The literature describing 31P, 1H, 13C, 23Na and 19F MRS in vivo in human cancers is reviewed. Cancers have typical metabolic characteristics in 31P and 1H MRS including high levels of phospholipid metabolites and a cellular pH more alkaline than normal. These alone are not specific for cancer but are diagnostic in appropriate clinical settings. Some metabolic characteristics appear to be prognostic indices and correlation with treatment response is emerging as an important potentially cost-effective use of MRS in oncology. 19F MRS examines pharmacokinetics of 5-fluorouracil and by demonstrating its retention predicts response of a cancer to treatment. Current needs include improvement of diagnostic specificity by use of techniques like multivoxel MRS, proton decoupling of 31P, short echo time and fat-suppressed 1H MRS, 13C MRS direct or via 1H-observe, and statistical analysis of multiple spectral features. Trials in large populations in well defined clinical settings are needed to determine if MRS can provide independent prognostic indices useful in cancer management.

吸烟是导致过早发病和死亡的主要风险因素之一．随着磁共振技术的不断进步，磁共振波谱已成为临床诊断的重要手段之一．与磁共振成像的其他模态一样，磁共振波谱具有无创性的优点，能够反映大脑的神经递质及代谢物的浓度变化情况．吸烟是一个全球性的健康问题，近些年已有一些研究利用磁共振波谱观察吸烟对大脑的影响．针对这一广泛关注的社会问题，本文对吸烟人群的磁共振波谱研究进展进行了综述，总结了烟草对大脑代谢的影响．本综述为深入研究烟草依赖的神经生物学机制提供了新的视角，从技术层面为早期诊断、治疗及预防吸烟对大脑的负面影响提供了支持．

谷胱甘肽（GSH）是一种由谷氨酸、半胱氨酸和甘氨酸聚合而成的三肽，是人体还原驱动力的主要来源，能维持人体内部氧化还原平衡．谷胱甘肽还与生物体内自由基淬灭、肿瘤治疗密切相关．利用活体磁共振波谱（MRS）监测生物体中谷胱甘肽的含量变化对于理解其生物学作用具有重要意义．由于生物体内环境复杂，活体组织的磁共振信号往往重叠严重，难以实现特定分子的选择性检测及定量．本文介绍了一种利用核自旋单重态选择性检测活体谷胱甘肽分子的方法，通过在谷胱甘肽中选择合适的自旋体系，将其制备为单重态，借助梯度脉冲消除其它分子的信号，进而实现谷胱甘肽信号的选择性检测．该方法在健康人体大脑中得到了实验验证，理论和实验结果表明，与文献中报道的其它方法相比，本文的方法具有检测效率高、选择性强的特点．

MR offers unique tools for measuring molecular diffusion. This review focuses on the use of diffusion-weighted MR spectroscopy (DW-MRS) to non-invasively quantitate the translational displacement of endogenous metabolites in intact mammalian tissues. Most of the metabolites that are observed by in vivo MRS are predominantly located in the intracellular compartment. DW-MRS is of fundamental interest because it enables one to probe the in situ status of the intracellular space from the diffusion characteristics of the metabolites, while at the same time providing information on the intrinsic diffusion properties of the metabolites themselves. Alternative techniques require the introduction of exogenous probe molecules, which involves invasive procedures, and are also unable to measure molecular diffusion in and throughout intact tissues. The length scale of the process(es) probed by MR is in the micrometer range which is of the same order as the dimensions of many intracellular entities. DW-MRS has been used to estimate the dimensions of the cellular elements that restrict intracellular metabolite diffusion in muscle and nerve tissue. In addition, it has been shown that DW-MRS can provide novel information on the cellular response to pathophysiological changes in relation to a range of disorders, including ischemia and excitotoxicity of the brain and cancer.Copyright 2001 John Wiley & Sons, Ltd.

Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) is a powerful technique for probing microstructure, yet its information arises from the ubiquitous, non-specific water signal. By contrast, diffusion-weighted Magnetic Resonance Spectroscopy (DW-MRS) allows specific characterizations of tissues such as brain and muscle in vivo by quantifying the diffusion properties of MR-observable metabolites. Many brain metabolites are predominantly intracellular, and some of them are preferentially localized in specific brain cell populations, e.g., neurons and glia. Given the microstructural sensitivity of diffusion-encoding filters, investigation of metabolite diffusion properties using DW-MRS can thus provide exclusive cell and compartment-specific information. Furthermore, since many models and assumptions are used for quantification of water diffusion, metabolite diffusion may serve to generate a-priori information for model selection in DW-MRI. However, DW-MRS measurements are extremely challenging, from the acquisition to the accurate and correct analysis and quantification stages. In this review, we survey the state-of-the-art methods that have been developed for the robust acquisition, quantification and analysis of DW-MRS data and discuss the potential relevance of DW-MRS for elucidating brain microstructure in vivo. The review highlights that when accurate data on the diffusion of multiple metabolites is combined with accurate computational and geometrical modeling, DW-MRS can provide unique cell-specific information on the intracellular structure of brain tissue, in health and disease, which could serve as incentives for further application in vivo in human research and clinical MRI.Copyright © 2017 Elsevier Inc. All rights reserved.

Diffusion-weighted 1H magnetic resonance spectroscopy (DW-MRS) allows to quantify creatine-phosphocreatine brain diffusivity (ADC(tCr)), whose reduction in multiple sclerosis (MS) has been proposed as a proxy of energy dysfunction.

We employed diffusion-weighted magnetic resonance spectroscopy (DW-MRS), which allows to measure in vivo the diffusion properties of metabolites, to explore the functional neuro-axonal damage and the ongoing energetic dysregulation in multiple sclerosis (MS).

To study measurement repeatability and physiological determinants on measurement stability for phase contrast MRI (PC-MRI) measurements of cyclic volume changes (ΔV) of brain arteries, veins, and cerebrospinal fluid (CSF) compartments.Total cerebral blood flow (tCBF), total internal jugular flow (tJBF) and spinal CSF flow at C2-C3 level and CSF in the aqueduct was measured using five repetitions in 20 healthy subjects. After subtracting net flow, waveforms were integrated to calculate ΔV of arterial, venous, and cerebrospinal fluid compartments. The intraclass correlation coefficient (ICC) was used to measure repeatability. Systematic errors were investigated by a series of phantom measurements.For ΔV calculated from tCBF, tJBF and both CSF waveforms, the ICC was ≥0.85. ΔV from the tCBF waveform decreased linearly between repetitions (P = 0.012). Summed CSF and venous volume being shifted out from the cranium was correlated with ΔV calculated from the tCBF waveform (r = 0.75; P < 0.001). Systematic errors increased at resolutions <4 pixels per diameter.Repeatability of ΔV calculated from tCBF, tJBF, and CSF waveforms allows useful interpretations. The subject's time in the MR system and imaging resolution should be considered when interpreting volume changes. Summed CSF and venous volume changes was associated with arterial volume changes.Copyright © 2011 Wiley Periodicals, Inc.

Background Changes in blood volume in the intracranial arteries and the resulting oscillations of brain parenchyma have been presumed as main initiating factors of cerebrospinal fluid (CSF) pulsations. However, respiration has been recently supposed to influence CSF dynamics via thoracic pressure changes. Purpose To measure blood and CSF cervical flow and quantify the contribution of cardiac and respiratory cycles on the subsequent signal evolution. Material and Methods Sixteen volunteers were enrolled. All participant underwent two-dimensional fast field echo echo planar imaging (FFE-EPI). Regions of interest were placed on internal carotids, jugular veins, and rachidian canal to extract temporal profiles. Spectral analysis was performed to extract respiratory and cardiac frequencies. The contribution of respiration and cardiac activity was assessed to signal evolution by applying a multiple linear model. Results Mean respiratory frequency was 14.6 ± 3.9 cycles per min and mean heart rate was 66.8 ± 9 cycles per min. Cardiac contribution was higher than breathing for internal carotids, explaining 74.68% and 10.27% of the signal variance, respectively. For the jugular veins, respiratory component was higher than the cardiac one contributing 44.28% and 6.53% of the signal variance, respectively. For CSF, breathing and cardiac component contributed less than half of signal variance (12.61% and 23.23%, respectively). Conclusion Respiration and cardiac activity both influence fluid flow at the cervical level. Arterial inflow is driven by the cardiac pool whereas venous blood aspiration seems more due to thoracic pressure changes. CSF dynamics acts as a buffer between these two blood compartments.

