Clinical proton MR spectroscopy in central nervous system disorders

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling

PURPOSE

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a 2D linear-combination model (2D-LCM) of individual transients ("model-based FPC"). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates.

METHODS

We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the SD of those ground-truth errors, and amplitude Cramér Rao lower bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data.

RESULTS

2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of FPC and amplitudes performed substantially better at low-to-very-low SNR.

CONCLUSION

Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, for example, long TEs or strong diffusion weighting.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

PURPOSE

The interest in applying and modeling dynamic MRS has recently grown. Two-dimensional modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to one-dimensional (1D) modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.

METHODS

Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multitransient linear-combination modeling (LCM) with 1D-LCM of the average. A total of 2,500 data sets per condition with different noise representations of a 64-transient MRS experiment at six signal-to-noise levels for two separate spin systems (scyllo-inositol and gamma-aminobutyric acid) were analyzed. Additional data sets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by SDs and Cramér-Rao lower bounds (CRLBs).

RESULTS

Amplitude estimates for 1D- and 2D-LCM agreed well and showed a similar level of bias compared with the ground truth. Estimated CRLBs agreed well between both models and with ground-truth CRLBs. For correlated noise, the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.

CONCLUSION

Our results indicate that the model performance of 2D multitransient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as a necessary basis for further applications of 2D modeling.

Improvements in precision and accuracy of complex- relative to real-domain linear combination model spectral fitting not necessarily recovered by zero filling

Although the information obtained from in vivo proton magnetic resonance spectroscopy (1H MRS) presents a complex-valued spectrum, spectral quantification generally employs linear combination model (LCM) fitting using the real spectrum alone. There is currently no known investigation comparing fit results obtained from LCM fitting over the full complex data versus the real data and how these results might be affected by common spectral preprocessing procedure zero filling. Here, we employ linear combination modeling of simulated and measured spectral data to examine two major ideas: first, whether use of the full complex rather than real-only data can provide improvements in quantification by linear combination modeling and, second, to what extent zero filling might influence these improvements. We examine these questions by evaluating the errors of linear combination model fits in the complex versus real domains against three classes of synthetic data: simulated Lorentzian singlets, simulated metabolite spectra excluding the baseline, and simulated metabolite spectra including measured in vivo baselines. We observed that complex fitting provides consistent improvements in fit accuracy and precision across all three data types. While zero filling obviates the accuracy and precision benefit of complex fitting for Lorentzian singlets and metabolite spectra lacking baselines, it does not necessarily do so for complex spectra including measured in vivo baselines. Overall, performing linear combination modeling in the complex domain can improve metabolite quantification accuracy relative to real fits alone. While this benefit can be similarly achieved via zero filling for some spectra with flat baselines, this is not invariably the case for all baseline types exhibited by measured in vivo data.

Reproducibility Made Easy: A Tool for Methodological Transparency and Efficient Standardized Reporting based on the proposed MRSinMRS Consensus

Recent expert consensus publications have highlighted the issue of poor reproducibility in magnetic resonance spectroscopy (MRS) studies, mainly due to the lack of standardized reporting criteria, which affects their clinical applicability. To combat this, guidelines for minimum reporting standards (MRSinMRS) were introduced to aid journal editors and reviewers in ensuring the comprehensive documentation of essential MRS study parameters. Despite these efforts, the implementation of MRSinMRS standards has been slow, attributed to the diverse nomenclature used by different vendors, the variety of raw MRS data formats, and the absence of appropriate software tools for identifying and reporting necessary parameters. To overcome this obstacle, we have developed the REproducibility Made Easy (REMY) standalone toolbox. REMY software supports a range of MRS data formats from major vendors like GE (p. file), Siemens (twix, .rda, .dcm), Philips (.spar/.sdat), and Bruker (.method), facilitating easy data import and export through a user-friendly interface. REMY employs external libraries such as spec2nii and pymapVBVD to accurately read and process these diverse data formats, ensuring compatibility and ease of use for researchers in generating reproducible MRS research outputs. Users can select and import datasets, choose the appropriate vendor and data format, and then generate an MRSinMRS table, log file, and methodological documents in both Latex and PDF formats. REMY effectively populated key sections of the MRSinMRS table with data from all supported file types. Accurate generation of hardware parameters including field strength, manufacturer, and scanner software version were demonstrated. However, it could not input data for RF coil and additional hardware information due to their absence in the files. For the acquisition section, REMY accurately read and populated fields for the pulse sequence name, nominal voxel size, repetition time (TR), echo time (TE), number of acquisitions/excitations/shots, spectral width [Hz], and number of spectral points, significantly contributing to the completion of the Acquisition fields of the table. Furthermore, REMY generates a boilerplate methods text section for manuscripts.The use of REMY will facilitate more widespread adoption of the MRSinMRS checklist within the MRS community, making it easier to write and report acquisition parameters effectively.

Predicting performance in attention by measuring key metabolites in the PCC with 7T MRS

The posterior cingulate cortex (PCC) is a key hub of the default mode network and is known to play an important role in attention. Using ultra-high field 7 Tesla magnetic resonance spectroscopy (MRS) to quantify neurometabolite concentrations, this exploratory study investigated the effect of the concentrations of myo-inositol (Myo-Ins), glutamate (Glu), glutamine (Gln), aspartate or aspartic acid (Asp) and gamma-amino-butyric acid (GABA) in the PCC on attention in forty-six healthy participants. Each participant underwent an MRS scan and cognitive testing, consisting of a trail-making test (TMT A/B) and a test of attentional performance. After a multiple regression analysis and bootstrapping for correction, the findings show that Myo-Ins and Asp significantly influence (p < 0.05) attentional tasks. On one hand, Myo-Ins shows it can improve the completion times of both TMT A and TMT B. On the other hand, an increase in aspartate leads to more mistakes in Go/No-go tasks and shows a trend towards enhancing reaction time in Go/No-go tasks and stability of alertness without signal. No significant (p > 0.05) influence of Glu, Gln and GABA was observed.

GABA and glutamate response to social processing: a functional MRS feasibility study

Several studies have suggested that atypical social processing in neurodevelopmental conditions (e.g. autism) is associated with differences in excitation and inhibition, through changes in the levels of glutamate and gamma-aminobutyric acid (GABA). While associations between baseline metabolite levels and behaviours can be insightful, assessing the neurometabolic response of GABA and glutamate during social processing may explain altered neurochemical function in more depth. Thus far, there have been no attempts to determine whether changes in metabolite levels are detectable using functional MRS (fMRS) during social processing in a control population. We performed Mescher-Garwood point resolved spectroscopy edited fMRS to measure the dynamic response of GABA and glutamate in the superior temporal sulcus (STS) and visual cortex (V1) while viewing social stimuli, using a design that allows for analysis in both block and event-related approaches. Sliding window analyses were used to investigate GABA and glutamate dynamics at higher temporal resolution. The changes of GABA and glutamate levels with social stimulus were largely non-significant. A small decrease in GABA levels was observed during social stimulus presentation in V1, but no change was observed in STS. Conversely, non-social stimulus elicited changes in both GABA and glutamate levels in both regions. Our findings suggest that the current experimental design primarily captures effects of visual stimulation, not social processing. Here, we discuss the feasibility of using fMRS analysis approaches to assess changes in metabolite response.

Advances and prospects in deuterium metabolic imaging (DMI): a systematic review of in vivo studies

BACKGROUND

Deuterium metabolic imaging (DMI) has emerged as a promising non-invasive technique for studying metabolism in vivo. This review aims to summarize the current developments and discuss the futures in DMI technique in vivo.

METHODS

A systematic literature review was conducted based on the PRISMA 2020 statement by two authors. Specific technical details and potential applications of DMI in vivo were summarized, including strategies of deuterated metabolites detection, deuterium-labeled tracers and corresponding metabolic pathways in vivo, potential clinical applications, routes of tracer administration, quantitative evaluations of metabolisms, and spatial resolution.

