Magnetic Resonance Spectroscopy Data Research Articles

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning.

Deep learning-based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning-based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR). To investigate a convolutional neural network-based SR (CNN-SR) approach for simultaneous frequency-and-phase correction (FPC) of single-voxel Meshcher-Garwood point-resolved spectroscopy (MEGA-PRESS) MRS data. Retrospective. Forty thousand simulated MEGA-PRESS datasets generated from FID Appliance (FID-A) were used and split into the following: 32,000/4000/4000 for training/validation/testing. A 101 MEGA-PRESS medial parietal lobe data retrieved from the Big GABA were used as the in vivo datasets. 3T, MEGA-PRESS. Evaluation of frequency and phase offsets mean absolute errors were performed for the simulation dataset. Evaluation of the choline interval variance was performed for the in vivo dataset. The magnitudes of the offsets introduced were -20 to 20 Hz and -90° to 90° and were uniformly distributed for the simulation dataset at different signal-to-noise ratio (SNR) levels. For the in vivo dataset, different additional magnitudes of offsets were introduced: small offsets (0-5 Hz; 0-20°), medium offsets (5-10 Hz; 20-45°), and large offsets (10-20 Hz; 45-90°). Two-tailed paired t-tests for model performances in the simulation and in vivo datasets were used and a P-value <0.05 was considered statistically significant. CNN-SR model was capable of correcting frequency offsets (0.014 ± 0.010 Hz at SNR 20 and 0.058 ± 0.050 Hz at SNR 2.5 with line broadening) and phase offsets (0.104 ± 0.076° at SNR 20 and 0.416 ± 0.317° at SNR 2.5 with line broadening). Using in vivo datasets, CNN-SR achieved the best performance without (0.000055 ± 0.000054) and with different magnitudes of additional frequency and phase offsets (i.e., 0.000062 ± 0.000068 at small, -0.000033 ± 0.000023 at medium, 0.000067 ± 0.000102 at large) applied. The proposed CNN-SR method is an efficient and accurate approach for simultaneous FPC of single-voxel MEGA-PRESS MRS data. 4 TECHNICAL EFFICACY: Stage 2.

Predicting TERT promoter mutation status using 1H-MR spectroscopy and stretched-exponential model of diffusion-weighted imaging in IDH-wildtype diffuse astrocytic glioma without intense enhancement.

Isocitrate dehydrogenase (IDH)-wildtype diffuse astrocytic glioma with telomerase reverse transcriptase (TERT) promoter mutation is defined as glioblastoma by the WHO 2021 criteria, revealing that TERT promotor mutation is highly associated with tumor aggressiveness. The aim of this study was to identify features from MR spectroscopy (MRS) and multi-exponential models of DWI distinguishing wild-type TERT (TERTw) from TERT promoter mutation (TERTm) in IDH-wildtype diffuse astrocytic glioma. Participants comprised 25 adult patients with IDH-wildtype diffuse astrocytic glioma. Participants were classified into TERTw and TERTm groups. Point-resolved spectroscopy sequences were used for MRS data acquisition. DWI was performed with 13 different b-factors. Peak height ratios of NAA/Cr and Cho/Cr were calculated from MRS data. Mean apparent diffusion coefficient (ADC), perfusion fraction (f), diffusion coefficient (D), pseudo-diffusion coefficient (D*), distributed diffusion coefficient (DDC), and heterogeneity index (α) were obtained using multi-exponential models from DWI data. Each parameter was compared between TERTw and TERTm using the Mann-Whitney U test. Correlations between parameters derived from MRS and DWI were also evaluated. NAA/Cr and Cho/Cr were both higher for TERTw than for TERTm. The α of TERTw was smaller than that of TERTm, while the f of TERTw was higher than that of TERTm. NAA/Cr correlated negatively with α, but not with other DWI parameters. Cho/Cr did not show significant correlations with any DWI parameters. The combination of NAA/Cr and α may have merit in clinical situation to predict the TERT mutation status of IDH-wildtype diffuse astrocytic glioma without intense enhancement.

