The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling

PURPOSE

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates.

METHODS

We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data.

RESULTS

2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR.

CONCLUSION

Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

Glutamate dynamics and BOLD response during OCD symptom provocation in the lateral occipital cortex: A 7 Tesla fMRI-fMRS study

Obsessive-compulsive disorder (OCD) is linked with dysfunction in frontal-striatal, fronto-limbic, and visual brain regions. Research using proton magnetic resonance spectroscopy (1H-MRS) suggests that altered neurometabolite levels, like glutamate, may contribute to this dysfunction. However, static neurometabolite levels in OCD patients have shown inconsistent results, likely due to previous studies' limited focus on neurometabolite dynamics. We employ functional MRS (fMRS) and functional magnetic resonance imaging (fMRI) to explore these dynamics and brain activation during OCD symptom provocation. We utilized a combined 7-tesla fMRI-fMRS setup to examine task-related BOLD response and glutamate changes in the lateral occipital cortex (LOC) of 30 OCD participants and 34 matched controls during an OCD-specific symptom provocation task. The study examined main effects and between-group differences in brain activation and glutamate levels during the task. A whole sample task-effects analysis on data meeting predefined quality criteria showed significant glutamate increases (n = 41 (22 OCD, 19 controls), mean change: 3.2 %, z = 3.75, p < .001) and task activation (n = 54 (26 OCD, 28 controls), p < .001) in the LOC during OCD blocks compared to neutral blocks. However, no differences in task-induced glutamate dynamics or activation between groups were found, nor a correlation between glutamate levels and task activation. We were able to measure task-induced increases in glutamate and BOLD levels, emphasizing its feasibility for OCD research. The absence of group differences highlights the need for further exploration to discern to what extent neurometabolite dynamics differ between OCD patients and controls. Once established, future studies can use pre-post intervention fMRS-fMRI to probe the effects of therapies modulating glutamate pathways in OCD.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

PURPOSE

The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.

METHODS

Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs).

RESULTS

Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.

CONCLUSION

Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.

Considerations for event-related gamma-aminobutyric acid functional magnetic resonance spectroscopy

The use of sequential proton magnetic resonance spectroscopy (MRS) to follow glutamate and gamma-aminobutyric acid (GABA) changes during functional task-based paradigms, functional MRS (fMRS), has increased. This technique has been used to investigate GABA dynamics during both sensory and behavioural tasks, usually with long 'block design' paradigms. Recently, there has been an increase in interest in the use of short stimuli and 'event-related' tasks. While changes in glutamate can be readily followed by collecting multiple individual transients (or shots), measurement of GABA, especially at 3 T, is usually performed using editing techniques like Mescher-Garwood point-resolved spectroscopy (MEGA-PRESS), which by its nature is a dual shot approach. This poses problems when considering an event-related experiment, where it is unclear when GABA may change, or how this may affect the individual subspectra of the MEGA-PRESS acquisition. To address this issue, MEGA-PRESS data were simulated to reflect the effect of a transient change in GABA concentration due to a short event-related stimulus. The change in GABA was simulated for both the ON and OFF subspectra, and the effect of three different conditions (increase only during ON acquisition, increase during OFF acquisition and increase across both) on the corresponding edited GABA spectrum was modelled. Results show that a transient increase in GABA that only occurs during the ON subspectral acquisition, while not changing the results much from when GABA is changed across both conditions, will give a much larger change in the edited GABA spectrum than a transient increase that occurs only during the OFF subspectral acquisition. These results suggest that researchers should think carefully about the design of any event-related fMRS studies using MEGA-PRESS, as well as the analysis of other functional paradigms where transient changes in GABA may be expected. Experimental design considerations are therefore discussed, and suggestions are made.

Metabolite T2 relaxation times decrease across the adult lifespan in a large multi-site cohort

Purpose

Relaxation correction is crucial for accurately estimating metabolite concentrations measured using in vivo magnetic resonance spectroscopy (MRS). However, the majority of MRS quantification routines assume that relaxation values remain constant across the lifespan, despite prior evidence of T2 changes with aging for multiple of the major metabolites. Here, we comprehensively investigate correlations between T2 and age in a large, multi-site cohort.

Methods

We recruited approximately 10 male and 10 female participants from each decade of life: 18-29, 30-39, 40-49, 50-59, and 60+ years old (n=101 total). We collected PRESS data at 8 TEs (30, 50, 74, 101, 135, 179, 241, and 350 ms) from voxels placed in white-matter-rich centrum semiovale (CSO) and gray-matter-rich posterior cingulate cortex (PCC). We quantified metabolite amplitudes using Osprey and fit exponential decay curves to estimate T2.

