Analysis of macromolecule resonances in 1H NMR spectra of human brain

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.

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

OBJECTIVE

Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation.

METHODS

Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen in training compared to the end-to-end deep learning method.

RESULTS

Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods.

CONCLUSION

This study provides an intelligent, reliable and robust MRS quantification.

SIGNIFICANCE

QNet is the first LLS quantification aided by deep learning.

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.

Optimization of the flip angles of narrow-band editing pulses in J-difference edited MRS of lactate at 3T

PURPOSE

Application of highly selective editing RF pulses provides a means of minimizing co-editing of contaminants in J-difference MRS (MEGA), but it causes reduction in editing yield. We examined the flip angles (FAs) of narrow-band editing pulses to maximize the lactate edited signal with minimal co-editing of threonine.

METHODS

The effect of editing-pulse FA on the editing performance was examined, with numerical and phantom analyses, for bandwidths of 17.6-300 Hz in MEGA-PRESS editing of lactate at 3T. The FA and envelope of 46 ms Gaussian editing pulses were tailored to maximize the lactate edited signal at 1.3 ppm and minimize co-editing of threonine. The optimized editing-pulse FA MEGA scheme was tested in brain tumor patients.

RESULTS

Simulation and phantom data indicated that the optimum FA of MEGA editing pulses is progressively larger than 180° as the editing-pulse bandwidth decreases. For 46 ms long 17.6 Hz bandwidth Gaussian pulses and other given sequence parameters, the lactate edited signal was maximum at the first and second editing-pulse FAs of 241° and 249°, respectively. The edit-on and difference-edited lactate peak areas of the optimized FA MEGA were greater by 43% and 25% compared to the 180°-FA MEGA, respectively. In-vivo data confirmed the simulation and phantom results. The lesions of the brain tumor patients showed elevated lactate and physiological levels of threonine.

CONCLUSION

The lactate MEGA editing yield is significantly increased with editing-pulse FA much larger than 180° when the editing-pulse bandwidth is comparable to the lactate quartet frequency width.

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.

J-Difference editing (MEGA) of lactate in the human brain at 3T

PURPOSE

The need to detect and quantify brain lactate accurately by MRS has stimulated the development of editing sequences based on J coupling effects. In J-difference editing of lactate, threonine can be co-edited and it contaminates lactate estimates due to the spectral proximity of the coupling partners of their methyl protons. We therefore implemented narrow-band editing 180° pulses (E180) in MEGA-PRESS acquisitions to resolve separately the 1.3-ppm resonances of lactate and threonine.

METHODS

Two 45.3-ms rectangular E180 pulses, which had negligible effects 0.15-ppm away from the carrier frequency, were implemented in a MEGA-PRESS sequence with TE 139 ms. Three acquisitions were designed to selectively edit lactate and threonine, in which the E180 pulses were tuned to 4.1 ppm, 4.25 ppm, and a frequency far off resonance. Editing performance was validated with numerical analyses and acquisitions from phantoms. The narrow-band E180 MEGA and another MEGA-PRESS sequence with broad-band E180 pulses were evaluated in six healthy subjects.

RESULTS

The 45.3-ms E180 MEGA offered a difference-edited lactate signal with lower intensity and reduced contamination from threonine compared to the broad-band E180 MEGA. The 45.3 ms E180 pulse had MEGA editing effects over a frequency range larger than seen in the singlet-resonance inversion profile. Lactate and threonine in healthy brain were both estimated to be 0.4 ± 0.1 mM, with reference to N-acetylaspartate at 12 mM.

CONCLUSION

Narrow-band E180 MEGA editing minimizes threonine contamination of lactate spectra and may improve the ability to detect modest changes in lactate levels.

In vivo cortical glutathione response to oral fumarate therapy in relapsing-remitting multiple sclerosis: A single-arm open-label phase IV trial using 7-Tesla 1H MRS

BACKGROUND

This is an open-label, single-arm, single-center pilot study using 7-Tesla in vivo proton magnetic resonance spectroscopy (1H MRS) to measure brain cortical glutathione concentration at baseline before and during the use of oral fumarates as a disease-modifying therapy for multiple sclerosis. The primary endpoint of this research was the change in prefrontal cortex glutathione concentration relative to a therapy-naïve baseline after one year of oral fumarate therapy.

METHODS

Brain glutathione concentrations were examined by 1H MRS in single prefrontal and occipital cortex cubic voxels (2.5 × 2.5 × 2.5 cm3) before and during initiation of oral fumarate therapy (120 mg b.i.d. for 7 days and 240 mg b.i.d. thereafter). Additional measurements of related metabolites glutamate, glutamine, myoinositol, total N-acetyl aspartate, and total choline were also acquired in voxels centered on the same regions. Seven relapsing-remitting multiple sclerosis patients (4 f / 3 m, age range 28-50 years, mean age 40 years) naïve to fumarate therapy were scanned at pre-therapy baseline and after 1, 3, 6 and 12 months of therapy. A group of 8 healthy volunteers (4 f / 4 m, age range 33-48 years, mean age 41 years) was also scanned at baseline and Month 6 to characterize 1H-MRS measurement reproducibility over a comparable time frame.

RESULTS

In the multiple sclerosis cohort, general linear models demonstrated a significant positive linear relationship between prefrontal glutathione and time either linearly across all time points (+0.05 ± 0.02 mM/month, t(27) = 2.6, p = 0.02) or specifically for factor variable Month 12 (+0.6 ± 0.3 mM/12 months, t(24) = 2.2, p = 0.04) relative to baseline. No such effects of time on glutathione concentration were demonstrated in the occipital cortex or in the healthy volunteer group. Changes in occipital total choline were further observed in the multiple sclerosis cohort as well as prefrontal total choline and occipital glutamine and myoinositol in the control cohort throughout the study duration.

CONCLUSIONS

While the open-label single-arm pilot study design and abbreviated control series cannot support firm conclusions about the influence of oral fumarate therapy independent of test-retest factors or normal biological variation in a state of either health or disease, these results do justify further investigation at a larger scale into the potential relationship between prefrontal cortex glutathione increases and oral fumarate therapy in relapsing-remitting multiple sclerosis.

