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Purpose: Medulloblastoma is the most common malignant brain tumor occurring in childhood and is a significant cause of morbidity and mortality in pediatric oncology. More intense treatment strategies are recommended for patients displaying high-risk factors; however, considerable variation in outcome remains, indicating a need for improved predictive markers. In this study, 1H magnetic resonance spectroscopy (MRS) was used to investigate noninvasive molecular biomarkers of survival in medulloblastoma.Experimental Design: MRS was performed on a series of 35 biopsy-confirmed medulloblastoma cases. One case was excluded because of poor quality MRS. The prognostic value of MRS detectable biomarkers was investigated using Cox regression, retrospectively (N = 15). A subsequent validation analysis (N = 19) was also performed to reduce the chance of type I errors. Where available, high-resolution ex vivo MRS of biopsy tissue was used to confirm biomarker assignments.Results: The retrospective analysis revealed that creatine, glutamate, and glycine were markers of survival (P < 0.01). The validation analysis showed that glutamate was a robust marker, with a hazard ration (HR) of 8.0 for the full dataset (P = 0.0003, N = 34). A good correlation between in vivo and ex vivo MRS glutamate/total-choline was found (P = 0.001), validating the in vivo assignment. Ex vivo glutamate/total-choline was also associated with survival (P < 0.01).Conclusion: The identification of glutamate as a predictive biomarker of survival in pediatric medulloblastoma provides a clinically viable risk factor and highlights the importance of more detailed studies into the metabolism of this disease. Noninvasive biomarker detection using MRS may offer improved disease monitoring and potential for widespread use following multicenter validation. Clin Cancer Res; 20(17); 4532–9. ©2014 AACR.

Supplementary Methods (Text 1). A description of the treatment protocols used during the study period. Supplementary Figure 1. Example medulloblastoma fits where the lowest smooth black line is the baseline; the middle black line represents the acquired spectrum following pre-processing; the red line shows the spectral fit and the top black line shows the residual between the fit and acquired spectrum. A) shows a distorted baseline leading to qc failure whereas B) is an example of an ideal smooth baseline.

IntroductionEvidence suggests a potentially causal role of interleukin 6 (IL-6), a pleiotropic cytokine that generally promotes inflammation, in the pathogenesis of psychosis. However, no interventional studies in patients with psychosis, stratified using inflammatory markers, have been conducted to assess the therapeutic potential of targeting IL-6 in psychosis and to elucidate potential mechanism of effect. Tocilizumab is a humanised monoclonal antibody targeting the IL-6 receptor to inhibit IL-6 signalling, licensed in the UK for treatment of rheumatoid arthritis. The primary objective of this study is to test whether IL-6 contributes to the pathogenesis of first episode psychosis and to examine potential mechanisms by which IL-6 affects psychotic symptoms. A secondary objective is to examine characteristics of inflammation-associated psychosis.Methods and analysisA proof-of-concept study employing a randomised, parallel-group, double-blind, placebo-controlled design testing the effect of IL-6 inhibition on anhedonia in patients with psychosis. Approximately 60 participants with a diagnosis of schizophrenia and related psychotic disorders (ICD-10 codes F20, F22, F25, F28, F29) with evidence of low-grade inflammation (IL-6≥0.7 pg/mL) will receive either one intravenous infusion of tocilizumab (4.0 mg/kg; max 800 mg) or normal saline. Psychiatric measures and blood samples will be collected at baseline, 7, 14 and 28 days post infusion. Cognitive and neuroimaging data will be collected at baseline and 14 days post infusion. In addition, approximately 30 patients with psychosis without evidence of inflammation (IL-6Ethics and disseminationThe study is sponsored by the University of Bristol and has been approved by the Cambridge East Research Ethics Committee (reference: 22/EE/0010; IRAS project ID: 301682). Study findings will be published in peer-review journals. Findings will also be disseminated by scientific presentation and other means.Trial registration numberISRCTN23256704.