RESULTS

Of the 2,248 articles initially retrieved, 34 were finally included, highlighting 2 strategies for detecting deuterated metabolites: direct and indirect DMI. Various deuterated tracers (e.g., [6,6'-2H2]glucose, [2,2,2'-2H3]acetate) were utilized in DMI to detect and quantify different metabolic pathways such as glycolysis, tricarboxylic acid cycle, and fatty acid oxidation. The quantifications (e.g., lactate level, lactate/glutamine and glutamate ratio) hold promise for diagnosing malignancies and assessing early anti-tumor treatment responses. Tracers can be administered orally, intravenously, or intraperitoneally, either through bolus administration or continuous infusion. For metabolic quantification, both serial time point methods (including kinetic analysis and calculation of area under the curves) and single time point quantifications are viable. However, insufficient spatial resolution remains a major challenge in DMI (e.g., 3.3-mL spatial resolution with 10-min acquisition at 3 T).

CONCLUSIONS

Enhancing spatial resolution can facilitate the clinical translation of DMI. Furthermore, optimizing tracer synthesis, administration protocols, and quantification methodologies will further enhance their clinical applicability.

RELEVANCE STATEMENT

Deuterium metabolic imaging, a promising non-invasive technique, is systematically discussed in this review for its current progression, limitations, and future directions in studying in vivo energetic metabolism, displaying a relevant clinical potential.

KEY POINTS

• Deuterium metabolic imaging (DMI) shows promise for studying in vivo energetic metabolism. • This review explores DMI's current state, limits, and future research directions comprehensively. • The clinical translation of DMI is mainly impeded by limitations in spatial resolution.

Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI

OBJECTIVE

To develop a novel multi-TE MR spectroscopic imaging (MRSI) approach to enable label-free, simultaneous, high-resolution mapping of several molecules and their biophysical parameters in the brain.

METHODS

The proposed method uniquely integrated an augmented molecular-component-specific subspace model for multi-TE 1H-MRSI signals, an estimation-theoretic experiment optimization (nonuniform TE selection) for molecule separation and parameter estimation, a physics-driven subspace learning strategy for spatiospectral reconstruction and molecular quantification, and a new accelerated multi-TE MRSI acquisition for generating high-resolution data in clinically relevant times. Numerical studies, phantom and in vivo experiments were conducted to validate the optimized experiment design and demonstrate the imaging capability offered by the proposed method.

RESULTS

The proposed TE optimization improved estimation of metabolites, neurotransmitters and their T2's over conventional TE choices, e.g., reducing variances of neurotransmitter concentration by  ∼  40% and metabolite T2 by  ∼  60%. Simultaneous metabolite and neurotransmitter mapping of the brain can be achieved at a nominal resolution of 3.4 × 3.4 × 6.4 mm 3. High-resolution, 3D metabolite T2 mapping was made possible for the first time. The translational potential of the proposed method was demonstrated by mapping biochemical abnormality in a post-traumatic epilepsy (PTE) patient.

CONCLUSION

The feasibility for high-resolution mapping of metabolites/neurotransmitters and metabolite T2's within clinically relevant time was demonstrated. We expect our method to offer richer information for revealing and understanding metabolic alterations in neurological diseases.

SIGNIFICANCE

A novel multi-TE MRSI approach was presented that enhanced the technological capability of multi-parametric molecular imaging of the brain. The proposed method presents new technology development and application opportunities for providing richer molecular level information to uncover and comprehend metabolic changes relevant in various neurological applications.

MR Imaging in Ataxias: Consensus Recommendations by the Ataxia Global Initiative Working Group on MRI Biomarkers

With many viable strategies in the therapeutic pipeline, upcoming clinical trials in hereditary and sporadic degenerative ataxias will benefit from non-invasive MRI biomarkers for patient stratification and the evaluation of therapies. The MRI Biomarkers Working Group of the Ataxia Global Initiative therefore devised guidelines to facilitate harmonized MRI data acquisition in clinical research and trials in ataxias. Recommendations are provided for a basic structural MRI protocol that can be used for clinical care and for an advanced multi-modal MRI protocol relevant for research and trial settings. The advanced protocol consists of modalities with demonstrated utility for tracking brain changes in degenerative ataxias and includes structural MRI, magnetic resonance spectroscopy, diffusion MRI, quantitative susceptibility mapping, and resting-state functional MRI. Acceptable ranges of acquisition parameters are provided to accommodate diverse scanner hardware in research and clinical contexts while maintaining a minimum standard of data quality. Important technical considerations in setting up an advanced multi-modal protocol are outlined, including the order of pulse sequences, and example software packages commonly used for data analysis are provided. Outcome measures most relevant for ataxias are highlighted with use cases from recent ataxia literature. Finally, to facilitate access to the recommendations by the ataxia clinical and research community, examples of datasets collected with the recommended parameters are provided and platform-specific protocols are shared via the Open Science Framework.

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

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

15N Hyperpolarization of Metronidazole Antibiotic in Aqueous Media Using Phase-Separated Signal Amplification by Reversible Exchange with Parahydrogen

Metronidazole is a prospective hyperpolarized MRI contrast agent with potential hypoxia sensing utility for applications in cancer, stroke, neurodegenerative diseases, etc. We demonstrate a pilot procedure for production of ∼30 mM hyperpolarized [15N3]metronidazole in aqueous media by using a phase-separated SABRE-SHEATH hyperpolarization method, with nitrogen-15 polarization exceeding 2.2% on all three 15N sites achieved in less than 2 min. The 15N polarization T1 of ∼12 min is reported for the 15NO2 group at the clinically relevant field of 1.4 T in the aqueous phase, demonstrating a remarkably long lifetime of the hyperpolarized state. The produced aqueous solution of [15N3]metronidazole that contained only ∼100 μM of residual Ir was deemed biocompatible via validation through the MTT colorimetric test for assessing cell metabolic activity using human embryotic kidney HEK293T cells. This low-cost and ultrafast hyperpolarization procedure represents a major advance for the production of a biocompatible HP [15N3]metronidazole (and potentially other hyperpolarized drugs) formulation for MRI sensing applications.

An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis

MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.

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.

Model-based frequency-and-phase correction of 1H MRS data with 2D linear-combination modeling

Purpose

Retrospective frequency-and-phase correction (FPC) methods attempt to remove frequency-and-phase variations between transients to improve the quality of the averaged MR spectrum. However, traditional FPC methods like spectral registration struggle at low SNR. Here, we propose a method that directly integrates FPC into a two-dimensional linear-combination model (2D-LCM) of individual transients ('model-based FPC'). We investigated how model-based FPC performs compared to the traditional approach, i.e., spectral registration followed by 1D-LCM in estimating frequency-and-phase drifts and, consequentially, metabolite level estimates.

Methods

We created synthetic in-vivo-like 64-transient short-TE sLASER datasets with 100 noise realizations at 5 SNR levels and added randomly sampled frequency and phase variations. We then used this synthetic dataset to compare the performance of 2D-LCM with the traditional approach (spectral registration, averaging, then 1D-LCM). Outcome measures were the frequency/phase/amplitude errors, the standard deviation of those ground-truth errors, and amplitude Cramér Rao Lower Bounds (CRLBs). We further tested the proposed method on publicly available in-vivo short-TE PRESS data.

Results

2D-LCM estimates (and accounts for) frequency-and-phase variations directly from uncorrected data with equivalent or better fidelity than the conventional approach. Furthermore, 2D-LCM metabolite amplitude estimates were at least as accurate, precise, and certain as the conventionally derived estimates. 2D-LCM estimation of frequency and phase correction and amplitudes performed substantially better at low-to-very-low SNR.

Conclusion

Model-based FPC with 2D linear-combination modeling is feasible and has great potential to improve metabolite level estimation for conventional and dynamic MRS data, especially for low-SNR conditions, e.g., long TEs or strong diffusion weighting.

Test-Retest Reproducibility of Reduced-Field-of-View Density-Weighted CRT MRSI at 3T

Quantifying an imaging modality's ability to reproduce results is important for establishing its utility. In magnetic resonance spectroscopic imaging (MRSI), new acquisition protocols are regularly introduced which improve upon their precursors with respect to signal-to-noise ratio (SNR), total acquisition duration, and nominal voxel resolution. This study has quantified the within-subject and between-subject reproducibility of one such new protocol (reduced-field-of-view density-weighted concentric ring trajectory (rFOV-DW-CRT) MRSI) by calculating the coefficient of variance of data acquired from a test-retest experiment. The posterior cingulate cortex (PCC) and the right superior corona radiata (SCR) were selected as the regions of interest (ROIs) for grey matter (GM) and white matter (WM), respectively. CVs for between-subject and within-subject were consistently around or below 15% for Glx, tCho, and Myo-Ins, and below 5% for tNAA and tCr.

