Characterization of the macromolecule baseline in localized (1)H-MR spectra of human brain

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.

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.

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 spatially localized MRS by a novel deep learning approach without spectral fitting

PURPOSE

To propose a novel end-to-end deep learning model to quantify absolute metabolite concentrations from in vivo J-point resolved spectroscopy (JPRESS) without using spectral fitting.

METHODS

A novel encoder-decoder-style neural network was created, which was trained to predict metabolite concentrations and individual component signals concurrently from 3T JPRESS data in the time domain. The training data set contained 100 000 samples created by spin-density simulations using experimentally used RF pulses. Concentrations, phase, frequencies, linewidths, and T2 relaxation times in the training data set were varied over a large range with uniform distributions. Random synthesized noise and extraneous signals were added to the data set. Two thousand validation samples were created similarly to the training data set but with mean concentrations close to in vivo values. An in vivo test was conducted with 20 samples acquired from the human brain.

RESULTS

Both validation and in vivo test results showed that the proposed model successfully predicted metabolite concentrations as well as individual metabolite signals without involving spectral fitting, while extraneous peaks or unregistered signals were filtered out. Compared with the short-TE spectral fitting by LCModel, the proposed method had the advantage that the undesired correlations between the estimated concentrations and noise levels and between metabolites were eliminated or substantially reduced.

CONCLUSION

The proposed method provides a working deep learning model that directly maps in vivo JPRESS data to metabolite concentrations. Because spectral fitting is not used, the trained model does not depend on the assumptions associated with parameter tuning when applied to in vivo data.

Association of default-mode network neurotransmitters and inter-network functional connectivity in first episode psychosis

Multiple psychiatric disorders are characterized by a failure to suppress default-mode network (DMN) activity during tasks and by weaker anti-correlations between DMN and other brain networks at rest. However, the cellular and molecular mechanisms underlying this phenomenon are poorly understood. At the cellular level, neuronal activity is regulated by multiple neurochemical processes including cycling of glutamate and GABA, the major excitatory and inhibitory neurotransmitters in brain. By combining functional MRI and magnetic resonance spectroscopy techniques, it has been shown that the neurotransmitter concentrations in DMN modulate not only functional activity during cognitive tasks, but also the functional connectivity between DMN and other brain networks such as frontoparietal executive control network (CN) at rest in the healthy brain. In the current study, we extend previous research to first episode psychosis (FEP) patients and their relatives. We detected higher glutamate (Glu) levels in the medial prefrontal cortex (MPFC) in FEP compared to healthy controls without a significant difference in GABA. We also observed a significantly lower functional anti-correlated connectivity between critical nodes within the DMN (MPFC) and CN (DLPFC) in FEP. Furthermore, the relationship between MPFC Glu and GABA concentrations and the functional anti-correlation that is seen in healthy people was absent in FEP patients. These findings imply that both the DMN Glu level and the interaction between DMN and CN are affected by the illness, as is the association between neurochemistry and functional connectivity. A better understanding of this observation could provide opportunities for developing novel treatment strategies for psychosis.

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.

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.

Click here for author‐reader discussions

Continuous and intermittent theta burst stimulation to the visual cortex do not alter GABA and glutamate concentrations measured by magnetic resonance spectroscopy

BACKGROUND

Theta burst stimulation (TBS), a form of repetitive transcranial magnetic stimulation (rTMS), uses repeated high-frequency bursts to non-invasively modulate neural processes in the brain. An intermittent TBS (iTBS) protocol is generally considered "excitatory," while continuous TBS (cTBS) is considered "inhibitory." However, the majority of work that has led to these effects being associated with the respective protocols has been done in the motor cortex, and it is well established that TMS can have variable effects across the brain.

OBJECTIVES AND METHOD

We investigated the effects of iTBS and cTBS to the primary visual cortex (V1) on composite levels of gamma-aminobutyric acid + co-edited macromolecules (GABA+) and glutamate + glutamine (Glx) since these are key inhibitory and excitatory neurotransmitters, respectively. Participants received a single session of cTBS, iTBS, or sham TBS to V1. GABA+ and Glx were quantified in vivo at the stimulation site using spectral-edited proton magnetic resonance spectroscopy (¹ H-MRS) at 3T. Baseline pre-TBS GABA+ and Glx levels were compared to immediate post-TBS and 1 h post-TBS levels.

RESULTS

There were no significant changes in GABA+ or Glx following either of the TBS conditions. Visual cortical excitability, measured using phosphene thresholds, remained unchanged following both cTBS and iTBS conditions. There was no relationship between excitability thresholds and GABA+ or Glx levels. However, TBS did alter the relationship between GABA+ and Glx for up to 1 h following stimulation.

CONCLUSIONS

These findings demonstrate that a single session of TBS to the visual cortex can be used without significant effects on the tonic levels of these key neurotransmitters; and add to our understanding that TBS has differential effects at visual, motor, and frontal cortices.

The association between language-based task-functional magnetic resonance imaging hemodynamics and baseline GABA+ and glutamate-glutamine measured in pre-supplementary motor area: A pilot study in an aging model

Aging is a natural phenomenon that elicits slow and progressive cerebrovascular and neurophysiological changes that eventually lead to cognitive decline. The objective of this pilot study is to examine the association of GABA+ and glutamate-glutamine (Glx) complex with language-based blood oxygen level dependent (BOLD) hemodynamics in an aging model. More specifically, using standard BOLD we will first attempt to validate whether previously reported findings for BOLD amplitude and resting neurochemical relationships hold in an aging model. Secondly, we will investigate how our recently established neurosensitized task-BOLD energetics relate to resting GABA+ and Glx, especially accounting for titration of task difficulty. To support the above endeavors, we optimize the baseline fitting for edited magnetic resonance spectroscopy (MRS) difference spectra to sensitize GABA+ and Glx concentrations to aging-related differences. We identify a spline-knot spacing of 0.6ppm to yield the optimal aging-related differences in GABA+ and Glx. The optimized MRS values were then graduated to relate to task-BOLD hemodynamics. Our results did not replicate previous findings that relate task-BOLD amplitude and resting GABA+ and Glx. However, we did identify neurochemistry relationships with the vascularly-driven dispersion component of the hemodynamic response function, specifically in older participants. In terms of neuro-sensitized BOLD energetics and the underlying role of GABA+ and Glx, our data suggests that the task demands are supported by both neurometabolites depending on the difficulty of the task stimuli. Another novelty is that we developed task-based functional parcellation of pre-SMA using both groups. In sum, we are the first to demonstrate that multimodal task-fMRI and MRS studies are beneficial to improve our understanding of the aging brain physiology, and to set the platform to better inform approaches for clinical care in aging-related neurovascular diseases. We also urge future studies to replicate our findings in a larger population incorporating a lifespan framework.

The macromolecular MR spectrum does not change with healthy aging

PURPOSE

To acquire the mobile macromolecule (MM) spectrum from healthy participants, and to investigate changes in the signals with age and sex.

