Cramér-Rao bounds: an evaluation tool for quantitation

Plasma p-tau181 and GFAP reflect 7T MR-derived changes in Alzheimer's disease: A longitudinal study of structural and functional MRI and MRS

BACKGROUND

Associations between longitudinal changes of plasma biomarkers and cerebral magnetic resonance (MR)-derived measurements in Alzheimer's disease (AD) remain unclear.

METHODS

In a study population (n = 127) of healthy older adults and patients within the AD continuum, we examined associations between longitudinal plasma amyloid beta 42/40 ratio, tau phosphorylated at threonine 181 (p-tau181), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and 7T structural and functional MR imaging and spectroscopy using linear mixed models.

RESULTS

Increases in both p-tau181 and GFAP showed the strongest associations to 7T MR-derived measurements, particularly with decreasing parietal cortical thickness, decreasing connectivity of the salience network, and increasing neuroinflammation as determined by MR spectroscopy (MRS) myo-inositol.

DISCUSSION

Both plasma p-tau181 and GFAP appear to reflect disease progression, as indicated by 7T MR-derived brain changes which are not limited to areas known to be affected by tau pathology and neuroinflammation measured by MRS myo-inositol, respectively.

HIGHLIGHTS

This study leverages high-resolution 7T magnetic resonance (MR) imaging and MR spectroscopy (MRS) for Alzheimer's disease (AD) plasma biomarker insights. Tau phosphorylated at threonine 181 (p-tau181) and glial fibrillary acidic protein (GFAP) showed the largest changes over time, particularly in the AD group. p-tau181 and GFAP are robust in reflecting 7T MR-based changes in AD. The strongest associations were for frontal/parietal MR changes and MRS neuroinflammation.

PyAMARES, an Open-Source Python Library for Fitting Magnetic Resonance Spectroscopy Data

Background/Objectives: Magnetic resonance spectroscopy (MRS) is a valuable tool for studying metabolic processes in vivo. While numerous quantification methods exist, the advanced method for accurate, robust, and efficient spectral fitting (AMARES) is among the most used. This study introduces pyAMARES, an open-source Python implementation of AMARES, addressing the need for a flexible, user-friendly, and versatile MRS quantification tool within the Python ecosystem. Methods: PyAMARES was developed as a Python library, implementing the AMARES algorithm with additional features such as multiprocessing capabilities and customizable objective functions. The software was validated against established AMARES implementations (OXSA and jMRUI) using both simulated and in vivo MRS data. Monte Carlo simulations were conducted to assess robustness and accuracy across various signal-to-noise ratios and parameter perturbations. Results: PyAMARES utilizes spreadsheet-based prior knowledge and fitting parameter settings, enhancing flexibility and ease of use. It demonstrated comparable performance to existing software in terms of accuracy, precision, and computational efficiency. In addition to conventional AMARES fitting, pyAMARES supports fitting without prior knowledge, frequency-selective AMARES, and metabolite residual removal from mobile macromolecule (MM) spectra. Utilizing multiple CPU cores significantly enhances the performance of pyAMARES. Conclusions: PyAMARES offers a robust, flexible, and user-friendly solution for MRS quantification within the Python ecosystem. Its open-source nature, comprehensive documentation, and integration with popular data science tools enhance reproducibility and collaboration in MRS research. PyAMARES bridges the gap between traditional MRS fitting methods and modern machine learning frameworks, potentially accelerating advancements in metabolic studies and clinical applications.

Leveraging ultra-high field (7T) MRI in psychiatric research

Non-invasive brain imaging has played a critical role in establishing our understanding of the neural properties that contribute to the emergence of psychiatric disorders. However, characterizing core neurobiological mechanisms of psychiatric symptomatology requires greater structural, functional, and neurochemical specificity than is typically obtainable with standard field strength MRI acquisitions (e.g., 3T). Ultra-high field (UHF) imaging at 7 Tesla (7T) provides the opportunity to identify neurobiological systems that confer risk, determine etiology, and characterize disease progression and treatment outcomes of major mental illnesses. Increases in scanner availability, regulatory approval, and sequence availability have made the application of UHF to clinical cohorts more feasible than ever before, yet the application of UHF approaches to the study of mental health remains nascent. In this technical review, we describe core neuroimaging methodologies which benefit from UHF acquisition, including high resolution structural and functional imaging, single (1H) and multi-nuclear (e.g., 31P) MR spectroscopy, and quantitative MR techniques for assessing brain tissue iron and myelin. We discuss advantages provided by 7T MRI, including higher signal- and contrast-to-noise ratio, enhanced spatial resolution, increased test-retest reliability, and molecular and neurochemical specificity, and how these have begun to uncover mechanisms of psychiatric disorders. Finally, we consider current limitations of UHF in its application to clinical cohorts, and point to ongoing work that aims to overcome technical hurdles through the continued development of UHF hardware, software, and protocols.

Diffusion magnetic resonance spectroscopy captures microglial reactivity related to gut-derived systemic lipopolysaccharide: A preliminary study

Neuroinflammation is a key component underlying multiple neurological disorders, yet non-invasive and cost-effective assessment of in vivo neuroinflammatory processes in the central nervous system remains challenging. Diffusion weighted magnetic resonance spectroscopy (dMRS) has shown promise in addressing these challenges by measuring diffusivity properties of different neurometabolites, which can reflect cell-specific morphologies. Prior work has demonstrated dMRS utility in capturing microglial reactivity in the context of lipopolysaccharide (LPS) challenges and serious neurological disorders, detected as changes of microglial metabolite diffusivity properties. However, the extent to which such dMRS metrics are capable of detecting subtler and more nuanced levels of neuroinflammation in populations without overt neuropathology is unknown. Here we examined the relationship between intrinsic, gut-derived levels of systemic LPS and dMRS-based apparent diffusion coefficients (ADC) of choline, creatine, and N-acetylaspartate (NAA) in two brain regions: the thalamus and the corona radiata. Higher plasma LPS concentrations were significantly associated with increased ADC of choline and NAA in the thalamic region, with no such relationships observed in the corona radiata for any of the metabolites examined. As such, dMRS may have the sensitivity to measure microglial reactivity across populations with highly variable levels of neuroinflammation, and holds promising potential for widespread applications in both research and clinical settings.

GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex

The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.

TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets

PURPOSE

To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI data based on Torch's auto-differentiation and optimization framework.

METHODS

TensorFit was implemented in Python based on Torch's auto-differentiation to fit individual metabolites in MRS spectra. The underlying time domain and/or frequency domain fitting model is based on a linear combination of metabolite spectroscopic response. The computational time efficiency and accuracy of TensorFit were tested on simulated and in vivo MRS data and compared against TDFDFit and QUEST.

RESULTS

TensorFit demonstrates a significant improvement in computation speed, achieving a 165-times acceleration compared with TDFDFit and 115 times against QUEST. TensorFit showed smaller percentual errors on simulated data compared with TDFDFit and QUEST. When tested on in vivo data, it performed similarly to TDFDFit with a 2% better fit in terms of mean squared error while obtaining a 169-fold speedup.

CONCLUSION

TensorFit enables fast and robust metabolite fitting in large MRSI data sets compared with conventional metabolite fitting methods. This tool could boost the clinical applicability of large 3D MRSI by enabling the fitting of large MRSI data sets within computation times acceptable in a clinical environment.

Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Proton magnetic resonance spectroscopy (1H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 ± 5.3/9.3 ± 5.4), a medulloblastoma (ages 6.9 ± 3.5/6.5 ± 4.4), or a pilocytic astrocytoma (8.0 ± 3.6/6.3 ± 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw 1H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression 1H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical 1H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression 1H-MRS may have better diagnostic performance for paediatric brain tumours.

Uncertainty propagation in absolute metabolite quantification for in vivo MRS of the human brain

PURPOSE

Absolute spectral quantification is the standard method for deriving estimates of the concentration from metabolite signals measured using in vivo proton MRS (1 H-MRS). This method is often reported with minimum variance estimators, specifically the Cramér-Rao lower bound (CRLB) of the metabolite signal amplitude's scaling factor from linear combination modeling. This value serves as a proxy for SD and is commonly reported in MRS experiments. Characterizing the uncertainty of absolute quantification, however, depends on more than simply the CRLB. The uncertainties of metabolite-specific (T1m , T2m ), reference-specific (T1ref , T2ref ), and sequence-specific (TR , TE ) parameters are generally ignored, potentially leading to an overestimation of precision. In this study, the propagation of uncertainty is used to derive a comprehensive estimate of the overall precision of concentrations from an internal reference.