Purpose Accurate baseline modeling is essential for reliable MRS analysis and interpretation-particularly at short echo-times, where enhanced metabolite information coincides with elevated baseline interference. The degree of baseline smoothness is a key analysis parameter for metabolite estimation, and in this study, a new method is presented to estimate its optimal value. Methods An adaptive baseline fitting algorithm (ABfit) is described, incorporating a spline basis into a frequency-domain analysis model, with a penalty parameter to enforce baseline smoothness. A series of candidate analyses are performed over a range of smoothness penalties, as part of a 4-stage algorithm, and the Akaike information criterion is used to estimate the appropriate penalty. ABfit is applied to a set of simulated spectra with differing baseline features and experimentally acquired 2D MRSI-both at a field strength of 3 Tesla. Results Simulated analyses demonstrate metabolite errors result from 2 main sources: bias from an inflexible baseline (underfitting) and increased variance from an overly flexible baseline (overfitting). In the case of an ideal flat baseline, ABfit is shown to correctly estimate a highly rigid baseline, and for more realistic spectra a reasonable compromise between bias and variance is found. Analysis of experimentally acquired data demonstrates good agreement with known correlations between metabolite ratios and the contributing volumes of gray and white matter tissue. Conclusions ABfit has been shown to perform accurate baseline estimation and is suitable for fully automated routine MRS analysis.

Edited MRS sequences are widely used for studying γ-aminobutyric acid (GABA) in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multisite study. GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate. After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors' hardware, presenting across algorithms. Scale differences across algorithms were also observed. Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An interclass correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases. Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies. publishedVersion

Our understanding of how information unfolds when we recall events from memory remains limited. In this study, we investigate whether the reconstruction of visual object memories follows a backward trajectory along the ventral visual stream with respect to perception, such that their neural feature representations are gradually reinstated from late areas close to the hippocampus backwards to lower-level sensory areas. We use multivariate analyses of fMRI activation patterns to map the constituent features of the object memories onto the brain during retrieval, and EEG-fMRI fusion to track the temporal evolution of the reactivated patterns. Participants studied new associations between verbs and randomly paired object images in an encoding phase, and subsequently recalled the objects when presented with the corresponding verb cue. Decoding reactivated memory features from fMRI activity revealed that retrieval patterns were dominated by conceptual features, represented in comparatively late visual and parietal areas. Representational-similarity-based fusion then allowed us to map the EEG patterns that emerged at each given time point of a trial onto the spatially resolved fMRI patterns. This fusion suggests that memory reconstruction proceeds backwards along the ventral visual stream from anterior fronto-temporal to posterior occipital and parietal regions, in line with a semantic-to-perceptual gradient. A linear regression on the peak time points of reactivated brain regions statistically confirms that the temporal progression is reversed with respect to encoding. Together, the results shed light onto the spatio-temporal trajectories along which memories are reconstructed during associative retrieval, and which features of an image are reconstructed when in time and where in the brain.

Clinical coding uses a classification system to assign standard codes to clinical terms and so facilitates good clinical practice through audit, service design and research. However, despite clinical coding being mandatory for inpatient activity, this is often not so for outpatient services, where most neurological care is delivered. Recent reports by the UK National Neurosciences Advisory Group and NHS England’s ‘Getting It Right First Time’ initiative recommend implementing outpatient coding. The UK currently has no standardised system for outpatient neurology diagnostic coding. However, most new attendances at general neurology clinics appear to be classifiable with a limited number of diagnostic terms. We present the rationale for diagnostic coding and its benefits, and the need for clinical engagement to develop a system that is pragmatic, quick and easy to use. We outline a scheme developed in the UK that could be used elsewhere.

MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi‐class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi‐site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi‐class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave‐one‐out and k‐fold cross‐validation. Metabolites identified as crucial in tumour classification include myo‐inositol (P < 0.05, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N‐acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave‐one‐out cross‐validation was 85% for 1.5 T 1H‐MRS through support vector machine and 75% for 3 T 1H‐MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Edited MRS sequences are widely used for studying GABA in the human brain. Several algorithms are available for modelling these data, deriving metabolite concentration estimates through peak fitting or a linear combination of basis spectra. The present study compares seven such algorithms, using data obtained in a large multi-site study.GABA-edited (GABA+, TE = 68 ms MEGA-PRESS) data from 222 subjects at 20 sites were processed via a standardised pipeline, before modelling with FSL-MRS, Gannet, AMARES, QUEST, LCModel, Osprey and Tarquin, using standardised vendor-specific basis sets (for GE, Philips and Siemens) where appropriate.After referencing metabolite estimates (to water or creatine), systematic differences in scale were observed between datasets acquired on different vendors’ hardware, presenting across algorithms. Scale differences across algorithms were also observed.Using the correlation between metabolite estimates and voxel tissue fraction as a benchmark, most algorithms were found to be similarly effective in detecting differences in GABA+. An inter-class correlation across all algorithms showed single-rater consistency for GABA+ estimates of around 0.38, indicating moderate agreement. Upon inclusion of a basis set component explicitly modelling the macromolecule signal underlying the observed 3.0 ppm GABA peaks, single-rater consistency improved to 0.44. Correlation between discrete pairs of algorithms varied, and was concerningly weak in some cases.Our findings highlight the need for consensus on appropriate modelling parameters across different algorithms, and for detailed reporting of the parameters adopted in individual studies to ensure reproducibility and meaningful comparison of outcomes between different studies.HighlightsGABA-edited MRS data from 222 healthy adults across 20 research sites were analyzedData were modelled using seven different algorithms, yielding GABA+ and Glx estimatesModerate agreement was seen across all the tested algorithmsAdding a component to represent co-edited macromolecule signals improved concordanceBaseline modelling emerged as major factor differentiating outcomes

