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

GABA and glutamate response to social processing: a functional MRS feasibility study

Several studies have suggested that atypical social processing in neurodevelopmental conditions (e.g. autism) is associated with differences in excitation and inhibition, through changes in the levels of glutamate and gamma-aminobutyric acid (GABA). While associations between baseline metabolite levels and behaviours can be insightful, assessing the neurometabolic response of GABA and glutamate during social processing may explain altered neurochemical function in more depth. Thus far, there have been no attempts to determine whether changes in metabolite levels are detectable using functional MRS (fMRS) during social processing in a control population. We performed Mescher-Garwood point resolved spectroscopy edited fMRS to measure the dynamic response of GABA and glutamate in the superior temporal sulcus (STS) and visual cortex (V1) while viewing social stimuli, using a design that allows for analysis in both block and event-related approaches. Sliding window analyses were used to investigate GABA and glutamate dynamics at higher temporal resolution. The changes of GABA and glutamate levels with social stimulus were largely non-significant. A small decrease in GABA levels was observed during social stimulus presentation in V1, but no change was observed in STS. Conversely, non-social stimulus elicited changes in both GABA and glutamate levels in both regions. Our findings suggest that the current experimental design primarily captures effects of visual stimulation, not social processing. Here, we discuss the feasibility of using fMRS analysis approaches to assess changes in metabolite response.

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.

GABA-edited MEGA-PRESS at 3 T: Does a measured macromolecule background improve linear combination modeling?

PURPOSE

The J-difference edited γ-aminobutyric acid (GABA) signal is contaminated by other co-edited signals-the largest of which originates from co-edited macromolecules (MMs)-and is consequently often reported as "GABA+." MM signals are broader and less well-characterized than the metabolites, and are commonly approximated using a Gaussian model parameterization. Experimentally measured MM signals are a consensus-recommended alternative to parameterized modeling; however, they are relatively under-studied in the context of edited MRS.

METHODS

To address this limitation in the literature, we have acquired GABA-edited MEGA-PRESS data with pre-inversion to null metabolite signals in 13 healthy controls. An experimental MM basis function was derived from the mean across subjects. We further derived a new parameterization of the MM signals from the experimental data, using multiple Gaussians to accurately represent their observed asymmetry. The previous single-Gaussian parameterization, mean experimental MM spectrum and new multi-Gaussian parameterization were compared in a three-way analysis of a public MEGA-PRESS dataset of 61 healthy participants.

RESULTS

Both the experimental MMs and the multi-Gaussian parameterization exhibited reduced fit residuals compared to the single-Gaussian approach (p = 0.034 and p = 0.031, respectively), suggesting they better represent the underlying data than the single-Gaussian parameterization. Furthermore, both experimentally derived models estimated larger MM fractional contribution to the GABA+ signal for the experimental MMs (58%) and multi-Gaussian parameterization (58%), compared to the single-Gaussian approach (50%).

CONCLUSIONS

Our results indicate that single-Gaussian parameterization of edited MM signals is insufficient and that both experimentally derived GABA+ spectra and their parameterized replicas improve the modeling of GABA+ spectra.

Neurochemical Predictors of Generalized Learning Induced by Brain Stimulation and Training

Methods of cognitive enhancement for humans are most impactful when they generalize across tasks. However, the extent to which such "transfer" is possible via interventions is widely debated. In addition, the contribution of excitatory and inhibitory processes to such transfer is unknown. Here, in a large-scale neuroimaging individual differences study with humans (both sexes), we paired multitasking training and noninvasive brain stimulation (transcranial direct current stimulation, tDCS) over multiple days and assessed performance across a range of paradigms. In addition, we varied tDCS dosage (1.0 and 2.0 mA), electrode montage (left or right prefrontal regions), and training task (multitasking vs a control task) and assessed GABA and glutamate concentrations via ultrahigh field 7T magnetic resonance spectroscopy. Generalized benefits were observed in spatial attention, indexed by visual search performance, when multitasking training was combined with 1.0 mA stimulation targeting either the left or right prefrontal cortex (PFC). This transfer effect persisted for ∼30 d post intervention. Critically, the transferred benefits associated with right prefrontal tDCS were predicted by pretraining concentrations of glutamate in the PFC. Thus, the effects of this combined stimulation and training protocol appear to be linked predominantly to excitatory brain processes.

Integrated Short-TE and Hadamard-edited Multi-Sequence (ISTHMUS) for Advanced MRS

Background

To examine data quality and reproducibility using ISTHMUS, which has been implemented as the standardized MR spectroscopy sequence for the multi-site Healthy Brain and Child Development (HBCD) study.

Methods

ISTHMUS is the consecutive acquisition of short-TE PRESS (32 transients) and long-TE HERCULES (224 transients) data with dual-TE water reference scans. Voxels were positioned in the centrum semiovale, dorsal anterior cingulate cortex, posterior cingulate cortex and bilateral thalamus regions. After acquisition, ISTHMUS data were separated into the PRESS and HERCULES portions for analysis and modeled separately using Osprey. In vivo experiments were performed in 10 healthy volunteers (6 female; 29.5±6.6 years). Each volunteer underwent two scans on the same day. Differences in metabolite measurements were examined. T2 correction based on the dual-TE water integrals were compared with: 1) T2 correction based the default white matter and gray matter T2 reference values in Osprey; 2) shorter WM and GM T2 values from recent literature; and 3) reduced CSF fractions.

Results

No significant difference in linewidth was observed between PRESS and HERCULES. Bilateral thalamus spectra had produced significantly higher (p<0.001) linewidth compared to the other three regions. Linewidth measurements were similar between scans, with scan-to-scan differences under 1 Hz for most subjects. Paired t-tests indicated a significant difference only in PRESS NAAG between the two thalamus scans (p=0.002). T2 correction based on shorter T2 values showed better agreement to the dual-TE water integral ratio.

Conclusions

ISTHMUS facilitated and standardized acquisition and post-processing and reduced operator workload to eliminate potential human error.