Results

Older age was correlated with shorter T2 for tNAA, tCr3.0, tCr3.9, tCho, Glx, and tissue water in CSO and PCC; rs = -0.21 to -0.65, all p<0.05, FDR-corrected for multiple comparisons. These associations remained statistically significant when controlling for cortical atrophy. T2 values did not differ across the adult lifespan for mI. By region, T2 values were longer in the CSO for tNAA, tCr3.0, tCr3.9, Glx, and tissue water and longer in the PCC for tCho and mI.

Conclusion

These findings underscore the importance of considering metabolite T2 changes with aging in MRS quantification. We suggest that future 3T work utilize the equations presented here to estimate age-specific T2 values instead of relying on uniform default values.

Universal dynamic fitting of magnetic resonance spectroscopy

PURPOSE

Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type.

METHODS

A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting.

RESULTS

Analysis of synthetic functional 1H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data.

CONCLUSION

A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.

Functional Magnetic Resonance Spectroscopy of Prolonged Motor Activation using Conventional and Spectral GLM Analyses

Background

Functional MRS (fMRS) is a technique used to measure metabolic changes in response to increased neuronal activity, providing unique insights into neurotransmitter dynamics and neuroenergetics. In this study we investigate the response of lactate and glutamate levels in the motor cortex during a sustained motor task using conventional spectral fitting and explore the use of a novel analysis approach based on the application of linear modelling directly to the spectro-temporal fMRS data.

Methods

fMRS data were acquired at a field strength of 3 Tesla from 23 healthy participants using a short echo-time (28ms) semi-LASER sequence. The functional task involved rhythmic hand clenching over a duration of 8 minutes and standard MRS preprocessing steps, including frequency and phase alignment, were employed. Both conventional spectral fitting and direct linear modelling were applied, and results from participant-averaged spectra and metabolite-averaged individual analyses were compared.

Results

We observed a 20% increase in lactate in response to the motor task, consistent with findings at higher magnetic field strengths. However, statistical testing showed some variability between the two averaging schemes and fitting algorithms. While lactate changes were supported by the direct spectral modelling approach, smaller increases in glutamate (2%) were inconsistent. Exploratory spectral modelling identified a 4% decrease in aspartate, aligning with conventional fitting and observations from prolonged visual stimulation.

Conclusion

We demonstrate that lactate dynamics in response to a prolonged motor task are observed using short-echo time semi-LASER at 3 Tesla, and that direct linear modelling of fMRS data is a useful complement to conventional analysis. Future work includes mitigating spectral confounds, such as scalp lipid contamination and lineshape drift, and further validation of our novel direct linear modelling approach through experimental and simulated datasets.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling

Purpose

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a two-dimensional linear-combination model (2D-LCM) of individual transients ('model-based FPC'). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates.

Methods

We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the standard deviation of those ground-truth errors, and amplitude Cramér Rao Lower Bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data.

Results

2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of frequency and phase correction and amplitudes performed substantially better at low-to-very-low SNR.

Conclusion

Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs or strong diffusion weighting.

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

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

GABA, glutamatergic dynamics and BOLD contrast assessed concurrently using functional MRS during a cognitive task

A recurring issue in functional neuroimaging is how to link task-driven haemodynamic blood oxygen level dependent functional MRI (BOLD-fMRI) responses to underlying neurochemistry at the synaptic level. Glutamate and γ-aminobutyric acid (GABA), the major excitatory and inhibitory neurotransmitters respectively, are typically measured with MRS sequences separately from fMRI, in the absence of a task. The present study aims to resolve this disconnect, developing acquisition and processing techniques to simultaneously assess GABA, glutamate and glutamine (Glx) and BOLD in relation to a cognitive task, at 3 T. Healthy subjects (N = 81) performed a cognitive task (Eriksen flanker), which was presented visually in a task-OFF, task-ON block design, with individual event onset timing jittered with respect to the MRS readout. fMRS data were acquired from the medial anterior cingulate cortex during task performance, using an adapted MEGA-PRESS implementation incorporating unsuppressed water-reference signals at a regular interval. These allowed for continuous assessment of BOLD activation, through T2 *-related changes in water linewidth. BOLD-fMRI data were additionally acquired. A novel linear model was used to extract modelled metabolite spectra associated with discrete functional stimuli, building on well established processing and quantification tools. Behavioural outcomes from the flanker task, and activation patterns from the BOLD-fMRI sequence, were as expected from the literature. BOLD response assessed through fMRS showed a significant correlation with fMRI, specific to the fMRS-targeted region of interest; fMRS-assessed BOLD additionally correlated with lengthening of response time in the incongruent flanker condition. While no significant task-related changes were observed for GABA+, a significant increase in measured Glx levels (~8.8%) was found between task-OFF and task-ON periods. These findings verify the efficacy of our protocol and analysis pipelines for the simultaneous assessment of metabolite dynamics and BOLD. As well as establishing a robust basis for further work using these techniques, we also identify a number of clear directions for further refinement in future studies.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

Purpose

The interest in applying and modeling dynamic MRS has recently grown. 2D modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to 1D modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.