PurposeThe use of multiple data formats in the MRS community currently hinders data sharing and integration. NIfTI-MRS is proposed as a standard MR spectroscopy data format, which is implemented as an extension to the neuroimaging informatics technology initiative (NIfTI) format.Using this standardised format will facilitate data sharing, ease algorithm development, and encourage the integration of MRS analysis with other imaging modalities.MethodsA file format based on the NIfTI header extension framework was designed to incorporate essential spectroscopic metadata and additional encoding dimensions. A detailed description of the specification is provided. An open-source command-line conversion program is implemented to enable conversion of single-voxel and spectroscopic imaging data to NIfTI-MRS. To provide visualisation of data in NIfTI-MRS, a dedicated plugin is implemented for FSLeyes, the FSL image viewer.ResultsAlongside online documentation, ten example datasets are provided in the proposed format. In addition, minimal examples of NIfTI-MRS readers have been implemented. The conversion software, spec2nii, currently converts fourteen formats to NIfTI-MRS, including DICOM and vendor proprietary formats.ConclusionThe proposed format aims to solve the issue of multiple data formats being used in the MRS community. By providing a single conversion point, it aims to simplify the processing and analysis of MRS data, thereby lowering the barrier to use of MRS. Furthermore, it can serve as the basis for open data sharing, collaboration, and interoperability of analysis programs. It also opens possibility of greater standardisation and harmonisation. By aligning with the dominant format in neuroimaging, NIfTI-MRS enables the use of mature tools present in the imaging community, demonstrated in this work by using a dedicated imaging tool, FSLeyes, as a viewer.

Purpose Heating of gradient coils and passive shim components is a common cause of instability in the B0 field, especially when gradient intensive sequences are used. The aim of the study was to set a benchmark for typical drift encountered during MR spectroscopy (MRS) to assess the need for real-time field-frequency locking on MRI scanners by comparing field drift data from a large number of sites. Method A standardized protocol was developed for 80 participating sites using 99 3T MR scanners from 3 major vendors. Phantom water signals were acquired before and after an EPI sequence. The protocol consisted of: minimal preparatory imaging; a short pre-fMRI PRESS; a ten-minute fMRI acquisition; and a long post-fMRI PRESS acquisition. Both pre- and post-fMRI PRESS were non-water suppressed. Real-time frequency stabilization/adjustment was switched off when appropriate. Sixty scanners repeated the protocol for a second dataset. In addition, a three-hour post-fMRI MRS acquisition was performed at one site to observe change of gradient temperature and drift rate. Spectral analysis was performed using MATLAB. Frequency drift in pre-fMRI PRESS data were compared with the first 5:20 minutes and the full 30:00 minutes of data after fMRI. Median (interquartile range) drifts were measured and showed in violin plot. Paired t-tests were performed to compare frequency drift pre- and post-fMRI. A simulated in vivo spectrum was generated using FID-A to visualize the effect of the observed frequency drifts. The simulated spectrum was convolved with the frequency trace for the most extreme cases. Impacts of frequency drifts on NAA and GABA were also simulated as a function of linear drift. Data from the repeated protocol were compared with the corresponding first dataset using Pearson's and intraclass correlation coefficients (ICC). Results Of the data collected from 99 scanners, 4 were excluded due to various reasons. Thus, data from 95 scanners were ultimately analyzed. For the first 5:20 min (64 transients), median (interquartile range) drift was 0.44 (1.29) Hz before fMRI and 0.83 (1.29) Hz after. This increased to 3.15 (4.02) Hz for the full 30 min (360 transients) run. Average drift rates were 0.29 Hz/min before fMRI and 0.43 Hz/min after. Paired t-tests indicated that drift increased after fMRI, as expected (p < 0.05). Simulated spectra convolved with the frequency drift showed that the intensity of the NAA singlet was reduced by up to 26%, 44 % and 18% for GE, Philips and Siemens scanners after fMRI, respectively. ICCs indicated good agreement between datasets acquired on separate days. The single site long acquisition showed drift rate was reduced to 0.03 Hz/min approximately three hours after fMRI. Discussion This study analyzed frequency drift data from 95 3T MRI scanners. Median levels of drift were relatively low (5-min average under 1 Hz), but the most extreme cases suffered from higher levels of drift. The extent of drift varied across scanners which both linear and nonlinear drifts were observed.

