PCC Glu/tCr and increases in ACC GSH/tCr were associated with improvement in depressive symptoms. LIMITATIONS: Sample size. CONCLUSIONS: These preliminary results suggest a role of neurochemicals and neurometabolites in the neurobiology of LLD and antidepressant treatment response.

BackgroundAccurate quantification of in vivo proton magnetic resonance spectra involves modeling with a linear combination of known metabolite basis functions. Basis sets can be generated by numerical simulation using the quantum mechanical density-matrix formalism. Accurate simulations for a basis set require correct sequence timings, and pulse shapes and durations.PurposeTo present a cloud-based spectral simulation tool ‘MRSCloud’. It allows community users of MRS to simulate a vendor- and sequence-specific basis set online in a convenient and timeefficient manner. This tool can simulate basis sets for 3 major MR scanner vendors (GE, Philips, Siemens), including conventional acquisitions and spectral editing schemes (MEGA, HERMES, HERCULES) with PRESS and semi-LASER localization.Study TypeProspective.SpecimenN/AField Strength/SequenceSimulations of 3T basis sets for conventional and spectral-editing sequences (MEGA, HERMES, HERCULES) with PRESS and sLASER localizations.AssessmentSimulated metabolite basis functions generated by MRSCloud are compared to those generated by FID-A and MARSS, and a phantom-acquired basis-set from LCModel.Statistical TestsIntraclass correlation coefficients (ICC) were calculated to measure the agreement between individual metabolite basis functions generated using different packages. Statistical analysis was performed using R in RStudio.ResultsSimulation time for a full basis set is approximately 1 hour. ICCs between MRSCloud and FID-A were at least 0.98 and ICCs between MRSCloud and MARSS were at least 0.96. ICCs between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for Gln at 0.68 and highest for NAA at 0.96.Data ConclusionSubstantial reductions in runtime have been achieved by implementing the 1D projection method, coherence-order filtering, and pre-calculation of propagators. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets. The generated basis set has been successfully used with LCModel.

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. publishedVersion

Functional magnetic resonance spectroscopy (fMRS) can be used to investigate neurometabolic responses to external stimuli in-vivo, but findings are inconsistent. We performed a systematic review and meta-analysis on fMRS studies of the primary neurotransmitters Glutamate (Glu), Glx (Glutamate + Glutamine), and GABA. Data were extracted, grouped by metabolite, stimulus domain, and brain region, and analysed by determining standardized effect sizes. The quality of individual studies was rated. When results were analysed by metabolite type small to moderate effect sizes of 0.29-0.47 (p < 0.05) were observed for changes in Glu and Glx regardless of stimulus domain and brain region, but no significant effects were observed for GABA. Further analysis suggests that Glu, Glx and GABA responses differ by stimulus domain or task and vary depending on the time course of stimulation and data acquisition. Here, we establish effect sizes and directionality of GABA, Glu and Glx response in fMRS. This work highlights the importance of standardised reporting and minimal best practice for fMRS research.

PurposeTwo main approaches are used for spectral analysis of edited data: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach will improve quantification of HERMES data compared with simple peak fitting.MethodsA test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE 80 ms for HERMES and TE 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variance (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate.ResultsThe reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0% to 9.9%. For MEGA-PRESS data, the GSH reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3% and 8.8% respectively.ConclusionLinear combination modeling with simulated basis functions substantially improves the reproducibility of GSH quantification for HERMES data.

Theta burst stimulation (TBS), a form of repetitive transcranial magnetic stimulation (rTMS), uses repeated high-frequency bursts to non-invasively modulate neural processes in the brain. An intermittent TBS (iTBS) protocol is generally considered "excitatory," while continuous TBS (cTBS) is considered "inhibitory." However, the majority of work that has led to these effects being associated with the respective protocols has been done in the motor cortex, and it is well established that TMS can have variable effects across the brain.We investigated the effects of iTBS and cTBS to the primary visual cortex (V1) on composite levels of gamma-aminobutyric acid + co-edited macromolecules (GABA+) and glutamate + glutamine (Glx) since these are key inhibitory and excitatory neurotransmitters, respectively. Participants received a single session of cTBS, iTBS, or sham TBS to V1. GABA+ and Glx were quantified in vivo at the stimulation site using spectral-edited proton magnetic resonance spectroscopy (There were no significant changes in GABA+ or Glx following either of the TBS conditions. Visual cortical excitability, measured using phosphene thresholds, remained unchanged following both cTBS and iTBS conditions. There was no relationship between excitability thresholds and GABA+ or Glx levels. However, TBS did alter the relationship between GABA+ and Glx for up to 1 h following stimulation.These findings demonstrate that a single session of TBS to the visual cortex can be used without significant effects on the tonic levels of these key neurotransmitters; and add to our understanding that TBS has differential effects at visual, motor, and frontal cortices.

PURPOSE: To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex. METHODS: 102 volunteers (49M/53F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm(3), pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a ‘clean’ MM spectrum using the Osprey software. Pearson’s correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. RESULTS: MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > 0.004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < 0.05) negative linear association with age in the WM-predominant CSO (R = –0.29) and GM-predominant PCC regions (R = –0.57) respectively while CSF increased significantly with age in both regions. CONCLUSION: Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.