Metabolite T1 relaxation times decrease across the adult lifespan

Relaxation correction is an integral step in quantifying brain metabolite concentrations measured by in vivo magnetic resonance spectroscopy (MRS). While most quantification routines assume constant T1 relaxation across age, it is possible that aging alters T1 relaxation rates, as is seen for T2 relaxation. Here, we investigate the age dependence of metabolite T1 relaxation times at 3 T in both gray- and white-matter-rich voxels using publicly available metabolite and metabolite-nulled (single inversion recovery TI = 600 ms) spectra acquired at 3 T using Point RESolved Spectroscopy (PRESS) localization. Data were acquired from voxels in the posterior cingulate cortex (PCC) and centrum semiovale (CSO) in 102 healthy volunteers across 5 decades of life (aged 20-69 years). All spectra were analyzed in Osprey v.2.4.0. To estimate T1 relaxation times for total N-acetyl aspartate at 2.0 ppm (tNAA2.0) and total creatine at 3.0 ppm (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 between T1 and subject age were evaluated. Spearman correlations revealed that estimated T1 relaxation times of tNAA2.0 (rs = -0.27; p < 0.006) and tCr3.0 (rs = -0.40; p < 0.001) decreased significantly with age in white-matter-rich CSO, and less steeply for tNAA2.0 (rs = -0.228; p = 0.005) and (not significantly for) tCr3.0 (rs = -0.13; p = 0.196) in graymatter-rich PCC. The analysis harnessed a large publicly available cross-sectional dataset to test an important hypothesis, that metabolite T1 relaxation times change with age. This preliminary study stresses the importance of further work to measure age-normed metabolite T1 relaxation times for accurate quantification of metabolite levels in studies of aging.

7 T characterization of excitatory and inhibitory systems of acute pain in healthy female participants

Current understanding of the physiological underpinnings of normative pain processing is incomplete. Enhanced knowledge of these systems is necessary to advance our understanding of pain processes as well as to develop effective therapeutic interventions. Previous neuroimaging research suggests a network of interrelated brain regions that seem to be implicated in the processing and experience of pain. Among these, the dorsal anterior cingulate cortex (dACC) plays an important role in the affective aspects of pain signals. The current study leveraged functional MRS to investigate the underlying dynamic shifts in the neurometabolic signature of the human dACC at rest and during acute pain. Results provide support for increased glutamate levels following acute pain administration. Specifically, a 4.6% increase in glutamate was observed during moderate pressure pain compared with baseline. Exploratory analysis also revealed meaningful changes in dACC gamma aminobutyric acid in response to pain stimulation. These data contribute toward the characterization of neurometabolic shifts, which lend insight into the role of the dACC in the pain network. Further research in this area with larger sample sizes could contribute to the development of novel therapeutics or other advances in pain-related outcomes.

1H and 31P magnetic resonance spectroscopy reveals potential pathogenic and biomarker metabolite alterations in Lafora disease

Lafora disease is a fatal teenage-onset progressive myoclonus epilepsy and neurodegenerative disease associated with polyglucosan bodies. Polyglucosans are long-branched and as a result precipitation- and aggregation-prone glycogen. In mouse models, downregulation of glycogen synthase, the enzyme that elongates glycogen branches, prevents polyglucosan formation and rescues Lafora disease. Mouse work, however, has not yet revealed the mechanisms of polyglucosan generation, and few in vivo human studies have been performed. Here, non-invasive in vivo magnetic resonance spectroscopy (1H and 31P) was applied to test scan feasibility and assess neurotransmitter balance and energy metabolism in Lafora disease towards a better understanding of pathogenesis. Macromolecule-suppressed gamma-aminobutyric acid (GABA)-edited 1H magnetic resonance spectroscopy and 31P magnetic resonance spectroscopy at 3 and 7 tesla, respectively, were performed in 4 Lafora disease patients and a total of 21 healthy controls (12 for the 1H magnetic resonance spectroscopy and 9 for the 31PMRS). Spectra were processed using in-house software and fit to extract metabolite concentrations. From the 1H spectra, we found 33% lower GABA concentrations (P = 0.013), 34% higher glutamate + glutamine concentrations (P = 0.011) and 24% lower N-acetylaspartate concentrations (P = 0.0043) in Lafora disease patients compared with controls. From the 31P spectra, we found 34% higher phosphoethanolamine concentrations (P = 0.016), 23% lower nicotinamide adenine dinucleotide concentrations (P = 0.003), 50% higher uridine diphosphate glucose concentrations (P = 0.004) and 225% higher glucose 6-phosphate concentrations in Lafora disease patients versus controls (P = 0.004). Uridine diphosphate glucose is the substrate of glycogen synthase, and glucose 6-phosphate is its extremely potent allosteric activator. The observed elevated uridine diphosphate glucose and glucose 6-phosphate levels are expected to hyperactivate glycogen synthase and may underlie the generation of polyglucosans in Lafora disease. The increased glutamate + glutamine and reduced GABA indicate altered neurotransmission and energy metabolism, which may contribute to the disease's intractable epilepsy. These results suggest a possible basis of polyglucosan formation and potential contributions to the epilepsy of Lafora disease. If confirmed in larger human and animal model studies, measurements of the dysregulated metabolites by magnetic resonance spectroscopy could be developed into non-invasive biomarkers for clinical trials.

Diffusion of brain metabolites highlights altered brain microstructure in type C hepatic encephalopathy: a 9.4 T preliminary study

Introduction

Type C hepatic encephalopathy (HE) is a decompensating event of chronic liver disease leading to severe motor and cognitive impairment. The progression of type C HE is associated with changes in brain metabolite concentrations measured by 1H magnetic resonance spectroscopy (MRS), most noticeably a strong increase in glutamine to detoxify brain ammonia. In addition, alterations of brain cellular architecture have been measured ex vivo by histology in a rat model of type C HE. The aim of this study was to assess the potential of diffusion-weighted MRS (dMRS) for probing these cellular shape alterations in vivo by monitoring the diffusion properties of the major brain metabolites.

Methods

The bile duct-ligated (BDL) rat model of type C HE was used. Five animals were scanned before surgery and 6- to 7-week post-BDL surgery, with each animal being used as its own control. 1H-MRS was performed in the hippocampus (SPECIAL, TE = 2.8 ms) and dMRS in a voxel encompassing the entire brain (DW-STEAM, TE = 15 ms, diffusion time = 120 ms, maximum b-value = 25 ms/μm2) on a 9.4 T scanner. The in vivo MRS acquisitions were further validated with histological measures (immunohistochemistry, Golgi-Cox, electron microscopy).

Results

The characteristic 1H-MRS pattern of type C HE, i.e., a gradual increase of brain glutamine and a decrease of the main organic osmolytes, was observed in the hippocampus of BDL rats. Overall increased metabolite diffusivities (apparent diffusion coefficient and intra-stick diffusivity-Callaghan's model, significant for glutamine, myo-inositol, and taurine) and decreased kurtosis coefficients were observed in BDL rats compared to control, highlighting the presence of osmotic stress and possibly of astrocytic and neuronal alterations. These results were consistent with the microstructure depicted by histology and represented by a decline in dendritic spines density in neurons, a shortening and decreased number of astrocytic processes, and extracellular edema.