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.

Joint spectral quantification of MR spectroscopic imaging using linear tangent space alignment-based manifold learning

PURPOSE

To develop a manifold learning-based method that leverages the intrinsic low-dimensional structure of MR Spectroscopic Imaging (MRSI) signals for joint spectral quantification.

METHODS

A linear tangent space alignment (LTSA) model was proposed to represent MRSI signals. In the proposed model, the signals of each metabolite were represented using a subspace model and the local coordinates of the subspaces were aligned to the global coordinates of the underlying low-dimensional manifold via linear transform. With the basis functions of the subspaces predetermined via quantum mechanics simulations, the global coordinates and the matrices for the local-to-global coordinate alignment were estimated by fitting the proposed LTSA model to noisy MRSI data with a spatial smoothness constraint on the global coordinates and a sparsity constraint on the matrices.

RESULTS

The performance of the proposed method was validated using numerical simulation data and in vivo proton-MRSI experimental data acquired on healthy volunteers at 3T. The results of the proposed method were compared with the QUEST method and the subspace-based method. In all the compared cases, the proposed method achieved superior performance over the QUEST and the subspace-based methods both qualitatively in terms of noise and artifacts in the estimated metabolite concentration maps, and quantitatively in terms of spectral quantification accuracy measured by normalized root mean square errors.

CONCLUSION

Joint spectral quantification using linear tangent space alignment-based manifold learning improves the accuracy of MRSI spectral quantification.

Feasibility of transient nuclear Overhauser effect imaging in brain at 7 T

PURPOSE

The nuclear Overhauser effect (NOE) quantification from the steady-state NOE imaging suffers from multiple confounding non-NOE-specific sources, including direct saturation, magnetization transfer, and relevant chemical exchange species, and is affected by B₀ and B₁ ⁺ inhomogeneities. The B₀ -dependent and B₁ ⁺ -dependent data needed for deconvolving these confounding effects would increase the scan time substantially, leading to other issues such as patient tolerability. Here, we demonstrate the feasibility of brain lipid mapping using an easily implementable transient NOE (tNOE) approach.

METHODS

This 7T study used a frequency-selective inversion pulse at a range of frequency offsets between 1.0 and 5.0 parts per million (ppm) and -5.0 and -1.0 ppm relative to bulk water peak. This was followed by a fixed/variable mixing time and then a single-shot 2D turbo FLASH readout. The feasibility of tNOE measurements is demonstrated on bovine serum albumin phantoms and healthy human brains.

RESULTS

The tNOE measurements from bovine serum albumin phantoms were found to be independent of physiological pH variations. Both bovine serum albumin phantoms and human brains showed broad tNOE contributions centered at approximately -3.5 ppm relative to water peak, with presumably aliphatic moieties in lipids and proteins being the dominant contributors. Less prominent tNOE contributions of approximately +2.5 ppm relative to water, presumably from aromatic moieties, were also detected. These aromatic signals were free from any CEST signals.

CONCLUSION

In this study, we have demonstrated the feasibility of tNOE in human brain at 7 T. This method is more scan-time efficient than steady-state NOE and provides NOE measurement with minimal confounders.

The early days of ex vivo 1 H, 13 C, and 31 P nuclear magnetic resonance in the laboratory of Dr. Robert G. Shulman from 1975 to 1995

This paper provides a brief description of the early use of ex vivo nuclear magnetic resonance (NMR) studies of tissue and tissue extracts performed in the laboratory of Dr. Robert G. Shulman from 1975 through 1995 at Bell Laboratories, then later at Yale University. During that period, ex vivo NMR provided critical information in support of resonance assignments and the quantitation of concentrations for magnetic resonance spectroscopy studies. The period covered saw rapid advances in magnet technology, starting with studies of microorganisms in vertical bore high-resolution NMR studies, then by 1981 studies of small mammals in a horizontal bore magnet, and then studies of humans in 1984. Ex vivo NMR played a critical role in all these studies. A general strategy developed in the lab for using ex vivo NMR to support in vivo studies is presented, as well as illustrative examples.

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.

Prospective Investigation of Glutamate Levels and Percentage Gray Matter in the Medial Prefrontal Cortex in Females at Risk for Postpartum Depression

BACKGROUND

The substantial female hormone fluctuations associated with pregnancy and postpartum have been linked to a greater risk of developing depressive symptoms, particularly in high-risk women (HRW), i.e. those with histories of mood sensitivity to female hormone fluctuations. We have shown that glutamate (Glu) levels in the medial prefrontal cortex (MPFC) decrease during perimenopause, a period of increased risk of developing a major depressive episode. Our team has also demonstrated that percentage gray matter (%GM), another neural correlate of maternal brain health, decreases in the MPFC during pregnancy.

OBJECTIVE

To investigate MPFC Glu levels and %GM from late pregnancy up to 7 weeks postpartum in HRW and healthy pregnant women (HPW).

METHODS

Single-voxel spectra were acquired from the MPFC of 41 HPW and 22 HRW using 3- Tesla in vivo proton magnetic resonance spectroscopy at five different time points.

RESULTS

We observed a statistically significant interaction between time and group for the metabolite Glu, with Glu levels being lower for HRW during pregnancy and early postpartum (p<0.05). MPFC %GM was initially lower during pregnancy and then significantly increased over time in both groups (p<0.01).

CONCLUSION

This investigation suggests that the vulnerability towards PPD is associated with unique fluctuations of MPFC Glu levels during pregnancy and early postpartum period. Our results also suggest that the decline in MPFC %GM associated with pregnancy seems to progressively recover over time. Further investigations are needed to determine the specific role that female hormones play on the physiological changes in %GM during pregnancy and postpartum.