METHODS

102 volunteers (49 M/53 F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). Spectral data were acquired at 3T using PRESS localization with a voxel size of 30 × 26 × 26 mm³ , pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled to eliminate residual metabolite signals, which were then subtracted out to yield a "clean" MM spectrum using the Osprey software. Pearson's correlation coefficient was calculated between integrals and age for the 14 MM signals. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes.

RESULTS

MM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM signals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > .004). No differences were observed between sex groups. WM and GM voxel fractions showed a significant (p < .05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions.

CONCLUSION

Our findings suggest that a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups.

In vivo macromolecule signals in rat brain 1 H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times

PURPOSE

Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) ¹ H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T₂ relaxation times for seven MM components.

METHODS

A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo ¹ H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel.

RESULTS

A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T₂ ranged between 12 and 24 ms for seven MM peaks.

CONCLUSION

Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

The cross-sectional interplay between neurochemical profile and brain connectivity

Neurochemical profile and brain connectivity are both critical aspects of brain function. However, our knowledge of their interplay across development is currently poor. We combined single-voxel magnetic resonance spectroscopy and resting functional magnetic resonance imaging in a cross-sectional sample spanning from childhood to adulthood which was reassessed in ~1.5 years (N = 293). We revealed the developmental trajectories of 20 neurochemicals in two key developmental brain regions (the intraparietal sulcus, IPS, and the middle frontal gyrus, MFG). We found that certain neurochemicals exhibited similar developmental trajectories across the two regions, while other trajectories were region-specific. Crucially, we mapped the connectivity of the brain regions IPS and MFG to the rest of the brain across development as a function of regional glutamate and GABA concentration. We demonstrated that glutamate concentration within the IPS is modulated by age in explaining IPS connectivity with frontal, temporal and parietal regions. In mature participants, higher glutamate within the IPS was related to more negative connectivity while the opposite pattern was found for younger participants. Our findings offer specific developmental insights on the interplay between the brain's resting activity and the glutamatergic system both of which are crucial for regulating normal functioning and are dysregulated in several clinical conditions.

Simultaneous quantification of GABA, Glx and GSH in the neonatal human brain using magnetic resonance spectroscopy

Balance between inhibitory and excitatory neurotransmitter systems and the protective role of the major antioxidant glutathione (GSH) are central to early healthy brain development. Disruption has been implicated in the early life pathophysiology of psychiatric disorders and neurodevelopmental conditions including Autism Spectrum Disorder. Edited magnetic resonance spectroscopy (MRS) methods such as HERMES have great potential for providing important new non-invasive insights into these crucial processes in human infancy. In this work, we describe a systematic approach to minimise the impact of specific technical challenges inherent to acquiring MRS data in a neonatal population, including automatic segmentation, full tissue-correction and optimised GABA+ fitting and consider the minimum requirements for a robust edited-MRS acquisition. With this approach we report for the first time simultaneous GABA+, Glx (glutamate + glutamine) and GSH concentrations in the neonatal brain (n = 18) in two distinct regions (thalamus and anterior cingulate cortex (ACC)) using edited MRS at 3T. The improved sensitivity provided by our method allows specific regional neurochemical differences to be identified including: significantly lower Glx and GSH ratios to total creatine in the thalamus compared to the ACC (p < 0.001 for both), and significantly higher GSH levels in the ACC following tissue-correction (p < 0.01). Furthermore, in contrast to adult GABA+ which can typically be accurately fitted with a single peak, all neonate spectra displayed a characteristic doublet GABA+ peak at 3 ppm, indicating a lower macromolecule (MM) contribution to the 3 ppm signal in neonates. Relatively high group-level variance shows the need to maximise voxel size/acquisition time in edited neonatal MRS acquisitions for robust estimation of metabolites. Application of this method to study how these levels and balance are altered by early-life brain injury or genetic risk can provide important new knowledge about the pathophysiology underlying neurodevelopmental disorders.

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

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Rapid acquisition diffusion MR spectroscopy of metabolites in human brain

Few studies have focused on metabolite diffusion in the human brain using ¹ H-MRS due to significant technical challenges. Moreover, such studies have required lengthy acquisition times and are therefore impractical to implement clinically. By first characterizing and then minimizing the effects of linear and oscillating eddy currents, which arise from the diffusion gradients, and by implementing phase-cycle and slice-order strategies, as well as introducing a new phase-alignment methodology, we report a method that allows data acquisition requiring 20 seconds per spectrum. This remained feasible, even for b-values >8000 s/mm² , with a rapid acquisition diffusion MRS methodology. It has allowed the nonlinear characterization of signal intensity with multiple b-values, and has improved the measurement of rotationally invariant diffusion parameters via six-direction, six b-value diffusion tensor spectroscopy (DTS) in 12 minutes at 4.7 T. The shorter DTS acquisition will enable its application to white matter regions not aligned with the gradients and permit clinical studies in a feasible time.

Longitudinal quantification of metabolites and macromolecules reveals age- and sex-related changes in the healthy Fischer 344 rat brain

Normal aging is associated with numerous biological changes, including altered brain metabolism and tissue chemistry. In vivo characterization of the neurochemical profile during aging is possible using magnetic resonance spectroscopy, a powerful noninvasive technique capable of quantifying brain metabolites involved in physiological processes that become impaired with age. A prominent macromolecular signal underlies those of brain metabolites and is particularly visible at high fields; parameterization of this signal into components improves quantification and expands the number of biomarkers comprising the neurochemical profile. The present study reports, for the first time, the simultaneous absolute quantification of brain metabolites and individual macromolecules in aging male and female Fischer 344 rats, measured longitudinally using proton magnetic resonance spectroscopy at 7 T. We identified age- and sex-related changes in neurochemistry, with prominent differences in metabolites implicated in anaerobic energy metabolism, antioxidant defenses, and neuroprotection, as well as numerous macromolecule changes. These findings contribute to our understanding of the neurobiological processes associated with healthy aging, critical for the proper identification and management of pathologic aging trajectories. This article is part of the Virtual Special Issue titled COGNITIVE NEUROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect athttps://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

Metabolic underpinnings of activated and deactivated cortical areas in human brain

Neuroimaging with functional MRI (fMRI) identifies activated and deactivated brain regions in task-based paradigms. These patterns of (de)activation are altered in diseases, motivating research to understand their underlying biochemical/biophysical mechanisms. Essentially, it remains unknown how aerobic metabolism of glucose to lactate (aerobic glycolysis) and excitatory-inhibitory balance of glutamatergic and GABAergic neuronal activities vary in these areas. In healthy volunteers, we investigated metabolic distinctions of activating visual cortex (VC, a task-positive area) using a visual task and deactivating posterior cingulate cortex (PCC, a task-negative area) using a cognitive task. We used fMRI-guided J-edited functional MRS (fMRS) to measure lactate, glutamate plus glutamine (Glx) and γ-aminobutyric acid (GABA), as indicators of aerobic glycolysis and excitatory-inhibitory balance, respectively. Both lactate and Glx increased upon activating VC, but did not change upon deactivating PCC. Basal GABA was negatively correlated with BOLD responses in both brain areas, but during functional tasks GABA decreased in VC upon activation and GABA increased in PCC upon deactivation, suggesting BOLD responses in relation to baseline are impacted oppositely by task-induced inhibition. In summary, opposite relations between BOLD response and GABAergic inhibition, and increases in aerobic glycolysis and glutamatergic activity distinguish the BOLD response in (de)activated areas.