Proton Free Induction Decay MRSI at 7T in the Human Brain Using an Egg-Shaped Modified Rosette K-Space Trajectory

Purpose

1.1 Proton ( 1 H)-MRSI via spatial-spectral encoding poses high demands on gradient hardware at ultra-high fields and high-resolutions. Rosette trajectories help alleviate these problems, but at reduced SNR-efficiency due to their k-space densities not matching any desired k-space filter. We propose modified rosette trajectories, which more closely match a Hamming filter, and thereby improve SNR performance while still staying within gradient hardware limitations and without prolonging scan time.

Methods

1.2Analytical and synthetic simulations were validated with phantom and in vivo measurements at 7 T. The rosette and modified rosette trajectories were measured in five healthy volunteers in six minutes in a 2D slice in the brain. A 3D sequence was measured in one volunteer within 19 minutes. The SNR, linewidth, CRLBs, lipid contamination and data quality of the proposed modified rosette trajectory were compared to the rosette trajectory.

Results

1.3Using the modified rosette trajectories, an improved k-space weighting function was achieved resulting in an increase of up to 12% in SNR compared to rosette's dependent on the two additional trajectory parameters. Similar results were achieved for the theoretical SNR calculation based on k-space densities, as well as when using the pseudo-replica method for simulated, in-vivo and phantom data. The CRLBs improved slightly, but non-significantly for the modified rosette trajectories, while the linewidths and lipid contamination remained similar.

Conclusion

1.4By improving the rosette trajectory's shape, modified rosette trajectories achieved higher SNR at the same scan time and data quality.

Investigating the structural network underlying brain-immune interactions using combined histopathology and neuroimaging: a critical review for its relevance in acute and long COVID-19

Current views on immunity support the idea that immunity extends beyond defense functions and is tightly intertwined with several other fields of biology such as virology, microbiology, physiology and ecology. It is also critical for our understanding of autoimmunity and cancer, two topics of great biological relevance and for critical public health considerations such as disease prevention and treatment. Central to this review, the immune system is known to interact intimately with the nervous system and has been recently hypothesized to be involved not only in autonomic and limbic bio-behaviors but also in cognitive function. Herein we review the structural architecture of the brain network involved in immune response. Furthermore, we elaborate upon the implications of inflammatory processes affecting brain-immune interactions as reported recently in pathological conditions due to SARS-Cov-2 virus infection, namely in acute and post-acute COVID-19. Moreover, we discuss how current neuroimaging techniques combined with ad hoc clinical autopsies and histopathological analyses could critically affect the validity of clinical translation in studies of human brain-immune interactions using neuroimaging. Advances in our understanding of brain-immune interactions are expected to translate into novel therapeutic avenues in a vast array of domains including cancer, autoimmune diseases or viral infections such as in acute and post-acute or Long COVID-19.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy

PURPOSE

Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE.

METHODS

Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression.

RESULTS

The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory.

CONCLUSION

This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Global trends in neonatal MRI brain neuroimaging research over the last decade: a bibliometric analysis

Background

Neuroimaging plays a central role in the evaluation, treatment, and prognosis of neonates. In recent years, the exploration of the developing brain has been a major focus of research for researchers and clinicians. In this study, we conducted bibliometric and visualization analyses of the related studies in the field of neonatal magnetic resonance imaging (MRI) brain neuroimaging from the past 10 years, and summarized its research status, hotspots, and frontier development trends.

Methods

The Web of Science core collection database was used as the literature source from which to retrieve the relevant papers and reviews in the field of neonatal MRI brain neuroimaging published in the Science Citation Index-Expanded from 2013 to 2022. VOSviewer and CiteSpace were used to conduct bibliometric and visualization analyses of the annual publication volume, countries, institutions, journals, authors, co-cited literature, and the overall distribution of keywords.

Results

We retrieved 3,568 papers and reviews published from 2013 to 2022. The number of publications increased during this period. The United States (US) and the United Kingdom were the largest contributors, with the US receiving the highest H-index and number of citations. The institutions that published the most were the University of London and Harvard University. The research mainly focused on cerebral cortex, brain tissue, brain structure network, artificial intelligence algorithm, automatic image segmentation, and premature infants.

Conclusions

This study reveals the research status and hotspots of magnetic resonance imaging in the field of neonatal brain neuroimaging in the past decade, which helps researchers to better understand the research status, hotspots, and frontier development trends.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

Purpose

The interest in applying and modeling dynamic MRS has recently grown. 2D modeling yields advantages for the precision of metabolite estimation in interrelated MRS data. However, it is unknown whether including all transients simultaneously in a 2D model without averaging (presuming a stable signal) performs similarly to 1D modeling of the averaged spectrum. Therefore, we systematically investigated the accuracy, precision, and uncertainty estimation of both described model approaches.

Methods

Monte Carlo simulations of synthetic MRS data were used to compare the accuracy and uncertainty estimation of simultaneous 2D multi-transient LCM with 1D-LCM of the average. 2,500 datasets per condition with different noise representations of a 64-transient MRS experiment at 6 signal-to-noise levels for two separate spin systems (scyllo-inositol and GABA) were analyzed. Additional datasets with different levels of noise correlation were also analyzed. Modeling accuracy was assessed by determining the relative bias of the estimated amplitudes against the ground truth, and modeling precision was determined by standard deviations and Cramér-Rao Lower Bounds (CRLB).

Results

Amplitude estimates for 1D- and 2D-LCM agreed well and showed similar level of bias compared to the ground truth. Estimated CRLBs agreed well between both models and with ground truth CRLBs. For correlated noise the estimated CRLBs increased with the correlation strength for the 1D-LCM but remained stable for the 2D-LCM.

Conclusion

Our results indicate that the model performance of 2D multi-transient LCM is similar to averaged 1D-LCM. This validation on a simplified scenario serves as necessary basis for further applications of 2D modeling.

The intra-individual reliability of 1 H-MRS measurement in the anterior cingulate cortex across 1 year

Magnetic resonance spectroscopy (MRS) is the primary method that can measure the levels of metabolites in the brain in vivo. To achieve its potential in clinical usage, the reliability of the measurement requires further articulation. Although there are many studies that investigate the reliability of gamma-aminobutyric acid (GABA), comparatively few studies have investigated the reliability of other brain metabolites, such as glutamate (Glu), N-acetyl-aspartate (NAA), creatine (Cr), phosphocreatine (PCr), or myo-inositol (mI), which all play a significant role in brain development and functions. In addition, previous studies which predominately used only two measurements (two data points) failed to provide the details of the time effect (e.g., time-of-day) on MRS measurement within subjects. Therefore, in this study, MRS data located in the anterior cingulate cortex (ACC) were repeatedly recorded across 1 year leading to at least 25 sessions for each subject with the aim of exploring the variability of other metabolites by using the index coefficient of variability (CV); the smaller the CV, the more reliable the measurements. We found that the metabolites of NAA, tNAA, and tCr showed the smallest CVs (between 1.43% and 4.90%), and the metabolites of Glu, Glx, mI, and tCho showed modest CVs (between 4.26% and 7.89%). Furthermore, we found that the concentration reference of the ratio to water results in smaller CVs compared to the ratio to tCr. In addition, we did not find any time-of-day effect on the MRS measurements. Collectively, the results of this study indicate that the MRS measurement is reasonably reliable in quantifying the levels of metabolites.