Macromolecular-Suppressed GABA-Edited MR Spectroscopy in the Posterior Cingulate Cortex of Patients With Acute Mild Traumatic Brain Injury

BACKGROUND

Mild traumatic brain injury (mTBI) causes a number of molecular and cellular alterations. There is evidence of an imbalance between the main excitatory (glutamate, Glu) and the main inhibitory (gamma-aminobutyric acid [GABA]) neurotransmitters following mTBI. In vivo human GABA-Glu balance studies following mTBI are sparse.

PURPOSE

To investigate the effect of acute mTBI on the GABA concentration measured in the posterior cingulate cortex (PCC) of pediatric patients by using the macromolecular (MM)-suppressed GABA J-editing technique.

STUDY TYPE

Prospective patient and phantom.

PARTICIPANTS

A total of 14 pediatric patients (mean age 16.0 ± 1.7) with acute mTBI (<3 days after trauma; Glasgow Coma Scale 15) and 16 healthy volunteers (mean age 16.9 ± 2.8). Phantom: 524 cm³ sphere containing 10 mM glycine, 10 mM GABA.

FIELD STRENGTH/SEQUENCE

A 3 T, MEGA-PRESS pulse sequence.

ASSESSMENT

GABA spectra were processed in Gannet software. MM-suppressed GABA editing efficiency was derived from the phantom study. Absolute GABA and glutamate + glutamine (Glx) concentrations were quantified using different types of correction and compared between groups. N-acetyl aspartate (NAA) and choline (Cho) levels relative to tCr were also compared.

STATISTICAL TESTS

Shapiro-Wilk test, Mann-Whitney U test, Student t-test, Pearson or Spearman correlations. P < 0.01 was considered statistically significant.

RESULTS

The MM-suppressed GABA editing efficiency was 0.63. GABA signal fit error was <16% for all participants. The GABA concentration in the PCC of the mTBI group was significantly different from that in healthy controls: GABA/tCr was higher by 27%, absolute GABA concentration with different types of correction was higher by ≈17%. No significant differences were observed in Glx concentrations (P ≥ 0.32) or in Glx/tCr (P ≥ 0.1), NAA/tCr (P = 0.55), and Cho/tCr levels (P = 0.85).

DATA CONCLUSION

We report an increase in the GABA concentration in the PCC region in acute mTBI pediatric patients. This may suggest activation of GABA synthesis and impairment of the GABAergic system after acute mTBI.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

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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Insights from auditory cortex for GABA+ magnetic resonance spectroscopy studies of aging

Changes in levels of the inhibitory neurotransmitter γ-aminobutyric acid (GABA) may underlie aging-related changes in brain function. GABA and co-edited macromolecules (GABA+) can be measured with MEGA-PRESS magnetic resonance spectroscopy (MRS). The current study investigated how changes in the aging brain impact the interpretation of GABA+ measures in bilateral auditory cortices of healthy young and older adults. Structural changes during aging appeared as decreasing proportion of grey matter in the MRS volume of interest and corresponding increase in cerebrospinal fluid. GABA+ referenced to H₂ O without tissue correction declined in aging. This decline persisted after correcting for tissue differences in MR-visible H₂ O and relaxation times but vanished after considering the different abundance of GABA+ in grey and white matter. However, GABA+ referenced to creatine and N-acetyl aspartate (NAA), which showed no dependence on tissue composition, decreased in aging. All GABA+ measures showed hemispheric asymmetry in young but not older adults. The study also considered aging-related effects on tissue segmentation and the impact of co-edited macromolecules. Tissue segmentation differed significantly between commonly used algorithms, but aging-related effects on tissue-corrected GABA+ were consistent across methods. Auditory cortex macromolecule concentration did not change with age, indicating that a decline in GABA caused the decrease in the compound GABA+ measure. Most likely, the macromolecule contribution to GABA+ leads to underestimating an aging-related decrease in GABA. Overall, considering multiple GABA+ measures using different reference signals strengthened the support for an aging-related decline in auditory cortex GABA levels.

Neurometabolic and functional changes of default-mode network relate to clinical recovery in first-episode psychosis patients: A longitudinal 1H-MRS and fMRI study

BACKGROUND

Antipsychotic treatment has improved the disrupted functional connectivity (FC) and neurometabolites levels of the default mode network (DMN) in schizophrenia patients, but a direct relationship between FC change, neurometabolic level alteration, and symptom improvement has not been built. This study examined the association between the alterations in DMN FC, the changes of neurometabolites levels in the medial prefrontal cortex (MPFC), and the improvementsinpsychopathology in a longitudinal study of drug-naïve first-episode psychosis (FEP) patients.

METHODS

Thirty-two drug-naïve FEP patients and 30 matched healthy controls underwent repeated assessments with the Positive and Negative Syndrome Scale (PANSS) and 3T proton magnetic resonance spectroscopy as well as resting-state functional magnetic resonance imaging. The levels of γ-aminobutyric acid, glutamate, N-acetyl-aspartate in MPFC, and the FC of DMN were measured. After 8-week antipsychotic treatment, 24 patients were re-examined.

RESULTS

After treatment, the changes in γ-aminobutyric acid were correlated with the alterations of FC between the MPFC and DMN, while the changes in N-acetyl-aspartate were associated with the alterations of FC between the posterior cingulate cortex/precuneus and DMN. The FC changes of both regions were correlated with patients PANSS positive score reductions. The structural equation modeling analyses revealed that the changes of DMN FC mediated the relationship between the changes of neurometabolites and the symptom improvements of the patients.

CONCLUSIONS

The derived neurometabolic-functional changes underlying the clinical recovery provide insights into the prognosis of FEP patients. It is noteworthy that this is an exploratory study, and future work with larger sample size is needed to validate our findings.