MR spectroscopy in pediatric neuroradiology

Magnetic resonance spectroscopy (MRS), being able to identify and measure some brain components (metabolites) in pathologic lesions and in normal-appearing tissue, offers a valuable additional diagnostic tool to assess several pediatric neurological diseases. In this review we will illustrate the basic principles and clinical applications of brain proton (H¹; hydrogen) MRS (H¹MRS), by now the only MRS method widely available in clinical practice. Performing H¹MRS in the brain is inherently less complicated than in other tissues (e.g., liver, muscle), in which spectra are heavily affected by magnetic field inhomogeneities, respiration artifacts, and dominating signals from the surrounding adipose tissues. H¹MRS in pediatric neuroradiology has some advantages over acquisitions in adults (lack of motion due to children sedation and lack of brain iron deposition allow optimal results), but it requires a deep knowledge of pediatric pathologies and familiarity with the developmental changes in spectral patterns, particularly occurring in the first two years of life. Examples from our database, obtained mainly from a 1.5 Tesla clinical scanner in a time span of 15 years, will demonstrate the efficacy of H¹MRS in the diagnosis of a wide range of selected pediatric pathologies, like brain tumors, infections, neonatal hypoxic-ischemic encephalopathy, metabolic and white matter disorders.

Accumulation of Macromolecules in Idiopathic Normal Pressure Hydrocephalus

The clearance system in the brain is not completely understood. The aim of this study was to prove the presence of the “glymphatic system” in the human brain using magnetic resonance spectroscopy (MRS). Spectral data of the brain white matter were obtained from healthy volunteers and patients with hydrocephalic dementia and used to measure intracerebral metabolites, including macromolecules (MMs) and lipids. Data were transferred from the MRS scanners to a workstation, and metabolites were quantified with the spectrogram-based eddy current method and water scaling. MM levels were significantly higher in patients with a slow gait and executive dysfunction due to normal pressure hydrocephalus (NPH) than in asymptomatic volunteers (p <0.01). In contrast, the N-acetyl aspartate (NAA) level was significantly lower in patients with executive dysfunction than in asymptomatic volunteers (p <0.01). There were no statistically significant differences in metabolites, including alanine, aspartate, creatine, γ-amino butyric acid, D-glucose, glutamine, glutamate, glycerophosphorylcholine, phosphorylcholine, lactate, myoinositol, N-acetyl-aspartyl-glutamate, scyllo-inositol, taurine, creatine methylene, and guanine, in the centrum semiovale between patients with NPH and asymptomatic volunteers. We quantitatively evaluated cerebral metabolites, particularly in the centrum semiovale, with MRS. In the brain of patients with a slow gait and executive dysfunction due to NPH, MRS revealed significantly higher MM levels and lower NAA levels compared to healthy volunteers. Therefore, it may be concluded that the patients have a dysfunctional glymphatic system in the brain.

Macromolecule suppressed GABA levels show no relationship with age in a pediatric sample

The inhibitory neurotransmitter γ-Aminobutyric acid (GABA) plays a crucial role in cortical development. Therefore, characterizing changes in GABA levels during development has important implications for the study of healthy development and developmental disorders. Brain GABA levels can be measured non-invasively using GABA-edited magnetic resonance spectroscopy (MRS). However, the most commonly used editing technique to measure GABA results in contamination of the GABA signal with macromolecules (MM). Therefore, GABA measured using this technique is often referred to as GABA+ . While few in number, previous studies have shown GABA+ levels increase with age during development. However, these studies are unable to specify whether it is specifically GABA that is increasing or, instead, if levels of MM increase. In this study, we use a GABA-editing technique specifically designed to suppress the MM signal (MM-supp GABA). We find no relationship between MM-supp GABA and age in healthy children aged 7-14 years. These findings suggest that the relationship between GABA+ and age is driven by changes in MM levels, not by changes in GABA levels. Moreover, these findings highlight the importance of accounting for MM levels in MRS quantification.

Assessing differential effects of single and accelerated low-frequency rTMS to the visual cortex on GABA and glutamate concentrations

BACKGROUND

The application of repetitive transcranial magnetic stimulation (rTMS) for therapeutic use in visual-related disorders and its underlying mechanisms in the visual cortex is under-investigated. Additionally, there is little examination of rTMS adverse effects particularly with regards to visual and cognitive function. Neural plasticity is key in rehabilitation and recovery of function; thus, effective therapeutic strategies must be capable of modulating plasticity. Glutamate and γ-aminobutyric acid (GABA)-mediated changes in the balance between excitation and inhibition are prominent features in visual cortical plasticity.

OBJECTIVES AND METHOD

We investigated the effects of low-frequency (1 Hz) rTMS to the visual cortex on levels of neurotransmitters GABA and glutamate to determine the therapeutic potential of 1 Hz rTMS for visual-related disorders. Two rTMS regimes commonly used in clinical applications were investigated: participants received rTMS to the visual cortex either in a single 20-min session or five accelerated 20-min sessions (not previously investigated at the visual cortex). Proton (1H) magnetic resonance spectroscopy for in vivo quantification of GABA (assessed via GABA+) and glutamate (assessed via Glx) concentrations was performed pre- and post-rTMS.

RESULTS

GABA+ and Glx concentrations were unaltered following a single session of rTMS to the visual cortex. One day of accelerated rTMS significantly reduced GABA+ concentration for up to 24 hr, with levels returning to baseline by 1-week post-rTMS. Basic visual and cognitive function remained largely unchanged.

CONCLUSION

Accelerated 1 Hz rTMS to the visual cortex has greater potential for approaches targeting plasticity or in cases with altered GABAergic responses in visual disorders. Notably, these results provide preliminary insight into a critical window of plasticity with accelerated rTMS (e.g., 24 hr) in which adjunct therapies may offer better functional outcome. We describe detailed procedures to enable further exploration of these protocols.

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

PURPOSE

Gamma-aminobutyric acid (GABA) abnormalities have been implicated in a range of neuropsychiatric disorders. Despite substantial interest in probing GABA in vivo, human imaging studies relying on magnetic resonance spectroscopy (MRS) have generally been hindered by technical challenges, including GABA's relatively low concentration and spectral overlap with other metabolites. Although past studies have shown moderate-to-strong test-retest repeatability and reliability of GABA within certain brain regions, many of these studies have been limited by small sample sizes.

METHODS

GABA+ (macromolecular-contaminated) test-retest reliability and repeatability were assessed via a Meshcher-Garwood point resolved spectroscopy (MEGA-PRESS) MRS sequence in the rostral anterior cingulate cortex (rACC; n = 21) and dorsolateral prefrontal cortex (dlPFC; n = 20) in healthy young adults. Data were collected on a 3T scanner (Siemens Prisma, Siemens Healthcare, Erlangen, Germany) and GABA+ results were reported in reference to both total creatine (GABA+/tCr) and water (GABA+/water).