Diffusion weighted spectroscopy can provide microstructural information that is specific to compartmental geometry. So far, in human brain, apparent diffusion coefficients (ADCs) of only the metabolites N-acetyl aspartate, creatine (tCr) and choline (tCho) have been assessed. High field MR at 7 T allows the collection and analysis of diffusion weighted spectroscopy data of additional metabolites of interest such as glutamate (Glu), N-acetyl aspartyl glutamate, and glutamine (Gln), which are of interest due to their different compartmentalization and role in brain physiology. In this study, we performed (1)H diffusion weighted spectroscopy at 7 T using a diffusion-weighted PRESS sequence in parietal white matter (n = 6) and occipital grey matter (n = 7). Data were analyzed using the LCmodel. ADCs could reliably be obtained of N-acetyl aspartate, tCr, tCho, Glu, Gln in grey and white matter, and N-acetyl aspartyl glutamate in white matter. Significant differences in ADC values were observed between grey and white matter for all metabolites. ADCs in grey matter were consistently lower than in white matter. These differences can probably be attributed to different compartmentalization as well as to the differential impact of diffusion time on ADC of different molecules under conditions of restricted diffusion.Copyright © 2011 Wiley Periodicals, Inc.

There is much interest in using magnetic resonance diffusion imaging to provide information on anatomical connectivity in the brain by measuring the diffusion of water in white matter tracts. Among the measures, the most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies local tract directionality and integrity. Many multi-subject imaging studies are using FA images to localize brain changes related to development, degeneration and disease. In a recent paper, we presented a new approach, tract-based spatial statistics (TBSS), which aims to solve crucial issues of cross-subject data alignment, allowing localized cross-subject statistical analysis. This works by transforming the data from the centers of the tracts that are consistent across a study's subjects into a common space. In this protocol, we describe the MRI data acquisition and analysis protocols required for TBSS studies of localized change in brain connectivity across multiple subjects.

Amyloid-β deposition in Alzheimer's disease is thought to start while individuals are still cognitively unimpaired and it is hypothesized that after an early phase of fast accumulation, a plateau is reached by the time of cognitive decline. However, few longitudinal Pittsburgh compound B-positron emission tomography studies have tested this hypothesis, and with conflicting results. The purpose of this work is to further our understanding of the dynamics of amyloid-β deposition in a large longitudinal cohort. A total of 32 patients with Alzheimer's disease, 49 subjects with mild cognitive impairment and 103 healthy controls underwent two Pittsburgh compound B-positron emission tomography scans 18 months apart. For each participant, a parametric map of Pittsburgh compound B-positron emission tomography rate of change was created [(follow-up scan - baseline scan)/follow-up duration] and entered in a voxelwise three-way analysis of covariance, with clinical status (healthy controls, mild cognitive impairment or Alzheimer's disease), disease progression (clinical conversion from healthy controls to mild cognitive impairment or Alzheimer's disease, or from mild cognitive impairment to Alzheimer's disease) and Pittsburgh compound B status (positive versus negative) as independent factors. Only a significant effect of the Pittsburgh compound B status was found: both Pittsburgh compound B-positive and -negative subjects showed a significant increase in amyloid-β deposition, with this increase being significantly higher in Pittsburgh compound B-positive individuals. This finding suggests either that Pittsburgh compound B-negative individuals have slower rates of amyloid-β accumulation than positive, or that the proportion of individuals showing significant increase in amyloid-β deposition, termed 'Pittsburgh compound B accumulators', is higher within the Pittsburgh compound B-positive group than within the Pittsburgh compound B-negative group. The bimodal distribution of the individual rates of neocortical amyloid-β accumulation observed support the existence of 'Pittsburgh compound B non-accumulators' and 'Pittsburgh compound B accumulators' and different clustering analyses led to a consistent threshold to separate these two subgroups (0.014-0.022 standardized uptake value ratio(pons)/year). The voxelwise three-way analysis of covariance was thus recomputed with the 'Pittsburgh compound B accumulators' only and the results were almost unchanged, with the Pittsburgh compound B-positive group showing higher accumulation than the Pittsburgh compound B-negative group. Finally, a significant negative correlation was found between Pittsburgh compound B rate of change and Pittsburgh compound B baseline burden, but only in the Pittsburgh compound B-positive group (r= -0.24; P=0.025). Higher rates of amyloid-β deposition are associated with higher amyloid-β burden suggesting that amyloid-β deposition does not reach a plateau when cognitive impairments manifest but is instead an ongoing process present even at the Alzheimer's disease stage. amyloid-β accumulation also seems to slow down at the latest stages of the process, i.e. in participants with the highest amyloid burden. Furthermore, this study identified the existence of Pittsburgh compound 'accumulators' and 'non-accumulators', notably within the Pittsburgh compound B-negative group, which may be a relevant concept for future studies.