Discussion

dMRS enables non-invasive and longitudinal monitoring of the diffusion behavior of brain metabolites, reflecting in the present study the globally altered brain microstructure in BDL rats, as confirmed ex vivo by histology. These findings give new insights into metabolic and microstructural abnormalities associated with high brain glutamine and its consequences in type C HE.

Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

Brain cell structure and function reflect neurodevelopment, plasticity, and aging; and changes can help flag pathological processes such as neurodegeneration and neuroinflammation. Accurate and quantitative methods to noninvasively disentangle cellular structural features are needed and are a substantial focus of brain research. Diffusion-weighted MRS (dMRS) gives access to diffusion properties of endogenous intracellular brain metabolites that are preferentially located inside specific brain cell populations. Despite its great potential, dMRS remains a challenging technique on all levels: from the data acquisition to the analysis, quantification, modeling, and interpretation of results. These challenges were the motivation behind the organization of the Lorentz Center workshop on "Best Practices & Tools for Diffusion MR Spectroscopy" held in Leiden, the Netherlands, in September 2021. During the workshop, the dMRS community established a set of recommendations to execute robust dMRS studies. This paper provides a description of the steps needed for acquiring, processing, fitting, and modeling dMRS data, and provides links to useful resources.

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.

Magnetic Resonance Spectroscopy (MRS) in Alzheimer's Disease

MRS is a noninvasive technique to measure different metabolites in the brain. Changes in the levels of certain metabolites can be used as surrogate markers for Alzheimer's disease. They can potentially be used for diagnosis, prediction of prognosis, or even assessing response to treatment.There are different techniques for MRS acquisitions including STimulated Echo Acquisition Mode (STEAM) and Point Resolved Spectroscopy (PRESS). In terms of localization, single or multi-voxel methods can be used. Based on current data: 1. NAA, marker of neuronal integrity and viability, reduces in AD with longitudinal changes over the time as the disease progresses. There are data claiming that reduction of NAA is associated with tau accumulation, early neurodegenerative processes, and cognitive decline. Therefore, it can be used as a stage biomarker for AD to assess the severity of the disease. With advancement of disease modifying therapies, there is a potential role for NAA in the future to be used as a marker of response to treatment. 2. mI, marker of glial cell proliferation and activation, is associated with AB pathology and has early changes in the course of the disease. The NAA/mI ratio can be predictive of AD development with high specificity and can be utilized in the clinical setting to stratify cases for further evaluation with PET for potential treatments. 3. The changes in the level of other metabolites such as Chol, Glu, Gln, and GABA are controversial because of the lack of standardization of MRS techniques, current technical limitations, and possible region specific changes. 4. Ultrahigh field MRS and more advanced techniques can overcome many of these limitations and enable us to measure more metabolites with higher accuracy. 5. Standardization of MRS techniques, validation of metabolites' changes against PET using PET-guided technique, and longitudinal follow-ups to investigate the temporal changes of the metabolites in relation to other biomarkers and cognition will be crucial to confirm the utility of MRS as a potential noninvasive biomarker for AD.

Multi-echo single-shot spectroscopy combined with simultaneous 2D model fitting for fast and accurate measurement of metabolite-specific concentrations and T2 relaxation times

The purpose of the current study was to develop a novel single-voxel MR spectroscopy acquisition scheme to simultaneously determine metabolite-specific concentrations and transverse relaxation times within realistic clinical scan times. Partly truncated multi-TE data are acquired as an echo train in a single acquisition (multi-echo single-shot [MESS]). A 2D multiparametric model fitting approach combines truncated, low-resolved short TE data with fully sampled, highly resolved, longer TE data to yield concentration and T2 estimates for major brain metabolites simultaneously. Cramer-Rao lower bounds (CRLB) are used as a measure of performance. The novel scheme was compared with traditional multi-echo multi-shot methods. In silico, in vitro, and in vivo experiments support the findings. MESS schemes, requiring only 2 min 12 s for the acquisition of three echo times, provide valid concentration and relaxation estimates for multiple metabolites and outperform traditional methods for simultaneous determinations of metabolite-specific T2 s and concentrations, with improvements ranging from 5% to 30% for T2 s and from 10% to 50% for concentrations. However, substantial unsuppressed residual water signals may hamper the method's reproducibility, as observed in an initial experiment setup that prioritizes short TEs with severely truncated acquisition for the benefit of signal-to-noise ratio (SNR). Nevertheless, CRLB have been confirmed to be well suited as design criteria, and within-session repeatability approaches CRLB when residual water is removed in postprocessing by exploiting longer and less truncated data recordings. MESS MRS combined with 2D model fitting promises comparable accuracy, increased precision, or inversely shorter experimental times compared with traditional approaches. However, the optimal design must be investigated as a trade-off between SNR, the truncation factor, and TE batch selections, all of which influence the robustness of estimations.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

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.

Individual Differences in Decision Strategy Relate to Neurochemical Excitability and Cortical Thickness

The speed-accuracy trade-off (SAT), whereby faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during the performance of almost all daily tasks. To date, computational modeling has implicated the latent decision variable of response caution (thresholds), the amount of evidence required for a decision to be made, in the SAT. Previous imaging has associated frontal regions, notably the left prefrontal cortex and the presupplementary motor area (pre-SMA), with the setting of such caution levels. In addition, causal brain stimulation studies, using transcranial direct current stimulation (tDCS), have indicated that while both of these regions are involved in the SAT, their role appears to be dissociable. tDCS efficacy to impact decision-making processes has previously been linked with neurochemical concentrations and cortical thickness of stimulated regions. However, to date, it is unknown whether these neurophysiological measures predict individual differences in the SAT, and brain stimulation effects on the SAT. Using ultra-high field (7T) imaging, here we report that instruction-based adjustments in caution are associated with both neurochemical excitability (the balance between GABA+ and glutamate) and cortical thickness across a range of frontal regions in both sexes. In addition, cortical thickness, but not neurochemical concentrations, was associated with the efficacy of left prefrontal and superior medial frontal cortex (SMFC) stimulation to modulate performance. Overall, our findings elucidate key neurophysiological predictors, frontal neural excitation, of individual differences in latent psychological processes and the efficacy of stimulation to modulate these.SIGNIFICANCE STATEMENT The speed-accuracy trade-off (SAT), faster decisions increase the likelihood of an error, reflects a cognitive strategy humans must engage in during most daily tasks. The SAT is often investigated by explicitly instructing participants to prioritize speed or accuracy when responding to stimuli. Using ultra-high field (7T) magnetic resonance imaging (MRI), we found that individual differences in the extent to which participants adjust their decision strategies with instruction related to neurochemical excitability (ratio of GABA+ to glutamate) and cortical thickness in the frontal cortex. Moreover, brain stimulation to the left prefrontal cortex and the superior medial frontal cortex (SMFC) modulated performance, with the efficacy specifically related to cortical thickness. This work sheds new light on the neurophysiological basis of decision strategies and brain stimulation.