Continuous Ingestion of Lacticaseibacillus rhamnosus JB-1 during Chronic Stress Ensures Neurometabolic and Behavioural Stability in Rats

The intestinal microbiome composition and dietary supplementation with psychobiotics can result in neurochemical alterations in the brain, which are possible due to the presence of the brain–gut–microbiome axis. In the present study, magnetic resonance spectroscopy (MRS) and behavioural testing were used to evaluate whether treatment with Lacticaseibacillus rhamnosus JB-1 (JB-1) bacteria alters brain metabolites’ levels and behaviour during continuous exposure to chronic stress. Twenty Wistar rats were subjected to eight weeks of a chronic unpredictable mild stress protocol. Simultaneously, half of them were fed with JB-1 bacteria, and the second half was given a daily placebo. Animals were examined at three-time points: before starting the stress protocol and after five and eight weeks of stress onset. In the elevated plus maze behavioural test the placebo group displayed increased anxiety expressed by almost complete avoidance of exploration, while the JB-1 dietary supplementation mitigated anxiety which resulted in a longer exploration time. Hippocampal MRS measurements demonstrated a significant decrease in glutamine + glutathione concentration in the placebo group compared to the JB-1 bacteria-supplemented group after five weeks of stress. With the progression of stress, the decrease of glutamate, glutathione, taurine, and macromolecular concentrations were observed in the placebo group as compared to baseline. The level of brain metabolites in the JB-1-supplemented rats were stable throughout the experiment, with only the taurine level decreasing between weeks five and eight of stress. These data indicated that the JB-1 bacteria diet might stabilize levels of stress-related neurometabolites in rat brain and could prevent the development of anxiety/depressive-like behaviour.

Quantifying the excitatory-inhibitory balance: A comparison of SemiLASER and MEGA-SemiLASER for simultaneously measuring GABA and glutamate at 7T

The importance of the excitatory-inhibitory (E/I) balance in a wide range of cognitive and behavioral processes has prompted a commensurate interest in methods for reliably quantifying it. Proton Magnetic Resonance Spectroscopy (¹H-MRS) remains the only method capable of safely and non-invasively measuring the concentrations of the brain's major excitatory (glutamate) and inhibitory (γ-aminobutyric-acid, GABA) neurotransmitters in-vivo. MRS relies on spectral Mescher-Garwood (MEGA) editing techniques at 3T to distinguish GABA from its overlapping resonances. However, with the increased spectral resolution at ultrahigh field strengths of 7T and above, non-edited spectroscopic techniques become potential viable alternatives to MEGA based approaches, and also address some of their shortcomings, such as signal loss, sensitivity to transmitter inhomogeneities and temporal resolution. We present a comprehensive comparison of both edited and non-edited strategies at 7T for simultaneously quantifying glutamate and GABA from the dorsal anterior cingulate cortex (dACC), and evaluate their reproducibility and relative bias. The combined root-mean-square test-retest reproducibility of Glu and GABA (CV_E/I) was as low as 13.3% for unedited MRS at TE=80 ms using SemiLASER localization, while edited MRS at TE=80 ms yielded CV_E/I=20% and 21% for asymmetric and symmetric MEGA editing, respectively. An unedited SemiLASER acquisition using a shorter echo time of TE=42 ms yielded CV_E/I as low as 24.9%. Our results show that non-edited sequences at an echo time of 80 ms provide better reproducibility than either edited sequences at the same TE, or non-edited sequences at a shorter TE of 42 ms. This is supported by numerical simulations and is driven in part by a pseudo-singlet appearance of the GABA multiplets at TE=80 ms, and the excellent spectral resolution at 7T. Our results uphold a transition to non-edited MRS for monitoring the E/I balance at ultrahigh fields, and stress the importance of using a properly-optimized echo time.

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 evidence of differential frontal cortex metabolic abnormalities in progressive and relapsing-remitting multiple sclerosis

The pathophysiology of progressive multiple sclerosis remains elusive, significantly limiting available disease-modifying therapies. Proton MRS (¹ H-MRS) enables in vivo measurement of small molecules implicated in multiple sclerosis, but its application to key metabolites glutamate, γ-aminobutyric acid (GABA), and glutathione has been sparse. We employed, at 7 T, a previously validated ¹ H-MRS protocol to measure glutamate, GABA, and glutathione, as well as glutamine, N-acetyl aspartate, choline, and myoinositol, in the frontal cortex of individuals with relapsing-remitting (N = 26) or progressive (N = 21) multiple sclerosis or healthy control adults (N = 25) in a cross-sectional analysis. Only individuals with progressive multiple sclerosis demonstrated reduced glutamate (F_2,65  = 3.424, p = 0.04; 12.40 ± 0.62 mM versus control 13.17 ± 0.95 mM, p = 0.03) but not glutamine (F_2,65  = 0.352, p = 0.7; 4.71 ± 0.35 mM versus control 4.84 ± 0.42 mM), reduced GABA (F_2,65  = 3.89, p = 0.03; 1.29 ± 0.23 mM versus control 1.47 ± 0.25 mM, p = 0.05), and possibly reduced glutathione (F_2,65  = 0.352, p = 0.056; 2.23 ± 0.46 mM versus control 2.51 ± 0.48 mM, p < 0.1). As a group, multiple sclerosis patients demonstrated significant negative correlations between disease duration and glutamate or GABA (ρ = -0.4, p = 0.02) but not glutamine or glutathione. Alone, only relapsing-remitting multiple sclerosis patients exhibited a significant negative correlation between disease duration and GABA (ρ = -0.5, p = 0.03). Taken together, these results indicate that frontal cortex metabolism is differentially disturbed in progressive and relapsing-remitting multiple sclerosis.