To introduce a new toolkit for simulation and processing of magnetic resonance spectroscopy (MRS) data, and to demonstrate some of its novel features.The FID appliance (FID-A) is an open-source, MATLAB-based software toolkit for simulation and processing of MRS data. The software is designed specifically for processing data with multiple dimensions (eg, multiple radiofrequency channels, averages, spectral editing dimensions). It is equipped with functions for importing data in the formats of most major MRI vendors (eg, Siemens, Philips, GE, Agilent) and for exporting data into the formats of several common processing software packages (eg, LCModel, jMRUI, Tarquin). This paper introduces the FID-A software toolkit and uses examples to demonstrate its novel features, namely 1) the use of a spectral registration algorithm to carry out useful processing routines automatically, 2) automatic detection and removal of motion-corrupted scans, and 3) the ability to perform several major aspects of the MRS computational workflow from a single piece of software. This latter feature is illustrated through both high-level processing of in vivo GABA-edited MEGA-PRESS MRS data, as well as detailed quantum mechanical simulations to generate an accurate LCModel basis set for analysis of the same data.All of the described processing steps resulted in a marked improvement in spectral quality compared with unprocessed data. Fitting of MEGA-PRESS data using a customized basis set resulted in improved fitting accuracy compared with a generic MEGA-PRESS basis set.The FID-A software toolkit enables high-level processing of MRS data and accurate simulation of in vivo MRS experiments. Magn Reson Med 77:23-33, 2017. © 2015 Wiley Periodicals, Inc.© 2015 Wiley Periodicals, Inc.

Magnetic resonance spectroscopy (MRS) has been shown to be a potentially important medical diagnostic tool. The success of MRS depends on the quantitative data analysis, i.e., the interpretation of the signal in terms of relevant physical parameters, such as frequencies, decay constants, and amplitudes. A variety of time-domain algorithms to extract parameters have been developed. On the one hand, there are so-called blackbox methods. Minimal user interaction and limited incorporation of prior knowledge are inherent to this type of method. On the other hand, interactive methods exist that are iterative, require user involvement, and allow inclusion of prior knowledge. We focus on blackbox methods. The computationally most intensive part of these blackbox methods is the computation of the singular value decomposition (SVD) of a Hankel matrix. Our goal is to reduce the needed computational time without affecting the accuracy of the parameters of interest. To this end, algorithms based on the Lanczos method are suitable because the main computation at each step, a matrix-vector product, can be efficiently performed by means of the fast Fourier transform exploiting the structure of the involved matrix. We compare the performance in terms of accuracy and efficiency of four algorithms: the classical SVD algorithm based on the QR decomposition, the Lanczos algorithm, the Lanczos algorithm with partial reorthogonalization, and the implicitly restarted Lanczos algorithm. Extensive simulation studies show that the latter two algorithms perform best.