Comparative study of bilateral putamen for patients with severe Parkinson's disease detected by 1H magnetic resonance spectroscopy

Background

The diagnosis of Parkinson's disease (PD) is challenging because the clinical symptoms overlap with other neurodegenerative diseases. The discovery of reliable biomarkers is highly expected to facilitate clinical diagnosis. Through the analysis of the 1H magnetic resonance spectroscopy (1H-MRS) in the putamen, the purpose of the study was to discuss the possibility of the difference in metabolite concentrations between the left and right putamen as biomarkers for patients with severe PD.

Methods

We collected 1H-MRS of unilateral or bilateral putamen from 41 patients and used the independent sample t-test and paired t-test to analyze 4 metabolite concentrations, including choline (Cho), total N-acetyl aspartate (tNAA), total creatine (tCr), and combined glutamate and glutamine; Bonferroni correction was used to correct P values for multiple comparisons. We designed 4 controlled experiments as follows: (I) PD patients versus healthy controls (HCs) in the left putamen; (II) PD patients versus HCs in the right putamen; (III) the left putamen versus the right putamen for PD patients; and (IV) the left putamen versus the right putamen for HCs.

Results

No statistically significant differences (P>0.05) were detected among 4 metabolites in the ipsilateral and bilateral putamen for the PD and HCs groups, except for tCr in the left putamen (PD 6.426±0.557, HCs 6.026±0.460, P=0.046) for ipsilateral comparisons.

Conclusions

In the bilateral putamen of severe PD patients, there was no statistically significant difference in the 4 metabolites. The difference (P<0.05) in tCr in the left putamen might be a potential biomarker to distinguish HCs from severe patients in clinic. This might provide a reference for the clinical diagnosis and acquisition strategy of 1H-MRS in severe PD.

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

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

GABA, glutamate and excitatory-inhibitory ratios measured using short-TE STEAM MRS at 7-Tesla: Effects of macromolecule basis sets and baseline parameters

Rationale and objectives

Macromolecules (MMs) affect the precision and accuracy of neurochemical quantification in magnetic resonance spectroscopy. A measured MM basis is increasingly used in LCModel analysis combined with a spline baseline, whose stiffness is controlled by a parameter named DKNTMN. The effects of measured MM basis and DKNTMN were investigated.

Materials and methods

Twenty-six healthy subjects were prospectively enrolled and scanned twice using a short echo-time Stimulated Echo Acquisition Mode (STEAM) at 7-T. Using LCModel, analyses were conducted using the simulated MM basis (MMsim) with DKNTMN 0.15 and an MM basis measured inhouse (MMmeas) with DKNTMN of 0.15, 0.30, 0.60 and 1.00. Cramér-Rao lower bound (CRLB) and the concentrations of gamma-aminobutyric acid (GABA), glutamate and excitatory-inhibitory ratio (EIR), in addition to MMs were statistically analyzed. Measurement stability was evaluated using coefficient of variation (CV).

Results

CRLBs of GABA were significantly lower when using MMsim than MMmeas; those of glutamate were 2-3. GABA concentrations were significantly higher in the analysis using MMsim than MMmeas where concentrations were significantly higher with DKNTMN of 0.15 or 0.30 than 0.60 or 1.00. Difference in glutamate concentration was not significant. EIRs showed the same difference as in GABA depending on the DKNTMN values. CVs between test-retest scans were relatively stable for glutamate but became larger as DKNTMN increased for GABA and EIR.

Conclusion

Neurochemical quantification depends on the parameters of the basis sets used for fitting. Analysis using MMmeas with DKNTMN of 0.30 conformed best to previous studies and is recommended.

High field brain proton magnetic resonance spectroscopy and volumetry in children with chronic, compensated liver disease - A pilot study

BACKGROUND

and rationale: There is increasing evidence that children or young adults having acquired liver disease in childhood display neurocognitive impairment which may become more apparent as they grow older. The molecular, cellular and morphological underpinnings of this clinical problem are incompletely understood.

AIM

Therefore, we used the advantages of highly-resolved proton magnetic resonance spectroscopy at ultra-high magnetic field to analyze the neurometabolic profile and brain morphometry of children with chronic, compensated liver disease, hypothesizing that with high field spectroscopy we would identify early evidence of rising brain glutamine and decreased myoinositol, such as has been described both in animals and humans with more significant liver disease.

METHODS

Patients (n = 5) and age-matched controls (n = 19) underwent 7T MR scans and short echo time ¹H MR spectra were acquired using the semi-adiabatic SPECIAL sequence in two voxels located in gray and white matter dominated prefrontal cortex, respectively. A 3D MP2RAGE sequence was also acquired for brain volumetry and T₁ mapping. Liver disease had to have developed at least 6 months before entering the study. Subjects underwent routine blood analysis and neurocognitive testing using validated methods within 3 months of MRI and MRS.

RESULTS

Five children currently aged 8-16 years with liver disease acquired in childhood were included. Baseline biological characteristics were similar among patients. There were no statistically significant differences between subjects and controls in brain metabolite levels or brain volumetry. Finally, there were minor neurocognitive fluctuations including attention deficit in one child, but none fell in the statistically significant range.

CONCLUSION

Children with chronic, compensated liver disease did not display an abnormal neurometabolic profile, neurocognitive abnormalities, or signal intensity changes in the globus pallidus. Despite the absence of neurometabolic changes, it is an opportunity to emphasize that it is only by developing the use of ¹H MRS at high field in the clinical arena that we will understand the significance and generalizability of these findings in children with CLD. Attention deficit was observed in one child. Healthy children displayed neurometabolic regional differences as previously reported in adult subjects.

Chronic 3,4-Methylenedioxymethamphetamine (MDMA) Use Is Related to Glutamate and GABA Concentrations in the Striatum But Not the Anterior Cingulate Cortex

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.

Single-Voxel MR Spectroscopy of Gliomas with s-LASER at 7T

BACKGROUND AND PURPOSE

Magnetic resonance spectroscopy (MRS)-a method of analysing metabolites in vivo-has been utilized in several studies of brain glioma biomarkers at lower field strengths. At ultra-high field strengths, MRS provides an improved signal-to-noise-ratio and spectral resolution, but 7T studies on patients with gliomas are sparse. The purpose of this exploratory study was to evaluate the potential clinical implication of the use of single-voxel MRS at 7T to assess metabolic information on lesions in a pilot cohort of patients with grade II and III gliomas.