High-resolution proton metabolic mapping of the human brain at 7 T using free induction decay rosette spectroscopic imaging

Magnetic resonance spectroscopic imaging (MRSI) provides information about the spatial distribution of metabolites in the brain. These metabolite maps can be valuable in diagnosing central nervous system pathology. However, MRSI generally suffers from a long acquisition time, poor spatial resolution, and a low metabolite signal-to-noise ratio (SNR). Ultrahigh field strengths (≥ 7 T) can benefit MRSI with an improved SNR and allow high-resolution metabolic mapping. Non-Cartesian spatial-spectral encoding techniques, such as rosette spectroscopic imaging, can efficiently sample spatial and temporal domains, which significantly reduces the imaging time and enables high-resolution metabolic mapping in a clinically relevant scan time. In the current study, high-resolution (in-plane resolution of 2 × 2 mm2 ) mapping of proton (1 H) metabolites in the human brain at 7 T, is demonstrated. Five healthy subjects participated in the study. Using a time-efficient rosette trajectory and short TR/TE free induction decay MRSI, high-resolution maps of 1 H metabolites were obtained in a clinically relevant imaging time (6 min). Suppression of the water signal was achieved with an optimized water suppression enhanced through T1 effects approach and lipid removal was performed using L2 -regularization in the postprocessing. Spatial distributions of N-acetyl-aspartate, total choline, creatine, N-acetyl-aspartyl glutamate, myo-inositol, and glutamate were generated with Cramer-Rao lower bounds of less than 20%.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Neurometabolic alterations in children and adolescents with functional neurological disorder

OBJECTIVES

In vivo magnetic resonance spectroscopy (MRS) was used to investigate neurometabolic homeostasis in children with functional neurological disorder (FND) in three regions of interest: supplementary motor area (SMA), anterior default mode network (aDMN), and posterior default mode network (dDMN). Metabolites assessed included N-acetyl aspartate (NAA), a marker of neuron function; myo-inositol (mI), a glial-cell marker; choline (Cho), a membrane marker; glutamate plus glutamine (Glx), a marker of excitatory neurotransmission; γ-aminobutyric acid (GABA), a marker of inhibitor neurotransmission; and creatine (Cr), an energy marker. The relationship between excitatory (glutamate and glutamine) and inhibitory (GABA) neurotransmitter (E/I) balance was also examined.

METHODS

MRS data were acquired for 32 children with mixed FND (25 girls, 7 boys, aged 10.00 to 16.08 years) and 41 healthy controls of similar age using both short echo point-resolved spectroscopy (PRESS) and Mescher-Garwood point-resolved spectroscopy (MEGAPRESS) sequences in the three regions of interest.

RESULTS

In the SMA, children with FND had lower NAA/Cr, mI/Cr (trend level), and GABA/Cr ratios. In the aDMN, no group differences in metabolite ratios were found. In the pDMN, children with FND had lower NAA/Cr and mI/Cr (trend level) ratios. While no group differences in E/I balance were found (FND vs. controls), E/I balance in the aDMN was lower in children with functional seizures-a subgroup within the FND group. Pearson correlations found that increased arousal (indexed by higher heart rate) was associated with lower mI/Cr in the SMA and pDMN.

CONCLUSIONS

Our findings of multiple differences in neurometabolites in children with FND suggest dysfunction on multiple levels of the biological system: the neuron (lower NAA), the glial cell (lower mI), and inhibitory neurotransmission (lower GABA), as well as dysfunction in energy regulation in the subgroup with functional seizures.

Multiparametric MR Evaluation of the Photoperiodic Regulation of Hypothalamic Structures in Sheep

Most organisms on earth, humans included, have developed strategies to cope with environmental day-night and seasonal cycles to survive. For most of them, their physiological and behavioral functions, including the reproductive function, are synchronized with the annual changes of day length, to ensure winter survival and subsequent reproductive success in the following spring. Sheep are sensitive to photoperiod, which also regulates natural adult neurogenesis in their hypothalamus. We postulate that the ovine model represents a good alternative to study the functional and metabolic changes occurring in response to photoperiodic changes in hypothalamic structures of the brain. Here, the impact of the photoperiod on the neurovascular coupling and the metabolism of the hypothalamic structures was investigated at 3T using BOLD fMRI, perfusion-MRI and proton magnetic resonance spectroscopy (1H-MRS). A longitudinal study involving 8 ewes was conducted during long days (LD) and short days (SD) revealing significant BOLD, rCBV and metabolic changes in hypothalamic structures of the ewe brain between LD and SD. More specifically, the transition between LD and SD revealed negative BOLD responses to hypercapnia at the beginning of SD period followed by significant increases in BOLD, rCBV, Glx and tNAA concentrations towards the end of the SD period. These observations suggest longitudinal mechanisms promoting the proliferation and differentiation of neural stem cells within the hypothalamic niche of breeding ewes. We conclude that multiparametric MRI studies including 1H-MRS could be promising non-invasive translational techniques to investigate the existence of natural adult neurogenesis in-vivo in gyrencephalic brains.

Accuracy of Proton Magnetic Resonance Spectroscopy in Distinguishing Neoplastic From Non-neoplastic Brain Lesions

OBJECTIVE

To evaluate the advantage of a combination of magnetic resonance spectroscopy (MRS) and magnetic resonance imaging (MRI) over MRI in the diagnosis of intracranial mass lesions to differentiate between neoplastic and non-neoplastic lesions and compare them with histopathology and clinical data as gold standard.

METHODOLOGY

This was a descriptive cross-sectional study conducted at the Department of Radiology, Apollo Hospital located in Jubilee Hills, Hyderabad. In the present study, a total of 60 patients of all ages with brain masses found through MRI with positive clinical symptoms, regardless of gender, were included. We also involved patients with non-brain cancers suspected of spreading to the brain.

RESULT

MRI identified 63% of lesions as neoplastic and 37% as non-neoplastic. Combining MRI and MRS increased accuracy, with 65% of the lesions diagnosed as neoplastic and 35% as non-neoplastic, demonstrating that MRS significantly enhances diagnostic precision compared to MRI alone.

CONCLUSION

 This study aimed to see how combining MRI and MRS helps diagnose brain masses, comparing with histopathology as the gold standard. MRI alone identified 63% as neoplastic, but MRI with MRS improved accuracy (65%). MRI sensitivity was 87.80%, but combined with MRS, it increased to 92.68%. Thus, the study concluded that the combination of MRI and MRS is more accurate than MRI alone.

Unique magnetic resonance spectroscopy profile of intracranial meningiomas compared to gliomas: a systematic review

BACKGROUND AND PURPOSE

The goal of this study was to systematically review the metabolic profile of meningiomas using magnetic resonance spectroscopy in comparison to gliomas, as measured by mean metabolite ratios.

METHODS

Following the PRISMA guidelines, a systematic literature review was performed using the PubMed, Ovid Embase, Web of Science, and the Cochrane databases from inception to May 2021. Studies were selected based on predetermined inclusion and exclusion criteria.

RESULTS

Eight studies were ultimately selected with 207 patients included. Fifty-nine patients were diagnosed with meningioma (age = 48.4, 66.7% female) and 148 patients diagnosed with glioma (age = 56.4, 49.2% female). Three studies reported elevated Cho/Cr in meningiomas compared to gliomas (5.71 vs. 1.46, p < 0.05, 7.02 vs. 2.62, p < 0.05, and 4.64 vs. 2.52, p = 0.001). One study reported Ala/Cr to be significantly elevated in meningiomas compared to gliomas (1.30 vs. undetectable, p < 0.001). One study reported myo-Inositol/Cr to be significantly elevated in meningiomas in comparison to gliomas (1.44 vs. 1.08, p < 0.05). One study reported Glu/Cr to be significantly elevated in meningiomas in comparison to gliomas (3.47 vs. 0.89, p = 0.002). Two studies reported Cho/NAA to be significantly elevated in meningiomas in comparison to gliomas (4.46 vs. 2.6, p = 0.004, and 5.8 vs. 2.55, p < 0.05). Two studies reported NAA/Cr was significantly elevated in gliomas compared to meningiomas (undetectable vs. 1.54, p < 0.001 and undetectable vs. 0.58, p < 0.05).

CONCLUSIONS

Significant differences in metabolite ratios between tumor types were reported in Cho/Cr, Ala/Cr, Glu/Cr, Cho/NAA, myoI/Cr and NAA/Cr between meningiomas and gliomas.

Preliminary MRS study of critical values of relevant brain metabolites in elderly Chinese patients with post-stroke cognitive impairment

Objective

Proton magnetic resonance spectroscopy (1H-MRS) was applied in this study to detect metabolite changes in the brain of post-stroke cognitive impairment (PSCI) and normal volunteers. The levels of N-acetylaspartate (NAA) and creatinine (Cr) and in the frontal lobe, hippocampus and cingulate gyrus were measured to distinguish patients with post-stroke cognitive impairment (PSCI) and normal control group (NC). The relationship between them and cognitive function was explored and a critical value of the metabolite ratio was predicted. This study may serve as a reference for the diagnosis of cognitive dysfunction after stroke.