METHODS

The propagated uncertainty is calculated using analytical derivations and Monte Carlo simulations and subsequently analyzed across a set of commonly measured metabolites and macromolecules. The effect of measurement error from experimentally obtained quantification parameters is estimated using published uncertainties and CRLBs from in vivo 1 H-MRS literature.

RESULTS

The additive effect of propagated measurement uncertainty from applied quantification correction factors can result in up to a fourfold increase in the concentration estimate's coefficient of variation compared to the CRLB alone. A case study analysis reveals similar multifold increases across both metabolites and macromolecules.

CONCLUSION

The precision of absolute metabolite concentrations derived from 1 H-MRS experiments is systematically overestimated if the uncertainties of commonly applied corrections are neglected as sources of error.

Implantable theranostic device for in vivo real-time NMR evaluation of drug impact in brain tumors

The evaluation of the efficacy of a drug is a fundamental step in the development of new treatments or in personalized therapeutic strategies and patient management. Ideally, this evaluation should be rapid, possibly in real time, easy to perform and reliable. In addition, it should be associated with as few adverse effects as possible for the patient. In this study, we present a device designed to meet these goals for assessing therapeutic response. This theranostic device is based on the use of magnetic resonance imaging and spectroscopy for the diagnostic aspect and on the application of the convection-enhanced delivery technique for the therapeutic aspect. The miniaturized device is implantable and can be used in vivo in a target tissue. In this study, the device was applied to rodent glioma models with local administration of choline kinase inhibitor and acquisition of magnetic resonance images and spectra at 7 Tesla. The variations in the concentration of key metabolites measured by the device during the administration of the molecules demonstrate the relevance of the approach and the potential of the device.

The Potential of Phase Constraints for Non-Fourier Radiofrequency-Encoded MRI

In modern magnetic resonance imaging, it is common to use phase constraints to reduce sampling requirements along Fourier-encoded spatial dimensions. In this work, we investigate whether phase constraints might also be beneficial to reduce sampling requirements along spatial dimensions that are measured using non-Fourier encoding techniques, with direct relevance to approaches that use tailored spatially-selective radiofrequency (RF) pulses to perform spatial encoding along the slice dimension in a 3D imaging experiment. In the first part of the paper, we use the Cramér-Rao lower bound to examine the potential estimation theoretic benefits of using phase constraints. The results suggest that phase constraints can be used to improve experimental efficiency and enable acceleration, but only if the RF encoding matrix is complex-valued and appropriately designed. In the second part of the paper, we use simulations of RF-encoded data to test the benefits of phase constraints combined with optimized RF-encodings, and find that the theoretical benefits are indeed borne out empirically. These results provide new insights into the potential benefits of phase constraints for RF-encoded data, and provide a solid theoretical foundation for future practical explorations.

Map-based B0 shimming for single voxel brain spectroscopy at 7T

While B0 shimming is an important requirement for in vivo brain spectroscopy, for single voxel spectroscopy (SVS), the role for advanced shim methods has been questioned. Specifically, with the small spatial dimensions of the voxel, the extent to which inhomogeneities higher than second order exist and the ability of higher order shims to correct them is controversial. To assess this, we acquired SVS from two loci of neurophysiological interest, the rostral prefrontal cortex (rPFC; 8 cc) and hippocampus (Hc; 9 cc). The rPFC voxel was placed using SUsceptibility Managed Optimization (SUMO) and an initial B0 map that covers the entire cerebrum to cerebellum. In each location, we compared map-based shimming (Bolero) with projection-based shimming (FAST(EST)MAP). We also compared vendor-provided spherical harmonic first- and second-order shims with additional third- and fourth-order shim hardware. The 7T SVS acquisition used stimulated echo acquisition mode (STEAM) TR/TM/TE of 6 s/20 ms/8 ms, a tissue water acquisition for concentration reference, and LCModel for spectral analysis. In the rPFC (n = 7 subjects), Bolero shimming with first- and second-order shims reduced the residual inhomogeneity σ B 0 from 9.8 ± 4.5 Hz with FAST(EST)MAP to 6.5 ± 2.0 Hz. The addition of third- and fourth-order shims further reduced σ B 0 to 4.0 ± 0.8 Hz. In the Hc (n = 7 subjects), FAST(EST)MAP, Bolero with first- and second-order shims, and Bolero with first- to fourth-order shims achieved σ B 0 values of 8.6 ± 1.9, 5.6 ± 1.0, and 4.6 ± 0.9 Hz, respectively. The spectral linewidth,Δ v σ B 0 , was estimated with a Voigt lineshape using σ B 0 and T2 = 130 ms.Δ v σ B 0 significantly correlated with the Cramer-Rao lower bounds and concentrations of several metabolites, including glutamate and glutamine in the rPFC. In both loci, if the B0 distribution is well described by a Gaussian model, the variance of the metabolite concentrations is reduced, consistent with the LCModel fit based on a unimodal lineshape. Overall, the use of the high order and map-based B0 shim methods improved the accuracy and consistency of spectroscopic data.

Echo time optimization for in-vivo measurement of unsaturated lipid resonances using J-difference-edited MRS

PURPOSE

Measuring lipid composition provides more information than just total lipid content. Hence, the non-invasive measurement of unsaturated lipid protons with both high efficiency and precision is of pressing need. This study was to optimize echo time (TE) for the best resolving of J-difference editing of unsaturated lipid resonances.

METHODS

The TE dependence of J-difference-edited (JDE) MRS was verified in the density-matrix simulation, soybean oil phantom, in-vivo experiments of white adipose tissue (WAT), and skeletal muscles using single-voxel MEGA-PRESS sequence at 3T. The peak SNRs and Cramér-Rao lower bounds (CRLBs) acquired at the proposed TE of 45 ms and previously published TE of 70 ms were compared (eight pairs) in WAT, extramyocelluar lipids (EMCLs), and intramyocellular lipids (IMCLs). The lipid composition in skeletal muscles was compared between healthy males (n = 7) and females (n = 7).

RESULTS

The optimal TE was suggested as 45 ms. Compared to 70 ms, the mean signal gains at TE of 45 ms were 151% in WAT, 168% in EMCL, 204% in IMCL for allylic resonance, and 52% in EMCL for diallylic resonance. CRLBs were significantly reduced at TE of 45 ms in WAT, EMCL, IMCL for allylic resonance and in EMCL for diallylic resonance. With TE of 45 ms, significant gender differences were found in the lipid composition in EMCL pools, while no difference in IMCL pools.

CONCLUSION

The JDE-MRS protocol with TE of 45 ms allows improved quantification of unsaturated lipid resonances in vivo and future lipid metabolism investigations.

Quantification of short echo time MRS signals with improved version of QUantitation based on quantum ESTimation algorithm

Magnetic resonance spectroscopy offers information about metabolite changes in the organism, which can be used in diagnosis. While short echo time proton spectra exhibit more distinguishable metabolites compared with proton spectra acquired with long echo times, their quantification (and providing estimates of metabolite concentrations) is more challenging. They are hampered by a background signal, which originates mainly from macromolecules (MM) and mobile lipids. An improved version of the quantification algorithm QUantitation based on quantum ESTimation (QUEST), with MM prior knowledge (QUEST-MM), dedicated to proton signals and invoking appropriate prior knowledge on MM, is proposed and tested. From a single acquisition, it enables better metabolite quantification, automatic estimation of the background, and additional automatic quantification of MM components, thus improving its applicability in the clinic. The proposed algorithm may facilitate studies that involve patients with pathological MM in the brain. QUEST-MM and three QUEST-based strategies for quantifying short echo time signals are compared in terms of bias-variance trade-off and Cramér-Rao lower bound estimates. The performances of the methods are evaluated through extensive Monte Carlo studies. In particular, the histograms of the metabolite and MM amplitude distributions demonstrate the performances of the estimators. They showed that QUEST-MM works better than QUEST (Subtract approach) and is a good alternative to QUEST when measured MM signal is unavailable or unsuitable. Quantification with QUEST-MM is shown for 1 H in vivo rat brain signals obtained with the SPECIAL pulse sequence at 9.4 T, and human brain signals obtained, respectively, with STEAM at 4 T and PRESS at 3 T. QUEST-MM is implemented in jMRUI and will be available for public use from version 7.1.

Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting

PURPOSE

To propose a novel end-to-end deep learning model to quantify absolute metabolite concentrations from in vivo J-point resolved spectroscopy (JPRESS) without using spectral fitting.

METHODS

A novel encoder-decoder-style neural network was created, which was trained to predict metabolite concentrations and individual component signals concurrently from 3T JPRESS data in the time domain. The training data set contained 100 000 samples created by spin-density simulations using experimentally used RF pulses. Concentrations, phase, frequencies, linewidths, and T2 relaxation times in the training data set were varied over a large range with uniform distributions. Random synthesized noise and extraneous signals were added to the data set. Two thousand validation samples were created similarly to the training data set but with mean concentrations close to in vivo values. An in vivo test was conducted with 20 samples acquired from the human brain.

RESULTS

Both validation and in vivo test results showed that the proposed model successfully predicted metabolite concentrations as well as individual metabolite signals without involving spectral fitting, while extraneous peaks or unregistered signals were filtered out. Compared with the short-TE spectral fitting by LCModel, the proposed method had the advantage that the undesired correlations between the estimated concentrations and noise levels and between metabolites were eliminated or substantially reduced.

CONCLUSION

The proposed method provides a working deep learning model that directly maps in vivo JPRESS data to metabolite concentrations. Because spectral fitting is not used, the trained model does not depend on the assumptions associated with parameter tuning when applied to in vivo data.

Vespa: Integrated applications for RF pulse design, spectral simulation and MRS data analysis

PURPOSE

The Vespa package (Versatile Simulation, Pulses, and Analysis) is described and demonstrated. It provides workflows for developing and optimizing linear combination modeling (LCM) fitting for 1 H MRS data using intuitive graphical user interface interfaces for RF pulse design, spectral simulation, and MRS data analysis. Command line interfaces for embedding workflows in MR manufacturer platforms and utilities for synthetic dataset creation are included. Complete provenance is maintained for all steps in workflows.

THEORY AND METHODS

Vespa is written in Python for compatibility across operating systems. It embeds the PyGAMMA spectral simulation library for spectral simulation. Multiprocessing methods accelerate processing and visualization. Applications use the Vespa database for results storage and cross-application access. Three projects demonstrate pulse, sequence, simulation, and data analysis workflows: (1) short TE semi-LASER single-voxel spectroscopy (SVS) LCM fitting, (2) optimizing MEGA-PRESS (MEscher-GArwood Point RESolved Spectroscopy) flip angle and LCM fitting, and (3) creating a synthetic short TE dataset.

RESULTS

The LCM workflows for in vivo basis set creation and spectral analysis showed reasonable results for both the short TE semi-LASER and MEGA-PRESS. Examples of pulses, simulations, and data fitting are shown in Vespa application interfaces for various steps to demonstrate the interactive workflow.

CONCLUSION

Vespa provides an efficient and extensible platform for characterizing RF pulses, pulse design, spectral simulation optimization, and automated LCM fitting via an interactive platform. Modular design and command line interface make it easy to embed in other platforms. As open source, it is free to the MRS community for use and extension. Vespa source code and documentation are available through GitHub.

Ethanol Kinetics in the Human Brain Determined by Magnetic Resonance Spectroscopy

In many parts of the world, ethanol is a widely consumed substance that displays its effect in the brain, the target organ for desired, but also negative impact. In a previous study, the ethanol concentrations were analyzed in different regions of the brain by magnetic resonance spectroscopy (MRS). In this study, the same method is used to demonstrate the kinetics of the ethanol concentration in the human brain after oral ethanol uptake. A drinking study was performed with 10 healthy participants. After the uptake of ethanol in a calculated amount leading to a plasma ethanol concentration of 0.92 g/L (19.95 mM corresponding to a blood ethanol concentration of 0.7 g/kg), brain ethanol concentrations were continuously measured by means of MRS on a 3 Tesla human magnetic resonance imaging (MRI) system. For the data acquisition a single-voxel sLASER sequence was used, with the volume of interest located in the occipital cortex. Intermittently, blood samples were taken and plasma was analyzed for ethanol using headspace gas chromatography with flame ionization detection (HS-GC-FID). The obtained MRS brain ethanol curves showed distinct inter-individual differences; however, a good intra-individual correlation of plasma and brain ethanol concentrations was observed. The results suggest a rapid equilibration between blood and brain. The ethanol concentrations measured in the brain were substantially lower than the measured plasma ethanol results, suggesting an MRS visibility of about 63% for ethanol in brain tissue. The maximum individual ethanol concentrations in the brain (normalized to water content) ranged between 7.1 and 14.1 mM across the cohort, while the highest measured plasma concentrations were in the range between 0.35 g/L (9.41 mM) and 0.95 g/L (20.52 mM).

The psychosis human connectome project: Design and rationale for studies of visual neurophysiology

Visual perception is abnormal in psychotic disorders such as schizophrenia. In addition to hallucinations, laboratory tests show differences in fundamental visual processes including contrast sensitivity, center-surround interactions, and perceptual organization. A number of hypotheses have been proposed to explain visual dysfunction in psychotic disorders, including an imbalance between excitation and inhibition. However, the precise neural basis of abnormal visual perception in people with psychotic psychopathology (PwPP) remains unknown. Here, we describe the behavioral and 7 tesla MRI methods we used to interrogate visual neurophysiology in PwPP as part of the Psychosis Human Connectome Project (HCP). In addition to PwPP (n = 66) and healthy controls (n = 43), we also recruited first-degree biological relatives (n = 44) in order to examine the role of genetic liability for psychosis in visual perception. Our visual tasks were designed to assess fundamental visual processes in PwPP, whereas MR spectroscopy enabled us to examine neurochemistry, including excitatory and inhibitory markers. We show that it is feasible to collect high-quality data across multiple psychophysical, functional MRI, and MR spectroscopy experiments with a sizable number of participants at a single research site. These data, in addition to those from our previously described 3 tesla experiments, will be made publicly available in order to facilitate further investigations by other research groups. By combining visual neuroscience techniques and HCP brain imaging methods, our experiments offer new opportunities to investigate the neural basis of abnormal visual perception in PwPP.

In vivo phosphorus magnetic resonance spectroscopic imaging of the whole human liver at 7 T using a phosphorus whole-body transmit coil and 16-channel receive array: Repeatability and effects of principal component analysis-based denoising

Quantitative three-dimensional (3D) imaging of phosphorus (³¹ P) metabolites is potentially a promising technique with which to assess the progression of liver disease and monitor therapy response. However, ³¹ P magnetic resonance spectroscopy has a low sensitivity and commonly used ³¹ P surface coils do not provide full coverage of the liver. This study aimed to overcome these limitations by using a ³¹ P whole-body transmit coil in combination with a 16-channel ³¹ P receive array at 7 T. Using this setup, we determined the repeatability of whole-liver ³¹ P magnetic resonance spectroscopic imaging (³¹ P MRSI) in healthy subjects and assessed the effects of principal component analysis (PCA)-based denoising on the repeatability parameters. In addition, spatial variations of ³¹ P metabolites within the liver were analyzed. 3D ³¹ P MRSI data of the liver were acquired with a nominal voxel size of 20 mm isotropic in 10 healthy volunteers twice on the same day. Data were reconstructed without denoising, and with PCA-based denoising before or after channel combination. From the test-retest data, repeatability parameters for metabolite level quantification were determined for 12 ³¹ P metabolite signals. On average, ³¹ P MR spectra from 100 ± 25 voxels in the liver were analyzed. Only voxels with contamination from skeletal muscle or the gall bladder were excluded and no voxels were discarded based on (low) signal-to-noise ratio (SNR). Repeatability for most quantified ³¹ P metabolite levels in the liver was good to excellent, with an intrasubject variability below 10%. PCA-based denoising increased the SNR ~ 3-fold, but did not improve the repeatability for mean liver ³¹ P metabolite quantification with the fitting constraints used. Significant spatial heterogeneity of various ³¹ P metabolite levels within the liver was observed, with marked differences for the phosphomonoester and phosphodiester metabolites between the left and right lobe. In conclusion, using a ³¹ P whole-body transmit coil in combination with a 16-channel ³¹ P receive array at 7 T allowed ³¹ P MRSI acquisitions with full liver coverage and good to excellent repeatability.