The purpose of this study is to present a cloud-based spectral simulation tool "MRSCloud," which allows MRS users to simulate a vendor-specific and sequence-specific basis set online in a convenient and time-efficient manner. This tool can simulate basis sets for GE, Philips, and Siemens MR scanners, including conventional acquisitions and spectral editing schemes with PRESS and semi-LASER localization at 3 T.The MRSCloud tool was built on the spectral simulation functionality in the FID-A software package. We added three extensions to accelerate computation (ie, one-dimensional projection method, coherence pathways filters, and precalculation of propagators). The RF waveforms were generated based on vendors' generic pulse shapes and timings. Simulations were compared within MRSCloud using different numbers of spatial resolution (21 × 21, 41 × 41, and 101 × 101). Simulated metabolite basis functions from MRSCloud were compared with those generated by the generic FID-A and MARSS, and a phantom-acquired basis set from LCModel. Intraclass correlation coefficients were calculated to measure the agreement between individual metabolite basis functions. Statistical analysis was performed using R in RStudio.Simulation time for a full PRESS basis set is approximately 11 min on the server. The interclass correlation coefficients ICCs were at least 0.98 between MRSCloud and FID-A and were at least 0.96 between MRSCloud and MARSS. The interclass correlation coefficients between simulated MRSCloud basis spectra and acquired LCModel basis spectra were lowest for glutamine at 0.68 and highest for N-acetylaspartate at 0.96.Substantial reductions in runtime have been achieved. High ICC values indicated that the accelerating features are running correctly and produce comparable and accurate basis sets.© 2022 International Society for Magnetic Resonance in Medicine.

We describe an overarching organization of large-scale connectivity that situates the default-mode network at the opposite end of a spectrum from primary sensory and motor regions. This topography, based on the differentiation of connectivity patterns, is also embedded in the spatial distance along the cortical surface between these respective systems. In addition, this connectivity gradient accounts for the respective positions of canonical networks and captures a functional spectrum from perception and action to more abstract cognitive functions. These results suggest that the default-mode network consists of regions at the top of a representational hierarchy that describe the current cognitive landscape in the most abstract terms.

Many MRS paradigms produce 2D spectral-temporal datasets, including diffusion-weighted, functional, and hyperpolarized and enriched (carbon-13, deuterium) experiments. Conventionally, temporal parameters-such as T, T, or diffusion constants-are assessed by first fitting each spectrum independently and subsequently fitting a temporal model (1D fitting). We investigated whether simultaneously fitting the entire dataset using a single spectral-temporal model (2D fitting) would improve the precision of the relevant temporal parameter.We derived a Cramer Rao lower bound for the temporal parameters for both 1D and 2D approaches for 2 experiments: a multi-echo experiment designed to estimate metabolite T s, and a functional MRS experiment designed to estimate fractional change ( ) in metabolite concentrations. We investigated the dependence of the relative standard deviation (SD) of T in multi-echo and in functional MRS.When peaks were spectrally distant, 2D fitting improved precision by approximately 20% relative to 1D fitting, regardless of the experiment and other parameter values. These gains increased exponentially as peaks drew closer. Dependence on temporal model parameters was weak to negligible.Our results strongly support a 2D approach to MRS fitting where applicable, and particularly in nuclei such as hydrogen and deuterium, which exhibit substantial spectral overlap.© 2022 The Author. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Dynamic (2D) MRS is a collection of techniques where acquisitions of spectra are repeated under varying experimental or physiological conditions. Dynamic MRS comprises a rich set of contrasts, including diffusion-weighted, relaxation-weighted, functional, edited, or hyperpolarized spectroscopy, leading to quantitative insights into multiple physiological or microstructural processes. Conventional approaches to dynamic MRS analysis ignore the shared information between spectra, and instead proceed by independently fitting noisy individual spectra before modeling temporal changes in the parameters. Here, we propose a universal dynamic MRS toolbox which allows simultaneous fitting of dynamic spectra of arbitrary type.A simple user-interface allows information to be shared and precisely modeled across spectra to make inferences on both spectral and dynamic processes. We demonstrate and thoroughly evaluate our approach in three types of dynamic MRS techniques. Simulations of functional and edited MRS are used to demonstrate the advantages of dynamic fitting.Analysis of synthetic functional H-MRS data shows a marked decrease in parameter uncertainty as predicted by prior work. Analysis with our tool replicates the results of two previously published studies using the original in vivo functional and diffusion-weighted data. Finally, joint spectral fitting with diffusion orientation models is demonstrated in synthetic data.A toolbox for generalized and universal fitting of dynamic, interrelated MR spectra has been released and validated. The toolbox is shared as a fully open-source software with comprehensive documentation, example data, and tutorials.© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