RESULTS

Results showed strong test-retest repeatability (mean GABA+/tCr coefficient of variation [CV] = 4.6%; mean GABA+/water CV = 4.0%) and reliability (GABA+/tCr intraclass correlation coefficient [ICC] = 0.77; GABA+/water ICC = 0.87) in the dlPFC. The rACC showed acceptable (but comparatively lower) repeatability (mean GABA+/tCr CV = 8.0%; mean GABA+/water CV = 7.5%), yet low-moderate reliability (GABA+/tCr ICC = 0.40; GABA+/water ICC = 0.44).

CONCLUSION

The present study found excellent GABA+ MRS repeatability and reliability in the dlPFC. The rACC showed inferior results, possibly because of a combination of shimming impedance and measurement error. These data suggest that MEGA-PRESS can be utilized to reliably distinguish participants based on dlPFC GABA+ levels, whereas the mixed results in the rACC merit further investigation.

A novel method to measure T1 -relaxation times of macromolecules and quantification of the macromolecular resonances

PURPOSE

Macromolecular peaks underlying metabolite spectra influence the quantification of metabolites. Therefore, it is important to understand the extent of contribution from macromolecules (MMs) in metabolite quantification. However, to model MMs more accurately in spectral fitting, differences in T₁ relaxation times among individual MM peaks must be considered. Characterization of T₁ -relaxation times for all individual MM peaks using a single inversion recovery technique is difficult due to eventual contributions from metabolites. On the contrary, a double inversion recovery (DIR) technique provided flexibility to acquire MM spectra spanning a range of longitudinal magnetizations with minimal metabolite influence. Thus, a novel method to determine T₁ -relaxation times of individual MM peaks is reported in this work.

METHODS

Extensive Bloch simulations were performed to determine inversion time combinations for a DIR technique that yielded adequate MM signal with varying longitudinal magnetizations while minimizing metabolite contributions. MM spectra were acquired using DIR-metabolite-cycled semi-LASER sequence. LCModel concentrations were fitted to the DIR signal equation to calculate T₁ -relaxation times.

RESULTS

T₁ -relaxation times of MMs range from 204 to 510 ms and 253 to 564 ms in gray- and white-matter rich voxels respectively at 9.4T. Additionally, concentrations of 13 MM peaks are reported.

CONCLUSION

A novel DIR method is reported in this work to calculate T₁ -relaxation times of MMs in the human brain. T₁ -relaxation times and relaxation time corrected concentrations of individual MMs are reported in gray- and white-matter rich voxels for the first time at 9.4T.

Regional GABA Concentrations Modulate Inter-network Resting-state Functional Connectivity

Coordinated activity within and differential activity between large-scale neuronal networks such as the default mode network (DMN) and the control network (CN) is a critical feature of brain organization. The CN usually exhibits activations in response to cognitive tasks while the DMN shows deactivations; in addition, activity between the two networks is anti-correlated at rest. To address this issue, we used functional MRI to measure whole-brain BOLD signal during resting-state and task-evoked conditions, and MR spectroscopy (MRS) to quantify GABA and glutamate concentrations, in nodes within the DMN and CN (MPFC and DLPFC, respectively) in 19 healthy individuals at 3 Tesla. We found that GABA concentrations in the MPFC were significantly associated with DMN deactivation during a working memory task and with anti-correlation between DMN and CN at rest and during task performance, while GABA concentrations in the DLPFC weakly modulated DMN-CN anti-correlation in the opposite direction. Highlighting specificity, glutamate played a less significant role related to brain activity. These findings indicate that GABA in the MPFC is potentially involved in orchestrating between-network brain activity at rest and during task performance.

Effect of Age on GABA+ and Glutathione in a Pediatric Sample

BACKGROUND AND PURPOSE

Gamma-aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the human brain and is implicated in several neuropathologies. Glutathione is a major antioxidant in the brain and is considered a marker of oxidative stress. Several studies have reported age-related declines in GABA levels in adulthood, but the trajectory of both GABA and glutathione during childhood has not been well explored. The aim of this study is to establish how GABA and glutathione vary with age during early development.

MATERIALS AND METHODS

Twenty-three healthy children (5.6-13.9 years of age) were recruited for this study. MR imaging/MR spectroscopy experiments were conducted on a 3T MR scanner. A 27-mL MR spectroscopy voxel was positioned in the frontal lobe. J-difference edited MR spectroscopy was used to spectrally edit GABA and glutathione. Data were analyzed using the Gannet software, and GABA+ (GABA + macromolecules/homocarnosine) and glutathione were quantified using water (GABA+_H2O and Glutathione_H2O) and Cr (GABA+/Cr and glutathione/Cr) as concentration references. Also, the relative gray matter contribution to the voxel volume (GM_ratio) was estimated from structural images. Pearson correlation coefficients were used to examine the association between age and GABA+_H2O (and glutathione_H2O), between age and GABA+/Cr (and glutathione/Cr), and between age and GM_ratio.

RESULTS

Both GABA+_H2O (r = 0.63, P = .002) and GABA+/Cr (r = 0.48, P = .026) significantly correlated with age, whereas glutathione measurements and GM_ratio did not.

CONCLUSIONS

We demonstrate increases in GABA and no differences in glutathione with age in a healthy pediatric sample. This study provides insight into neuronal maturation in children and may facilitate better understanding of normative behavioral development and the pathophysiology of developmental disorders.

Effects of noise and linewidth on in vivo analysis of glutamate at 3 T

Magnetic resonance spectroscopy (MRS) can noninvasively detect metabolites in vivo, including glutamate (Glu). However, quantification is known to be affected by the overlaps among metabolite resonance lines and background macromolecule signals. We found that adding a moderate amount of noise or line broadening (2 Hz) caused large variations in concentration of Glu and other metabolites, when determined by LCModel analysis of in vivo short-echo time (TE) spectra. Theses variations were largely attributed to strong spectral baselines in short TE spectra, especially near 2.35 ppm, as well as overlapping metabolite resonance lines. To address this issue, we acquired in vivo data at 3 T using both short-TE and the multiple echo time J-resolved point-resolved spectroscopy (JPRESS) MRS techniques. We found that one-dimensional (1D) JPRESS, by simultaneously fitting the two cross-sections of JPRESS at J = 0 and J = 7.5 Hz, was highly resistant to variations in noise levels and spectral linewidths. Our results demonstrate that LCModel analysis of short-TE data is highly sensitive to variations in noise levels and spectral linewidths and this sensitivity is greatly reduced by 1D JPRESS given its substantially reduced baselines and enhanced spectral resolution.

