Purpose The purpose of this study is to describe the Gannet toolkit for the quantitative batch analysis of gamma-aminobutyric acid (GABA) -edited MRS data. Materials and Methods Using MEGA-PRESS editing and standard acquisition parameters, four MEGA-PRESS spectra were acquired in three brain regions in 10 healthy volunteers. These 120 datasets were processed without user intervention with Gannet, a Matlab-based tool that takes raw time-domain data input, processes it to generate the frequency-domain edited spectrum, and applies a simple modeling procedure to estimate GABA concentration relative to the creatine or, if provided, the unsuppressed water signal. A comparison of four modeling approaches is also presented. Results All data were successfully processed by Gannet. Coefficients of variation across subjects ranged from 11% for the occipital region to 17% for the dorsolateral prefrontal region. There was no clear difference in fitting performance between the simple Gaussian model used by Gannet and the other more complex models presented. Conclusion Gannet, the GABA Analysis Toolkit, can be used to process and quantify GABA-edited MRS spectra without user intervention.

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.

Purpose To develop a tissue correction for GABA-edited MRS that appropriately addresses differences in voxel gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) fractions. Methods Simulations compared the performance of tissue correction approaches. Corrections were then applied to in vivo data from sixteen healthy volunteers, acquired at 3T. GM, WM and CSF fractions were determined from T1-weighted images. Corrections for CSF content, GM/WM GABA content, and water relaxation of the three compartments are combined into a single, fully corrected measurement. Results Simulations show that CSF correction increases the dependence of GABA measurements on GM/WM fraction, by an amount equal to the fraction of CSF. Furthermore, GM correction substantially (and non-linearly) increases the dependence of GABA measurements on GM/WM fraction, for example, by a factor of over four when the voxel GM tissue fraction is 50%. At this tissue fraction, GABA is over estimated by a factor of 1.5. For the in vivo data, correcting for voxel composition increased measured GABA values (p<0.001 for all regions), but did not reduce inter-subject variance (p>0.5 for all regions). Corrected GABA values differ significantly based on the segmentation procedure used (p<0.0001) and tissue parameter assumptions made (p<0.0001). Conclusion We introduce a comprehensive tissue correction factor that adjusts GABA measurements to correct for different voxel compositions of GM, WM, and CSF.

Background Abnormal responses to tactile stimuli are a common feature of Autism Spectrum Disorder (ASD). Several lines of evidence suggest that GABAergic function, which has a crucial role in tactile processing, is altered in ASD. In this study, we determine whether in vivo GABA levels are altered in children with ASD, and whether alterations in GABA levels are associated with abnormal tactile function in these children. Methods GABA-edited MRS was acquired in 37 children with Autism and 35 Typically Developing Children from voxels over primary sensorimotor and occipital cortices. Children performed tactile tasks previously shown to be altered in ASD, linked to inhibitory mechanisms. Detection threshold was measured with- and without the presence of a slowly increasing sub-threshold stimulus. Amplitude discrimination was measured with- and without the presence of an adapting stimulus, and frequency discrimination was measured. Results Sensorimotor GABA levels were significantly reduced in children with autism compared to healthy controls. Occipital GABA levels were normal. Sensorimotor GABA levels correlated with dynamic detection threshold as well as with the effect of sub-threshold stimulation. Sensorimotor GABA levels also correlated with amplitude discrimination after adaptation (an effect absent in autism) and frequency discrimination in controls, but not in children with autism. Conclusions GABA levels correlate with behavioral measures of inhibition. Children with autism have reduced GABA, associated with abnormalities in tactile performance. We show here that altered in vivo GABA levels might predict abnormal tactile information processing in ASD and that the GABA system may be a future target for therapies.

Magnetic resonance spectroscopy (MRS) is the only biomedical imaging method that can noninvasively detect endogenous signals from the neurotransmitter γ-aminobutyric acid (GABA) in the human brain. Its increasing popularity has been aided by improvements in scanner hardware and acquisition methodology, as well as by broader access to pulse sequences that can selectively detect GABA, in particular J-difference spectral editing sequences. Nevertheless, implementations of GABA-edited MRS remain diverse across research sites, making comparisons between studies challenging. This large-scale multi-vendor, multi-site study seeks to better understand the factors that impact measurement outcomes of GABA-edited MRS. An international consortium of 24 research sites was formed. Data from 272 healthy adults were acquired on scanners from the three major MRI vendors and analyzed using the Gannet processing pipeline. MRS data were acquired in the medial parietal lobe with standard GABA+ and macromolecule- (MM-) suppressed GABA editing. The coefficient of variation across the entire cohort was 12% for GABA+ measurements and 28% for MM-suppressed GABA measurements. A multilevel analysis revealed that most of the variance (72%) in the GABA+ data was accounted for by differences between participants within-site, while site-level differences accounted for comparatively more variance (20%) than vendor-level differences (8%). For MM-suppressed GABA data, the variance was distributed equally between site- (50%) and participant-level (50%) differences. The findings show that GABA+ measurements exhibit strong agreement when implemented with a standard protocol. There is, however, increased variability for MM-suppressed GABA measurements that is attributed in part to differences in site-to-site data acquisition. This study’s protocol establishes a framework for future methodological standardization of GABA-edited MRS, while the results provide valuable benchmarks for the MRS community.

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Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (FMRI) is most commonly used in a semi-quantitative manner to infer changes in brain activity. Despite the basis of the image contrast lying in the cerebral venous blood oxygenation level, quantification of absolute cerebral metabolic rate of oxygen consumption (CMRO 2 ) has only recently been demonstrated. Here we examine two approaches to the calibration of FMRI signal to measure absolute CMRO 2 using hypercapnic and hyperoxic respiratory challenges. The first approach is to apply hypercapnia and hyperoxia separately but interleaved in time and the second is a combined approach in which we apply hyperoxic challenges simultaneously with different levels of hypercapnia. Eleven healthy volunteers were studied at 3T using a dual gradient-echo spiral readout pulsed arterial spin labelling (ASL) imaging sequence. Respiratory challenges were conducted using an automated system of dynamic end-tidal forcing. A generalised BOLD signal model was applied, within a Bayesian estimation framework, that aims to explain the effects of modulation of CBF and arterial oxygen content to estimate venous deoxyhaemoglobin concentration ([dHb] 0 ). Using CBF measurements combined with the estimated oxygen extraction fraction (OEF), absolute CMRO 2 was calculated. The interleaved approach to hypercapnia and hyperoxia, as well as yielding estimates of CMRO 2 and OEF demonstrated a significant increase in regional CBF, venous oxygen saturation (SvO 2 ) (a decrease in OEF) and absolute CMRO 2 in visual cortex in response to a continuous (20 minute) visual task, demonstrating the potential for the method in measuring long term changes in CMRO 2 . The combined approach to oxygen and carbon dioxide modulation, as well as taking less time to acquire data, yielded whole brain grey matter estimates of CMRO 2 and OEF of 184±45 μmol/100g/min and 0.42±0.12 respectively, along with additional estimates of the vascular parameters α = 0.33±0.06, the exponent relating relative increases in CBF to CBV, and β = 1.35±0.13, the exponent relating deoxyhaemoglobin concentration to the relaxation rate R 2 *. Maps of cerebrovascular and cerebral metabolic parameters were also calculated. We show that combined modulation of oxygen and carbon dioxide can offer an experimentally more efficient approach to estimating OEF and absolute CMRO 2 along with the additional vascular parameters that form an important part of the commonly used calibrated FMRI signal model.