Some features of magnetic resonance spectroscopy metabolites in diagnostics of epidermoid tumor of the posterior cranial fossa

This article provides an analysis of the data of magnetic resonance spectroscopy in dermoid tumor of the postcranial fossa, the features of metabolites in the focus and along the periphery of the tumor process. Information about the surgical treatment of cholesteatoma by suboccipital paramedian access in the early and late postoperative period is given.

Measuring Brain Temperature in Youth Bipolar Disorder Using a Novel Magnetic Resonance Imaging Approach: A Proof-of-concept Study.

There is evidence of alterations in mitochondrial energy metabolism and cerebral blood flow (CBF) in adults and youth with bipolar disorder (BD). Brain thermoregulation is based on the balance of heat-producing metabolism and heat-dissipating mechanisms, including CBF. To examine brain temperature, and its relation to CBF, in relation to BD and mood symptom severity in youth. This study included 25 youth participants (age 17.4 ± 1.7 years; 13 BD, 12 control group (CG)). Magnetic resonance spectroscopy data were acquired to obtain brain temperature in the left anterior cingulate cortex (ACC) and the left precuneus. Regional estimates of CBF were provided by arterial spin labeling imaging. Analyses used general linear regression models, covarying for age, sex, and psychiatric medications. Brain temperature was significantly higher in BD compared to CG in the precuneus. A higher ratio of brain temperature to CBF was significantly associated with greater depression symptom severity in both the ACC and precuneus within BD. Analyses examining the relationship of brain temperature or CBF with depression severity score did not reveal any significant finding in the ACC or the precuneus. The current study provides preliminary evidence of increased brain temperature in youth with BD, in whom reduced thermoregulatory capacity is putatively associated with depression symptom severity. Evaluation of brain temperature and CBF in conjunction may provide valuable insight beyond what can be gleaned by either metric alone. Larger prospective studies are warranted to further evaluate brain temperature and its association with CBF concerning BD.

Neurochemistry-enriched dynamic causal models of magnetoencephalography, using magnetic resonance spectroscopy

We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters’ concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals’ neurophysiological observations. At the second level, individuals’ 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

A 3D method called mdMRS for post-processing Magnetic Resonance Spectroscopy data

The data obtained from a scanner for Magnetic Resonance Spectroscopy (MRS) is three-dimensional (3D) since the FID data from the scanner has spectrum values which are 2D complex numbers and 1D time values. This paper describes an MRS frequency-based post-processing method that has the advantage that it quickly determines the values needed for metabolite intensities and concentrations, even for most overlapping spectral terms.The method of this paper does not depend on area under a curve or a user-chosen basis set. As few as three valid points corresponding to a peak on the spectral curve are all that is needed, other than similar information on a reference metabolite, for concentration determination of the associated metabolite. All four of the key values of peak location, amplitude, phase angle, and damping constant are determined simultaneously with high accuracy, dependent upon noise, and with computational simplicity from only three complex-valued constants. The theory behind mdMRS uses the knowledge that the projection of the 3D spectrum to the complex plane is a simple circle. Several concepts from complex variables theory are important, such as the Linear Fractional Transformation (LFT) that maps the frequency axis to that circle. The duality linking an LFT with a 2 × 2 Moebius matrix enables a fast iteration process that sharpens the four key value estimates on each iteration. The iteration removes all other terms when considering one of them. Applied to initial estimates, this leads to increasingly accurate output.To be useful to clinicians and to researchers developing treatments based on metabolite concentrations, the goal for post-processing of the data include both fast and accurate computations, with speed sufficient to provide results “on console” and output provided as concentrations. Besides the number of protons associated with a metabolite, concentrations are determined using both amplitude and damping constant values. Since the methods of mdMRS provide both of those characteristics simultaneously, the time from data collection to metabolite concentration output is minimized. The name of this new post-processing method was chosen since the attributes of the method help bring that goal closer to reality for Medical Doctors.