Data format specification for the NIfTI-MRS file format. NIfTI-MRS is a NIfTI derived format for storing magnetic resonance spectroscopy (MRS) and spectroscopic imaging (MRSI) data. Generated from the specification at the NIfTI-MRS GitHub repository. Documentationfor the format is available online.

Brain tumours are the most common cause of cancer death in children. Molecular studies have greatly improved our understanding of these tumours but tumour metabolism is underexplored. Metabolites measured in vivo have been reported as prognostic biomarkers of these tumours but analysis of surgically resected tumour tissue allows a more extensive set of metabolites to be measured aiding biomarker discovery and providing validation of in vivo findings. In this study, metabolites were quantified across a range of paediatric brain tumours using 1H-High-Resolution Magic Angle Spinning nuclear magnetic resonance spectroscopy (HR-MAS) and their prognostic potential investigated. HR-MAS was performed on pre-treatment frozen tumour tissue from a single centre. Univariate and multivariate Cox regression was used to examine the ability of metabolites to predict survival. The models were cross validated using C-indices and further validated by splitting the cohort into two. Higher concentrations of glutamine were predictive of a longer overall survival, whilst higher concentrations of lipids were predictive of a shorter overall survival. These metabolites were predictive independent of diagnosis, as demonstrated in multivariate Cox regression models. Whilst accurate quantification of metabolites such as glutamine in vivo is challenging, metabolites show promise as prognostic markers due to development of optimised detection methods and increasing use of 3 T clinical scanners.

UK neurology has many different models of provision and a shortage of essential clinical staff. Services are sometimes unsatisfactory and there is much variation and inequality, especially in areas outside London where there are far fewer consultants. Some hospitals have much better staffed and resourced neurological services than others which may have far less provision or even no neurology service at all. There is no national strategy or agreed model of service delivery – local areas have evolved individual arrangements, often dictated by consultant availability. We describe, with clear operational details, a neurology network model in a large population, with outcomes. In many areas with limited resources it could, by re-organisation of current services, be considered instead of existing separate, variable and potentially inequitable local arrangements.

Background 1H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI. Methods Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end. Results Radiologists’ principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. Conclusions MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.

Proton Magnetic Resonance Spectroscopy ((1)H MRS) provides non-invasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Whilst most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges towards obtaining reliable good-quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the popular point resolved spectroscopy (PRESS) localization sequence was found to be unacceptably high at 3T, and the use of the semi-adiabatic localization by adiabatic selective refocusing (semi-LASER) sequence is a recommended solution. The incorporation of simulated metabolite basis-sets into analysis routines is recommended for reliably capturing the full spectral detail available from short echo time acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B(0)) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. We anticipate the implementation of these recommendations will strengthen current clinical applications and advance progress towards developing and validating new MRS biomarkers for clinical use.

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Highlights • Altered hippocampal subfields functional connectivity in ischemic stroke patients. • Decreased connectivity between the hippocampus and inferior parietal lobe in the patients. • Increased connectivity in the hippocampus and cerebellum in the patients. • The degree of connectivity changes associated with the patients’ impaired memory function., Background Ischemic stroke with cognitive impairment is a considerable risk factor for developing dementia. Identifying imaging markers of cognitive impairment following ischemic stroke will help to develop prevention strategies against post-stroke dementia. Methods We investigated the hippocampal functional connectivity (FC) pattern following ischemic stroke, using resting-state fMRI (rs-fMRI). Thirty-three cognitively impaired patients after ischemic stroke and sixteen age-matched controls with no known history of neurological disorder were recruited for the study. No patient had a direct ischaemic insult to hippocampus on the examination of brain imaging. Seven subfields of hippocampus were used as seeds region for FC analyses. Results Across all hippocampal subfields, FC with the inferior parietal lobule was reduced in stroke patients as compared with healthy controls. This decreased FC included both supramarginal gyrus and angular gyrus. The FC of hippocampal subfields with cerebellum was increased. Importantly, the degree of the altered FC between hippocampal subfields and inferior parietal lobule was associated with their impaired memory function. Conclusion Our results demonstrated that decreased hippocampal-inferior parietal lobule connectivity was associated with cognitive impairment in patients with ischemic stroke. These findings provide novel insights into the role of hippocampus in cognitive impairment following ischemic stroke.