On Ambiguity in Linear Inverse Problems: Entrywise Bounds on Nearly Data-Consistent Solutions and Entrywise Condition Numbers

Ill-posed linear inverse problems appear frequently in various signal processing applications. It can be very useful to have theoretical characterizations that quantify the level of ill-posedness for a given inverse problem and the degree of ambiguity that may exist about its solution. Traditional measures of ill-posedness, such as the condition number of a matrix, provide characterizations that are global in nature. While such characterizations can be powerful, they can also fail to provide full insight into situations where certain entries of the solution vector are more or less ambiguous than others. In this work, we derive novel theoretical lower- and upper-bounds that apply to individual entries of the solution vector, and are valid for all potential solution vectors that are nearly data-consistent. These bounds are agnostic to the noise statistics and the specific method used to solve the inverse problem, and are also shown to be tight. In addition, our results also lead us to introduce an entrywise version of the traditional condition number, which provides a substantially more nuanced characterization of scenarios where certain elements of the solution vector are less sensitive to perturbations than others. Our results are illustrated in an application to magnetic resonance imaging reconstruction, and we include discussions of practical computation methods for large-scale inverse problems, connections between our new theory and the traditional Cramér-Rao bound under statistical modeling assumptions, and potential extensions to cases involving constraints beyond just data-consistency.

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

PURPOSE

Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T₂ , T₁ , or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter.

METHODS

We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T₂ s, and a functional MRS experiment designed to estimate fractional change ( δ $$ \delta $$ ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T₂ in multi-echo and δ $$ \delta $$ in functional MRS.

RESULTS

When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible.

CONCLUSION

Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.

Cramér-Rao Bounds for DoA Estimation of Sparse Bayesian Learning with the Laplace Prior

In this paper, we derive the Cramér-Rao lower bounds (CRLB) for direction of arrival (DoA) estimation by using sparse Bayesian learning (SBL) and the Laplace prior. CRLB is a lower bound on the variance of the estimator, the change of CRLB can indicate the effect of the specific factor to the DoA estimator, and in this paper a Laplace prior and the three-stage framework are used for the DoA estimation. We derive the CRLBs under different scenarios: (i) if the unknown parameters consist of deterministic and random variables, a hybrid CRLB is derived; (ii) if all the unknown parameters are random, a Bayesian CRLB is derived, and the marginalized Bayesian CRLB is obtained by marginalizing out the nuisance parameter. We also derive the CRLBs of the hyperparameters involved in the three-stage model and explore the effect of multiple snapshots to the CRLBs. We compare the derived CRLBs of SBL, finding that the marginalized Bayesian CRLB is tighter than other CRLBs when SNR is low and the differences between CRLBs become smaller when SNR is high. We also study the relationship between the mean squared error of the source magnitudes and the CRLBs, including numerical simulation results with a variety of antenna configurations such as different numbers of receivers and different noise conditions.

Improving the reproducibility of proton magnetic resonance spectroscopy brain thermometry: Theoretical and empirical approaches

In proton magnetic resonance spectroscopy (¹ H MRS)-based thermometry of brain, averaging temperatures measured from more than one reference peak offers several advantages, including improving the reproducibility (i.e., precision) of the measurement. This paper proposes theoretically and empirically optimal weighting factors to improve the weighted average of temperatures measured from three references. We first proposed concepts of equivalent noise and equivalent signal-to-noise ratio in terms of frequency measurement and a concept of relative frequency that allows the combination of different peaks in a spectrum for improving the precision of frequency measurement. Based on these, we then derived a theoretically optimal weighting factor and proposed an empirical weighting factor, both involving equivalent noise levels, for a weighted average of temperatures measured from three references (i.e., the singlets of NAA, Cr, and Ch in the ¹ H MR spectrum). We assessed these two weighting factors by comparing their errors in measurement of temperatures with the errors of temperatures measured from individual references; we also compared these two new weighting factors with two previously proposed weighting factors. These errors were defined as the standard deviations in repeated measurements or in Monte Carlo studies. Both the proposed theoretical and empirical weighting factors outperformed the two previously proposed weighting factors as well as the three individual references in all phantom and in vivo experiments. In phantom experiments with 4- or 10-Hz line broadening, the theoretical weighting factor outperformed the empirical one, but the latter was superior in all other repeated and Monte Carlo tests performed on phantom and in vivo data. The proposed weighting factors are superior to the two previously proposed weighting factors and can improve the reproducibility of temperature measurement using ¹ H MRS-based thermometry.

Stabilization of parameter estimates from multiexponential decay through extension into higher dimensions

Analysis of multiexponential decay has remained a topic of active research for over 200 years. This attests to the widespread importance of this problem and to the profound difficulties in characterizing the underlying monoexponential decays. Here, we demonstrate the fundamental improvement in stability and conditioning of this classic problem through extension to a second dimension; we present statistical analysis, Monte-Carlo simulations, and experimental magnetic resonance relaxometry data to support this remarkable fact. Our results are readily generalizable to higher dimensions and provide a potential means of circumventing conventional limits on multiexponential parameter estimation.

Single-voxel short-TR multi-TI multi-TE STEAM MRS for water-fat relaxometry

PURPOSE

To propose a short-TR multi-TI multi-TE (SHORTIE, ['shȯr-tē]) STEAM single-voxel MRS acquisition scheme for the simultaneous assessment of T₁ relaxation, T₂ relaxation, and the proton density fat fraction at reduced scan times when compared with conventional long-TR multi-TI STEAM and long-TR multi-TE STEAM single-voxel MRS.

METHODS

Theoretical analysis for multi-TI (TI = 10, 100, 500, 1500 ms; scan time = 2:43 minutes), multi-TE (TE = 12, 15, 20, 25 ms; scan time = 2:24 minutes), and SHORTIE STEAM (all TI and TE combinations; scan time = 2:52 minutes) was carried out including Cramér-Rao lower bound and parameter estimation efficiency analysis for T₁ (150-2000 ms) and T₂ (5-150 ms) relaxation. The SHORTIE STEAM acquisition was compared with multi-TI STEAM and multi-TE STEAM in water-fat phantoms and in a human in vivo study of the adipose tissue depot in the supraclavicular fossa in 7 volunteers at 3 T.

RESULTS

Cramér-Rao lower bound analysis revealed similar to increased variances for T₁ and T₂ estimators for SHORTIE STEAM. Parameter efficiency analysis demonstrated superior performance of SHORTIE, particularly for shorter T₁ and T₂ when compared with multi-TI STEAM and multi-TE STEAM. For the phantom data, linear regression and Bland-Altmann analysis yielded a slope/intercept/mean difference of 1.07/-15.40/-17.18 for T₁ (in ms; r = 0.999), 0.93/+1.32/+1.09 for T₂ (in ms; r = 0.995), and 0.98/-0.04/+0.78 for the fat fraction (in percent; r = 0.999); and for the in vivo data 1.08/+1.77/-62.2 for T₁ (r = 0.994), 0.88/+6.69/-1.55 for T₂ (r = 0.884), and 0.56/+34.40/-0.46 for the fat fraction (r = 0.673), respectively.

CONCLUSION

The SHORTIE STEAM acquisition allows shorter scan times for the simultaneous probing of relaxation properties and spectral content in the water-fat environment when compared with combined long-TR multi-TI, and long-TR multi-TE STEAM.

Cartesian dictionary-based native T1 and T2 mapping of the myocardium

PURPOSE

To implement and evaluate a new dictionary-based technique for native myocardial T₁ and T₂ mapping using Cartesian sampling.

METHODS

The proposed technique (Multimapping) consisted of single-shot Cartesian image acquisitions in 10 consecutive cardiac cycles, with inversion pulses in cycle 1 and 5, and T₂ preparation (TE: 30 ms, 50 ms, and 70 ms) in cycles 8-10. Multimapping was simulated for different T₁ and T₂ , where entries corresponding to the k-space centers were matched to acquired data. Experiments were performed in a phantom, 16 healthy subjects, and 3 patients with cardiovascular disease.