Comparison of different approaches to manage multi-site magnetic resonance spectroscopy clinical data analysis.

The effects caused by differences in data acquisition can be substantial and may impact data interpretation in multi-site/scanner studies using magnetic resonance spectroscopy (MRS). Given the increasing use of multi-site studies, a better understanding of how to account for different scanners is needed. Using data from a concussion population, we compare ComBat harmonization with different statistical methods in controlling for site, vendor, and scanner as covariates to determine how to best control for multi-site data. The data for the current study included 545 MRS datasets to measure tNAA, tCr, tCho, Glx, and mI to study the pediatric concussion acquired across five sites, six scanners, and two different MRI vendors. For each metabolite, the site and vendor were accounted for in seven different models of general linear models (GLM) or mixed-effects models while testing for group differences between the concussion and orthopedic injury. Models 1 and 2 controlled for vendor and site. Models 3 and 4 controlled for scanner. Models 5 and 6 controlled for site applied to data harmonized by vendor using ComBat. Model 7 controlled for scanner applied to data harmonized by scanner using ComBat. All the models controlled for age and sex as covariates. Models 1 and 2, controlling for site and vendor, showed no significant group effect in any metabolites, but the vendor and site were significant factors in the GLM. Model 3, which included a scanner, showed a significant group effect for tNAA and tCho, and the scanner was a significant factor. Model 4, controlling for the scanner, did not show a group effect in the mixed model. The data harmonized by the vendor using ComBat (Models 5 and 6) had no significant group effect in both the GLM and mixed models. Lastly, the data harmonized by the scanner using ComBat (Model 7) showed no significant group effect. The individual site data suggest there were no group differences. Using data from a large clinical concussion population, different analysis techniques to control for site, vendor, and scanner in MRS data yielded different results. The findings support the use of ComBat harmonization for clinical MRS data, as it removes the site and vendor effects.

Real-Time Semi-Automated and Automated Voxel Placement using fMRI Targets for Repeated Acquisition Magnetic Resonance Spectroscopy

BackgroundCurrently, magnetic resonance spectroscopy (MRS) is dependent on the investigative team to manually prescribe, or demarcate, the desired tissue volume-of-interest. The need for a new method to automate precise voxel placements is warranted to improve the utility and interpretability of MRS data. New MethodWe propose and validate robust and real-time methods to automate MRS voxel placement using functionally defined coordinates within the prefrontal cortex. Data were collected and analyzed using two independent prospective studies: 1) two independent imaging days with each consisting of a multi-session sandwich design (MRS data only collected on one of the days determined based on scan time) and 2) a longitudinal design. Participants with fibromyalgia syndrome (N = 50) and major depressive disorder (N = 35) underwent neuroimaging. MRS acquisitions were acquired at 3-tesla. Evaluation of the reproducibility of spatial location and tissue segmentation was assessed for: 1) manual, 2) semi-automated, and 3) automated voxel prescription approaches ResultsVariability of voxel grey and white matter tissue composition was reduced using automated placement protocols. Spatially, post- to pre-voxel center-of-gravity distance was reduced and voxel overlap increased significantly across datasets using automated compared to manual procedures Comparison with existing methodsManual prescription, the current standard in the field, can produce inconsistent data across repeated acquisitions. Using automated voxel placement, we found reduced variability and more consistent voxel placement across multiple acquisitions ConclusionsThese results demonstrate the within subject reliability and reproducibility of a method for reducing variability introduced by spatial inconsistencies during MRS acquisitions. The proposed method is a meaningful advance toward improved consistency of MRS data in neuroscience and can be utilized for multi-session and longitudinal studies.

Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data

PurposeWhile the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. MethodWe present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. ResultThe validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. ConclusionThe proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.