Whole-slice mapping of GABA and GABA+ at 7T via adiabatic MEGA-editing, real-time instability correction, and concentric circle readout

An adiabatic MEscher-GArwood (MEGA)-editing scheme, using asymmetric hyperbolic secant editing pulses, was developed and implemented in a B1+-insensitive, 1D-semiLASER (Localization by Adiabatic SElective Refocusing) MR spectroscopic imaging (MRSI) sequence for the non-invasive mapping of γ-aminobutyric acid (GABA) over a whole brain slice. Our approach exploits the advantages of edited-MRSI at 7T while tackling challenges that arise with ultra-high-field-scans. Spatial-spectral encoding, using density-weighted, concentric circle echo planar trajectory readout, enabled substantial MRSI acceleration and an improved point-spread-function, thereby reducing extracranial lipid signals. Subject motion and scanner instabilities were corrected in real-time using volumetric navigators optimized for 7T, in combination with selective reacquisition of corrupted data to ensure robust subtraction-based MEGA-editing. Simulations and phantom measurements of the adiabatic MEGA-editing scheme demonstrated stable editing efficiency even in the presence of ±0.15 ppm editing frequency offsets and B1+ variations of up to ±30% (as typically encountered in vivo at 7T), in contrast to conventional Gaussian editing pulses. Volunteer measurements were performed with and without global inversion recovery (IR) to study regional GABA levels and their underlying, co-edited, macromolecular (MM) signals at 2.99 ppm. High-quality in vivo spectra allowed mapping of pure GABA and MM-contaminated GABA+ (GABA + MM) along with Glx (Glu + Gln), with high-resolution (eff. voxel size: 1.4 cm3) and whole-slice coverage in 24 min scan time. Metabolic ratio maps of GABA/tNAA, GABA+/tNAA, and Glx/tNAA were correlated linearly with the gray matter fraction of each voxel. A 2.15-fold increase in gray matter to white matter contrast was observed for GABA when enabling IR, which we attribute to the higher abundance of macromolecules at 2.99 ppm in the white matter than in the gray matter. In conclusion, adiabatic MEGA-editing with 1D-semiLASER selection is as a promising approach for edited-MRSI at 7T. Our sequence capitalizes on the benefits of ultra-high-field MRSI while successfully mitigating the challenges related to B0/B1+ inhomogeneities, prolonged scan times, and motion/scanner instability artifacts. Robust and accurate 2D mapping has been shown for the neurotransmitters GABA and Glx.

Regional excitation-inhibition balance predicts default-mode network deactivation via functional connectivity

Deactivation of the default mode network (DMN) is one of the most reliable observations from neuroimaging and has significant implications in development, aging, and various neuropsychiatric disorders. However, the neural mechanism underlying DMN deactivation remains elusive. As the coordination of regional neurochemical substrates and interregional neural interactions are both essential in support of brain functions, a quantitative description of how they impact DMN deactivation may provide new insights into the mechanism. Using an n-back working memory task fMRI and magnetic resonance spectroscopy, we probed the pairwise relationship between task-induced deactivation, interregional functional connectivity and regional excitation-inhibition balance (evaluated by glutamate/GABA ratio) in the posterior cingulate cortex/precuneus (PCC/PCu). Task-induced PCC/PCu deactivation correlated with its excitation-inhibition balance and interregional functional connectivity, where participants with lower glutamate/GABA ratio, stronger intra-DMN connections and stronger antagonistic DMN-SN (salience network)/ECN (executive control network) inter-network connections had greater PCC/PCu deactivation. Mediation analyses revealed that the DMN-SN functional interactions partially mediated the relationship between task-induced deactivation and the excitation-inhibition balance at the PCC/PCu. The triple-relationship discovered in the present study has the potential to bridge DMN-deactivation related findings from various neuroimaging modalities and may provide new insights into the neural mechanism of DMN deactivation. Moreover, this finding may have significant implications for neuropsychiatric disorders related to the DMN dysfunction and suggests an integrated application of pharmacological and neuromodulation-based strategies for rescuing DMN deactivation deficits.

Altered macromolecular pattern and content in the aging human brain

The resonances originating from proteins underlie those of metabolites in brain ¹ H nuclear magnetic resonance (NMR) spectra. These resonances have different physical properties from those of metabolites, such as shorter T₁ and T₂ relaxation time constants. The age dependence of the macromolecular pattern and content in the human brain was investigated with a focus on adults over 66 years of age using ultrahigh-field in vivo magnetic resonance spectroscopy. Eighteen young and 23 cognitively normal older adults were studied at 7 T. Metabolite spectra were acquired in the occipital cortex and the posterior cingulate cortex with single-voxel stimulated echo acquisition mode (STEAM) spectroscopy in 14 young and 20 older adults. Macromolecular spectra were acquired in the occipital cortex using an inversion recovery STEAM sequence in four young and three older adults. The macromolecular pattern was apparent over the 0.5-4.5-ppm range in the inversion recovery spectra and the 0.5-2-ppm range in the metabolite spectra. Macromolecular content was quantified from metabolite spectra using LCModel and from inversion recovery spectra using integration. Age-associated differences in the macromolecular pattern were apparent via both types of spectra, with the largest difference observed for the 1.7- and 2-ppm macromolecular resonances. A higher macromolecular content was observed in the older adults for both brain regions. Age-specific macromolecular spectra are needed when comparing metabolite spectra from subjects of differing ages because of age-associated differences in macromolecular pattern. Age-associated pattern and content differences may provide information about the aging process.

Improved resolution of glutamate, glutamine and γ-aminobutyric acid with optimized point-resolved spectroscopy sequence timings for their simultaneous quantification at 9.4 T

Glutamine (Gln), glutamate (Glu) and γ-aminobutyric acid (GABA) are relevant brain metabolites that can be measured with magnetic resonance spectroscopy (MRS). This work optimizes the point-resolved spectroscopy (PRESS) sequence echo times, TE₁ and TE₂ , for improved simultaneous quantification of the three metabolites at 9.4 T. Quantification was based on the proton resonances of Gln, Glu and GABA at ≈2.45, ≈2.35 and ≈2.28 ppm, respectively. Glu exhibits overlap with both Gln and GABA; in addition, the Gln peak is contaminated by signal from the strongly coupled protons of N-acetylaspartate (NAA), which resonate at about 2.49 ppm. J-coupling evolution of the protons was characterized numerically and verified experimentally. A {TE₁ , TE₂ } combination of {106 ms, 16 ms} minimized the NAA signal in the Gln spectral region, whilst retaining Gln, Glu and GABA peaks. The efficacy of the technique was verified on phantom solutions and on rat brain in vivo. LCModel was employed to analyze the in vivo spectra. The average T₂ -corrected Gln, Glu and GABA concentrations were found to be 3.39, 11.43 and 2.20 mM, respectively, assuming a total creatine concentration of 8.5 mM. LCModel Cramér-Rao lower bounds (CRLBs) for Gln, Glu and GABA were in the ranges 14-17%, 4-6% and 16-19%, respectively. The optimal TE resulted in concentrations for Gln and GABA that agreed more closely with literature concentrations compared with concentrations obtained from short-TE spectra acquired with a {TE₁ , TE₂ } combination of {12 ms, 9 ms}. LCModel estimations were also evaluated with short-TE PRESS and with the optimized long TE of {106 ms, 16 ms}, using phantom solutions of known metabolite concentrations. It was shown that concentrations estimated with LCModel can be inaccurate when combined with short-TE PRESS, where there is peak overlap, even when low (<20%) CRLBs are reported.