1H Magnetic Resonance Spectroscopy to Understand the Biological Basis of ALS, Diagnose Patients Earlier, and Monitor Disease Progression

At present, limited biomarkers exist to reliably understand, diagnose, and monitor the progression of amyotrophic lateral sclerosis (ALS), a fatal neurological disease characterized by motor neuron death. Standard MRI technology can only be used to exclude a diagnosis of ALS, but ¹H-MRS technology, which measures neurochemical composition, may provide the unique ability to reveal biomarkers that are specific to ALS and sensitive enough to diagnose patients at early stages in disease progression. In this review, we present a summary of current theories of how mitochondrial energetics and an altered glutamate/GABA neurotransmitter flux balance play a role in the pathogenesis of ALS. The theories are synthesized into a model that predicts how pathogenesis impacts glutamate and GABA concentrations. When compared with the results of all MRS studies published to date that measure the absolute concentrations of these neurochemicals in ALS patients, results were variable. However, when normalized for neuronal volume using the MRS biomarker N-acetyl aspartate (NAA), there is clear evidence for an elevation of neuronal glutamate in nine out of thirteen studies reviewed, an observation consistent with the predictions of the model of increased activity of glutamatergic neurons and excitotoxicity. We propose that this increase in neuronal glutamate concentration, in combination with decreased neuronal volume, is specific to the pathology of ALS. In addition, when normalized to glutamate levels, there is clear evidence for a decrease in neuronal GABA in three out of four possible studies reviewed, a finding consistent with a loss of inhibitory regulation contributing to excessive neuronal excitability. The combination of a decreased GABA/Glx ratio with an elevated Glx/NAA ratio may enhance the specificity for ¹H-MRS detection of ALS and ability to monitor glutamatergic and GABAergic targeted therapeutics. Additional longitudinal studies calculating the exact value of these ratios are needed to test these hypotheses and understand how ratios may change over the course of disease progression. Proposed modifications to the experimental design of the reviewed ¹H MRS studies may also increase the sensitivity of the technology to changes in these neurochemicals, particularly in early stages of disease progression.

Improved prospective frequency correction for macromolecule-suppressed GABA editing with metabolite cycling at 3T

PURPOSE

To combine metabolite cycling with J-difference editing (MC MEGA) to allow for prospective frequency correction at each transient without additional acquisitions and compare it to water-suppressed MEGA-PRESS (WS MEGA) editing with intermittent prospective frequency correction.

METHODS

Macromolecule-suppressed gamma aminobutyric acid (GABA)-edited experiments were performed in a phantom and in the occipital lobe (OCC) (n = 12) and medial prefrontal cortex (mPFC) (n = 8) of the human brain. Water frequency consistency and average offset over acquisition time were compared. GABA multiplet patterns, signal intensities, and choline subtraction artifacts were evaluated. In vivo GABA concentrations were compared and related to frequency offset in the OCC.

RESULTS

MC MEGA was more stable with 21% and 32% smaller water frequency SDs in the OCC and mPFC, respectively. MC MEGA also had 39% and 40% smaller average frequency offsets in the OCC and mPFC, respectively. Phantom GABA multiplet patterns and signal intensities were similar. In vivo GABA concentrations were smaller in MC MEGA than in WS MEGA, with median (interquartile range) of 2.52 (0.27) and 2.29 (0.19) institutional units (i.u.), respectively in the OCC scans without prior DTI, and 0.99 (0.3) and 1.72 (0.5), respectively in the mPFC. OCC WS MEGA GABA concentrations, but not MC MEGA GABA concentrations were moderately correlated with frequency offset. mPFC WS MEGA spectra contained significantly more subtraction artifacts than MC MEGA spectra.

CONCLUSION

MC MEGA is feasible and allows for prospective frequency correction at every transient. MC MEGA GABA concentrations were not biased by frequency offsets and contained less subtraction artifacts compared to WS MEGA.

Relaxation-corrected macromolecular model enables determination of 1 H longitudinal T1 -relaxation times and concentrations of human brain metabolites at 9.4T

PURPOSE

Ultrahigh field MRS has improved characterization of the neurochemical profile. To compare results obtained at 9.4T to those from lower field strengths, it is of interest to quantify the concentrations of metabolites measured. Thus, measuring T₁ -relaxation times is necessary to correct for T₁ -weighting that occurs in acquisitions for single-voxel spectroscopy and spectroscopic imaging. A macromolecule (MM) simulation model was developed to fit MM contributions to the short TE inversion series used to measure T₁ -relaxation times.

METHODS

An inversion series with seven time points was acquired with metabolite-cycled STEAM to estimate T₁ -relaxation times of metabolites. A short TE was employed in this study to retain signals from metabolites with short T₂ -relaxation times and J-couplings. The underlying macromolecule spectrum was corrected by developing a sequence-specific, relaxation-corrected simulated MM model. Quantification of metabolite peaks was performed using internal water referencing and relaxation corrections.

RESULTS

T₁ -relaxation times for metabolites range from approximately 750 to approximately 2000 ms and approximately 1000 to approximately 2400 ms in gray matter (GM)- and white matter (WM)- rich voxels, respectively. Quantification of metabolites was compared between GM and WM voxels, as well as between results that used a simulated MM spectrum against those that used an experimentally acquired MM spectrum. Metabolite concentrations are reported in mmol/kg quantities.

CONCLUSION

T₁ -relaxation times are reported for nonsinglet resonances for the first time at 9.4T by use of a MM simulation model to account for contributions from the MM spectrum. In addition to T₁ -relaxation times, quantification results of metabolites from GM- and WM-rich voxels are reported.

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.

Fast, regional three-dimensional hybrid (1D-Hadamard 2D-rosette) proton MR spectroscopic imaging in the human temporal lobes

1 H-MRSI is commonly performed with gradient phase encoding, due to its simplicity and minimal radio frequency (RF) heating (specific absorption rate). Its two well-known main problems-(i) "voxel bleed" due to the intrinsic point-spread function, and (ii) chemical shift displacement error (CSDE) when slice-selective RF pulses are used, which worsens with increasing volume of interest (VOI) size-have long become accepted as unavoidable. Both problems can be mitigated with Hadamard multislice RF encoding. This is demonstrated and quantified with numerical simulations, in a multislice phantom and in five healthy young adult volunteers at 3 T, targeting a 2-cm thick temporal lobe VOI through the bilateral hippocampus. This frequently targeted region (e.g. in epilepsy and Alzheimer's disease) is subject to strong, 1-2 ppm.cm-1 regional B0, susceptibility gradients that can dramatically reduce the signal-to-noise ratio (SNR) and water suppression effectiveness. The chemical shift imaging (CSI) sequence used a 3-ms Shinnar-Le Roux (SLR) 90° RF pulse, acquiring eight steps in the slice direction. The Hadamard sequence acquired two overlapping slices using the same SLR 90° pulses, under twofold stronger gradients that proportionally halved the CSDE. Both sequences used 2D 20 × 20 rosette spectroscopic imaging (RSI) for in-plane spatial localization and both used RF and gradient performance characteristics that are easily met by all modern MRI instruments. The results show that Hadamard spectroscopic imaging (HSI) suffered dramatically less signal bleed within the VOI compared with CSI (<1% vs. approximately 26% in simulations; and 5%-8% vs. >50%) in a phantom specifically designed to test these effects. The voxels' SNR per unit volume per unit time was also 40% higher for HSI. In a group of five healthy volunteers, we show that HSI with in-plane 2D-RSI facilitates fast, 3D multivoxel encoding at submilliliter spatial resolution, over the bilateral human hippocampus, in under 10 min, with negligible CSDE, spectral and spatial contamination and more than 6% improved SNR per unit time per unit volume.