Primary motor area-related injury of anterior central gyrus in Parkinson's disease with dyskinesia: A study based on MRS and Q-space

IntroductionBy using magnetic resonance spectroscopy (MRS) and Q-Space imaging technology, this research analyzes the imaging characteristics of white matter fibers in the primary motor cortex and posterior limbs of the subcortical internal capsule in parkinsonian patients with motor disorders. The correlation among the changes in axonal function and structure in the cerebral cortex and subcortical cortex and motor disorder is further revealed. MethodsFirst, motor function and clinical condition of 20 patients with Parkinson's disease is assessed the third section of the Unified Parkinson's Scale and H&Y Parkinson's Clinical Staging Scale. Magnetic resonance (MR) scanning is performed with 1H-MRS. Secondly, the range maps of N-acetylaspartic acid (NAA), Choline (Cho), and Creatine (Cr) in the region of interest (the primary motor area of anterior central cortex gyrus, i.e. M1 region) are obtained, and the ratios of NAA/Cr and Cho are calculated. Third, Q-Space MR diffusion imaging technique is used to collect Q-Space images, and a Dsi-studio workstation is used to post-process the images. The fraction anisotropic (FA), generalized fraction anisotropic (GFA), and apparent diffusion coefficient (ADC) parameters of Q-Space in the primary motor cortex and the region of interest in the posterior limb of the internal capsule are obtained. Finally, the parameters of MRS and Q-Space in the experimental group and the control group are further analyzed by SPSS statistical software. ResultsAfter assessing with Parkinson's score scale, there is obvious motor dysfunction in the experimental group. The average clinical stage of H&Y is 3.0 ± 0.31. In the analysis of MRS data, the ratio of NAA/Cr in the primary motor area of the anterior central gyrus in the experimental group is significantly lower than that in the control group (P < 0.05). In the ADC map obtained by Q-Space imaging technique, the ADC value in the primary motor area of the anterior central gyrus in the experimental group is higher than that in the control group (P < 0.05), and the difference is statistically significant (P < 0.05). There is no significant difference between the experimental group and the control group (P > 0.05) in FA and GFA values of the posterior limb of capsule to characterize the characteristics of white matter fibers. ConclusionsIn parkinsonian patients with motor dysfunction, there are apparent functional and structural changes in the primary motor area neurons and peripheral white matter of the anterior central gyrus, and no obvious damage to the axonal structure of the descending fibers in the cortex.

Altered behavior, brain structure, and neurometabolites in a rat model of autism-specific maternal autoantibody exposure

Maternal immune dysregulation is a prenatal risk factor for autism spectrum disorder (ASD). Importantly, a clinically relevant connection exists between inflammation and metabolic stress that can result in aberrant cytokine signaling and autoimmunity. In this study we examined the potential for maternal autoantibodies (aAbs) to disrupt metabolic signaling and induce neuroanatomical changes in the brains of exposed offspring. To accomplish this, we developed a model of maternal aAb exposure in rats based on the clinical phenomenon of maternal autoantibody-related ASD (MAR-ASD). Following confirmation of aAb production in rat dams and antigen-specific immunoglobulin G (IgG) transfer to offspring, we assessed offspring behavior and brain structure longitudinally. MAR-ASD rat offspring displayed a reduction in pup ultrasonic vocalizations and a pronounced deficit in social play behavior when allowed to freely interact with a novel partner. Additionally, longitudinal in vivo structural magnetic resonance imaging (sMRI) at postnatal day 30 (PND30) and PND70, conducted in a separate cohort of animals, revealed sex-specific differences in total and regional brain volume. Treatment-specific effects by region appeared to converge on midbrain and cerebellar structures in MAR-ASD offspring. Simultaneously, in vivo 1H magnetic resonance spectroscopy (1H-MRS) data were collected to examine brain metabolite levels in the medial prefrontal cortex. Results showed that MAR-ASD offspring displayed decreased levels of choline-containing compounds and glutathione, accompanied by increased taurine compared to control animals. Overall, we found that rats exposed to MAR-ASD aAbs present with alterations in behavior, brain structure, and neurometabolites; reminiscent of findings observed in clinical ASD.