1 H-MRS processing parameters affect metabolite quantification: The urgent need for uniform and transparent standardization

Proton magnetic resonance spectroscopy (¹ H-MRS) can be used to quantify in vivo metabolite levels, such as lactate, γ-aminobutyric acid (GABA) and glutamate (Glu). However, there are considerable analysis choices which can alter the accuracy or precision of ¹ H-MRS metabolite quantification. It is currently unknown to what extent variations in the analysis pipeline used to quantify ¹ H-MRS data affect outcomes. The purpose of this study was to evaluate whether the quantification of identical ¹ H-MRS scans across independent and experienced research groups would yield comparable results. We investigated the influence of model parameters and spectral quantification software on fitted metabolite concentration values. Sixty spectra in 30 individuals (repeated measures) were acquired using a 7-T MRI scanner. Data were processed by four independent research groups with the freedom to choose their own individualized and optimal parameter settings using LCModel software. Data were processed a second time in one group using an independent software package (NMRWizard) for an additional comparison with a different post-processing platform. Correlations across research groups of the ratio between the highest and, arguably, the most relevant resonances for neurotransmission [N-acetyl aspartate (NAA), N-acetyl aspartyl glutamate (NAAG) and Glu] over the total creatine [creatine (Cr) + phosphocreatine (PCr)] concentration, using Pearson's product-moment correlation coefficient (r), were calculated. Mean inter-group correlations using LCModel software were 0.87, 0.88 and 0.77 for NAA/Cr + PCr, NAA + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. The mean correlations when comparing NMRWizard results with LCModel fitting results at University Medical Center Utrecht (UMCU) were 0.87, 0.89 and 0.71 for NAA/Cr + PCr, NAA + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. Metabolite quantification using identical ¹ H-MRS data was influenced by processing parameters, basis sets and software choice. Locally preferred processing choices affected metabolite quantification, even when using identical software. Our results reinforce the notion that standard practices should be established to regularize outcomes of ¹ H-MRS studies, and that basis sets used for processing should be made available to the scientific community.

Global brain metabolic quantification with whole-head proton MRS at 3 T

Total N-acetyl-aspartate + N-acetyl-aspartate-glutamate (NAA), total creatine (Cr) and total choline (Cho) proton MRS (1 H-MRS) signals are often used as surrogate markers in diffuse neurological pathologies, but spatial coverage of this methodology is limited to 1%-65% of the brain. Here we wish to demonstrate that non-localized, whole-head (WH) 1 H-MRS captures just the brain's contribution to the Cho and Cr signals, ignoring all other compartments. Towards this end, 27 young healthy adults (18 men, 9 women), 29.9 ± 8.5 years old, were recruited and underwent T1 -weighted MRI for tissue segmentation, non-localizing, approximately 3 min WH 1 H-MRS (TE /TR /TI  = 5/10/940 ms) and 30 min 1 H-MR spectroscopic imaging (MRSI) (TE /TR  = 35/2100 ms) in a 360 cm3 volume of interest (VOI) at the brain's center. The VOI absolute NAA, Cr and Cho concentrations, 7.7 ± 0.5, 5.5 ± 0.4 and 1.3 ± 0.2 mM, were all within 10% of the WH: 8.6 ± 1.1, 6.0 ± 1.0 and 1.3 ± 0.2 mM. The mean NAA/Cr and NAA/Cho ratios in the WH were only slightly higher than the "brain-only" VOI: 1.5 versus 1.4 (7%) and 6.6 versus 5.9 (11%); Cho/Cr were not different. The brain/WH volume ratio was 0.31 ± 0.03 (brain ≈ 30% of WH volume). Air-tissue susceptibility-driven local magnetic field changes going from the brain outwards showed sharp gradients of more than 100 Hz/cm (1 ppm/cm), explaining the skull's Cr and Cho signal losses through resonance shifts, line broadening and destructive interference. The similarity of non-localized WH and localized VOI NAA, Cr and Cho concentrations and their ratios suggests that their signals originate predominantly from the brain. Therefore, the fast, comprehensive WH-1 H-MRS method may facilitate quantification of these metabolites, which are common surrogate markers in neurological disorders.

1D-spectral editing and 2D multispectral in vivo1H-MRS and 1H-MRSI - Methods and applications

This article reviews the methodological aspects of detecting low-abundant J-coupled metabolites via 1D spectral editing techniques and 2D nuclear magnetic resonance (NMR) methods applied in vivo, in humans, with a focus on the brain. A brief explanation of the basics of J-evolution will be followed by an introduction to 1D spectral editing techniques (e.g., J-difference editing, multiple quantum coherence filtering) and 2D-NMR methods (e.g., correlation spectroscopy, J-resolved spectroscopy). Established and recently developed methods will be discussed and the most commonly edited J-coupled metabolites (e.g., neurotransmitters, antioxidants, onco-markers, and markers for metabolic processes) will be briefly summarized along with their most important applications in neuroscience and clinical diagnosis.

Proton and multinuclear magnetic resonance spectroscopy in the human brain at ultra-high field strength: A review

Magnetic Resonance Spectroscopy (MRS) allows for a non-invasive and non-ionizing determination of in vivo tissue concentrations and metabolic turn-over rates of more than 20 metabolites and compounds in the central nervous system of humans. The aim of this review is to give a comprehensive overview about the advantages, challenges and advances of ultra-high field MRS with regard to methodological development, discoveries and applications from its beginnings around 15 years ago up to the current state. The review is limited to human brain and spinal cord application at field strength of 7T and 9.4T and includes all relevant nuclei (¹H, ³¹P, ¹³C).

Simultaneous mapping of metabolites and individual macromolecular components via ultra-short acquisition delay 1 H MRSI in the brain at 7T

Purpose

Short‐echo‐time proton MR spectra at 7T feature nine to 10 distinct macromolecule (MM) resonances that overlap with the signals of metabolites. Typically, a metabolite‐nulled in vivo MM spectrum is included in the quantification`s prior knowledge to provide unbiased metabolite quantification. However, this MM model may fail if MMs are pathologically altered. In addition, information about the individual MM peaks is lost. In this study, we aimed to create an improved MM model by parameterization of the in vivo MM spectrum into individual components, and to use this new model to quantify free induction decay MR spectroscopic imaging (FID‐MRSI) data.

Methods

The measured in vivo MM spectrum was parameterized using advanced method for accurate, robust, and efficient spectral fitting (AMARES) and Hankel‐Lanczos singular value decomposition algorithms from which six different MM models were derived. Soft constraints were applied to avoid over‐parameterization. All MM models were combined with simulated metabolite spectra to form complete basis sets. FID‐MRSI data from 14 healthy volunteers were quantified via LCModel, and the results were compared between all basis sets.

Results

The MM model using nine individual AMARES‐parameterized MM components with additional soft constraints achieved the most reliable results. Nine MMs and seven metabolites were mapped simultaneously over the whole slice.