RESULTS

Multimapping phantom measurements showed good agreement with reference values for both T₁ and T₂ , with no discernable heart-rate dependency for T₁ and T₂ within the range of myocardium. In vivo mean T₁ in healthy subjects was significantly higher using Multimapping (T₁ = 1114 ± 14 ms) compared to the reference (T₁ = 991 ± 26 ms) (p < 0.01). Mean Multimapping T₂ (47.1 ± 1.3 ms) and T₂ spatial variability (5.8 ± 1.0 ms) was significantly lower compared to the reference (T₂ = 54.7 ± 2.2 ms, p < 0.001; spatial variability = 8.4 ± 2.0 ms, p < 0.01). Increased T₁ and T₂ was detected in all patients using Multimapping.

CONCLUSIONS

Multimapping allows for simultaneous native myocardial T₁ and T₂ mapping with a conventional Cartesian trajectory, demonstrating promising in vivo image quality and parameter quantification results.

Results and interpretation of a fitting challenge for MR spectroscopy set up by the MRS study group of ISMRM

PURPOSE

Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness.

METHODS

Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with.

RESULTS

Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR.

CONCLUSIONS

Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.

A pilot spectroscopy study of adversity in adolescents

Background

Childhood adversity is a global health problem affecting 25-50% of children worldwide. Few prior studies have examined the underlying neurochemistry of adversity in adolescents. This cross-sectional study examined spectroscopic markers of trauma in a cohort of adolescents with major depressive disorder (MDD) and healthy controls. We hypothesized that historical adversity would have a negative relationship with spectroscopic measures of glutamate metabolites in anterior cingulate cortex.

Methods

Adolescent participants (aged 13-21) underwent a semi-structured diagnostic interview and clinical assessment, which included the self-report Childhood Trauma Questionnaire (CTQ), a 28-item assessment of childhood adversity. Proton magnetic resonance spectroscopy (1H-MRS) scans at 3 Tesla of an anterior cingulate cortex (ACC) voxel (8 cm3) encompassing both hemispheres were collected using a 2-dimensional J-averaged sequence to assess N-acetylaspartate (NAA), Glx (glutamate+glutamine) and [NAA]/[Glx] concentrations. Generalized linear models assessed the relationships between CTQ scores and metabolite levels in ACC.

Results

Thirty-nine participants (17 healthy controls, 22 depressed participants) underwent 1H-MRS and completed the CTQ measures. There were decrements in [NAA]/[Glx] ratio in the ACC of participants with childhood adversity while no significant relationship between CTQ total score and any of the ACC metabolites was found in the combined sample. Exploratory results revealed a positive association between Glx levels and CTQ scores in depressed participants. Conversely the [NAA]/[Glx] ratio had a negative association with total CTQ scores in the depressed participants. Emotional Abuse Scale showed a significant negative relationship with [NAA]/[Glx] ratio in the combined sample when adjusted for depression severity.

Conclusions

Our findings suggest that childhood adversity may impact brain neurochemical profiles. Further longitudinal studies should examine neurochemical correlates of childhood adversity throughout development and in populations with other psychiatric disorders.

Combined diffusion-relaxometry microstructure imaging: Current status and future prospects

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

Evidence against altered excitatory/inhibitory balance in the posteromedial cortex of young adult APOE E4 carriers: A resting state 1H-MRS study

A strategy to gain insight into early changes that may predispose people to Alzheimer's disease (AD) is to study the brains of younger cognitively healthy people that are at increased genetic risk of AD. The Apolipoprotein (APOE) E4 allele is the strongest genetic risk factor for AD, and several neuroimaging studies comparing APOE E4 carriers with non-carriers at age ∼20-30 years have detected hyperactivity (or reduced deactivation) in posteromedial cortex (PMC), a key hub of the default network (DN), which has a high susceptibility to early amyloid deposition in AD. Transgenic mouse models suggest such early network activity alterations may result from altered excitatory/inhibitory (E/I) balance, but this is yet to be examined in humans. Here we test the hypothesis that PMC fMRI hyperactivity could be underpinned by altered levels of excitatory (glutamate) and/or inhibitory (GABA) neurotransmitters in this brain region. Forty-seven participants (20 APOE E4 carriers and 27 non-carriers) aged 18-25 years underwent resting-state proton magnetic resonance spectroscopy (1H-MRS), a non-invasive neuroimaging technique to measure glutamate and GABA in vivo. Metabolites were measured in a PMC voxel of interest and in a comparison voxel in the occipital cortex (OCC). There was no difference in either glutamate or GABA between the E4 carriers and non-carriers in either MRS voxel, or in the ratio of glutamate to GABA, a measure of E/I balance. Default Bayesian t-tests revealed evidence in support of this null finding. Our findings suggest that PMC hyperactivity in APOE E4 carriers is unlikely to be associated with, or possibly may precede, alterations in local resting-state PMC neurotransmitters, thus informing our understanding of the spatio-temporal sequence of early network alterations underlying APOE E4 related AD risk.

Assessment of measurement precision in single-voxel spectroscopy at 7 T: Toward minimal detectable changes of metabolite concentrations in the human brain in vivo

PURPOSE

To introduce a study design and statistical analysis framework to assess the repeatability, reproducibility, and minimal detectable changes (MDCs) of metabolite concentrations determined by in vivo MRS.

METHODS

An unbalanced nested study design was chosen to acquire in vivo MRS data within different repeatability and reproducibility scenarios. A spin-echo, full-intensity acquired localized (SPECIAL) sequence was employed at 7 T utlizing three different inversion pulses: a hyperbolic secant (HS), a gradient offset independent adiabaticity (GOIA), and a wideband, uniform rate, smooth truncation (WURST) pulse. Metabolite concentrations, Cramér-Rao lower bounds (CRLBs) and coefficients of variation (CVs) were calculated. Both Bland-Altman analysis and a restricted maximum-likelihood estimation (REML) analysis were performed to estimate the different variance contributions of the repeatability and reproducibility of the measured concentration. A Bland-Altmann analysis of the spectral shape was performed to assess the variance of the spectral shape, independent of quantification model influences.

RESULTS

For the used setup, minimal detectable changes of brain metabolite concentrations were found to be between 0.40 µmol/g and 2.23 µmol/g. CRLBs account for only 16 % to 74 % of the total variance of the metabolite concentrations. The application of gradient-modulated inversion pulses in SPECIAL led to slightly improved repeatability, but overall reproducibility appeared to be limited by differences in positioning, calibration, and other day-to-day variations throughout different sessions.

CONCLUSION

A framework is introduced to estimate the precision of metabolite concentrations obtained by MRS in vivo, and the minimal detectable changes for 13 metabolite concentrations measured at 7 T using SPECIAL are obtained.

Fisher information for optimal planning of X-ray diffraction experiments

Fisher information is a powerful mathematical tool suitable for quantification of data `informativity' and optimization of the experimental setup and measurement conditions. Here, it is applied to X-ray diffraction and an informational approach to choosing the optimal measurement configuration is proposed. The core idea is maximization of the information which can be extracted from the measured data set by the selected analysis technique, over the sets of accessible reflections and measurement geometries. The developed approach is applied to high-resolution X-ray diffraction measurements and microstructure analysis of multilayer samples, and its efficiency and consistency are demonstrated with the results of more straightforward Monte Carlo simulations.

Medial Prefrontal Cortex Glutamate Is Reduced in Schizophrenia and Moderated by Measurement Quality: A Meta-analysis of Proton Magnetic Resonance Spectroscopy Studies

BACKGROUND

Magnetic resonance spectroscopy studies measuring brain glutamate separately from glutamine are helping elucidate schizophrenia pathophysiology. An expanded literature and improved methodologies motivate an updated meta-analysis examining effects of measurement quality and other moderating factors in characterizing abnormal glutamate levels in schizophrenia.

METHODS

Searching previous meta-analyses and the MEDLINE database identified 83 proton magnetic resonance spectroscopy datasets published through March 25, 2020. Three quality metrics were extracted-Cramér-Rao lower bound (CRLB), line width, and coefficient of variation. Pooled effect sizes (Hedges' g) were calculated with random-effects, inverse variance-weighted models. Moderator analyses were conducted using quality metrics, field strength, echo time, medication, age, and stage of illness.