METHODS

We scanned seven patients and seven healthy controls using the semi-localization by adiabatic-selective refocusing sequence on a Philips Achieva 7T system with a standard dual-transmit head coil. The metabolic ratios were calculated relative to water and total creatine. Additionally, 2-hydroxyglutarate (2-HG) MRS was carried out in four of the patients, and the 2-HG concentration was calculated relative to water.

RESULTS

When comparing the tumour data to control regions in both patients and healthy controls, we found that the choline/creatine and myo-inositol/creatine ratios were significantly increased and that the N-acetylaspartate/creatine and the neurotransmitter glutamate/creatine ratios were significantly decreased. The N-acetylaspartate/water and glutamate/water ratios were also significantly decreased. The lactate/water and lactate/creatine ratios showed increases, although not significant. The GABA/water ratio was significantly decreased, but the GABA/creatine ratio was not. MRS spectra showed the presence of 2-HG in three of the four patients studied. Three of the patients, including the MRS 2-HG-negative patient, were operated on, and all of them had the IDH mutation.

CONCLUSION

Our findings were consistent with the existing literature on 3T and 7T MRS.

A comprehensive guide to MEGA-PRESS for GABA measurement

The aim of this guideline is to provide a series of evidence-based recommendations that allow those new to using MEGA-PRESS to produce high-quality data for the measurement of GABA levels using edited magnetic resonance spectroscopy with the MEGA-PRESS sequence at 3T. GABA is the main inhibitory neurotransmitter of the central nervous system and has been increasingly studied due to its relevance in many clinical disorders of the central nervous system. MEGA-PRESS is the most widely used method for quantification of GABA at 3T, but is technically challenging and operates at a low signal-to-noise ratio. Therefore, the acquisition of high-quality MRS data relies on avoiding numerous pitfalls and observing important caveats. The guideline was developed by a working party that consisted of experts in MRS and experts in guideline development and implementation, together with key stakeholders. Strictly following a translational framework, we first identified evidence using a systematically conducted scoping literature review, then synthesized and graded the quality of evidence that formed recommendations. These recommendations were then sent to a panel of 21 world leaders in MRS for feedback and approval using a modified-Delphi process across two rounds. The final guideline consists of 23 recommendations across six domains essential for GABA MRS acquisition (Parameters, Practicalities, Data acquisition, Confounders, Quality/reporting, Post-processing). Overall, 78% of recommendations were formed from high-quality evidence, and 91% received agreement from over 80% of the expert panel. These 23 expert-reviewed recommendations and accompanying extended documentation form a readily useable guideline to allow those new to using MEGA-PRESS to design appropriate MEGA-PRESS study protocols and generate high-quality data.

Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias

PURPOSE

The aims of this work are (1) to explore deep learning (DL) architectures, spectroscopic input types, and learning designs toward optimal quantification in MR spectroscopy of simulated pathological spectra; and (2) to demonstrate accuracy and precision of DL predictions in view of inherent bias toward the training distribution.

METHODS

Simulated 1D spectra and 2D spectrograms that mimic an extensive range of pathological in vivo conditions are used to train and test 24 different DL architectures. Active learning through altered training and testing data distributions is probed to optimize quantification performance. Ensembles of networks are explored to improve DL robustness and reduce the variance of estimates. A set of scores compares performances of DL predictions and traditional model fitting (MF).

RESULTS

Ensembles of heterogeneous networks that combine 1D frequency-domain and 2D time-frequency domain spectrograms as input perform best. Dataset augmentation with active learning can improve performance, but gains are limited. MF is more accurate, although DL appears to be more precise at low SNR. However, this overall improved precision originates from a strong bias for cases with high uncertainty toward the dataset the network has been trained with, tending toward its average value.

CONCLUSION

MF mostly performs better compared to the faster DL approach. Potential intrinsic biases on training sets are dangerous in a clinical context that requires the algorithm to be unbiased to outliers (i.e., pathological data). Active learning and ensemble of networks are good strategies to improve prediction performances. However, data quality (sufficient SNR) has proven as a bottleneck for adequate unbiased performance-like in the case of MF.

Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data

PURPOSE

While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data.

METHOD

We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study.

RESULT

The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly.

CONCLUSION

The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.

The Psychosis Human Connectome Project: Design and rationale for studies of visual neurophysiology

Visual perception is abnormal in psychotic disorders such as schizophrenia. In addition to hallucinations, laboratory tests show differences in fundamental visual processes including contrast sensitivity, center-surround interactions, and perceptual organization. A number of hypotheses have been proposed to explain visual dysfunction in psychotic disorders, including an imbalance between excitation and inhibition. However, the precise neural basis of abnormal visual perception in people with psychotic psychopathology (PwPP) remains unknown. Here, we describe the behavioral and 7 tesla MRI methods we used to interrogate visual neurophysiology in PwPP as part of the Psychosis Human Connectome Project (HCP). In addition to PwPP (n = 66) and healthy controls (n = 43), we also recruited first-degree biological relatives (n = 44) in order to examine the role of genetic liability for psychosis in visual perception. Our visual tasks were designed to assess fundamental visual processes in PwPP, whereas MR spectroscopy enabled us to examine neurochemistry, including excitatory and inhibitory markers. We show that it is feasible to collect high-quality data across multiple psychophysical, functional MRI, and MR spectroscopy experiments with a sizable number of participants at a single research site. These data, in addition to those from our previously described 3 tesla experiments, will be made publicly available in order to facilitate further investigations by other research groups. By combining visual neuroscience techniques and HCP brain imaging methods, our experiments offer new opportunities to investigate the neural basis of abnormal visual perception in PwPP.

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.

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

Magnetic resonance spectroscopy is a powerful, non-invasive, quantitative imaging technique that allows for the measurement of brain metabolites that has demonstrated utility in diagnosing and characterizing a broad range of neurological diseases. Its impact, however, has been limited due to small sample sizes and methodological variability in addition to intrinsic limitations of the method itself such as its sensitivity to motion. The lack of standardization from a data acquisition and data processing perspective makes it difficult to pool multiple studies and/or conduct multisite studies that are necessary for supporting clinically relevant findings. Based on the experience of the ENIGMA MRS work group and a review of the literature, this manuscript provides an overview of the current state of MRS data harmonization. Key factors that need to be taken into consideration when conducting both retrospective and prospective studies are described. These include (1) MRS acquisition issues such as pulse sequence, RF and B0 calibrations, echo time, and SNR; (2) data processing issues such as pre-processing steps, modeling, and quantitation; and (3) biological factors such as voxel location, age, sex, and pathology. Various approaches to MRS data harmonization are then described including meta-analysis, mega-analysis, linear modeling, ComBat and artificial intelligence approaches. The goal is to provide both novice and experienced readers with the necessary knowledge for conducting MRS data harmonization studies.

