Event-related functional magnetic resonance spectroscopy

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.

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.

NIMH perspectives on future directions in neuroimaging for mental health

NIMH's mission is to transform the understanding and treatment of mental illnesses through basic and clinical research, paving the way for prevention, recovery, and cure. New imaging techniques hold great promise for improving our understanding of the pathophysiology of mental illnesses, stratifying patients for treatment selection, and developing a personalized medicine approach. Here, we highlight emerging and promising new technologies that are likely to be vital in helping NIMH accomplish its mission, the potential for utilizing multimodal approaches to study mental illness, and considerations for data analytics and data sharing.

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.

Sequence building block for magnetic resonance spectroscopy on Siemens VE-series scanners

We present a sequence building block (SBB) that embeds magnetic resonance spectroscopy (MRS) into another sequence on the Siemens VE platform without any custom hardware. This enables dynamic studies such as functional MRS (fMRS), dynamic shimming and frequency correction, and acquisition of navigator images for motion correction. The SBB supports nonlocalised spectroscopy (free induction decay), STimulated Echo Acquisition Mode single voxel spectroscopy, and 1D, 2D and 3D phase-encoded chemical shift imaging. It can embed 1H or X-nuclear MRS into a 1H sequence; and 1H-MRS into an X-nuclear sequence. We demonstrate integration into the vendor's gradient-recalled echo sequence. We acquire test data in phantoms with three coils (31P/1H, 13C/1H and 2H/1H) and in two volunteers on a 7-T Terra MRI scanner. Fifteen lines of code are required to insert the SBB into a sequence. Spectra and images are acquired successfully in all cases in phantoms, and in human abdomen and calf muscle. Phantom comparison of signal-to-noise ratio and linewidth showed that the SBB has negligible effects on image and spectral quality, except that it sometimes produces a nuclear Overhauser effect (NOE) signal enhancement for multinuclear applications in line with conventional 1H NOE pulses. Our new SBB embeds MRS into a host imaging or spectroscopy sequence in 15 lines of code. It allows homonuclear and heteronuclear interleaving. The package is available through the standard C2P procedure. We hope this will lower the barrier for entry to studies applying dynamic fMRS and for online motion correction and B0-shim updating.

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.

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.

Dynamics of γ-aminobutyric acid concentration in the human brain in response to short visual stimulation

OBJECTIVE

To find a possible quantitative relation between activation-induced fast (< 10 s) changes in the γ-aminobutyric acid (GABA) level and the amplitude of a blood oxygen level-dependent contrast (BOLD) response (according to magnetic resonance spectroscopy [MRS] and functional magnetic resonance imaging [fMRI]).

MATERIALS AND METHODS

fMRI data and MEGA-PRESS magnetic resonance spectra [echo time (TE)/repetition time (TR) = 68 ms/1500 ms] of an activated area in the visual cortex of 33 subjects were acquired using a 3 T MR scanner. Stimulation was performed by presenting an image of a flickering checkerboard for 3 s, repeated with an interval of 13.5 s. The time course of GABA and creatine (Cr) concentrations and the width and height of resonance lines were obtained with a nominal time resolution of 1.5 s. Changes in the linewidth and height of n-acetylaspartate (NAA) and Cr signals were used to determine the BOLD effect.

RESULTS

In response to the activation, the BOLD-corrected GABA + /Cr ratio increased by 5.0% (q = 0.027) and 3.8% (q = 0.048) at 1.6 and 3.1 s, respectively, after the start of the stimulus. Time courses of Cr and NAA signal width and height reached a maximum change at the 6th second (~ 1.2-1.5%, q < 0.05).

CONCLUSION

The quick response of the observed GABA concentration to the short stimulus is most likely due to a release of GABA from vesicles followed by its packaging back into vesicles.

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.