Background Antibodies to the aquaporin-4 (AQP4) water channel in neuromyelitis optica spectrum disorder (NMOSD) are reported to trigger the complement cascade, which is implicated in astrocyte damage and subsequent neuronal injury. The PREVENT study demonstrated that the terminal complement inhibitor eculizumab reduces adjudicated relapse risk in patients with anti-AQP4 immunoglobulin G-positive (AQP4+) NMOSD. The objective of this analysis was to evaluate the efficacy of eculizumab in reducing relapse risk and its safety in AQP4+ NMOSD across clinically relevant subgroups in PREVENT. Methods In the randomized, double-blind, time-to-event, phase 3 PREVENT trial, 143 adults received eculizumab (maintenance dose, 1200 mg/2 weeks) or placebo (2:1), with stable-dose concomitant immunosuppressive therapy (IST) permitted (except rituximab and mitoxantrone). Post hoc analyses of relapses and adverse events were performed for prespecified and post hoc subgroups based on concomitant IST and prior rituximab use, demographic and disease characteristics, and autoimmune comorbidity. Results The significant reduction in relapse risk observed for eculizumab versus placebo in the overall PREVENT population was consistently maintained across subgroups based on concomitant IST and previous rituximab use, age, sex, region, race, time since clinical onset of NMOSD, historical annualized relapse rate, baseline Expanded Disability Status Scale score, and history of another autoimmune disorder. The serious infection rate was lower with eculizumab than placebo regardless of rituximab use in the previous year, concomitant IST use, or history of another autoimmune disorder. Conclusion Across a wide range of clinically relevant AQP4+ NMOSD patient subgroups in PREVENT, eculizumab therapy was consistently effective versus placebo in reducing relapse risk, with no apparent increase in serious infection rate. Trial registration NCT01892345 (ClinicalTrials.gov).

With a 40‐year history of use for in vivo studies, the terminology used to describe the methodology and results of magnetic resonance spectroscopy (MRS) has grown substantially and is not consistent in many aspects. Given the platform offered by this special issue on advanced MRS methodology, the authors decided to describe many of the implicated terms, to pinpoint differences in their meanings and to suggest specific uses or definitions. This work covers terms used to describe all aspects of MRS, starting from the description of the MR signal and its theoretical basis to acquisition methods, processing and to quantification procedures, as well as terms involved in describing results, for example, those used with regard to aspects of quality, reproducibility or indications of error. The descriptions of the meanings of such terms emerge from the descriptions of the basic concepts involved in MRS methods and examinations. This paper also includes specific suggestions for future use of terms where multiple conventions have emerged or coexisted in the past., This work defines terms and explains concepts used to describe all aspects of MR spectroscopy, starting from the definition of the MR signal and its theoretical basis to acquisition methods, processing and quantification procedures, as well as terms involved in describing results, including quality aspects, reproducibility or indications of error. Also featured are specific suggestions for future use of terms where multiple conventions have emerged or previously coexisted.

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low-molecular-weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field which are all summarized at the end of the manuscript.

Although there is growing utilisation of intermediate care to improve the health and well-being of older adults with complex care needs, there is no international agreement on how it is defined, limiting comparability between studies and reducing the ability to scale effective interventions. To identify and define the characteristics of intermediate care models. A scoping review, a modified two-round electronic Delphi study involving 27 multi-professional experts from 13 countries, and a virtual consensus meeting were conducted. Sixty-six records were included in the scoping review, which identified four main themes: transitions, components, benefits and interchangeability. These formed the basis of the first round of the Delphi survey. After Round 2, 16 statements were agreed, refined and collapsed further. Consensus was established for 10 statements addressing the definitions, purpose, target populations, approach to care and organisation of intermediate care models. There was agreement that intermediate care represents time-limited services which ensure continuity and quality of care, promote recovery, restore independence and confidence at the interface between home and acute services, with transitional care representing a subset of intermediate care. Models are best delivered by an interdisciplinary team within an integrated health and social care system where a single contact point optimises service access, communication and coordination. This study identified key defining features of intermediate care to improve understanding and to support comparisons between models and studies evaluating them. More research is required to develop operational definitions for use in different healthcare systems.