RESULTS

Across 36 datasets (2086 participants), medial prefrontal cortex glutamate was significantly reduced in patients (g = -0.19, confidence interval [CI] = -0.07 to -0.32). CRLB and coefficient of variation quality subgroups significantly moderated this effect. Glutamate was significantly more reduced in studies with lower CRLB or coefficient of variation (g = -0.44, CI = -0.29 to -0.60, and g = -0.43, CI = -0.29 to -0.57, respectively). Studies using echo time ≤20 ms also showed significantly greater reduction in glutamate (g = -0.41, CI = -0.26 to -0.55). Across 11 hippocampal datasets, group differences and moderator effects were nonsignificant. Group effects in thalamus and dorsolateral prefrontal cortex were also nonsignificant.

CONCLUSIONS

High-quality measurements reveal consistently reduced medial prefrontal cortex glutamate in schizophrenia. Stricter CRLB criteria and reduced nuisance variance may increase the sensitivity of future studies examining additional regions and the pathophysiological significance of abnormal glutamate levels in schizophrenia.

From Molecular to Behavior: Higher Order Occipital Cortex in Major Depressive Disorder

Medial prefrontal cortex (MPFC) and other regions like the occipital cortex (OC) exhibit abnormal neural activity in major depressive disorder (MDD). Their relationship to specific biochemical, psychophysical, and psychopathological changes remains unclear, though. For that purpose, we focus on a particular subregion in OC, namely middle temporal (MT) visual area that is known to mediate the perception of visual motion. Using high-field 7 T magnetic resonance imaging (MRI), including resting state functional MRI and proton magnetic resonance spectroscopy, the amplitude of low-frequency fluctuations (ALFF) of the blood oxygen level-dependent signal in MT, MT-seeded functional connectivity (FC), and gamma-aminobutyric acid (GABA) in MT were investigated. Applying the vision motion psychophysical task, the motion suppression index of subjects was also examined. We demonstrate significantly elevated neural variability (as measured by ALFF) in MT together with decreases in both MT GABA and motion suppression in our MDD sample. Unlike in healthy subjects, MT neural variability no longer modulates the relationship of MT GABA and motion suppression in MDD. MT also exhibits reduction in global inter-regional FC to MPFC in MDD. Finally, elevated MT ALFF relates to specifically retardation in behavior as measured by the Hamilton subscore. Together, MT provides a strong candidate for biomarker in MDD.

Water-suppression cycling 3-T cardiac 1 H-MRS detects altered creatine and choline in patients with aortic or mitral stenosis

Cardiac proton spectroscopy (1 H-MRS) is widely used to quantify lipids. Other metabolites (e.g. creatine and choline) are clinically relevant but more challenging to quantify because of their low concentrations (approximately 10 mmol/L) and because of cardiac motion. To quantify cardiac creatine and choline, we added water-suppression cycling (WSC) to two single-voxel spectroscopy sequences (STEAM and PRESS). WSC introduces controlled residual water signals that alternate between positive and negative phases from transient to transient, enabling robust phase and frequency correction. Moreover, a particular weighted sum of transients eliminates residual water signals without baseline distortion. We compared WSC and the vendor's standard 'WET' water suppression in phantoms. Next, we tested repeatability in 10 volunteers (seven males, three females; age 29.3 ± 4.0 years; body mass index [BMI] 23.7 ± 4.1 kg/m2 ). Fat fraction, creatine concentration and choline concentration when quantified by STEAM-WET were 0.30% ± 0.11%, 29.6 ± 7.0 μmol/g and 7.9 ± 6.7 μmol/g, respectively; and when quantified by PRESS-WSC they were 0.30% ± 0.15%, 31.5 ± 3.1 μmol/g and 8.3 ± 4.4 μmol/g, respectively. Compared with STEAM-WET, PRESS-WSC gave spectra whose fitting quality expressed by Cramér-Rao lower bounds improved by 26% for creatine and 32% for choline. Repeatability of metabolite concentration measurements improved by 72% for creatine and 40% for choline. We also compared STEAM-WET and PRESS-WSC in 13 patients with severe symptomatic aortic or mitral stenosis indicated for valve replacement surgery (10 males, three females; age 75.9 ± 6.3 years; BMI 27.4 ± 4.3 kg/m2 ). Spectra were of analysable quality in eight patients for STEAM-WET, and in nine for PRESS-WSC. We observed comparable lipid concentrations with those in healthy volunteers, significantly reduced creatine concentrations, and a trend towards decreased choline concentrations. We conclude that PRESS-WSC offers improved performance and reproducibility for the quantification of cardiac lipids, creatine and choline concentrations in healthy volunteers at 3 T. It also offers improved performance compared with STEAM-WET for detecting altered creatine and choline concentrations in patients with valve disease.

Are Cramér-Rao lower bounds an accurate estimate for standard deviations in in vivo magnetic resonance spectroscopy?

Due to inherent time constraints for in vivo experiments, it is infeasible to repeat multiple MRS scans to estimate standard deviations on the desired measured parameters. As such, the Cramér-Rao lower bounds (CRLBs) have become the routine method to approximate standard deviations for in vivo experiments. Cramér-Rao lower bounds, however, as the name suggests, are theoretically a lower bound on the standard deviation and it is not clear if and under what circumstances this approximation is valid. Realistic synthetic 3 T spectra were used to investigate the relationship between estimated CRLBs, true CRLBs and standard deviations. Here we demonstrate that, although the CRLBs are theoretically truly a lower bound on the standard deviation (not an equality) for the problem typically encountered in quantification, they are still an adequate approximation to standard deviation as long as the model perfectly characterizes the data. In the case when the macromolecule basis deviates from the measured macromolecules it was shown that the CRLBs can deviate from standard deviations by approximately 50% for N-acetylaspartic acid, creatine and glutamate and of the order of 100% or more for myo-inositol and γ-aminobutyric acid. In the case when the model perfectly reflects the data the CRLBs are within approximately 10% of standard deviations for all metabolites. The result of the CRLB being within 10% of standard deviations means that, for an accurate model, novel quantification methods such as machine learning or deep learning will not be able to obtain substantially more precise estimates for the desired parameters than traditional maximum-likelihood estimation.

Metabolic underpinnings of activated and deactivated cortical areas in human brain

Neuroimaging with functional MRI (fMRI) identifies activated and deactivated brain regions in task-based paradigms. These patterns of (de)activation are altered in diseases, motivating research to understand their underlying biochemical/biophysical mechanisms. Essentially, it remains unknown how aerobic metabolism of glucose to lactate (aerobic glycolysis) and excitatory-inhibitory balance of glutamatergic and GABAergic neuronal activities vary in these areas. In healthy volunteers, we investigated metabolic distinctions of activating visual cortex (VC, a task-positive area) using a visual task and deactivating posterior cingulate cortex (PCC, a task-negative area) using a cognitive task. We used fMRI-guided J-edited functional MRS (fMRS) to measure lactate, glutamate plus glutamine (Glx) and γ-aminobutyric acid (GABA), as indicators of aerobic glycolysis and excitatory-inhibitory balance, respectively. Both lactate and Glx increased upon activating VC, but did not change upon deactivating PCC. Basal GABA was negatively correlated with BOLD responses in both brain areas, but during functional tasks GABA decreased in VC upon activation and GABA increased in PCC upon deactivation, suggesting BOLD responses in relation to baseline are impacted oppositely by task-induced inhibition. In summary, opposite relations between BOLD response and GABAergic inhibition, and increases in aerobic glycolysis and glutamatergic activity distinguish the BOLD response in (de)activated areas.