Methods

A total of 46 patients with PSCI (PSCI group, all patients are unilateral cerebral infarction or intracerebral haemorrhage) were screened by the Mini-Mental Status Examination (MMSE), and 35 healthy volunteers were selected as normal control group (NC group). The general information of gender, age, and education level was matched between the two groups. Two groups of subjects were examined using MRS and evaluated for cognitive function using the MMSE test and the Montreal Cognitive Assessment Scale (MoCA). The correlation between MRS and neurobehavioral scale (MMSE test and MoCA scale) was analysed, and the possible demarcation points of the brain metabolism of PSCI were evaluated.

Result

The MMSE and MoCA scores of patients with PSCI were lower significantly when compared with those of the NC group (P < 0.05). The NAA/Cr values of the bilateral hippocampus, bilateral frontal lobe and bilateral anterior and posterior cingulate gyrus in the PSCI group were lower than those in the NC group (P < 0.05). The NAA/Cr cut-off value for the right frontal lobe was 1.533, and the NAA/Cr sensitivity, specificity and Youden index for the right frontal lobe were 0.943, 0.935, and 0.878.

Conclusion

NAA/Cr values in the MRS bilateral frontal, bilateral hippocampus and bilateral anterior and posterior cingulate gyrus were reduced in the cognitively impaired post-stroke patients compared to the normal control group. MRS was also found to be correlated with the score of neurobehavioral scale (MMSE test and MoCA scale) and the combination of the two could evaluate cognitive dysfunction more comprehensively and objectively. NAA/Cr value of the right frontal lobe < 1.533 indicated that PSCI may occur. In accordance with this cut-off point, PSCI could be detected as early as possible and timely intervention could be carried out.

Multimodal magnetic resonance imaging on brain structure and function changes in vascular cognitive impairment without dementia

Vascular cognitive impairment not dementia (VCIND) is one of the three subtypes of vascular cognitive impairment (VCI), with cognitive dysfunction and symptoms ranging between normal cognitive function and vascular dementia. The specific mechanisms underlying VCIND are still not fully understood, and there is a lack of specific diagnostic markers in clinical practice. With the rapid development of magnetic resonance imaging (MRI) technology, structural MRI (sMRI) and functional MRI (fMRI) have become effective methods for exploring the neurobiological mechanisms of VCIND and have made continuous progress. This article provides a comprehensive overview of the research progress in VCIND using multimodal MRI, including sMRI, diffusion tensor imaging, resting-state fMRI, and magnetic resonance spectroscopy. By integrating findings from these multiple modalities, this study presents a novel perspective on the neuropathological mechanisms underlying VCIND. It not only highlights the importance of multimodal MRI in unraveling the complex nature of VCIND but also lays the foundation for future research examining the relationship between brain structure, function, and cognitive impairment in VCIND. These new perspectives and strategies ultimately hold the potential to contribute to the development of more effective diagnostic tools and therapeutic interventions for VCIND.

Modern preoperative imaging and functional mapping in patients with intracranial glioma

Magnetic resonance imaging (MRI) in therapy-naïve intracranial glioma is paramount for neuro-oncological diagnostics, and it provides images that are helpful for surgery planning and intraoperative guidance during tumor resection, including assessment of the involvement of functionally eloquent brain structures. This study reviews emerging MRI techniques to depict structural information, diffusion characteristics, perfusion alterations, and metabolism changes for advanced neuro-oncological imaging. In addition, it reflects current methods to map brain function close to a tumor, including functional MRI and navigated transcranial magnetic stimulation with derived function-based tractography of subcortical white matter pathways. We conclude that modern preoperative MRI in neuro-oncology offers a multitude of possibilities tailored to clinical needs, and advancements in scanner technology (e. g., parallel imaging for acceleration of acquisitions) make multi-sequence protocols increasingly feasible. Specifically, advanced MRI using a multi-sequence protocol enables noninvasive, image-based tumor grading and phenotyping in patients with glioma. Furthermore, the add-on use of preoperatively acquired MRI data in combination with functional mapping and tractography facilitates risk stratification and helps to avoid perioperative functional decline by providing individual information about the spatial location of functionally eloquent tissue in relation to the tumor mass. KEY POINTS::   · Advanced preoperative MRI allows for image-based tumor grading and phenotyping in glioma.. · Multi-sequence MRI protocols nowadays make it possible to assess various tumor characteristics (incl. perfusion, diffusion, and metabolism).. · Presurgical MRI in glioma is increasingly combined with functional mapping to identify and enclose individual functional areas.. · Advancements in scanner technology (e. g., parallel imaging) facilitate increasing application of dedicated multi-sequence imaging protocols.. CITATION FORMAT: · Sollmann N, Zhang H, Kloth C et al. Modern preoperative imaging and functional mapping in patients with intracranial glioma. Fortschr Röntgenstr 2023; 195: 989 - 1000.

Optimized multi-voxel TE-averaged PRESS for glutamate detection in the human brain at 3T

PURPOSE

To optimize possible combinations of echo times (TE) for multi-voxel TE-averaged Point RESolved Spectroscopy (PRESS) while reducing the total number of TEs required to separate glutamate (Glu) and glutamine (Gln) within a clinically feasible scan time.

METHODS

General Approach to Magnetic resonance Mathematical Analysis (GAMMA) was used to implement 2D J-resolved PRESS technique, and the spectra of 14 individual brain metabolites were simulated at 64 different TEs. Monte Carlo simulations were used for selecting the best TE combinations to separate Glu and Gln using TE-averaged PRESS with a total number of two, three, four and five TEs. Single-voxel 1H-MRS data were acquired using 64 different TEs from a healthy volunteer on a clinical 3T MR scanner to validate the echo time combinations selected with simulations. Additionally, 2D 1H-MRSI data of eight healthy volunteers were acquired on a clinical 3T MR scanner using four different TEs that were determined by Monte Carlo simulations. Optimized TE-averaged PRESS spectra were created by averaging the spectra acquired at selected TEs. LCModel was used for spectral quantification. A Wilcoxon signed-rank test was used to detect statistically significant differences in Glu/Gln ratios between 35 ms PRESS and optimized TE-averaged PRESS data.

RESULTS

Glu could be clearly separated from Gln at 2.35 ppm, using optimized TE-averaged PRESS with only four TEs (35, 37, 40, and 42 ms) that were selected through Monte Carlo simulations. Glu/Gln ratios were significantly higher in the optimized TE-averaged PRESS data of healthy volunteers than in the 35 ms PRESS data (P = 0.008).

CONCLUSION

Optimized multi-voxel TE-averaged PRESS enabled faster and unobstructed quantification of Glu at multiple voxels in the human brain in vivo at 3T.

Regional sex differences in neurochemical profiles of healthy mice measured by magnetic resonance spectroscopy at 9.4 tesla

Objective

To determine sex differences in the neurochemical concentrations measured by in vivo proton magnetic resonance spectroscopy (1H MRS) of healthy mice on a genetic background commonly used for neurodegenerative disease models.

Methods

1H MRS data collected from wild type mice with C57BL/6 or related genetic backgrounds in seven prior studies were used in this retrospective analysis. To be included, data had to be collected at 9.4 tesla magnetic field using advanced 1H MRS protocols, with isoflurane anesthesia and similar animal handling protocols, and a similar number of datasets from male and female mice had to be available for the brain regions analyzed. Overall, 155 spectra from female mice and 166 spectra from male mice (321 in total), collected from six brain regions (brainstem, cerebellum, cortex, hippocampus, hypothalamus, and striatum) at various ages were included.

Results

Concentrations of taurine, total creatine (creatine + phosphocreatine), ascorbate, glucose and glutamate were consistently higher in male vs. female mice in most brain regions. Striatum was an exception with similar total creatine in male and female mice. The sex difference pattern in the hypothalamus was notably different from other regions. Interaction between sex and age was significant for total creatine and taurine in the cerebellum and hippocampus.

Conclusion

Sex differences in regional neurochemical levels are small but significant and age-dependent, with consistent male-female differences across most brain regions. The neuroendocrine region hypothalamus displays a different pattern of sex differences in neurochemical levels. Differences in energy metabolism and cellular density may underlie the differences, with higher metabolic rates in females and higher osmoregulatory and antioxidant capacity in males.