PURPOSE Subject motion and static field (B0 ) drift are known to reduce the quality of single voxel MR spectroscopy data due to incoherent averaging. Retrospective correction has previously been shown to improve data quality by adjusting the phase and frequency offset of each average to match a reference spectrum. In this work, a new method (RATS) is developed to be tolerant to large frequency shifts (>7 Hz) and baseline instability resulting from inconsistent water suppression. METHODS In contrast to previous approaches, the variable-projection method and baseline fitting is incorporated into the correction procedure to improve robustness to fluctuating baseline signals and optimization instability. RATS is compared to an alternative method, based on time-domain spectral registration (TDSR), using simulated data to model frequency, phase, and baseline instability. In addition, a J-difference edited glutathione in-vivo dataset is processed using both approaches and compared. RESULTS RATS offers improved accuracy and stability for large frequency shifts and unstable baselines. Reduced subtraction artifacts are demonstrated for glutathione edited MRS when using RATS, compared with uncorrected or TDSR corrected spectra. CONCLUSIONS The RATS algorithm has been shown to provide accurate retrospective correction of SVS MRS data in the presence of large frequency shifts and baseline instability. The method is rapid, generic and therefore readily incorporated into MRS processing pipelines to improve lineshape, SNR, and aid quality assessment.

Once a magnetic resonance spectroscopy (MRS) dataset has been acquired, several important steps must be taken to obtain the desired metabolite concentration measures. First, the data must be preprocessed to prepare them for analysis. Next, the intensity of the metabolite signal(s) of interest must be estimated. Finally, the measured metabolite signal intensities must be converted into scaled concentration units employing a quantitative reference signal to allow meaningful interpretation. In this paper, we will review these three main steps in the post-acquisition workflow of a single-voxel MRS experiment (preprocessing, analysis and quantification) and provide recommendations for best practices at each step.

BACKGROUND: (1)H-magnetic resonance spectroscopy (MRS) facilitates noninvasive diagnosis of pediatric brain tumors by providing metabolite profiles. Prospective studies of diagnostic accuracy and comparisons with conventional MRI are lacking. We aimed to evaluate diagnostic accuracy of MRS for childhood brain tumors and determine added clinical value compared with conventional MRI. METHODS: Children presenting to a tertiary pediatric center with brain lesions from December 2015 through 2017 were included. MRI and single-voxel MRS were acquired on 52 tumors and sequentially interpreted by 3 radiologists, blinded to histopathology. Proportions of correct diagnoses and interrater agreement at each stage were compared. Cases were reviewed to determine added value of qualitative radiological review of MRS through increased certainty of correct diagnosis, reduced number of differentials, or diagnosis following spectroscopist evaluation. Final diagnosis was agreed by the tumor board at study end. RESULTS: Radiologists’ principal MRI diagnosis was correct in 69%, increasing to 77% with MRS. MRI + MRS resulted in significantly more additional correct diagnoses than MRI alone (P = .035). There was a significant increase in interrater agreement when correct with MRS (P = .046). Added value following radiologist interpretation of MRS occurred in 73% of cases, increasing to 83% with additional spectroscopist review. First histopathological diagnosis was available a median of 9.5 days following imaging, with 25% of all patients managed without conclusive histopathology. CONCLUSIONS: MRS can improve the accuracy of noninvasive diagnosis of pediatric brain tumors and add value in the diagnostic pathway. Incorporation into practice has the potential to facilitate early diagnosis, guide treatment planning, and improve patient care.

BACKGROUND Metabolite concentrations are fundamental biomarkers of disease and prognosis. Magnetic resonance spectroscopy (MRS) is a noninvasive method for measuring metabolite concentrations; however, quantitation is affected by T2 relaxation. PURPOSE To estimate T2 relaxation times in pediatric brain tumors and assess how variation in T2 relaxation affects metabolite quantification. STUDY TYPE Retrospective. POPULATION Twenty-seven pediatric brain tumor patients (n = 17 pilocytic astrocytoma and n = 10 medulloblastoma) and 24 age-matched normal controls. FIELD STRENGTH/SEQUENCE Short- (30 msec) and long-echo (135 msec) single-voxel MRS acquired at 1.5T. ASSESSMENT T2 relaxation times were estimated by fitting signal amplitudes at two echo times to a monoexponential decay function and were used to correct metabolite concentration estimates for relaxation effects. STATISTICAL TESTS One-way analysis of variance (ANOVA) on ranks were used to analyze the mean T2 relaxation times and metabolite concentrations for each tissue group and paired Mann-Whitney U-tests were performed. RESULTS The mean T2 relaxation of water was measured as 181 msec, 123 msec, 90 msec, and 86 msec in pilocytic astrocytomas, medulloblastomas, basal ganglia, and white matter, respectively. The T2 of water was significantly longer in both tumor groups than normal brain (P

