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. Abbreviations: 1 H, proton; 13 C, carbon-13; B 0 , main magnetic field; B 1 , RF field; Cr, creatine; CRMVB, Cramér-Rao minimum variance bound; CSF, cerebrospinal fluid; d GM , water density of grey matter; d WM , water density of white matter; ERETIC, Electric Reference to Access in vivo Concentrations; f CSF , volume fraction of cerebrospinal fluid inside the MRS voxel; fCSF H2O , water mole fraction in cerebrospinal fluid; fGM, volume fraction of gray matter inside the MRS voxel; fGM H2O , water mole fraction in gray matter; FFT, fast Fourier transform; FID, free induction decay; FQN, fit quality number; FWHM, full width at half maximum; f WM , volume fraction of white matter inside the MRS voxel; fWM H2O , water mole fraction in white matter; GM, grey matter; GPC, glycerophosphocholine; [H 2 O] molal , water concentration in moles of water per kilogram of tissue water = 55.49 moles/kg; [H 2 O] molar , water concentration in moles of water per liter of tissue water; HERMES, Hadamard encoding and reconstruction of MEGA-edited spectroscopy; MEGA-PRESS, Mescher-Garwood point resolved spectroscopy; [M] GM /[M] WM , assumed ratio of grey matter to white matter metabolite concentrations; MM, macromolecules; [M]molal, metabolite concentration in moles of metabolite per kilogram of tissue water; [M]molar, metabolite concentration in moles of metabolite per liter of tissue water; MRSI, magnetic resonance spectroscopic imaging; NAA, N-acetylaspartate; NAAG, N-acetylaspartylglutamate; N M , number of protons contributing to metabolite signal; N P , number of points in FID/spectrum; N pc , number of phase encoding steps in one phase cycle; N RF , number of RF channels; N tra , number of transients;PCh, phosphocholine; PCr, phosphocreatine; RH2O CSF , relaxation scaling factor for water in cerebrospinal fluid; RH2O GM , relaxation scaling factor for water in grey matter; RH2O WM , relaxation scaling factor for water in white matter; RM, relaxation scaling factor for tissue metabolite signal; RM GM , relaxation scaling factor for metabolite in grey matter; RM WM , relaxation scaling factor for metabolite in white matter; S H2O , water signal intensity; SH2O obs , observed water signal intensity in the presence of relaxation; S M , metabolite signal intensity; SM obs , observed metabolite signal intensity in the presence of relaxation; SNR, signal-to-noise r...

Purpose Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data. Methods We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as “spectral registration.” Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels. Results Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation. Conclusions Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile.

Non-technical summaryInter-individual differences in regional GABA as assessed by magnetic resonance spectroscopy (MRS) relate to behavioural variation in humans. However, it is not clear what the relationship is between MRS measures of the concentration of neurotransmitters in a region and synaptic activity. Transcranial magnetic stimulation (TMS) techniques provide physiological measures of cortical excitation or inhibition. Here, we investigated the relationship between MRS and TMS measures of glutamatergic and GABAergic activity within the same individuals. We demonstrated a relationship between MRS-assessed glutamate levels and a TMS measure of global cortical excitability, suggesting that MRS measures of glutamate do reflect glutamatergic activity. However, there was no clear relationship between MRS-assessed GABA levels and TMS measures of synaptic GABAA or GABAB activity. A relationship was found between MRS-assessed GABA and a TMS protocol with less clearly understood physiological underpinnings. We speculate that this protocol may therefore reflect extrasynaptic GABA tone.

We previously demonstrated that network level functional connectivity in the human brain could be related to levels of inhibition in a major network node at baseline (Stagg et al., 2014). In this study, we build upon this finding to directly investigate the effects of perturbing M1 GABA and resting state functional connectivity using transcranial direct current stimulation (tDCS), a neuromodulatory approach that has previously been demonstrated to modulate both metrics. FMRI data and GABA levels, as assessed by Magnetic Resonance Spectroscopy, were measured before and after 20 min of 1 mA anodal or sham tDCS. In line with previous studies, baseline GABA levels were negatively correlated with the strength of functional connectivity within the resting motor network. However, although we confirm the previously reported findings that anodal tDCS reduces GABA concentration and increases functional connectivity in the stimulated motor cortex; these changes are not correlated, suggesting they may be driven by distinct underlying mechanisms.DOI: http://dx.doi.org/10.7554/eLife.08789.001

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.DOI: http://dx.doi.org/10.7554/eLife.01465.001

Although the ventromedial prefrontal cortex (vmPFC) has long been implicated in reward-guided decision making, its exact role in this process has remained an unresolved issue. Here, we show that vmPFC levels of GABA and glutamate in human volunteers are predictive of both behavioural performance and the dynamics of a neural value comparison signal in a manner as predicted by models of decision-making. These data provide evidence for a neural competition mechanism in vmPFC supporting value-guided choice.Organisms are constantly required to choose between options that differ in their expected reward value. Whilst neural signals reflecting these values are widespread throughout the brain 1-5 , the ventromedial prefrontal cortex (vmPFC) has attracted particular interest. Neural signals recorded in this region appear to reflect choice 1, 5 and damage to these areas in humans leads to deficits in decision-making 6 . However, the nature of the computations underlying these decisions has remained elusive.One popular mechanism for decision-making is competition by mutual inhibition -a class of models in which representations of each available option inhibit one another until activity remains in only one. Such models can be implemented in abstract 7 or biophysical 8 forms, and dynamic neural signals consistent with these models can be found in the vmPFC 9 . A crucial prediction is that performance will depend heavily on the levels of inhibition relative to excitation in the network. If the vmPFC implements a decision-making process based on such an inhibitory competition mechanism, then both behavioural performance and the neural dynamics of the vmPFC value comparison signal should depend on the levels of the major excitatory and inhibitory neurotransmitters, glutamate and GABA, in the vmPFC. Such a finding would tie a neurochemical underpinning to our computational understanding

No abstract

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.