Longitudinal cognitive function and brain metabolites in women receiving chemotherapy for stage 1 to 3 breast cancer: Observational study

Few proton magnetic resonance spectroscopy studies have explored chemotherapy-related biochemical changes in brain regions. This observational study aimed to longitudinally assess short-term cognitive changes and brain metabolite concentrations in women undergoing chemotherapy for breast cancer. We analyzed 11 women with newly diagnosed stage 1 to 3 breast cancer. Patients were evaluated via objective cognitive testing, and patient self-report tests. Patients were examined using single voxel proton magnetic resonance spectroscopy in the medial frontal cortex, posterior cingulate gyrus, and left thalamus at baseline and after the completion of chemotherapy on a 1.5 Tesla scanner. At the posttreatment evaluation as compared to baseline, 7 of the 10 (70%) patients reported worsening memory on the MD Anderson symptom inventory (annualized change = 1.82 ± 2.88, P = .08), while the delayed recall raw score of the Rey Osterrieth complex figure test did not change from pre- to post-chemotherapy (mean annualized change = 5.00 ± 14.38, P = .30). The annualized change in the creatine concentration in the posterior cingulate gyrus was statistically significant. The annualized change in the MD Anderson symptom inventory was negatively correlated with the annualized change in the medial frontal N-acetylaspartate (Spearman correlation coefficient [rho] = -0.78, P = .01) and positively correlated with the annualized change in the posterior cingulate gyrus creatine (rho = 0.66, P = .04). Annualized changes in the Rey Osterrieth complex figure test were positively correlated with annualized changes in choline (rho = 0.83, P = .01) in the medial frontal cortex, choline (rho = 0.76, P = .04) in the left thalamus, and creatine (rho = 0.73, P = .02) in the medial frontal cortex. Our data suggest that chemotherapy may lead to the worsening of self-reported memory function, which is associated with alterations in brain metabolites.

Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques

Multimodal imaging (MMI) has emerged as a powerful tool in clinical research, combining different imaging modes to acquire comprehensive information and enabling scientists and surgeons to study tissue identification, localization, metabolic activity, and molecular discovery, thus aiding in disease progression analysis. While multimodal instruments are gaining popularity, challenges such as non-standardized characteristics, custom software, inadequate commercial support, and integration issues with other instruments need to be addressed. The field of multimodal imaging or multiplexed imaging allows for simultaneous signal reproduction from multiple imaging strategies. Intraoperatively, MMI can be integrated into frameless stereotactic surgery. Recent developments in medical imaging modalities such as magnetic resonance imaging (MRI), and Positron Emission Topography (PET) have brought new perspectives to multimodal imaging, enabling early cancer detection, molecular tracking, and real-time progression monitoring. Despite the evidence supporting the role of MMI in surgical decision-making, there is a need for comprehensive studies to validate and perform integration at the intersection of multiple imaging technologies. They were integrating mass spectrometry-based technologies (e.g., imaging mass spectrometry (IMS), imaging mass cytometry (IMC), and Ion mobility mass spectrometry ((IM-IM) with medical imaging modalities, offering promising avenues for molecular discovery and clinical applications. This review emphasizes the potential of multi-omics approaches in tissue mapping using MMI integrated into desorption electrospray ionization (DESI) and matrix-assisted laser desorption ionization (MALDI), allowing for sequential analyses of the same section. By addressing existing knowledge gaps, this review encourages future research endeavors toward multi-omics approaches, providing a roadmap for future research and enhancing the value of MMI in molecular pathology for diagnosis.

Metabolic Insights into Iron Deposition in Relapsing-Remitting Multiple Sclerosis via 7 T Magnetic Resonance Spectroscopic Imaging

OBJECTIVE

To investigate the metabolic pattern of different types of iron accumulation in multiple sclerosis (MS) lesions, and compare metabolic alterations within and at the periphery of lesions and newly emerging lesions in vivo according to iron deposition.

METHODS

7 T MR spectroscopic imaging and susceptibility-weighted imaging was performed in 31 patients with relapsing-remitting MS (16 female/15 male; mean age, 36.9 ± 10.3 years). Mean metabolic ratios of four neuro-metabolites were calculated for regions of interest (ROI) of normal appearing white matter (NAWM), "non-iron" (lesion without iron accumulation on SWI), and three distinct types of iron-laden lesions ("rim": distinct rim-shaped iron accumulation; "area": iron deposition across the entire lesions; "transition": transition between "area" and "rim" accumulation shape), and for lesion layers of "non-iron" and "rim" lesions. Furthermore, newly emerging "non-iron" and "iron" lesions were compared longitudinally, as measured before their appearance and one year later.

RESULTS

Thirty-nine of 75 iron-containing lesions showed no distinct paramagnetic rim. Of these, "area" lesions exhibited a 65% higher mIns/tNAA (p = 0.035) than "rim" lesions. Comparing lesion layers of both "non-iron" and "rim" lesions, a steeper metabolic gradient of mIns/tNAA ("non-iron" +15%, "rim" +40%) and tNAA/tCr ("non-iron" -15%, "rim" -35%) was found in "iron" lesions, with the lesion core showing +22% higher mIns/tNAA (p = 0.005) and -23% lower tNAA/tCr (p = 0.048) in "iron" compared to "non-iron" lesions. In newly emerging lesions, 18 of 39 showed iron accumulation, with the drop in tNAA/tCr after lesion formation remaining significantly lower compared to pre-lesional tissue over time in "iron" lesions (year 0: p = 0.013, year 1: p = 0.041) as opposed to "non-iron" lesions (year 0: p = 0.022, year 1: p = 0.231).

CONCLUSION

7 T MRSI allows in vivo characterization of different iron accumulation types each presenting with a distinct metabolic profile. Furthermore, the larger extent of neuronal damage in lesions with a distinct iron rim was reconfirmed via reduced tNAA/tCr concentrations, but with metabolic differences in lesion development between (non)-iron-containing lesions. This highlights the ability of MRSI to further investigate different types of iron accumulation and suggests possible implications for disease monitoring.

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.

Ipsilesional Hippocampal GABA Is Elevated and Correlates With Cognitive Impairment and Maladaptive Neurogenesis After Cortical Stroke in Mice

BACKGROUND

Cognitive dysfunction is a frequent stroke sequela, but its pathogenesis and treatment remain unresolved. Involvement of aberrant hippocampal neurogenesis and maladaptive circuitry remodeling has been proposed, but their mechanisms are unknown. Our aim was to evaluate potential underlying molecular/cellular events implicated.

METHODS

Stroke was induced by permanent occlusion of the middle cerebral artery occlusion in 2-month-old C57BL/6 male mice. Hippocampal metabolites/neurotransmitters were analyzed longitudinally by in vivo magnetic resonance spectroscopy. Cognitive function was evaluated with the contextual fear conditioning test. Microglia, astrocytes, neuroblasts, interneurons, γ-aminobutyric acid (GABA), and c-fos were analyzed by immunofluorescence.

RESULTS

Approximately 50% of mice exhibited progressive post-middle cerebral artery occlusion cognitive impairment. Notably, immature hippocampal neurons in the impaired group displayed more severe aberrant phenotypes than those from the nonimpaired group. Using magnetic resonance spectroscopy, significant bilateral changes in hippocampal metabolites, such as myo-inositol or N-acetylaspartic acid, were found that correlated, respectively, with numbers of glia and immature neuroblasts in the ischemic group. Importantly, some metabolites were specifically altered in the ipsilateral hippocampus suggesting its involvement in aberrant hippocampal neurogenesis and remodeling processes. Specifically, middle cerebral artery occlusion animals with higher hippocampal GABA levels displayed worse cognitive outcome. Implication of GABA in this setting was supported by the amelioration of ischemia-induced memory deficits and aberrant hippocampal neurogenesis after blocking pharmacologically GABAergic neurotransmission, an intervention which was ineffective when neurogenesis was inhibited. These data suggest that GABA exerts its detrimental effect, at least partly, by affecting morphology and integration of newborn neurons into the hippocampal circuits.

CONCLUSIONS

Hippocampal GABAergic neurotransmission could be considered a novel diagnostic and therapeutic target for poststroke cognitive impairment.