Advanced design of MRI inversion pulses for inhomogeneous field conditions by optimal control

Non-selective inversion pulses find widespread use in MRI applications, where requirements on them are increasingly demanding. With the use of high and ultra-high field strength systems, robustness to Δ B 0 and B 1 + inhomogeneities, while tackling SAR and hardware limitations, has rapidly become important. In this work, we propose a time-optimal control framework for the optimization of Δ B 0 - and B 1 + -robust inversion pulses. Robustness is addressed by means of ensemble formulations, while allowing inclusion of hardware and energy limitations. The framework is flexible and performs excellently for various optimization goals. The optimization results are analyzed extensively in numerical experiments. Furthermore, they are validated, and compared with adiabatic RF pulses, in various phantom and in vivo measurements on a 3 T MRI system.

MP-PCA denoising for diffusion MRS data: promises and pitfalls

Diffusion-weighted (DW) magnetic resonance spectroscopy (MRS) suffers from a lower signal to noise ratio (SNR) compared to conventional MRS owing to the addition of diffusion attenuation. This technique can therefore strongly benefit from noise reduction strategies. In the present work, Marchenko-Pastur principal component analysis (MP-PCA) denoising is tested on Monte Carlo simulations and on in vivo DW-MRS data acquired at 9.4 T in rat brain and at 3 T in human brain. We provide a descriptive study of the effects observed following different MP-PCA denoising strategies (denoising the entire matrix versus using a sliding window), in terms of apparent SNR, rank selection, noise correlation within and across b-values and quantification of metabolite concentrations and fitted diffusion coefficients. MP-PCA denoising yielded an increased apparent SNR, a more accurate B0 drift correction between shots, and similar estimates of metabolite concentrations and diffusivities compared to the raw data. No spectral residuals on individual shots were observed but correlations in the noise level across shells were introduced, an effect which was mitigated using a sliding window, but which should be carefully considered.

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

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

Laterality in functional and metabolic state of the bulbectomised rat brain detected by ASL and 1H MRS: A pilot study

OBJECTIVES

Pilot study validating the animal model of depression - the bilateral olfactory bulbectomy in rats - by two nuclear magnetic resonance methods, indirectly detecting the metabolic state of the brain. Furthermore, the study focussed on potential differences in brain laterality.

METHODS

Arterial spin labelling assessed cerebral brain flow in prefrontal, sensorimotor, and piriform cortices, nucleus accumbens, hippocampus, thalamus, circle of Willis, and whole brain. Proton magnetic resonance spectroscopy provided information about relative metabolite concentrations in the cortex and hippocampus.

RESULTS

Arterial spin labelling found no differences in cerebral perfusion in the group comparison but revealed lateralisation in the thalamus of the control group and the sensorimotor cortex of the bulbectomized rats. Lower Cho/tCr and Cho/NAA levels were found in the right hippocampus in bulbectomized rats. The differences in lateralisation were shown in the hippocampus: mI/tCr in the control group, Cho/NAA, NAA/tCr, Tau/tCr in the model group, and in the cortex: NAA/tCr, mI/tCr in the control group.

CONCLUSION

Olfactory bulbectomy affects the neuronal and biochemical profile of the rat brain laterally and, as a model of depression, was validated by two nuclear magnetic resonance methods.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

On the relationship between GABA+ and glutamate across the brain

Equilibrium between excitation and inhibition (E/I balance) is key to healthy brain function. Conversely, disruption of normal E/I balance has been implicated in a range of central neurological pathologies. Magnetic resonance spectroscopy (MRS) provides a non-invasive means of quantifying in vivo concentrations of excitatory and inhibitory neurotransmitters, which could be used as diagnostic biomarkers. Using the ratio of excitatory and inhibitory neurotransmitters as an index of E/I balance is common practice in MRS work, but recent studies have shown inconsistent evidence for the validity of this proxy. This is underscored by the fact that different measures are often used in calculating E/I balance such as glutamate and Glx (glutamate and glutamine). Here we used a large MRS dataset obtained at ultra-high field (7 T) measured from 193 healthy young adults and focused on two brain regions - prefrontal and occipital cortex - to resolve this inconsistency. We find evidence that there is an inter-individual common ratio between GABA+ (γ-aminobutyric acid and macromolecules) and Glx in the occipital, but not prefrontal cortex. We further replicate the prefrontal result in a legacy dataset (n = 78) measured at high-field (3 T) strength. By contrast, with ultra-high field MRS data, we find extreme evidence that there is a common ratio between GABA+ and glutamate in both prefrontal and occipital cortices, which cannot be explained by participant demographics, signal quality, fractional tissue volume, or other metabolite concentrations. These results are consistent with previous electrophysiological and theoretical work supporting E/I balance. Our findings indicate that MRS-detected GABA+ and glutamate (but not Glx), are a reliable measure of E/I balance .

Short symmetric and highly selective asymmetric first and second order gradient modulated offset independent adiabaticity (GOIA) pulses for applications in clinical MRS and MRSI

Gradient modulated RF pulses, especially gradient offset independent adiabaticity (GOIA) pulses, are increasingly gaining attention for high field clinical magnetic resonance spectroscopy and spectroscopic imaging (MRS/MRSI) due to the lower peak B₁ amplitude and associated power demands achievable relative to its non-modulated adiabatic full passage counterparts. In this work we describe the development of two GOIA RF pulses: 1) A power efficient, 3.0 ms wideband uniform rate with smooth truncation (WURST) modulated RF pulse with 15 kHz bandwidth compatible with a clinically feasible peak B₁ amplitude of 0.87 kHz (or 20 µT), and 2) A highly selective asymmetric 6.66 ms RF pulse with 20 kHz bandwidth designed to achieve a single-sided, fractional transition width of only 1.7%. Effects of potential asynchrony between RF and gradient-modulated (GM) waveforms for 3 ms GOIA-WURST RF pulses was evaluated by simulation and experimentally. Results demonstrate that a 20+ µs asynchrony between RF and GM functions substantially degrades inversion performance when using large RF offsets to achieve translation. A projection-based method is presented that allows a quick calibration of RF and GM asynchrony on pre-clinical/clinical MR systems. The asymmetric GOIA pulse was implemented within a multi-pulse OVS sequence to achieve power efficient, highly-selective, and B₁ and T₁-independent signal suppression for extracranial lipid suppression. The developed GOIA pulses were utilized with linear gradient modulation (X, Y, Z gradient fields), and with second-order-field modulations (Z2, X2Y2 gradient fields) to provide elliptically-shaped regions-of-interest for MRS and MRSI acquisitions. Both described GOIA-RF pulses have substantial clinical value; specifically, the 3.0 ms GOIA-WURST pulse is beneficial to realize short TE sLASER localized proton MRS/MRSI sequences, and the asymmetric GOIA RF pulse has applications in highly selective outer volume signal suppression to allow interrogation of tissue proximal to extracranial lipids with full-intensity.

