Proton MRS (1 H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi-adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0 ) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

/pdf/methodological-consensus-on-clinical-proton-mrs-of-the-brain-1y0sojtvlm.pdf

Methodological consensus on clinical proton MRS of the brain: Review and 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.

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

The Proton magnetic resonance (1 H-MRS) spectrum contains information about the concentration of tissue metabolites within a predefined region of interest (a voxel). The conventional spectrum in some cases obscures information about less abundant metabolites due to limited separation and complex splitting of the metabolite peaks. One method to detect these metabolites is to reduce the complexity of the spectrum using editing. This review provides an overview of the one-dimensional editing methods available to interrogate these obscured metabolite peaks. These methods include sequence optimizations, echo-time averaging, J-difference editing methods (single BASING, dual BASING, and MEGA-PRESS), constant-time PRESS, and multiple quantum filtering. It then provides an overview of the brain metabolites whose detection can benefit from one or more of these editing approaches, including ascorbic acid, γ-aminobutyric acid, lactate, aspartate, N-acetyl aspartyl glutamate, 2-hydroxyglutarate, glutathione, glutamate, glycine, and serine. Magn Reson Med 77:1377-1389, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.26619

Edited 1 H magnetic resonance spectroscopy in vivo: Methods and metabolites.

The translation of MRS to clinical practice has been impeded by the lack of technical standardization. There are multiple methods of acquisition, post-processing, and analysis whose details greatly impact the interpretation of the results. These details are often not fully reported, making it difficult to assess MRS studies on a standardized basis. This hampers the reviewing of manuscripts, limits the reproducibility of study results, and complicates meta-analysis of the literature. In this paper a consensus group of MRS experts provides minimum guidelines for the reporting of MRS methods and results, including the standardized description of MRS hardware, data acquisition, analysis, and quality assessment. This consensus statement describes each of these requirements in detail and includes a checklist to assist authors and journal reviewers and to provide a practical way for journal editors to ensure that MRS studies are reported in full.

https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/pdf/10.1002/nbm.4484

Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts' consensus recommendations

Effects of cannabidiol on brain excitation and inhibition systems; a randomised placebo-controlled single dose trial during magnetic resonance spectroscopy in adults with and without autism spectrum disorder

Purpose

To introduce a new toolkit for simulation and processing of magnetic resonance spectroscopy (MRS) data, and to demonstrate some of its novel features.



Methods

The FID appliance (FID-A) is an open-source, MATLAB-based software toolkit for simulation and processing of MRS data. The software is designed specifically for processing data with multiple dimensions (eg, multiple radiofrequency channels, averages, spectral editing dimensions). It is equipped with functions for importing data in the formats of most major MRI vendors (eg, Siemens, Philips, GE, Agilent) and for exporting data into the formats of several common processing software packages (eg, LCModel, jMRUI, Tarquin). This paper introduces the FID-A software toolkit and uses examples to demonstrate its novel features, namely 1) the use of a spectral registration algorithm to carry out useful processing routines automatically, 2) automatic detection and removal of motion-corrupted scans, and 3) the ability to perform several major aspects of the MRS computational workflow from a single piece of software. This latter feature is illustrated through both high-level processing of in vivo GABA-edited MEGA-PRESS MRS data, as well as detailed quantum mechanical simulations to generate an accurate LCModel basis set for analysis of the same data.



Results

All of the described processing steps resulted in a marked improvement in spectral quality compared with unprocessed data. Fitting of MEGA-PRESS data using a customized basis set resulted in improved fitting accuracy compared with a generic MEGA-PRESS basis set.



Conclusions

The FID-A software toolkit enables high-level processing of MRS data and accurate simulation of in vivo MRS experiments. Magn Reson Med, 2015. © 2015 Wiley Periodicals, Inc.

Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit.

The earliest stages of sensorimotor learning involve learning the correspondence between movements and sensory results-a sensorimotor map. The present exploratory study investigated the neurochemical underpinnings of map acquisition by monitoring 25 participants as they acquired a new association between movements and sounds. Functional magnetic resonance spectroscopy was used to measure neurochemical concentrations in the left primary motor cortex during learning. Resting-state functional magnetic resonance imaging data were also collected before and after training to assess learning-related changes in functional connectivity. There were monotonic increases in γ-aminobutyric acid (GABA) and decreases in glucose during training, which extended into the subsequent rest period and, importantly, in the case of GABA correlated with the amount of learning: participants who showed greater behavioral learning showed greater GABA increase. The GABA change was furthermore correlated with changes in functional connectivity between the primary motor cortex and a cluster of voxels in the right intraparietal sulcus: greater increases in GABA were associated with greater strengthening of connectivity. Transiently, there were increases in lactate and reductions in aspartate, which returned to baseline at the end of training, but only lactate showed a statistical trend to correlate with the amount of learning. In summary, during the earliest stages of sensorimotor learning, GABA levels are linked on a subject-level basis to both behavioral learning and a strengthening of functional connections that persists beyond the training period. The findings are consistent with the idea that GABA-mediated inhibition is linked to maintenance of newly learned information.NEW & NOTEWORTHY Learning the mapping between movements and their sensory effects is a necessary step in the early stages of sensorimotor learning. There is evidence showing which brain areas are involved in early motor learning, but their role remains uncertain. Here, we show that GABA, a neurotransmitter linked to inhibitory processing, rises during and after learning and is involved in ongoing changes in resting-state networks.

https://journals.physiology.org/doi/pdf/10.1152/jn.00285.2020

T J Hennessy

Papers

Advanced processing and simulation of MRS data using the FID appliance (FID-A)-An open source, MATLAB-based toolkit.

Early stages of sensorimotor map acquisition: neurochemical signature in primary motor cortex and its relation to functional connectivity.