How to 19F MRI: applications, technique, and getting started

Magnetic resonance imaging (MRI) plays a significant role in the routine imaging workflow, providing both anatomical and functional information. 19F MRI is an evolving imaging modality where instead of 1H, 19F nuclei are excited. As the signal from endogenous 19F in the body is negligible, exogenous 19F signals obtained by 19F radiofrequency coils are exceptionally specific. Highly fluorinated agents targeting particular biological processes (i.e., the presence of immune cells) have been visualised using 19F MRI, highlighting its potential for non-invasive and longitudinal molecular imaging. This article aims to provide both a broad overview of the various applications of 19F MRI, with cancer imaging as a focus, as well as a practical guide to 19F imaging. We will discuss the essential elements of a 19F system and address common pitfalls during acquisition. Last but not least, we will highlight future perspectives that will enhance the role of this modality. While not an exhaustive exploration of all 19F literature, we endeavour to encapsulate the broad themes of the field and introduce the world of 19F molecular imaging to newcomers. 19F MRI bridges several domains, imaging, physics, chemistry, and biology, necessitating multidisciplinary teams to be able to harness this technology effectively. As further technical developments allow for greater sensitivity, we envision that 19F MRI can help unlock insight into biological processes non-invasively and longitudinally.

Neuropsychological Evaluation and Functional Magnetic Resonance Imaging Tasks in the Preoperative Assessment of Patients with Brain Tumors: A Systematic Review

BACKGROUND

Current surgical treatment of gliomas relies on a function-preserving, maximally safe resection approach. Functional Magnetic Resonance Imaging (fMRI) is a widely employed technology for this purpose. A preoperative neuropsychological evaluation should accompany this exam. However, only a few studies have reported both neuropsychological tests and fMRI tasks for preoperative planning-the current study aimed to systematically review the scientific literature on the topic.

METHODS

PRISMA guidelines were followed. We included studies that reported both neuropsychological tests and fMRI. Exclusion criteria were: no brain tumors, underage patients, no preoperative assessment, resting-state fMRI only, or healthy sample population/preclinical studies.

RESULTS

We identified 123 papers, but only 15 articles were included. Eight articles focused on language; three evaluated cognitive performance; single papers studied sensorimotor cortex, prefrontal functions, insular cortex, and cerebellar activation. Two qualitative studies focused on visuomotor function and language. According to some authors, there was a strong correlation between performance in presurgical neuropsychological tests and fMRI. Several papers suggested that selecting well-adjusted and individualized neuropsychological tasks may enable the development of personalized and more efficient protocols. The fMRI findings may also help identify plasticity phenomena to avoid unintentional damage during neurosurgery.

CONCLUSIONS

Most studies have focused on language, the most commonly evaluated cognitive function. The correlation between neuropsychological and fMRI results suggests that altered functions during the neuropsychological assessment may help identify patients who could benefit from an fMRI and, possibly, functions that should be tested. Neuropsychological evaluation and fMRI have complementary roles in the preoperative assessment.

Protocol to conduct functional magnetic resonance spectroscopy in different age groups of human participants

We present a protocol to conduct functional magnetic resonance spectroscopy (fMRS) in human participants before, during, and after training on a visual task. We describe steps for participant setup, volume-of-interest placement, fMRS measurement, and post-scan tests. We discuss the design, analysis, and interpretation of fMRS experiments. This protocol can be adapted to investigate the dynamics of chief excitatory and inhibitory neurotransmitters (glutamate and γ-aminobutyric acid, GABA, respectively) while participants perform or learn perceptual, motor, or cognitive tasks. For complete details on the use and execution of this protocol, please refer to Frank et al. (2022).1.

Altered Brain Energy Metabolism Related to Astrocytes in Alzheimer's Disease

OBJECTIVE

Increasing evidence suggests that reactive astrocytes are associated with Alzheimer's disease (AD). However, its underlying pathogenesis remains unknown. Given the role of astrocytes in energy metabolism, reactive astrocytes may contribute to altered brain energy metabolism. Astrocytes are primarily considered glycolytic cells, suggesting a preference for lactate production. This study aimed to examine alterations in astrocytic activities and their association with brain lactate levels in AD.

METHODS

The study included 30 AD and 30 cognitively unimpaired participants. For AD participants, amyloid and tau depositions were confirmed by positron emission tomography using [11 C]PiB and [18 F]florzolotau, respectively. Myo-inositol, an astroglial marker, and lactate in the posterior cingulate cortex were quantified by magnetic resonance spectroscopy. These magnetic resonance spectroscopy metabolites were compared with plasma biomarkers, including glial fibrillary acidic protein as another astrocytic marker, and amyloid and tau positron emission tomography.

RESULTS

Myo-inositol and lactate levels were higher in AD patients than in cognitively unimpaired participants (p < 0.05). Myo-inositol levels correlated with lactate levels (r = 0.272, p = 0.047). Myo-inositol and lactate levels were positively associated with the Clinical Dementia Rating sum-of-boxes scores (p < 0.05). Significant correlations were noted between myo-inositol levels and plasma glial fibrillary acidic protein, tau phosphorylated at threonine 181 levels, and amyloid and tau positron emission tomography accumulation in the posterior cingulate cortex (p < 0.05).

INTERPRETATION

We found high myo-inositol levels accompanied by increased lactate levels in the posterior cingulate cortex in AD patients, indicating a link between reactive astrocytes and altered brain energy metabolism. Myo-inositol and plasma glial fibrillary acidic protein may reflect similar astrocytic changes as biomarkers of AD. ANN NEUROL 2023.

Evaluation of deep learning models for quality control of MR spectra

Purpose

While 3D MR spectroscopic imaging (MRSI) provides valuable spatial metabolic information, one of the hurdles for clinical translation is its interpretation, with voxel-wise quality control (QC) as an essential and the most time-consuming step. This work evaluates the accuracy of machine learning (ML) models for automated QC filtering of individual spectra from 3D healthy control and patient datasets.

Methods

A total of 53 3D MRSI datasets from prior studies (30 neurological diseases, 13 brain tumors, and 10 healthy controls) were included in the study. Three ML models were evaluated: a random forest classifier (RF), a convolutional neural network (CNN), and an inception CNN (ICNN) along with two hybrid models: CNN + RF, ICNN + RF. QC labels used for training were determined manually through consensus of two MRSI experts. Normalized and cropped real-valued spectra was used as input. A cross-validation approach was used to separate datasets into training/validation/testing sets of aggregated voxels.

Results

All models achieved a minimum AUC of 0.964 and accuracy of 0.910. In datasets from neurological disease and controls, the CNN model produced the highest AUC (0.982), while the RF model achieved the highest AUC in patients with brain tumors (0.976). Within tumor lesions, which typically exhibit abnormal metabolism, the CNN AUC was 0.973 while that of the RF was 0.969. Data quality inference times were on the order of seconds for an entire 3D dataset, offering drastic time reduction compared to manual labeling.

Conclusion

ML methods accurately and rapidly performed automated QC. Results in tumors highlights the applicability to a variety of metabolic conditions.

Atlas-Based Adaptive Hadamard-Encoded MR Spectroscopic Imaging at 3T

BACKGROUND

This study aimed to develop a time-efficient method of acquiring simultaneous, dual-slice MR spectroscopic imaging (MRSI) for the evaluation of brain metabolism.

METHODS

Adaptive Hadamard-encoded pulses were developed and integrated with atlas-based automatic prescription. The excitation profiles were evaluated via simulation, phantom and volunteer experiments. The feasibility of γ-aminobutyric acid (GABA)-edited dual-slice MRSI was also assessed.

RESULTS

The signal between slices in the dual-band MRSI was less than 1% of the slice profiles. Data from a homemade phantom containing separate, interfacing compartments of creatine and acetate solutions demonstrated ~0.4% acetate signal contamination relative to the amplitude in the excited creatine compartment. The normalized signal-to-noise ratios from atlas-based acquisitions in volunteers were found to be comparable between dual-slice, Hadamard-encoded MRSI and 3D acquisitions. The mean and standard deviation of the coefficients of variation for NAA/Cho from the repeated volunteer scans were 8.2% ± 0.8% and 10.1% ± 3.7% in the top and bottom slices, respectively. GABA-edited, dual-slice MRSI demonstrated simultaneous detection of signals from GABA and coedited macromolecules (GABA+) from both superior grey and deep grey regions of volunteers.

CONCLUSION

This study demonstrated a fully automated dual-slice MRSI acquisition using atlas-based automatic prescription and adaptive Hadamard-encoded pulses.