Spectral fitting strategy to overcome the overlap between 2-hydroxyglutarate and lipid resonances at 2.25 ppm

PURPOSE

¹ H MRS provides a noninvasive tool for identifying mutations in isocitrate dehydrogenase (IDH). Quantification of the prominent 2-hydroxyglutarate (2HG) resonance at 2.25 ppm is often confounded by the lipid resonance at the same frequency in tumors with elevated lipids. We propose a new spectral fitting approach to separate these overlapped signals, therefore, improving 2HG evaluation.

METHODS

TE 97 ms PRESS was acquired at 3T from 42 glioma patients. New lipid basis sets were created, in which the small lipid 2.25-ppm signal strength was preset with reference to the lipid signal at 0.9 ppm, incorporating published fat relaxation data. LCModel fitting using the new lipid bases (Fitting method 2) was conducted along with fitting using the LCModel built-in lipid basis set (Fitting method 1), in which the lipid 2.25-ppm signal is assessed with reference to the lipid 1.3-ppm signal. In-house basis spectra of low-molecular-weight metabolites were used in both fitting methods.

RESULTS

Fitting method 2 showed marked improvement in identifying IDH mutational status compared with Fitting method 1. 2HG estimates from Fitting method 2 were overall smaller than those from Fitting method 1, which was because of differential assignment of the signal at 2.25 ppm to lipids. In receiver operating characteristic analysis, Fitting method 2 provided a complete distinction between IDH mutation and wild-type whereas Fitting method 1 did not.

CONCLUSION

The data suggest that ¹ H MR spectral fitting using the new lipid basis set provides a robust fitting strategy that improves 2HG evaluation in brain tumors with elevated lipids.

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.

MEGA-PRESS of GABA+: Influences of acquisition parameters

γ-aminobutyric acid (GABA) was the first molecule that was edited with MEGA-PRESS. GABA edited spectroscopy is challenged by limited selectivity of editing pulses. Coediting of resonances from macromolecules (MM) is the greatest single limitation of GABA edited spectroscopy. In this contribution, relative signal contributions from GABA, MM and homocarnosine to the total MEGA-PRESS edited signal at ~3 ppm, i.e., GABA+, are simulated at 3 tesla using several acquisition schemes. The base scheme is modeled after those currently supplied by vendors: it uses typical pulse shapes and lengths, it minimizes the first echo time (TE), and the delay between the editing pulses is kept at TE/2. Edited spectra are simulated for imperfect acquisition parameters such as incorrect frequency, larger chemical shift displacement, incorrect transmit B₁ -field calibration for localization and editing pulses, and longer TE. An alternative timing scheme and longer editing pulses are also considered. Additional simulations are performed for symmetric editing around the MM frequency to suppress the MM signal. The relative influences of these acquisition parameters on the constituents of GABA+ are examined from the perspective of modern experimental designs for investigating brain GABA concentration differences in healthy and diseased humans. Other factors that influence signal contributions, such as T₁ and T₂ relaxation times are also considered.

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.

Preprocessing, analysis and quantification in single-voxel magnetic resonance spectroscopy: experts' consensus recommendations

Once an MRS dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.

Atlas-based GABA mapping with 3D MEGA-MRSI: Cross-correlation to single-voxel MRS

The purpose of this work is to develop and validate a new atlas-based metabolite quantification pipeline for edited magnetic resonance spectroscopic imaging (MEGA-MRSI) that enables group comparisons of brain structure-specific GABA levels. By using brain structure masks segmented from high-resolution MPRAGE images and coregistering these to MEGA-LASER 3D MRSI data, an automated regional quantification of neurochemical levels is demonstrated for the example of the thalamus. Thalamic gamma-aminobutyric acid + coedited macromolecules (GABA+) levels from 21 healthy subjects scanned at 3 T were cross-validated both against a single-voxel MEGA-PRESS acquisition in the same subjects and same scan sessions, as well as alternative MRSI processing techniques (ROI approach, four-voxel approach) using Pearson correlation analysis. In addition, reproducibility was compared across the MRSI processing techniques in test-retest data from 14 subjects. The atlas-based approach showed a significant correlation with SV MEGA-PRESS (correlation coefficient r [GABA+] = 0.63, P < 0.0001). However, the actual values for GABA+, NAA, tCr, GABA+/tCr and tNAA/tCr obtained from the atlas-based approach showed an offset to SV MEGA-PRESS levels, likely due to the fact that on average the thalamus mask used for the atlas-based approach only occupied 30% of the SVS volume, ie, somewhat different anatomies were sampled. Furthermore, the new atlas-based approach showed highly reproducible GABA+/tCr values with a low median coefficient of variance of 6.3%. In conclusion, the atlas-based metabolite quantification approach enables a more brain structure-specific comparison of GABA+ and other neurochemical levels across populations, even when using an MRSI technique with only cm-level resolution. This approach was successfully cross-validated against the typically used SVS technique as well as other different MRSI analysis methods, indicating the robustness of this quantification approach.

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.