Methods

Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multi-transient LCM with 1D-LCM of the average. 2,500 datasets per condition with different noise representations of a 64-transient MRS experiment at 6 signal-to-noise levels for two separate spin systems (scyllo-inositol and GABA) were analyzed. Additional datasets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by standard deviations and Cramér-Rao Lower Bounds (CRLB).

Results

Amplitude estimates for 1D- and 2D-LCM agreed well and showed similar level of bias compared to the ground truth. Estimated CRLBs agreed well between both models and with ground truth CRLBs. For correlated noise the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.

Conclusion

Our results indicate that the model performance of 2D multi-transient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as necessary basis for further applications of 2D modeling.

Practical considerations of diffusion-weighted MRS with ultra-strong diffusion gradients

Introduction

Diffusion-weighted magnetic resonance spectroscopy (DW-MRS) offers improved cellular specificity to microstructure-compared to water-based methods alone-but spatial resolution and SNR is severely reduced and slow-diffusing metabolites necessitate higher b-values to accurately characterize their diffusion properties. Ultra-strong gradients allow access to higher b-values per-unit time, higher SNR for a given b-value, and shorter diffusion times, but introduce additional challenges such as eddy-current artefacts, gradient non-uniformity, and mechanical vibrations.

Methods

In this work, we present initial DW-MRS data acquired on a 3T Siemens Connectom scanner equipped with ultra-strong (300 mT/m) gradients. We explore the practical issues associated with this manner of acquisition, the steps that may be taken to mitigate their impact on the data, and the potential benefits of ultra-strong gradients for DW-MRS. An in-house DW-PRESS sequence and data processing pipeline were developed to mitigate the impact of these confounds. The interaction of TE, b-value, and maximum gradient amplitude was investigated using simulations and pilot data, whereby maximum gradient amplitude was restricted. Furthermore, two DW-MRS voxels in grey and white matter were acquired using ultra-strong gradients and high b-values.

Results

Simulations suggest T2-based SNR gains that are experimentally confirmed. Ultra-strong gradient acquisitions exhibit similar artefact profiles to those of lower gradient amplitude, suggesting adequate performance of artefact mitigation strategies. Gradient field non-uniformity influenced ADC estimates by up to 4% when left uncorrected. ADC and Kurtosis estimates for tNAA, tCho, and tCr align with previously published literature.

Discussion

In conclusion, we successfully implemented acquisition and data processing strategies for ultra-strong gradient DW-MRS and results indicate that confounding effects of the strong gradient system can be ameliorated, while achieving shorter diffusion times and improved metabolite SNR.

Neurometabolite changes in response to antidepressant medication: A systematic review of 1H-MRS findings

Selective serotonin reuptake inhibitors (SSRIs), serotonin and noradrenaline reuptake inhibitors (SNRIs), and (es)ketamine are used to treat major depressive disorder (MDD). These different types of medication may involve common neural pathways related to glutamatergic and GABAergic neurotransmitter systems, both of which have been implicated in MDD pathology. We conducted a systematic review of pharmacological proton Magnetic Resonance Spectroscopy (1H-MRS) studies in healthy volunteers and individuals with MDD to explore the potential impact of these medications on glutamatergic and GABAergic systems. We searched PubMed, Web of Science and Embase and included randomized controlled trials or cohort studies, which assessed the effects of SSRIs, SNRIs, or (es)ketamine on glutamate, glutamine, Glx or GABA using single-voxel 1H-MRS or Magnetic Resonance Spectroscopic Imaging (MRSI). Additionally, studies were included when they used a field strength > 1.5 T, and when a comparison of metabolite levels between antidepressant treatment and placebo or baseline with post-medication metabolite levels was done. We excluded animal studies, duplicate publications, or articles with 1H-MRS data already described in another included article. Twenty-nine studies were included in this review. Fifteen studies investigated the effect of administration or treatment with SSRIs or SNRIs, and fourteen studies investigated the effect of (es)ketamine on glutamatergic and GABAergic metabolite levels. Studies on SSRIs and SNRIs were highly variable, generally underpowered, and yielded no consistent findings across brain regions or specific populations. Although studies on (es)ketamine were also highly variable, some demonstrated an increase in glutamate levels in the anterior cingulate cortex in a time-dependent manner after administration. Our findings highlight the need for standardized study and acquisition protocols. Additionally, measuring metabolites dynamically over time or combining 1H-MRS with whole brain functional imaging techniques could provide valuable insights into the effects of these medications on glutamate and GABAergic neurometabolism.