The rare pediatric embryonal tumors retinoblastoma, medulloblastoma and neuroblastoma derive from neuroectodermal tissue and share similar histopathological features despite different anatomical locations and diverse clinical outcomes. As metabolism can reflect genetic and histological features, we investigated whether the metabolism of embryonal tumors reflects their similar histology, shared developmental and neural origins, or tumor location. We undertook metabolic profiling on 50 retinoblastoma, 39 medulloblastoma and 70 neuroblastoma using high resolution magic angle spinning magnetic resonance spectroscopy (1H-MRS). Mean metabolite concentrations identified several metabolites that were significantly different between the tumor groups including taurine, hypotaurine, glutamate, glutamine, GABA, phosphocholine, N-acetylaspartate, creatine, glycine and myoinositol, p < 0.0017. Unsupervised multivariate analysis found that each tumor group clustered separately, with a unique metabolic profile, influenced by their underlying clinical diversity. Taurine was notably high in all tumors consistent with prior evidence from embryonal tumors. Retinoblastoma and medulloblastoma were more metabolically similar, sharing features associated with the central nervous system (CNS). Neuroblastoma had features consistent with neural tissue, but also contained significantly higher myoinositol and altered glutamate-glutamine ratio, suggestive of differences in the underlying metabolism of embryonal tumors located outside of the CNS. Despite the histological similarities and shared neural metabolic features, we show that individual neuroectodermal derived embryonal tumors can be distinguished by tissue metabolic profile. Pathway analysis suggests the alanine-aspartate-glutamate and taurine-hypotaurine metabolic pathways may be the most pertinent pathways to investigate for novel therapeutic strategies. This work strengthens our understanding of the biology and metabolic pathways underlying neuroectodermal derived embryonal tumors of childhood.

Context Brain white matter hyperintensities are seen on routine clinical imaging in 46% of adults with congenital adrenal hyperplasia (CAH). The extent and functional relevance of these abnormalities have not been studied with quantitative magnetic resonance imaging (MRI) analysis. Objective To examine white matter microstructure, neural volumes, and central nervous system (CNS) metabolites in CAH due to 21-hydroxylase deficiency (21OHD) and to determine whether identified abnormalities are associated with cognition, glucocorticoid, and androgen exposure. Design, Setting, and Participants A cross-sectional study at a tertiary hospital including 19 women (18 to 50 years) with 21OHD and 19 age-matched healthy women. Main Outcome Measure Recruits underwent cognitive assessment and brain imaging, including diffusion weighted imaging of white matter, T1-weighted volumetry, and magnetic resonance spectroscopy for neural metabolites. We evaluated white matter microstructure by using tract-based spatial statistics. We compared cognitive scores, neural volumes, and metabolites between groups and relationships between glucocorticoid exposure, MRI, and neurologic outcomes. Results Patients with 21OHD had widespread reductions in white matter structural integrity, reduced volumes of right hippocampus, bilateral thalami, cerebellum, and brainstem, and reduced mesial temporal lobe total choline content. Working memory, processing speed, and digit span and matrix reasoning scores were reduced in patients with 21OHD, despite similar education and intelligence to controls. Patients with 21OHD exposed to higher glucocorticoid doses had greater abnormalities in white matter microstructure and cognitive performance. Conclusion We demonstrate that 21OHD and current glucocorticoid replacement regimens have a profound impact on brain morphology and function. If reversible, these CNS markers are a potential target for treatment., Using quantitative neuroimaging and cognitive assessment, we demonstrate that 21OHD and current glucocorticoid replacement regimens have a profound impact on brain morphology and function.

Background Magnetic resonance spectroscopy (MRS) aids noninvasive diagnosis of pediatric brain tumors, but use in clinical practice is not well documented. We aimed to review clinical use of MRS, establish added value in noninvasive diagnosis, and investigate potential impact on patient care. Methods Sixty-nine children with lesions imaged using MRS and reviewed by the tumor board from 2014 to 2016 met inclusion criteria. Contemporaneous MRI diagnosis, spectroscopy analysis, histopathology, and clinical information were reviewed. Final diagnosis was agreed on by the tumor board at study end. Results Five cases were excluded for lack of documented MRI diagnosis. The principal MRI diagnosis by pediatric radiologists was correct in 59%, increasing to 73% with addition of MRS. Of the 73%, 19.1% (95% CI, 9.1%-33.3%) were incorrectly diagnosed with MRI alone. MRS led to a significant improvement in correct diagnosis over all tumor types (P = .012). Of diagnoses correctly made with MRI, confidence increased by 37% when adding MRS, with no patients incorrectly re-diagnosed. Indolent lesions were diagnosed noninvasively in 85% of cases, with MRS a major contributor to 91% of these diagnoses. Of all patients, 39% were managed without histopathological diagnosis. MRS contributed to diagnosis in 68% of this group, modifying it in 12%. MRS influenced management in 33% of cases, mainly through avoiding and guiding biopsy and aiding tumor characterization. Conclusion MRS can improve accuracy and confidence in noninvasive diagnosis of pediatric brain lesions in clinical practice. There is potential to improve outcomes through avoiding biopsy of indolent lesions, aiding tumor characterization, and facilitating earlier family discussions and treatment planning.