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

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

Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations

Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI) allows for simultaneously mapping a number of molecules in the brain, and has been recognized as an important tool for studying in vivo biochemistry in various neuroscience and disease applications. However, separation of the metabolite and macromolecule (MM) signals present in the short-TE data with significant spectral overlaps remains a major technical challenge. This work introduces a new approach to solve this problem by integrating imaging physics and representation learning. Specifically, a mixed unsupervised and supervised learning-based strategy was developed to learn the metabolite and MM-specific low-dimensional representations using deep autoencoders. A constrained reconstruction formulation is proposed to integrate the MRSI spatiospectral encoding model and the learned representations as effective constraints for signal separation. An efficient algorithm was developed to solve the resulting optimization problem with provable convergence. Simulation and experimental results have been obtained to demonstrate the component-specific representation power of the learned models and the capability of the proposed method in separating metabolite and MM signals for practical short-TE [Formula: see text]-MRSI data.

Age-related parietal GABA alterations in children with autism spectrum disorder

GABA is the primary inhibitory neurotransmitter in the brain, and is essential to the balance of cortical excitation and inhibition. Reductions in GABA are proposed to result in an overly excitatory cortex that may cause, or contribute to, symptoms of autism spectrum disorder (ASD). This study employed a cross-sectional design to explore GABA+ differences in ASD and the impact of age, comparing 4-12 year olds with ASD (N = 24) to typically developing children (N = 35). GABA+ concentration was measured using edited magnetic resonance spectroscopy in the left parietal lobe. This study used a mixed model to investigate group differences between children with ASD and typically developing children. There was a significant difference in GABA+ levels between the groups, a significant effect of age and interaction between age and diagnostic group. The ASD group showed an association between GABA+ and age, with GABA+ levels gradually increasing with age (r = 0.59, p = 0.003). Typically developing children did not show age-related change in GABA+ concentration (r = 0.09, p = 0.60). By the age of 9, children with ASD showed GABA+ levels that were comparable to their typically developing peers. This study suggests that children with ASD have initially lower levels of GABA+ in the left parietal lobe compared to typically developing children, and that these initially lower levels of GABA+ increase with age in ASD within this region. It is suggested that this developmental shift of GABA+ levels within the left parietal lobe provides a possible explanation for the previously found reductions in childhood that does not persist in adults. LAY SUMMARY: This study measured levels of GABA in the left parietal lobe using magnetic resonance spectroscopy in children with ASD and typically developing children. GABA levels were initially lower in the ASD group, and increased with age, while GABA did not change with age in the typically developing group. This suggests that alterations in GABA signaling may be associated with ASD in childhood. Autism Res 2021, 14: 859-872. © 2021 International Society for Autism Research, Wiley Periodicals LLC.

INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis

In vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.

Intermittent Theta-Burst Stimulation Transcranial Magnetic Stimulation Increases GABA in the Medial Prefrontal Cortex: A Preliminary Sham-Controlled Magnetic Resonance Spectroscopy Study in Acute Bipolar Depression

Background: Magnetic resonance spectroscopy (MRS) has been used to identify gamma-aminobutyric acid (GABA) alterations in mood disorders, particularly in the medial prefrontal cortex (mPFC) where decreased concentrations have been associated with anhedonia. In major depressive disorder (MDD), prior work suggests that repetitive transcranial magnetic stimulation (rTMS) increases mPFC GABA concentrations proportional to antidepressant response. To our knowledge, this has not been examined in acute bipolar depression. Methods: As part of a multicentre 4-week randomized, double-blind, sham-controlled trial using intermittent theta-burst stimulation (iTBS) of the left dorsolateral prefrontal cortex (DLPFC) in individuals with acute bipolar depression, we quantified mPFC GABA and Glx (glutamate+glutamine) concentrations using a 3T MRS scan at baseline and after the intervention. Depressive symptoms were measured using the Montgomery-Asberg Depression Rating Scale (MADRS) and the Hamilton Depression Rating Scale-17 (HRDS-17), and anhedonia was measured using the Snaith-Hamilton Pleasure Scale (SHAPS). Results: The trial was terminated for futility and magnetic resonance spectroscopy data was acquired for 18 participants. At baseline, there were no associations between GABA or Glx concentrations and anhedonia, however GABA was negative correlated with depressive symptom severity on the HRDS-17. Compared to the sham-iTBS group, participants receiving active-iTBS had a significant increase in mPFC GABA concentrations. This was unrelated to antidepressant outcomes or improvements in anhedonia. Conclusion: Our data suggests that iTBS targeting the DLPFC is associated with physiological changes in the mPFC. In acute bipolar depression, our preliminary data suggests that mPFC GABA is dissociated from antidepressant iTBS treatment outcomes and anhedonia.

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.

Flexible MEGA editing scheme with asymmetric adiabatic pulses applied for T 2 measurement of lactate in human brain

PURPOSE

A flexible MEGA editing scheme which decouples the editing efficiency from TE is proposed and the utility of asymmetric adiabatic pulses for this new technique is explored. It is demonstrated that the method enables robust T 2 measurement of lactate in healthy human brain.

METHODS

The proposed variation of the MEGA scheme applies editing pulses in both acquired spectra, ensuring that the difference in J-evolution of the target resonance leads to maximal signal yield in the difference spectrum for arbitrary TE. A MEGA-sLASER sequence is augmented with asymmetric adiabatic editing pulses for enhanced flexibility and immunity to B 1 + miscalibration and inhomogeneities. The technique is validated and optimized for flexible lactate editing via a simple analytical model, numerical simulations and in vitro experiments. The T 2 relaxation constant of lactate is determined in vivo via multiple-TE measurements with the proposed method and a dedicated postprocessing and quantification approach.

RESULTS

Asymmetric adiabatic editing pulses improve robustness and facilitate efficient J-editing in sequences or protocols with strong timing constraints. Single voxel measurements using the proposed MEGA scheme in the occipital cortex of six healthy subjects yield a relaxation constant of T 2 = 171 ± 19  ms for the methyl resonance of lactate at a field strength of 3T.

CONCLUSIONS

The proposed MEGA editing scheme allows for novel kinds of J-editing experiments and promises to be an asset to robust T 2 measurement of lactate and potentially other J-coupled metabolites in vivo.