Investigating the effect of trigger delay on cardiac 31P MRS signals

The heart’s geometry and its metabolic activity vary over the cardiac cycle. The effect of these fluctuations on phosphorus (³¹P) magnetic resonance spectroscopy (MRS) data quality and metabolite ratios was investigated. 12 healthy volunteers were measured using a 7 T MR scanner and a cardiac ³¹P-¹H loop coil. ³¹P chemical shift imaging data were acquired untriggered and at four different times during the cardiac cycle using acoustic triggering. Signals of adenosine-triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi) and 2,3-diphosphoglycerate (2,3-DPG) and their fit quality as Cramér-Rao lower bounds (CRLB) were quantified including corrections for contamination by ³¹P signals from blood, flip angle, saturation and total acquisition time. The myocardial filling factor was estimated from cine short axis views. The corrected signals of PCr and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ-ATP were higher during end-systole and lower during diastasis than in untriggered acquisitions (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P<0.05$$\end{document}P<0.05). Signal intensities of untriggered scans were between those with triggering to end-systole and diastasis. Fit quality of PCr and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ-ATP peaks was best during end-systole when blood contamination of ATP and Pi signals was lowest. While metabolite ratios and pH remained stable over the cardiac cycle, signal amplitudes correlated strongly with myocardial voxel filling. Triggering of cardiac ³¹P MRS acquisitions improves signal amplitudes and fit quality if the trigger delay is set to end-systole. We conclude that triggering to end-systole is superior to triggering to diastasis.

Schulte R, Laustsen C. Comprehensive Literature Review of Hyperpolarized Carbon-13 MRI: The Road to Clinical Application

This review provides a comprehensive assessment of the development of hyperpolarized (HP) carbon-13 metabolic MRI from the early days to the present with a focus on clinical applications. The status and upcoming challenges of translating HP carbon-13 into clinical application are reviewed, along with the complexity, technical advancements, and future directions. The road to clinical application is discussed regarding clinical needs and technological advancements, highlighting the most recent successes of metabolic imaging with hyperpolarized carbon-13 MRI. Given the current state of hyperpolarized carbon-13 MRI, the conclusion of this review is that the workflow for hyperpolarized carbon-13 MRI is the limiting factor.

ACC Glu/GABA ratio is decreased in euthymic bipolar disorder I patients: possible in vivo neurometabolite explanation for mood stabilization

Bipolar disorder (BD) is characterized by unstable mood states ranging from mania to depression. Although there is some evidence that mood instability may result from an imbalance between excitatory glutamatergic and inhibitory GABA-ergic neurotransmission, few proton magnetic resonance spectroscopy (¹H-MRS) studies have measured these two neurometabolites simultaneously in BD. The enzyme glutamic acid decarboxylase (GAD1) catalyzes the decarboxylation of glutamate (Glu) to GABA, and its single nucleotide polymorphisms (SNPs) might influence Glu/GABA ratio. Thus, we investigated Glu/GABA ratio in the dorsal anterior cingulate cortex (dACC) of euthymic BD type I patients and healthy controls (HC), and assessed the influence of both mood stabilizers and GAD1 SNPs on this ratio. Eighty-eight subjects (50 euthymic BD type I patients and 38 HC) underwent 3T ¹H-MRS in the dACC (2 × 2 × 4.5 cm³) using a two-dimensional JPRESS sequence and all subjects were genotyped for 4 SNPs in the GAD1 gene. BD patients had lower dACC Glu/GABA ratio compared to HC, where this was influenced by anticonvulsant and antipsychotic medications, but not lithium. The presence of GAD1 rs1978340 allele A was associated with higher Glu/GABA ratio in BD, while patients without this allele taking mood stabilizers had a lower Glu/GABA ratio. The lowering of dACC Glu/GABA could be one explanation for the mood stabilizing action of anticonvulsants and antipsychotics in BD type I euthymia. Therefore, this putative role of Glu/GABA ratio and the influence of GAD1 genotype interacting with mood stabilization medication should be confirmed by further studies involving larger samples and other mood states.ClincalTrials.gov registration: NCT01237158.

Met carriers of the BDNF Val66Met polymorphism show reduced Glx/NAA in the pregenual ACC in two independent cohorts

The Met allele of the Val66Met SNP of the BDNF gene (rs6265) is associated with impaired activity-dependent release of brain-derived neurotrophic factor (BDNF), resulting in reduced synaptic plasticity, impaired glutamatergic neurotransmission, and morphological changes. While previous work has demonstrated Val66Met effects on magnetic resonance spectroscopy (MRS) markers of either glutamatergic metabolism (Glx) or neuronal integrity (NAA), no study has investigated Val66Met effects on these related processes simultaneously. As these metabolites share a metabolic pathway, the Glx/NAA ratio may be a more sensitive marker of changes associated with the Val66Met SNP. This ratio is increased in psychiatric disorders linked to decreased functioning in the anterior cingulate cortex (ACC). In this study, we investigated the correlation of the Val66Met polymorphism of the BDNF gene with Glx/NAA in the pregenual anterior cingulate cortex (pgACC) using MRS at 3 Tesla (T) (n = 30, all males) and 7 T (n = 98, 40 females). In both cohorts, Met carriers had lower Glx/NAA compared to Val homozygotes. Follow-up analyses using absolute quantification revealed that the Met carriers do not show decreased pgACC glutamate or glutamine levels, but instead show increased NAA compared to the Val homozygotes. This finding may in part explain conflicting evidence for Val66Met as a risk factor for developing psychiatric illnesses.

Review and prospect: NMR spectroscopy denoising and reconstruction with low-rank Hankel matrices and tensors

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical tool in chemistry, biology, and life science, but it suffers from relatively low sensitivity and long acquisition time. Thus, improving the apparent signal-to-noise ratio and accelerating data acquisition became indispensable. In this review, we summarize the recent progress on low-rank Hankel matrix and tensor methods, which exploit the exponential property of free-induction decay signals, to enable effective denoising and spectra reconstruction. We also outline future developments that are likely to make NMR spectroscopy a far more powerful technique.

Excitatory/inhibitory neuronal metabolic balance in mouse hippocampus upon infusion of [U-13C6]glucose

Hippocampus plays a critical role in linking brain energetics and behavior typically associated to stress exposure. In this study, we aimed to simultaneously assess excitatory and inhibitory neuronal metabolism in mouse hippocampus in vivo by applying ¹⁸FDG-PET and indirect ¹³C magnetic resonance spectroscopy (¹H-[¹³C]-MRS) at 14.1 T upon infusion of uniformly ¹³C-labeled glucose ([U-¹³C₆]Glc). Improving the spectral fitting by taking into account variable decoupling efficiencies of [U-¹³C₆]Glc and refining the compartmentalized model by including two γ-aminobutyric acid (GABA) pools permit us to evaluate the relative contributions of glutamatergic and GABAergic metabolism to total hippocampal neuroenergetics. We report that GABAergic activity accounts for ∼13% of total neurotransmission (V_NT) and ∼27% of total neuronal TCA cycle (V_TCA) in mouse hippocampus suggesting a higher V_TCA/V_NT ratio for inhibitory neurons compared to excitatory neurons. Finally, our results provide new strategies and tools for bringing forward the developments and applications of ¹³C-MRS in specific brain regions of small animals.

INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis

In vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.

Lower Ventromedial Prefrontal Cortex Glutamate Levels in Patients With Obsessive-Compulsive Disorder

Background: Recent studies using magnetic resonance spectroscopy (¹H-MRS) indicate that patients with obsessive-compulsive disorder (OCD) present abnormal levels of glutamate (Glu) and gamma aminobutyric acid (GABA) in the frontal and striatal regions of the brain. These abnormalities could be related to the hyperactivation observed in cortico-striatal circuits of patients with OCD. However, most of the previous ¹H-MRS studies were not capable of differentiating the signal from metabolites that overlap in the spectrum, such as Glu and glutamine (Gln), and referred to the detected signal as the composite measure-Glx (sum of Glu and Gln). In this study, we used a two-dimensional JPRESS ¹H-MRS sequence that allows the discrimination of overlapping metabolites by observing the differences in J-coupling, leading to higher accuracy in the quantification of all metabolites. Our objective was to identify possible alterations in the neurometabolism of OCD, focusing on Glu and GABA, which are key neurotransmitters in the brain that could provide insights into the underlying neurochemistry of a putative excitatory/inhibitory imbalance. Secondary analysis was performed including metabolites such as Gln, creatine (Cr), N-acetylaspartate, glutathione, choline, lactate, and myo-inositol. Methods: Fifty-nine patients with OCD and 42 healthy controls (HCs) underwent 3T ¹H-MRS in the ventromedial prefrontal cortex (vmPFC, 30 × 25 × 25 mm³). Metabolites were quantified using ProFit (version 2.0) and Cr as a reference. Furthermore, Glu/GABA and Glu/Gln ratios were calculated. Generalized linear models (GLMs) were conducted using each metabolite as a dependent variable and age, sex, and gray matter fraction (fGM) as confounding factors. GLM analysis was also used to test for associations between clinical symptoms and neurometabolites. Results: The GLM analysis indicated lower levels of Glu/Cr in patients with OCD (z = 2.540; p = 0.011). No other comparisons reached significant differences between groups for all the metabolites studied. No associations between metabolites and clinical symptoms were detected. Conclusions: The decreased Glu/Cr concentrations in the vmPFC of patients with OCD indicate a neurochemical imbalance in the excitatory neurotransmission that could be associated with the neurobiology of the disease and may be relevant for the pathophysiology of OCD.