Association between altered metabolism and genetic mutations in human glioma.

Molecular markers for classification of gliomas include isocitrate dehydrogenase (IDH) mutations and codeletion of chromosomal arms 1p and 19q (1p/19q). While mutations in IDH enzymes result in the well-characterized production of oncometabolite 2-hydroxyglutarate, dysregulation of other metabolites in IDH tumors is less characterized. Similarly, the effects of 1p/19q codeletion on cellular metabolism are also unclear. This study aimed to quantify changes in tumor metabolites in human glioma tissue as a function of both IDH mutation and 1p/19q codeletion. Deidentified human glioma tissue and associated clinical data were obtained from the Emory University Winship Cancer Institute tissue biobank from 14 patients (WHO grades II, III, and IV; seven female and seven male). Proton (1 H) high-resolution magic angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy data were acquired using a 600 MHz Bruker AVANCE III NMR spectrometer. Metabolite concentrations were calculated using LCModel. Differences in metabolite concentrations as a function of IDH mutation, 1p/19q codeletion, and survival status were determined using Mann-Whitney U tests. Concentrations of alanine, glutamine, and glutamate were significantly lower in glioma tissue with IDH mutations compared to tissue with IDH wildtype. Additionally, glutamate concentration was significantly lower in glioma tissue with 1p/19q codeletion compared to intact 1p/19q. Exploratory analysis revealed alanine concentration varied significantly as a function of survival status. Given the emerging landscape of glioma treatments that target metabolic dysregulation, an improved understanding of altered metabolism in molecular sub-types of gliomas, including those with IDH mutation and 1p/19q codeletion, is an important consideration for treatment stratification and personalized medicine.

Brain metabolites measured with magnetic resonance spectroscopy in pediatric concussion and orthopedic injury: An Advancing Concussion Assessment in Pediatrics (A-CAP)study.

Millions of children sustain a concussion annually. Concussion disrupts cellular signaling and neural pathways within the brain but the resulting metabolic disruptions are not well characterized. Magnetic resonance spectroscopy (MRS) can examine key brain metabolites (e.g., N-acetyl Aspartate (tNAA), glutamate (Glx), creatine (tCr), choline (tCho), and myo-Inositol (mI)) to better understand these disruptions. In this study, we used MRS to examine differences in brain metabolites between children and adolescents with concussion versus orthopedic injury. Children and adolescents with concussion (n=361) or orthopedic injury (OI) (n=184) aged 8 to 17 years were recruited from five emergency departments across Canada. MRS data were collected from the left dorsolateral prefrontal cortex (L-DLPFC) using point resolved spectroscopy (PRESS) at 3T at a mean of 12 days post-injury (median 10 days post-injury, range 2-33 days). Univariate analyses for each metabolite found no statistically significant metabolite differences between groups. Within each analysis, several covariates were statistically significant. Follow-up analyses designed to account for possible confounding factors including age, site, scanner,vendor, time since injury, and tissue type (and interactions as appropriate) did not find any metabolite group differences. In the largest sample of pediatric concussion studied with MRS to date, we found no metabolite differences between concussion and OI groups in the L-DLPFC. We suggest that at 2 weeks post-injury in a general pediatric concussion population, brain metabolites in the L-DLPFC are not specifically affected by brain injury.

Neurochemical correlations in short echo time proton magnetic resonance spectroscopy.