Conclusion

The proposed MM model may facilitate studies that involve patients with pathologically altered MMs. Magn Reson Med 79:1231–1240, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Towards a neurochemical profile of the amygdala using short-TE 1 H magnetic resonance spectroscopy at 3 T

The amygdala plays a key role in emotional learning and in the processing of emotions. As disturbed amygdala function has been linked to several psychiatric conditions, a knowledge of its biochemistry, especially neurotransmitter levels, is highly desirable. The spin echo full intensity acquired localized (SPECIAL) sequence, together with a transmit/receive coil, was used to perform very short-TE magnetic resonance spectroscopy at 3 T to determine the neurochemical profile in a spectroscopic voxel containing the amygdala in 21 healthy adult subjects. For spectral analysis, advanced data processing was applied in combination with a macromolecule baseline measured in the anterior cingulate for spectral fitting. The concentrations of total N-acetylaspartate, total creatine, total choline, myo-inositol and, for the first time, glutamate were quantified with high reliability (uncertainties far below 10%). For these metabolites, the inter-individual variability, reflected by the relative standard deviations for the cohort studied, varied between 12% (glutamate) and 22% (myo-inositol). Glutamine and glutathione could also be determined, albeit with lower precision. Retest on four subjects showed good reproducibility. The devised method allows the determination of metabolite concentrations in the amygdala voxel, including glutamate, provides an estimation of glutamine and glutathione, and may help in the study of disturbed amygdala metabolism in pathologies such as anxiety disorder, autism and major depression.

GABA editing with macromolecule suppression using an improved MEGA-SPECIAL sequence

PURPOSE

The most common γ-aminobutyric-acid (GABA) editing approach, MEGA-PRESS, uses J-editing to measure GABA distinct from larger overlapping metabolites, but suffers contamination from coedited macromolecules (MMs) comprising 40 to 60% of the observed signal. MEGA-SPECIAL is an alternative method with better MM suppression, but is not widely used primarily because of its relatively poor spatial localization. Our goal was to develop an improved MM-suppressed GABA editing sequence at 3 Tesla.

METHODS

We modified a single-voxel MEGA-SPECIAL sequence with an oscillating readout gradient for improved spatial localization, and used very selective 30-ms editing pulses for improved suppression of coedited MMs.

RESULTS

Simulation and in vivo experiments confirmed excellent MM suppression, insensitive to the range of B₀ frequency drifts typically encountered in vivo. Both intersubject and intrasubject studies showed that MMs, when suppressed by the improved MEGA-SPECIAL method, contributed approximately 40% to the corresponding MEGA-PRESS measurements. From the intersubject study, the coefficient of variation for GABA+/Cre (MEGA-PRESS) was 11.2% versus 7% for GABA/Cre (improved MEGA-SPECIAL), demonstrating significantly reduced variance (P = 0.005), likely coming from coedited MMs.

CONCLUSIONS

This improved MEGA-SPECIAL sequence provides unbiased GABA measurements with reduced variance as compared with conventional MEGA-PRESS. This approach is also relatively insensitive to the range of B₀ drifts typically observed in in vivo human studies. Magn Reson Med 79:41-47, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

In vivo diffusion MRS investigation of non-water molecules in biological tissues

Diffusion MRS of non-water molecules offers great potential in directly revealing various tissue microstructures and physiology at both cellular and subcellular levels. In brain, ¹ H diffusion MRS has been demonstrated as a new tool for probing normal tissue microstructures and their pathological changes. In skeletal muscle, ¹ H diffusion MRS could characterize slow and restricted intramyocellular lipid diffusion, providing a sensitive marker for metabolic alterations, while ³¹ P diffusion MRS can measure ATP and PCr diffusion, which may reflect the capacity of cellular energy transport, complementing the information from frequently used ³¹ P MRS in muscle. In intervertebral disk, ¹ H diffusion MRS can directly monitor extracellular matrix integrity by quantifying the mobility of macromolecules such as proteoglycans and collagens. In tumor tissue, ¹³ C diffusion MRS could probe intracellular glycolytic metabolism, while ¹ H diffusion MRS may separate the spectrally overlapped lactate and lipid resonances. In this review, recent diffusion MRS studies of these biologically relevant non-water molecules under normal and diseased conditions will be presented. Technical considerations for diffusion MRS experiments will be discussed. With advances in MRI hardware and diffusion methodology, diffusion MRS of non-water molecules is expected to provide increasingly valuable and biologically specific information on tissue microstructures and physiology, complementing the traditional diffusion MRI of small and ubiquitous water molecules. Copyright © 2016 John Wiley & Sons, Ltd.

In vivo estimation of gamma-aminobutyric acid levels in the neonatal brain

Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the brain, and plays a key role in brain development. However, the in vivo levels of brain GABA in early life are unknown. Using edited MRS, in vivo GABA can be detected as GABA+ signal with contamination of macromolecule signals. GABA+ is evaluated as the peak ratio of GABA+/reference compound, for which creatine (Cr) or water is typically used. However, the concentrations and T₁ and T₂ relaxation times of these references change during development. Thus, the peak ratio comparison between neonates and children may be inaccurate. The aim of this study was to measure in vivo neonatal brain GABA+ levels, and to investigate the dependency of GABA levels on brain region and age. The basal ganglia and cerebellum of 38 neonates and 12 children were measured using GABA-edited MRS. Two different approaches were used to obtain GABA+ levels: (i) multiplying the GABA/water ratio by the water concentration; and (ii) multiplying the GABA+/Cr by the Cr concentration. Neonates exhibited significantly lower GABA+ levels compared with children in both regions, regardless of the approach employed, consistent with previous ex vivo data. A similar finding of lower GABA+/water and GABA+/Cr in neonates compared with children was observed, except for GABA+/Cr in the cerebellum. This contrasting finding resulted from significantly lower Cr concentrations in the neonate cerebellum, which were approximately 52% of those of children. In conclusion, care should be taken to consider Cr concentrations when comparing GABA+/Cr levels between different-aged subjects.

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

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

The age dependence of T2 relaxation times of N-acetyl aspartate, creatine and choline in the human brain at 3 and 4T

Knowledge of the T2 age dependence is of importance for MRS clinical studies involving subject groups with a wide age range. A number of studies have focused on the age dependence of T2 values in the human brain, with rather conflicting results. The aim of this study was to analyze the age dependence of T2 values of N-acetyl aspartate (NAA), creatine (Cr) and choline (Cho) in the human brain using data acquired at 3T and 4T and to assess the influence of the macromolecule (MM) baseline handling on the obtained results. Two distinct groups of young and elderly controls have been measured at 3T (TE = 30-540 ms, 9 young and 11 elderly subjects) and 4T (TE = 10-180 ms, 18 young and 14 elderly subjects) using single-voxel spectroscopy. In addition, MM spectra were measured from two subjects using the inversion-recovery technique at 4T. All spectra were processed with LCModel using basis sets with different MM signals (measured or simulated) and also with MM signals included for a different TE range. Individual estimated T2 values were statistically analyzed using the R programming language for the age dependence of T2 values as well as the influence of the MM baseline handling. A significant decrease of T2 values of NAA and Cr in elderly subjects compared with young subjects was confirmed. The same trend was observed for Cho. Significantly higher T2 values calculated using the measured MM baseline for all studied metabolites at 4T were observed for both young and elderly subjects. To conclude, while the handling of MM and lipid signals may have a significant effect on estimated T2 values, we confirmed the age dependence of T2 values of NAA and Cr and the same trend for Cho in the human brain. Copyright © 2016 John Wiley & Sons, Ltd.