The Impact of Theta-Burst Stimulation on Cortical GABA and Glutamate in Treatment-Resistant Depression: A Surface-Based MRSI Analysis Approach

Background: Theta burst stimulation (TBS) belongs to one of the biological antidepressant treatment options. When applied bilaterally, excitatory intermittent TBS (iTBS) is commonly targeted to the left and inhibitory continuous TBS (cTBS) to the right dorsolateral prefrontal cortex. TBS was shown to influence neurotransmitter systems, while iTBS is thought to interfere with glutamatergic circuits and cTBS to mediate GABAergic neurotransmission.

Objectives: We aimed to expand insights into the therapeutic effects of TBS on the GABAergic and glutamatergic system utilizing 3D-multivoxel magnetic resonance spectroscopy imaging (MRSI) in combination with a novel surface-based MRSI analysis approach to investigate changes of cortical neurotransmitter levels in patients with treatment-resistant depression (TRD).

Methods: Twelve TRD patients (five females, mean age ± SD = 35 ± 11 years) completed paired MRSI measurements, using a GABA-edited 3D-multivoxel MEGA-LASER sequence, before and after 3 weeks of bilateral TBS treatment. Changes in cortical distributions of GABA+/tNAA (GABA+macromolecules relative to total N-acetylaspartate) and Glx/tNAA (Glx = mixed signal of glutamate and glutamine), were investigated in a surface-based region-of-interest (ROI) analysis approach.

Results: ANCOVAs revealed a significant increase in Glx/tNAA ratios in the left caudal middle frontal area (p_corr. = 0.046, F = 13.292), an area targeted by iTBS treatment. Whereas, contralateral treatment with cTBS evoked no alterations in glutamate or GABA concentrations.

Conclusion: This study demonstrates surface-based adaptions in the stimulation area to the glutamate metabolism after excitatory iTBS but not after cTBS, using a novel surface-based analysis of 3D-MRSI data. The reported impact of facilitatory iTBS on glutamatergic neurotransmission provides further insight into the neurobiological effects of TBS in TRD.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

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γ-aminobutyric acid measurement in the human brain at 7 T: Short echo-time or Mescher-Garwood editing

The purposes of the current study were to introduce a Mescher-Garwood (MEGA) semi-adiabatic spin-echo full-intensity localization (MEGA-sSPECIAL) sequence with macromolecule (MM) subtraction and to compare the test-retest reproducibility of γ-aminobutyric acid (GABA) measurements at 7 T using the sSPECIAL and MEGA-sSPECIAL sequences. The MEGA-sSPECIAL editing scheme using asymmetric adiabatic and highly selective Gaussian pulses was used to compare its GABA measurement reproducibility with that of short echo-time (TE) sSPECIAL. Proton magnetic resonance spectra were acquired in the motor cortex (M1) and medial prefrontal cortex (mPFC) using the sSPECIAL (TR/TE = 4000/16 ms) and MEGA-sSPECIAL sequences (TR/TE = 4000/80 ms). The metabolites were quantified using LCModel with unsuppressed water spectra. The concentrations are reported in institutional units. The test-retest reproducibility was evaluated by scanning each subject twice. Between-session reproducibility was assessed using coefficients of variation (CVs), Pearson's r correlation coefficients, and intraclass correlation coefficients (ICCs). Intersequence agreement was evaluated using Pearson's r correlation coefficients and Bland-Altman plots. Regarding GABA measurements by sSPECIAL, the GABA concentrations were 0.92 ± 0.31 (IU) in the M1 and 1.56 ± 0.49 (IU) in the mPFC. This demonstrated strong between-session correlation across both regions (r = 0.81, p < 0.01; ICC = 0.82). The CVs between the two scans were 21.8% in the M1 and 10.2% in the mPFC. On the other hand, the GABA measurements by MEGA-sSPECIAL were 0.52 ± 0.04 (IU) in the M1 and 1.04 ± 0.24 (IU) in the mPFC. MEGA-sSPECIAL demonstrated strong between-session correlation across the two regions (r = 0.98, p < 0.001; ICC = 0.98) and lower CVs than sSPECIAL, providing 4.1% in the M1 and 5.8% in the mPFC. The MEGA-editing method showed better reproducibility of GABA measurements in both brain regions compared with the short-TE sSPECIAL method. Thus it is a more sensitive method with which to detect small changes in areas with low GABA concentrations. In GABA-rich brain regions, GABA measurements can be achieved reproducibly using both methods.

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.

Effects of sex hormones on brain GABA and glutamate levels in a cis- and transgender cohort

Sex hormones affect the GABAergic and glutamatergic neurotransmitter system as demonstrated in animal studies. However, human research has mostly been correlational in nature. Here, we aimed at substantiating causal interpretations of the interaction between sex hormones and neurotransmitter function by using magnetic resonance spectroscopy imaging (MRSI) to study the effect of gender-affirming hormone treatment (GHT) in transgender individuals. Fifteen trans men (TM) with a DSM-5 diagnosis of gender dysphoria, undergoing GHT, and 15 age-matched cisgender women (CW), receiving no therapy, underwent MRSI before and after at least 12 weeks. Additionally, sex differences in neurotransmitter levels were evaluated in an independent sample of 80 cisgender men and 79 cisgender women. Mean GABA+ (combination of GABA and macromolecules) and Glx (combination of glutamate and glutamine) ratios to total creatine (GABA+/tCr, Glx/tCr) were calculated in five predefined regions-of-interest (hippocampus, insula, pallidum, putamen and thalamus). Linear mixed models analysis revealed a significant measurement by gender identity effect (p_corr. = 0.048) for GABA+/tCr ratios in the hippocampus, with the TM cohort showing decreased GABA+/tCr levels after GHT compared to CW. Moreover, analysis of covariance showed a significant sex difference in insula GABA+/tCr ratios (p_corr. = 0.049), indicating elevated GABA levels in cisgender women compared to cisgender men. Our study demonstrates GHT treatment-induced GABA+/tCr reductions in the hippocampus, indicating hormone receptor activation on GABAergic cells and testosterone-induced neuroplastic processes within the hippocampus. Moreover, elevated GABA levels in the female compared to the male insula highlight the importance of including sex as factor in future MRS studies. DATA AVAILABILITY STATEMENT: Due to data protection laws processed data is available from the authors upon reasonable request. Please contact rupert.lanzenberger@meduniwien.ac.at with any questions or requests.