Neurochemical concentrations determined by magnetic resonance spectroscopy (MRS) have been treated as statistically independent measurements in various clinical MRS studies. However, spectral overlap, independent of any biological effects, could lead to significant correlations between neurochemical concentrations extracted from spectral fitting of MRS data, confounding determination of correlations of biological origin. Short echo time (TE) proton MRS spectra are very crowded because of the comparatively narrow chemical shift dispersion of proton nuclear spins. In this study, the complex neurochemical correlations of spectral origin in short-TE MRS spectra were quantified. The effects of macromolecules and the background spectral baseline on metabolite-metabolite correlations were also analyzed. Our results demonstrate the importance of factoring in spectral correlations when correlating overlapping metabolite signals in short-TE spectra with clinical parameters.

Cycloartanes and pentacyclic triterpenes from Awka and ijebuode propolis and evaluation of their antimicrobial activity

Chemical investigation of propolis samples from southern Nigeria led to the isolation of cycloartanetype triterpenes namely: 24-methylene cycloartanol and ambonic acid along with pentacyclic triterpenes: Lupeol and α, β-amyrins. All compounds were identified and structures elucidated using proton nuclear magnetic resonance (1HNMR) spectroscopic data and comparison with literature.

Correction to ‘ORYX‐MRSI: A fully‐automated open‐source software for proton magnetic resonance spectroscopic imaging data analysis’

International Journal of Imaging Systems and TechnologyVolume 33, Issue 2 p. 770-770 CORRIGENDUMFree Access Correction to ‘ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis’ This article corrects the following: ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis Sevim Cengiz, Muhammed Yildirim, Abdullah Bas, Esin Ozturk-Isik, Volume 32Issue 4International Journal of Imaging Systems and Technology pages: 1068-1083 First Published online: May 17, 2022 First published: 27 January 2023 https://doi.org/10.1002/ima.22857AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat We regret to report a technical error in our manuscript entitled ‘ORYX-MRSI: A fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis’. The volume of interest (VOI) for all the MR spectroscopic metabolites, other than the reference metabolite, are shifted in space by their respective chemical shift amounts as calculated by Equation 2 due to the chemical shift error. Even though there is a shift in the VOIs of different metabolites, it was incorrect to shift the metabolite maps along with their corresponding VOIs. This technical error has been corrected in the Oryx-MRSI source code, which has been updated on our laboratory's GitHub page. Currently, if the chemical shift correction is set to ‘on’, the corresponding VOIs for the metabolites are shifted in space by their respective chemical shift amounts, and the metabolite intensities are evaluated at their respective locations. Additionally, the metabolite ratios are evaluated only in the region that is located at the intersection of the corresponding metabolite VOIs. Moreover, the tissue fractions are calculated only once at the reference VOI. As a result, Figure 5 is not accurate after correction and hence should not be considered for further discussion. The authors thank Wolfgang Bogner, Ph.D. and Gilbert Hangel, Ph.D. for their careful reviews of the published manuscript and apologize for this error. REFERENCE 1Cengiz S, Yildirim M, Bas A, Ozturk-Isik E. ORYX-MRSI: a fully-automated open-source software for proton magnetic resonance spectroscopic imaging data analysis. Int J Imaging Syst Technol. 2022; 32: 1068- 1083. doi:10.1002/ima.22748 Volume33, Issue2March 2023Pages 770-770 ReferencesRelatedInformation

13C NMR spectroscopic data of aporphine alkaloids.

This study involves aporphine alkaloids identified through 13C Nuclear Magnetic Resonance (NMR) spectroscopic data. For the present publication, articles were selected from several databases on aporphine alkaloids from 1994 to 2021. In this class, more than 700 compounds have been registered, with 221 were included in this section, among which 122 were characterized for the first time in the investigated period. The study also addresses their biosynthetic pathways, classifying substances according to their structural characteristics based on established literature. Furthermore, pharmacological activities related to the aporphine alkaloids highlighted in this section are also presented, giving an overview of the various applications of these compounds.

Exploiting reduced-symmetry ligands with pyridyl and imidazole donors to construct a second-generation stimuli-responsive heterobimetallic [PdPtL4]4+ cage.