In vivo γ-aminobutyric acid measurement in rats with spectral editing at 4.7T

Purpose

To evaluate the feasibility of spectral editing for quantification of γ‐aminobutyric acid (GABA) in the rat brain and to determine whether altered GABA concentration in the ventral striatum is a neural endophenotype associated with trait‐like impulsive behavior.

Materials and Methods

Spectra were acquired at 4.7T for 23 male Lister‐hooded rats that had been previously screened for extremely low and high impulsivity phenotypes on an automated behavioral task (n = 11 low‐impulsive; n = 12 high‐impulsive). Voxels of 3 × 7 × 4 mm³ (84 μL) centered bilaterally across the ventral striatum were used to evaluate GABA concentration ratios.

Results

Quantifiable GABA signals in the ventral striatum were obtained for all rats. Mean‐edited GABA to n‐acetyl aspartate (NAA) ratios in the ventral striatum were 0.22 (95% confidence interval [CI] [0.18, 0.25]). Mean GABA/NAA ratios in this region were significantly decreased by 28% in high‐impulsive rats compared to low‐impulsive rats (P = 0.02; 95% CI [–53%, –2%]).

Conclusion

These findings demonstrate that spectral editing at 4.7T is a feasible method to assess in vivo GABA concentrations in the rat brain. The results show that diminished GABA content in the ventral striatum may be a neural endophenotype associated with impulsivity. J. Magn. Reson. Imaging 2016;43:1308–1312.

Spectral-editing measurements of GABA in the human brain with and without macromolecule suppression

PURPOSE

The conventional spectral-editing experiment used to measure GABA in the human brain also contains a contribution from macromolecules (MM), and the combined GABA plus MM signal is often referred to as "GABA+". More recently, methods have been developed to estimate GABA free from MM contamination. In this study, the relationship between GABA acquired with MM suppression and conventional GABA+ measurements was examined.

METHODS

GABA-edited MEGA-PRESS experiments with and without MM suppression were performed in the sensorimotor and occipital cortex of 12 healthy subjects at 3 Tesla. The correlation between GABA+ and MM-suppressed GABA measures was then determined.

RESULTS

Across all data, a significant correlation between GABA+ and MM-suppressed GABA was found (r = 0.48; P = 0.02). Regionally, the sensorimotor voxel showed a trend toward a correlation of r = 0.53, P = 0.07 and the occipital voxel did not show a correlation, r = 0.058, P = 0.9.

CONCLUSION

GABA+ and MM-suppressed GABA are moderately correlated, but statistical power to reveal this relationship may vary regionally. The MM signal, while often assumed to be functionally irrelevant, appears to show inter-individual and inter-regional variance that impacts the correlation of GABA+ and MM-suppressed GABA.

Automated whole-brain N-acetylaspartate proton MRS quantification

Concentration of the neuronal marker, N-acetylaspartate (NAA), a quantitative metric for the health and density of neurons, is currently obtained by integration of the manually defined peak in whole-head proton ((1) H)-MRS. Our goal was to develop a full spectral modeling approach for the automatic estimation of the whole-brain NAA concentration (WBNAA) and to compare the performance of this approach with a manual frequency-range peak integration approach previously employed. MRI and whole-head (1) H-MRS from 18 healthy young adults were examined. Non-localized, whole-head (1) H-MRS obtained at 3 T yielded the NAA peak area through both manually defined frequency-range integration and the new, full spectral simulation. The NAA peak area was converted into an absolute amount with phantom replacement and normalized for brain volume (segmented from T1 -weighted MRI) to yield WBNAA. A paired-sample t test was used to compare the means of the WBNAA paradigms and a likelihood ratio test used to compare their coefficients of variation. While the between-subject WBNAA means were nearly identical (12.8 ± 2.5 mm for integration, 12.8 ± 1.4 mm for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, p = 0.026). The within-subject variability was 11.7% (±1.3 mm) for integration versus 7.0% (±0.8 mm) for spectral modeling, i.e., a 40% improvement. The (quantifiable) quality of the modeling approach was high, as reflected by Cramer-Rao lower bounds below 0.1% and vanishingly small (experimental - fitted) residuals. Modeling of the whole-head (1) H-MRS increases WBNAA quantification reliability by reducing its variability, its susceptibility to operator bias and baseline roll, and by providing quality-control feedback. Together, these enhance the usefulness of the technique for monitoring the diffuse progression and treatment response of neurological disorders.

Comparison of brain gray and white matter macromolecule resonances at 3 and 7 Tesla

PURPOSE

In proton MR spectra of the human brain, relatively broad macromolecule (MM) resonances underlie the narrower signals from metabolites. The purpose of this study was to quantify the MM profile in healthy human brain at 3T and 7T, both in gray matter (anterior cingulate cortex [ACC]) and white matter (centrum semiovale [CSO]).

METHODS

A water-suppressed, inversion-recovery pulse sequence was used to null metabolite signals and acquire MM spectra in 20 healthy volunteers using very similar methodology at both field strengths (n = 5 per region and field). The MM spectra were fitted with multiple Gaussian functions and quantified relative to the unsuppressed water signal from the same volume.

RESULTS

MM proton concentration values were in the range of 5-20 mmol/kg. No significant differences were found between the MM proton concentration measurements by region (P ≈ 0.8) nor by field strength (P ≈ 0.5). Linewidths of the well-resolved M1 peak were slightly more than double at 7T (43.0 ± 4.7 Hz in ACC, 45.6 ± 4.1 Hz in CSO) compared with 3T (19.8 ± 3.5 Hz in ACC, 20.0 ± 4.3 Hz in CSO).

CONCLUSION

The absence of differences in MM concentrations between white and gray matter implies that a single MM "baseline" may be adequate for spectral fitting of multiple brain regions when determining metabolite concentrations. Visibility of MM signals is similar at 3T and 7T.

Resting-state glutamate and GABA concentrations predict task-induced deactivation in the default mode network

Deactivation of the human brain's default mode network (DMN) is regarded as suppression of endogenous activity to support exogenous task-related processes. This phenomenon has important functional relevance and insufficient DMN deactivation has been implicated in several neuropsychiatric disorders. However, the neurochemical mechanism of the DMN's deactivation remains largely unknown. In the present study, we test the hypothesis that the major excitatory and inhibitory neurotransmitters, glutamate and GABA, respectively, are associated with DMN deactivation. We used magnetic resonance spectroscopy to measure neurotransmitter concentrations in the posterior cingulate cortex/precuneus (PCC/PCu), a key component of the DMN, and functional magnetic resonance imaging to evaluate DMN deactivation induced by an n-back working memory task. Our results demonstrate significant associations of glutamate and GABA with DMN deactivation. Specifically, high regional GABA concentration in the PCC/PCu area is associated with enhanced deactivation induced by the task in the same region, whereas high glutamate concentration is associated with reduced deactivation. Furthermore, the association between GABA and DMN deactivation increases with the cognitive loads. These neurochemical characteristics of DMN deactivation may provide novel insights toward better understanding of the DMN's functions under normal physiological conditions and dysfunctions in neuropsychiatric disorders.