A semi-LASER, single-voxel spectroscopic sequence with a minimal echo time of 20.1 ms in the human brain at 3 T

An optimized semi-LASER sequence that is capable of acquiring artefact-free data with an echo time (TE) of 20.1 ms on a standard clinical 3 T MR system was developed. Simulations were performed to determine the optimal TEs that minimize the expected Cramér-Rao lower bound (CRLB) as proxy for quantification accuracy of metabolites. Optimized RF pulses, crusher gradients and phase cycling were used to achieve the shortest TE in a semi-LASER sequence to date on a clinical system. Synthetic spectra were simulated using the density matrix formalism for TEs spanning from 20.1 to 220.1 ms. These simulations were used to calculate the expected CRLB for each of the 18 metabolites typically considered in ¹ H MRS. High quality spectra were obtained in six healthy volunteers in the prefrontal cortex, which is known for spurious echoes due to its proximity to the paranasal sinuses, and in the parietal-occipital cortex. Spectral transients were sufficient in quality to enable phase and frequency alignment prior to summation over all repetitions. Automated high-quality water suppression was obtained for all voxels without manual adjustment. The shortest TE minimized the CRLB for all brain metabolites except glycine due to its overlap with myo-inositol at this TE. It is also demonstrated that the CRLBs increase rapidly with TE for certain coupled metabolites.

Parameterization of metabolite and macromolecule contributions in interrelated MR spectra of human brain using multidimensional modeling

Macromolecular signals are crucial constituents of short echo-time ¹ H MR spectra with potential clinical implications in themselves as well as essential ramifications for the quantification of the usually targeted metabolites. Their parameterization, needed for general fitting models, is difficult because of their unknown composition. Here, a macromolecular signal parameterization together with metabolite signal quantification including relaxation properties is investigated by multidimensional modeling of interrelated 2DJ inversion-recovery (2DJ-IR) datasets. Simultaneous and iterative procedures for defining the macromolecular background (MMBG) as mono-exponentially or generally decaying signals over TE are evaluated. Varying prior knowledge and restrictions in the metabolite evaluation are tested to examine their impact on results and fitting stability for two sets of three-dimensional spectra acquired with metabolite-cycled PRESS from cerebral gray and white matter locations. One dataset was used for model optimization, and also examining the influence of prior knowledge on estimated parameters. The most promising model was applied to a second dataset. It turned out that the mono-exponential decay model appears to be inadequate to represent TE-dependent signal features of the MMBG. TE-adapted MMBG spectra were therefore determined. For a reliable overall quantification of implicated metabolite concentrations and relaxation times, a general fitting model had to be constrained in terms of the number of fitting variables and the allowed parameter space. With such a model in place, fitting precision for metabolite contents and relaxation times was excellent, while fitting accuracy is difficult to judge and bias was likely influenced by the type of fitting constraints enforced. In summary, the parameterization of metabolite and macromolecule contributions in interrelated MR spectra has been examined by using multidimensional modeling on complex 2DJ-IR datasets. A tightly restricted model allows fitting of individual subject data with high fitting precision documented in small Cramér-Rao lower bounds, good repeatability values and a relatively small spread of estimated concentration and relaxation values for a healthy subject cohort.

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.

Determination of Brain Metabolite T 1 Without Interference From Macromolecule Relaxation

BACKGROUND

J-coupled metabolites are often measured at a predetermined echo time in the presence of macromolecule signals, which complicates the measurement of metabolite T₁ .

PURPOSE

To evaluate the feasibility and benefits of measuring metabolite T₁ relaxation times without changing the overlapping macromolecule baseline signals.

STUDY TYPE

Prospective.

SUBJECTS

Five healthy volunteers (three females and two males; age = 27 ± 7 years).

FIELD STRENGTH/SEQUENCE

7T scanner using a point resolved spectroscopy (PRESS)-based spectral editing MR spectroscopy (MRS) sequence with inversion recovery (IR).

ASSESSMENT

F-tests were performed to evaluate if the new approach, which fitted all the spectra together and used the same baselines for the three different IR settings, significantly reduced the variances of the metabolite T₁ values compared to a conventional fitting approach.

STATISTICAL TESTS

Cramer-Rao lower bound (CRLB), within-subject coefficient of variation, and F-test.

RESULTS

The T₁ relaxation times of N-acetylaspartate (NAA), total creatine (tCr), total choline (tCho), myo-inositol (mI), and glutamate (Glu) were determined with CRLB values below 6%. Glutamine (Gln) T₁ was determined with a 17% CRLB, and the T₁ of γ-aminobutyric acid (GABA) was determined with a 34% CRLB. The new approach significantly reduced the variances (F-test P < 0.05) of NAA, Glu, Gln, tCr, tCho, and mI T₁ s compared to the conventional approach.

DATA CONCLUSION

Keeping macromolecule signals intact by using only long IR times allowed the use of a single macromolecule spectral model for different IR settings and significantly reduced the variances of NAA, Glu, Gln, tCr, tCho, and mI T₁ s.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 1.

Elevated homocarnosine and GABA in subject on isoniazid as assessed through 1H MRS at 7T

Typical magnetic resonance spectroscopy J-editing methods designed to quantify GABA suffer from contamination of both overlapping macromolecules and homocarnosine signal, introducing potential confounds. The aim of this study was to develop a novel method to assess accurately both the relative concentrations of homocarnosine as well as GABA free from overlapping creatine, homocarnosine and macromolecule signal. A novel method which utilized the combination of echo time STEAM and MEGA-sLASER magnetic resonance spectroscopy experiments at 7T were used to quantify the concentration of GABA and homocarnsoine independently, which are typically quantified in tandem. The metabolites GABA and homocarnosine were measured in brain of 6 healthy control subjects, and in a single subject medicated with isoniazid. It was found that (16.6±10.2)% of the supposed GABA signal in the brain originated from homocarnosine, and that isoniazid caused significantly elevated concentration of GABA and homocarnosine in a single subject compared to controls.

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.