Brain Spectroscopy Analysis in Retired Soccer Players With Chronic Exposure to Mild Traumatic Brain Injuries

Soccer players are at risk of suffering cranial injuries in the short and long term. There is growing concern that this may lead to traumatic brain injury in soccer players. Magnetic resonance spectroscopy (MRS) is an analytical method that enables the measurement of changes in brain metabolites that usually occur before significant structural changes. This study aimed to use MRS to compare variations in brain metabolite levels between retired soccer players and a control group. Twenty retired professional soccer players and 22 controls underwent magnetic resonance imaging, including MRS sequences and Mini-Mental State Examination (MMSE). Metabolite analysis was conducted based on absolute concentration and relative ratios. N-acetyl-aspartate, choline, glutamate, glutamine, and myoinositol were the metabolites of interest for the statistical analysis. Retired soccer players had an average age of 57.8 years, whereas the control group had an average age of 63.2 years. Median cognitive evaluation score, assessed using the MMSE, was 28 [26-29] for athletes and 29 [28-30] for controls (p = 0.01). Uni- and multi-variate analyses of the absolute concentration of metabolites (mM) between former athletes and controls did not yield any statistically significant results. Comparison of metabolites to creatine ratio concentrations did not yield any statistically significant results. There were no changes in concentrations of brain metabolites that indicated brain metabolic changes in retired soccer players compared with controls.

1H magnetic resonance spectroscopic imaging of deuterated glucose and of neurotransmitter metabolism at 7 T in the human brain

Impaired glucose metabolism in the brain has been linked to several neurological disorders. Positron emission tomography and carbon-13 magnetic resonance spectroscopic imaging (MRSI) can be used to quantify the metabolism of glucose, but these methods involve exposure to radiation, cannot quantify downstream metabolism, or have poor spatial resolution. Deuterium MRSI (2H-MRSI) is a non-invasive and safe alternative for the quantification of the metabolism of 2H-labelled substrates such as glucose and their downstream metabolic products, yet it can only measure a limited number of deuterated compounds and requires specialized hardware. Here we show that proton MRSI (1H-MRSI) at 7 T has higher sensitivity, chemical specificity and spatiotemporal resolution than 2H-MRSI. We used 1H-MRSI in five volunteers to differentiate glutamate, glutamine, γ-aminobutyric acid and glucose deuterated at specific molecular positions, and to simultaneously map deuterated and non-deuterated metabolites. 1H-MRSI, which is amenable to clinically available magnetic-resonance hardware, may facilitate the study of glucose metabolism in the brain and its potential roles in neurological disorders.

Event-related functional magnetic resonance spectroscopy

Proton-Magnetic Resonance Spectroscopy (MRS) is a non-invasive brain imaging technique used to measure the concentration of different neurochemicals. "Single-voxel" MRS data is typically acquired across several minutes, before individual transients are averaged through time to give a measurement of neurochemical concentrations. However, this approach is not sensitive to more rapid temporal dynamics of neurochemicals, including those that reflect functional changes in neural computation relevant to perception, cognition, motor control and ultimately behaviour. In this review we discuss recent advances in functional MRS (fMRS) that now allow us to obtain event-related measures of neurochemicals. Event-related fMRS involves presenting different experimental conditions as a series of trials that are intermixed. Critically, this approach allows spectra to be acquired at a time resolution in the order of seconds. Here we provide a comprehensive user guide for event-related task designs, choice of MRS sequence, analysis pipelines, and appropriate interpretation of event-related fMRS data. We raise various technical considerations by examining protocols used to quantify dynamic changes in GABA, the primary inhibitory neurotransmitter in the brain. Overall, we propose that although more data is needed, event-related fMRS can be used to measure dynamic changes in neurochemicals at a temporal resolution relevant to computations that support human cognition and behaviour.

Integrating Neuroimaging Measures in Nursing Research

BACKGROUND

Medical and scientific advancement worldwide has led to a longer lifespan. With the population aging comes the risk of developing cognitive decline. The incorporation of neuroimaging measures in evaluating cognitive changes is limited in nursing research. The aim of this review is to introduce nurse scientists to neuroimaging measures employed to assess the association between brain and cognitive changes.

METHODS

Relevant literature was identified by searching CINAHL, Web of Science, and PubMed databases using the following keywords: "neuroimaging measures," "aging," "cognition," "qualitative scoring," "cognitive ability," "molecular," "structural," and "functional."

RESULTS

Neuroimaging measures can be categorized into structural, functional, and molecular imaging approaches. The structural imaging technique visualizes the anatomical regions of the brain. Visual examination and volumetric segmentation of select structural sequences extract information such as white matter hyperintensities and cerebral atrophy. Functional imaging techniques evaluate brain regions and underlying processes using blood-oxygen-dependent signals. Molecular imaging technique is the real-time visualization of biological processes at the cellular and molecular levels in a given region. Examples of biological measures associated with neurodegeneration include decreased glutamine level, elevated total choline, and elevated Myo-inositol.

DISCUSSION

Nursing is at the forefront of addressing upstream factors impacting health outcomes across a lifespan of a population at increased risk of progressive cognitive decline. Nurse researchers can become more facile in using these measures both in qualitative and quantitative methodology by leveraging previously gathered neuroimaging clinical data for research purposes to better characterize the associations between symptom progression, disease risk, and health outcomes.

A Pilot Study of Short-Course Oral Vitamin A and Aerosolised Diffuser Olfactory Training for the Treatment of Smell Loss in Long COVID

Background: Olfactory dysfunction (OD) is a common neurosensory manifestation in long COVID. An effective and safe treatment against COVID-19-related OD is needed. Methods: This pilot trial recruited long COVID patients with persistent OD. Participants were randomly assigned to receive short-course (14 days) oral vitamin A (VitA; 25,000 IU per day) and aerosolised diffuser olfactory training (OT) thrice daily (combination), OT alone (standard care), or observation (control) for 4 weeks. The primary outcome was differences in olfactory function by butanol threshold tests (BTT) between baseline and end-of-treatment. Secondary outcomes included smell identification tests (SIT), structural MRI brain, and serial seed-based functional connectivity (FC) analyses in the olfactory cortical network by resting-state functional MRI (rs-fMRI). Results: A total of 24 participants were randomly assigned to receive either combination treatment (n = 10), standard care (n = 9), or control (n = 5). Median OD duration was 157 days (IQR 127-175). Mean baseline BTT score was 2.3 (SD 1.1). At end-of-treatment, mean BTT scores were significantly higher for the combination group than control (p < 0.001, MD = 4.4, 95% CI 1.7 to 7.2) and standard care (p = 0.009) groups. Interval SIT scores increased significantly (p = 0.009) in the combination group. rs-fMRI showed significantly higher FC in the combination group when compared to other groups. At end-of-treatment, positive correlations were found in the increased FC at left inferior frontal gyrus and clinically significant improvements in measured BTT (r = 0.858, p < 0.001) and SIT (r = 0.548, p = 0.042) scores for the combination group. Conclusions: Short-course oral VitA and aerosolised diffuser OT was effective as a combination treatment for persistent OD in long COVID.

SLIPMAT: a pipeline for extracting tissue-specific spectral profiles from 1H MR spectroscopic imaging data

¹H Magnetic Resonance Spectroscopy (MRS) is an important non-invasive tool for measuring brain metabolism, with numerous applications in the neuroscientific and clinical domains. In this work we present a new analysis pipeline (SLIPMAT), designed to extract high-quality, tissue-specific, spectral profiles from MR spectroscopic imaging data (MRSI). Spectral decomposition is combined with spatially dependant frequency and phase correction to yield high SNR white and grey matter spectra without partial-volume contamination. A subsequent series of spectral processing steps are applied to reduce unwanted spectral variation, such as baseline correction and linewidth matching, before direct spectral analysis with machine learning and traditional statistical methods. The method is validated using a 2D semi-LASER MRSI sequence, with a 5-minute duration, from data acquired in triplicate across 8 healthy participants. Reliable spectral profiles are confirmed with principal component analysis, revealing the importance of total-choline and scyllo-inositol levels in distinguishing between individuals - in good agreement with our previous work. Furthermore, since the method allows the simultaneous measurement of metabolites in grey and white matter, we show the strong discriminative value of these metabolites in both tissue types for the first time. In conclusion, we present a novel and time efficient MRSI acquisition and processing pipeline, capable of detecting reliable neuro-metabolic differences between healthy individuals, and suitable for the sensitive neurometabolic profiling of in-vivo brain tissue.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.