A new sequential metalation strategy that enables the assembly of a new more robust reduced symmetry heterobimetallic [PdPtL4]4+ cage C is reported. By exploiting a low-symmetry ditopic ligand (L) that features imidazole and pyridine donor units we were able to selectively form a [Pt(L)4]2+ "open-cage" complex. When this was treated with Pd(ii) ions the cage C assembled. 1H and DOSY nuclear magnetic resonance (NMR) spectroscopy and electrospray ionisation mass spectrometry (ESIMS) data were consistent with the quantitative formation of the cage and the heterobimetallic structure was confirmed by single crystal X-ray crystallography. The cage C was shown to bind anionic guest molecules. NMR studies suggested that these guests interacted with the cavity of the cage in a specific orientation and this was confirmed for the mesylate ion (MsO-) : C host-guest adduct using X-ray crystallography. In addition, the system was shown to be stimulus-responsive and could be opened and closed on demand when treated with appropriate stimuli. If a guest molecule was bound within the cage, the opening and closing was accompanied by the release and re-uptake of the guest molecule.

Evaluation of Two Post-Processing Analysis Methods of Proton Magnetic Resonance Spectroscopy in Glioma Tumors.

Magnetic resonance spectroscopy (MRS) is a non-invasive diagnostic and the neuroimaging method of choice for the noninvasive monitoring of brain metabolism in patients with glioma tumors. 1H-MRS is a reliable and non-invasive tool used to study glioma. However, the metabolite spectra obtained by 1H-MRS requires a specific quantification procedure for post-processing. According to our knowledge, no comparisons have yet been made between spectrum analysis software for quantification of gliomas metabolites. Current study aims to evaluate the difference between this two common software in quantifying cerebral metabolites. In this analytical study, we evaluate two post-processing software packages, java-based graphical for MR user interface packages (jMRUI) and totally automatic robust quantitation in NMR (TARQUIN) software. 1H-MRS spectrum from the brain of patients with gliomas tumors was collected for post-processing. AMARES algorithms were conducted to metabolite qualification on jMRUI software, and TARQUIN software were implemented with automated quantification algorithms. The study included a total of 30 subjects. For quantification, subjects were divided into a normal group (n=15) and group of gliomas (n=15). When calculated by TARQUIN, the mean metabolites ratio was typically lower than by jMRUI. While, the mean ratio of metabolites varied when quantified by jMRUI vs. TARQUIN, both methods apparent clinical associations. TARQUIN and jMRUI are feasible choices for the post-processing of cerebral MRS data obtained from glioma tumors.

Parallel detection of chemical reactions in a microfluidic platform using hyperpolarized nuclear magnetic resonance.

The sensitivity of NMR may be enhanced by more than four orders of magnitude via dissolution dynamic nuclear polarization (dDNP), potentially allowing real-time, in situ analysis of chemical reactions. However, there has been no widespread use of the technique for this application and the major limitation has been the low experimental throughput caused by the time-consuming polarization build-up process at cryogenic temperatures and fast decay of the hyper-intense signal post dissolution. To overcome this limitation, we have developed a microfluidic device compatible with dDNP-MR spectroscopic imaging methods for detection of reactants and products in chemical reactions in which up to 8 reactions can be measured simultaneously using a single dDNP sample. Multiple MR spectroscopic data sets can be generated under the same exact conditions of hyperpolarized solute polarization, concentration, pH, and temperature. A proof-of-concept for the technology is demonstrated by identifying the reactants in the decarboxylation of pyruvate via hydrogen peroxide (e.g. 2-hydroperoxy-2-hydroxypropanoate, peroxymonocarbonate and CO2). dDNP-MR allows tracing of fast chemical reactions that would be barely detectable at thermal equilibrium by MR. We envisage that dDNP-MR spectroscopic imaging combined with microfluidics will provide a new high-throughput method for dDNP enhanced MR analysis of multiple components in chemical reactions and for non-destructive in situ metabolic analysis of hyperpolarized substrates in biological samples for laboratory and preclinical research.