Bayesian deep learning-based 1 H-MRS of the brain: Metabolite quantification with uncertainty estimation using Monte Carlo dropout

PURPOSE

To develop a Bayesian convolutional neural network (BCNN) with Monte Carlo dropout sampling for metabolite quantification with simultaneous uncertainty estimation in deep learning-based proton MRS of the brain.

METHODS

Human brain spectra were simulated using basis spectra for 17 metabolites and macromolecules (N = 100 000) at 3.0 Tesla. In addition, actual in vivo spectra (N = 5) were modified by adjusting SNR and linewidth with increasing severity of spectral degradation (N = 50). A BCNN was trained on the simulated spectra to generate a noise-free, line-narrowed, macromolecule signal-removed, metabolite-only spectrum from a typical human brain spectrum. At inference, each input spectrum was Monte Carlo dropout sampled (50 times), and the resulting mean spectrum and variance spectrum were used for metabolite quantification and uncertainty estimation, respectively.

RESULTS

Using the simulated spectra, the mean absolute percent errors of the BCNN-predicted metabolite content were < 10% for Cr, Glu, Gln, mI, NAA, and Tau (< 5% for Glu, NAA, and mI). For all metabolites, the correlations (r's) between the ground-truth error and BCNN-predicted uncertainty ranged 0.72-0.94 (0.83 ± 0.06; p < 0.001). Using the modified in vivo spectra, the extent of variation in the estimated metabolite content against the increasing severity of spectral degradation tended to be smaller with BCNN than with linear combination of model spectra (LCModel). Overall, the variation in metabolite content tended to be more highly correlated with the uncertainty from BCNN than with the Cramér-Rao lower-bounds from LCModel (0.938 ± 0.019 vs. 0.881 ± 0.057 [p = 0.115]).

CONCLUSION

The BCNN with Monte Carlo dropout sampling may be used in deep learning-based MRS for the estimation of uncertainty in the machine-predicted metabolite content, which is important in the clinical application of deep learning-based MRS.

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints

We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoen-coder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE 1 H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE 1 H-MRSI of the brain, potentially useful for various clinical applications.

PET CMRglc mapping and 1H-MRS show altered glucose uptake and neurometabolic profiles in BDL rats

Type C hepatic encephalopathy (HE) is a complex neuropsychiatric disorder occurring as a consequence of chronic liver disease. Alterations in energy metabolism have been suggested in type C HE, but in vivo studies on this matter remain sparse and have reported conflicting results. Here, we propose a novel preclinical ¹⁸F-FDG PET methodology to compute quantitative 3D maps of the regional cerebral metabolic rate of glucose (CMR_glc) from a labelling steady-state PET image of the brain and an image-derived input function. This quantitative approach shows its strength when comparing groups of animals with divergent physiology, such as HE animals. PET CMR_glc maps were registered to an atlas and the mean CMR_glc from the hippocampus and the cerebellum were associated to the corresponding localized ¹H-MR spectroscopy acquisitions. This study provides for the first time local and quantitative information on both brain glucose uptake and neurometabolic profile alterations in a rat model of type C HE. A 2-fold lower brain glucose uptake, concomitant with an increase in brain glutamine and a decrease in the main osmolytes was observed in the hippocampus and in the cerebellum. These novel findings are an important step towards new insights into energy metabolism in the pathophysiology of HE.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Functional connectivity and neurotransmitter impairments of the salience brain network in chronic low back pain patients: a combined resting state functional MRI and 1H-MRS study

ABSTRACT

Functional re-organisation of the salience network (SN) has been proposed as one of the key pathomechanisms associated with central nociceptive processing in the chronic pain state. Being associated with an altered functional connectivity within the SN, these processes have been hypothesized to result from a loss of inhibitory function leading to node hyperexcitability and spontaneous pain. Combined resting-state (rs) BOLD fMRI and 1H-MR spectroscopy was applied to chronic back pain (CBP) patients and healthy subjects (HS) to assess deviations from functional integrity (weighted closeness centrality, wCC, derived from rs-fMRI), oscillatory BOLD characteristics (spectral power, SP), and neurotransmitter levels (GABA+, glutamate+glutamine) in two key SN nodes, anterior insular (aInsR) and anterior mid-cingulate (aMCC) cortices. In addition, examinations were repeated in CBP patients after a four week interdisciplinary multimodal pain treatment and in HS after four weeks to explore longitudinal, treatment-mediated changes in target variables. The aInsR and, to a lesser extent, the aMCC of patients exhibited significantly reduced wCC accompanied by a SP shift from a lower to a higher frequency band, indicating a desynchronization of their neuronal activity within the SN, possibly due to increased spontaneous activations. Without revealing neurotransmitter differences, patients alone showed significant positive associations between local GABA+ levels and wCC in aInsR, suggesting a stronger dependence of node synchronization on the inhibitory tone in the chronic pain state. However, this needs to be explored in future using MRS techniques that are more sensitive to detecting subtle neurotransmitter changes and also allow multifocal characterization of neurotransmitter tone.

Reproducibility of 7-T brain spectroscopy using an ultrashort echo time STimulated Echo Acquisition Mode sequence and automated voxel repositioning

Establishing the reproducibility of brain MRS is important for clinical studies so that researchers can evaluate changes in metabolites due to treatment or the course of a disease and better understand the brain in healthy and disordered states. Prior 7-T MRS reproducibility studies using the stimulated echo acquisition mode (STEAM) sequence have focused on the anterior cingulate cortex or posterior cingulate cortex and precuneus. The purpose of this study was to evaluate the reproducibility of metabolite measurements in the dorsolateral prefrontal cortex (DLPFC) using an ultrashort echo time (TE) STEAM sequence and automated voxel repositioning. Spectra were acquired during two scan sessions from nine subjects using the AutoAlign method for voxel repositioning. Reproducibility was evaluated with coefficients of variation (CVs) and percentage differences. The mean intrasubject CVs were less than 6% for the major metabolites glutamate, N-acetylaspartate, total creatine, total choline, and myo-inositol. The mean CVs were less than 20% for the smaller signals of GABA, glutamine, glutathione, and taurine. These results indicate that 7-T MRS using a STEAM sequence with ultrashort TE and automated voxel repositioning provides excellent reproducibility of metabolites in the DLPFC.

Evidence for distinct neuro-metabolic phenotypes in humans

Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with ¹H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.

In vivo spectral editing of phosphorylethanolamine

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.