Cortical glutamate and gamma-aminobutyric acid over the course of a provoked migraine attack, a 7 Tesla magnetic resonance spectroscopy study

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7T MRS separately measured glutamate, glutamine and GABA towards triggered attacks.

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Visual cortex GABA levels increased towards a preictal migraine state.

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Visual cortex glutamate and glutamine levels were stable across migraine states.

Enhanced activity of the glutamatergic system has been linked to migraine pathophysiology. The present study aimed to assess the involvement of the glutamatergic system in the onset of attacks. We provoked attacks by infusion of glyceryl trinitrate (GTN; 0.5 µg/kg/min over 20 min) in 24 female episodic migraineurs without aura and 13 female age-matched healthy controls. Over the course of a single day participants were scanned three times at fixed time slots (baseline before GTN infusion, 90 min and 270 min after start of GTN infusion). Single-volume proton magnetic resonance spectra (¹H–MRS) were acquired at 7 Tesla from a volume of interest (VOI, 2x2x3 cm) in the visual cortex. We assessed the concentrations of glutamate, its major precursor glutamine, and its product gamma-aminobutyric acid (GABA) over the course of a provoked attack. The preictal state was defined as the period after GTN infusion until the migraine-like headache started, independent of possible experienced premonitory symptoms, and the ictal state was defined as the period with provoked migraine-like headache. Data were analyzed using a linear mixed-effect model for repeated measures. Glutamate and glutamine levels did not change from interictal to the preictal and ictal state. GABA levels increased from interictal towards the preictal state for migraine patients compared with healthy controls. We conclude that high resolution 7T MRS is able to show changes in the glutamatergic system towards a triggered migraine attack, by revealing an increased GABA concentration associated with the onset of a migraine attack.

Multidimensional correlation spectroscopic imaging of exponential decays: From theoretical principles to in vivo human applications

Multiexponential modeling of relaxation or diffusion MR signal decays is a popular approach for estimating and spatially mapping different microstructural tissue compartments. While this approach can be quite powerful, it is also limited by the fact that one-dimensional multiexponential modeling is an ill-posed inverse problem with substantial ambiguities. In this article, we present an overview of a recent multidimensional correlation spectroscopic imaging approach to this problem. This approach helps to alleviate ill-posedness by making advantageous use of multidimensional contrast encoding (e.g., 2D diffusion-relaxation encoding or 2D relaxation-relaxation encoding) combined with a regularized spatial-spectral estimation procedure. Theoretical calculations, simulations, and experimental results are used to illustrate the benefits of this approach relative to classical methods. In addition, we demonstrate an initial proof-of-principle application of this kind of approach to in vivo human MRI experiments.

Quantifying the effect of dobutamine stress on myocardial Pi and pH in healthy volunteers: A 31 P MRS study at 7T

Purpose

Phosphorus spectroscopy (³¹P‐MRS) is a proven method to probe cardiac energetics. Studies typically report the phosphocreatine (PCr) to adenosine triphosphate (ATP) ratio. We focus on another ³¹P signal: inorganic phosphate (Pi), whose chemical shift allows computation of myocardial pH, with Pi/PCr providing additional insight into cardiac energetics. Pi is often obscured by signals from blood 2,3‐diphosphoglycerate (2,3‐DPG). We introduce a method to quantify Pi in 14 min without hindrance from 2,3‐DPG.

Methods

Using a ³¹P stimulated echo acquisition mode (STEAM) sequence at 7 Tesla that inherently suppresses signal from 2,3‐DPG, the Pi peak was cleanly resolved. Resting state UTE‐chemical shift imaging (PCr/ATP) and STEAM ³¹P‐MRS (Pi/PCr, pH) were undertaken in 23 healthy controls; pH and Pi/PCr were subsequently recorded during dobutamine infusion.

Results

We achieved a clean Pi signal both at rest and stress with good 2,3‐DPG suppression. Repeatability coefficient (8 subjects) for Pi/PCr was 0.036 and 0.12 for pH. We report myocardial Pi/PCr and pH at rest and during catecholamine stress in healthy controls. Pi/PCr was maintained during stress (0.098 ± 0.031 [rest] vs. 0.098 ± 0.031 [stress] P = .95); similarly, pH did not change (7.09 ± 0.07 [rest] vs. 7.08 ± 0.11 [stress] P = .81). Feasibility for patient studies was subsequently successfully demonstrated in a patient with cardiomyopathy.

Conclusion

We introduced a method that can resolve Pi using 7 Tesla STEAM ³¹P‐MRS. We demonstrate the stability of Pi/PCr and myocardial pH in volunteers at rest and during catecholamine stress. This protocol is feasible in patients and potentially of use for studying pathological myocardial energetics.

Comparison of ethanol concentrations in the human brain determined by magnetic resonance spectroscopy and serum ethanol concentrations

Aims

Ethanol is a widespread substance that inherits desired effects, but also negative consequences with regard to DUI or battery. Where required, the ethanol concentration is usually determined in peripheral venous blood samples, while the brain is the target organ of the ethanol effects. The aim of this study with three participants was the determination of the ethanol concentration in functionally relevant regions of the brain and the comparison with serum ethanol concentrations.

Design

After the uptake of ethanol in a calculated amount, leading to a serum ethanol concentration of 0.99 g/L, the ethanol concentrations in the brain were directly analyzed by means of magnetic resonance spectroscopy on a 3 Tesla human MRI system and normalized to the water content. The measurement voxels were located in the occipital cortex, the cerebellum, the frontal cortex, and the putamen and successively examined. Intermittently blood samples were taken, and serum was analyzed for ethanol using HS-GC-FID.

Findings and conclusions

Ethanol concentrations in brain regions normalized to the water content were lower than the measured serum ethanol results and rather homogenous within the three participants and the various regions of the brain. The maximum ethanol concentration in the brain (normalized to water content) was 0.68 g/L. It was measured in the frontal cortex, in which the highest results were gained. The maximum serum concentration was 1.19 g/L. The course of the brain ethanol curve seems to be flatter than the one of the serum ethanol concentrations.

Local glutamate in cingulate cortex subregions differentially correlates with affective network activations during face perception

The cingulate cortex is involved in emotion recognition/perception and regulation. Rostral and caudal subregions belong to different brain networks with distinct roles in affective perception. Despite recent accounts of the relevance of cingulate cortex glutamate (Glu) on blood-oxygen-level-dependent (BOLD) responses, the specificity of the subregional Glu levels during emotional tasks remains unclear. Seventy-two healthy participants (age = 27.33 ± 6.67, 32 women) performed an affective face-matching task and underwent magnetic resonance spectroscopy (MRS) at 7 Tesla. Correlations between the BOLD response during emotion perception and Glu concentration in the pregenual anterior cingulate cortex (pgACC) and anterior midcingulate cortex (aMCC) were compared on a whole-brain level. Post hoc specificity of the association with an affect was assessed. Lower Glu in the pgACC correlated with stronger activation differences between negative and positive faces in the left inferior and superior frontal gyrus (L IFG and L SFG). In contrast, lower Glu in the aMCC correlated with BOLD contrasts in the posterior cingulate cortex (PCC). Furthermore, negative face detection was associated with prolonged response time (RT). Our results demonstrate a subregion-specific involvement of cingulate cortex Glu in interindividual differences during viewing of affective facial expressions. Glu levels in the pgACC were correlated with frontal area brain activations, whereas Glu in the salience network component aMCC modulated responses in the PCC-precuneus. We show that region-specific metabolite mapping enables specific activation of different BOLD signals in the brain underlying emotional perception.