In vivo quantification of glutamate (Glu) and γ-aminobutyric acid (GABA) using MRS is often achieved using two separate sequences: a short-echo point resolved spectroscopy (PRESS) acquisition for Glu and a Mescher-Garwood PRESS (MEGA-PRESS) acquisition for GABA. The purpose of this study was to examine the agreement of Glu and Glx (the combined signal of glutamate + glutamine) quantified from two different GABA-edited MEGA-PRESS acquisitions (GABA plus macromolecules, GABA+, TE = 68 ms, and macromolecule suppressed, MMSup, TE = 80 ms) with Glu and Glx quantified from a short-echo PRESS (PRESS-35, TE = 35 ms) acquisition. Fifteen healthy male volunteers underwent a single scan session, in which data were acquired using the three acquisitions (GABA+, MMSup and PRESS-35) in both the sensorimotor and anterior cingulate cortices using a voxel size of 3 × 3 × 3 cm3 . Glx and Glu were quantified from the MEGA-PRESS data using both the OFF sub-spectra and the difference (DIFF) spectra. Agreement was assessed using correlation analyses, Bland-Altman plots and intraclass correlation coefficients. Glx quantified from the OFF sub-spectra from both the GABA+ and MMSup acquisitions showed poor agreement with PRESS-35 in both brain regions. In the sensorimotor cortex, Glu quantified from the OFF sub-spectra of GABA+ showed moderate agreement with PRESS-35 data, but this finding was not replicated in the anterior cingulate cortex. Glx and Glu quantified using the DIFF spectra of either MEGA-PRESS sequence were in poor agreement with the PRESS-35 data in both brain regions. In conclusion, Glx and Glu measured from MEGA-PRESS data generally showed poor agreement with Glx and Glu measured using PRESS-35.

Background Although there is evidence of suboptimal outcomes in older people with chronic pain, little emphasis has been placed on those in remote and rural settings. Objective To describe the perspectives of older people in the Scottish Highlands on their chronic pain management. Design Cross-sectional survey. Setting NHS Highland, the most remote and rural geographical health board in Scotland. Subjects Home-dwelling members of the public aged ≥70 years. Methods Anonymised questionnaires were mailed to a random sample of 1800 older people. Questionnaire items were demographics, nature of any chronic pain, management regimens and perceived effectiveness. Validated scales were the Pain Disability Questionnaire and the Tampa Scale for Kinesiophobia. Results Adjusted response rate was 39.3% (709/1755). One-quarter (25.0%, n = 177) were experiencing chronic pain, being more likely to live in deprived areas (P Conclusions Evidence of provision of appropriate integrated and person-centred chronic pain care is lacking.

SUMMARYNAD+ is modulated by conditions of metabolic stress and has been reported to decline with aging, but human data are sparse. Nicotinamide riboside (NR) supplementation ameliorates metabolic dysfunction in rodents. We aimed to establish whether oral NR supplementation in aged participants can increase the skeletal muscle NAD+ metabolome, and questioned if tissue NAD+ levels are depressed with aging. We supplemented 12 aged men with NR 1g per day for 21-days in a placebo-controlled, randomized, double-blind, crossover trial. Targeted metabolomics showed that NR elevated the muscle NAD+ metabolome, evident by increased nicotinic acid adenine dinucleotide and nicotinamide clearance products. Muscle RNA sequencing revealed NR-mediated downregulation of energy metabolism and mitochondria pathways. NR also depressed levels of circulating inflammatory cytokines. In an additional study, 31P magnetic resonance spectroscopy-based NAD+ measurement in muscle and brain showed no difference between young and aged individuals. Our data establish that oral NR is available to aged human muscle and identify anti-inflammatory effects of NR, while suggesting that NAD+ decline is not associated with chronological aging per se in human muscle or brain.