Inflammation of brain tissue is a complex response of the immune system to the presence of toxic compounds or to cell injury, leading to a cascade of pathological processes that include glial cell activation. Noninvasive MRI markers of glial reactivity would be very useful for in vivo detection and monitoring of inflammation processes in the brain, as well as for evaluating the efficacy of personalized treatments. Due to their specific location in glial cells, myo-inositol (mIns) and choline compounds (tCho) seem to be the best candidates for probing glial-specific intra-cellular compartments. However, their concentrations quantified using conventional proton MRS are not specific for inflammation. In contrast, it has been recently suggested that mIns intra-cellular diffusion, measured using diffusion-weighted MRS (DW-MRS) in a mouse model of reactive astrocytes, could be a specific marker of astrocytic hypertrophy. In order to evaluate the specificity of both mIns and tCho diffusion to inflammation-driven glial alterations, we performed DW-MRS in a volume of interest containing the corpus callosum and surrounding tissue of cuprizone-fed mice after 6 weeks of intoxication, and evaluated the extent of astrocytic and microglial alterations using immunohistochemistry. Both mIns and tCho apparent diffusion coefficients were significantly elevated in cuprizone-fed mice compared with control mice, and histologic evaluation confirmed the presence of severe inflammation. Additionally, mIns and tCho diffusion showed, respectively, strong and moderate correlations with histological measures of astrocytic and microglial area fractions, confirming DW-MRS as a promising tool for specific detection of glial changes under pathological conditions.© 2021 John Wiley & Sons, Ltd.

Systemic lupus erythematosus is an inflammatory autoimmune disease with multi-organ involvement. Central nervous system involvement in systemic lupus erythematosus is common and results in several neurological and psychiatric symptoms that are poorly linked to standard magnetic resonance imaging outcome. Magnetic resonance imaging methods sensitive to tissue microstructural changes, such as diffusion tensor imaging and magnetization transfer imaging, show some correlation with neuropsychiatric systemic lupus erythematosus (NPSLE) symptoms. Histological examination of NPSLE brains reveals presence of cerebral oedema, loss of neurons and myelinated axons, microglial proliferation and reactive astrocytosis, microinfacrts and diffuse ischaemic changes, all of which can affect both diffusion tensor imaging and magnetization transfer imaging in a non-specific manner. Here we investigated the underlying cell-type specific microstructural alterations in the brain of patients with systemic lupus erythematosus with and without a history of central nervous system involvement. We did so combining diffusion tensor imaging with diffusion-weighted magnetic resonance spectroscopy, a powerful tool capable of characterizing cell-specific cytomorphological changes based on diffusion of intracellular metabolites. We used a 7 T magnetic resonance imaging scanner to acquire T1-weighted images, diffusion tensor imaging datasets, and single volume diffusion-weighted magnetic resonance spectroscopy data from the anterior body of the corpus callosum of 13 patients with systemic lupus erythematosus with past NPSLE, 16 patients with systemic lupus erythematosus without past NPSLE, and 19 healthy control subjects. Group comparisons were made between patients with systemic lupus erythematosus with/without past NPSLE and healthy controls on diffusion tensor imaging metrics and on diffusion coefficients of three brain metabolites: the exclusively neuronal/axonal N-acetylaspartate, and the predominantly glial creatine + phosphocreatine and choline compounds. In patients with systemic lupus erythematosus with past NPSLE, significantly higher diffusion tensor imaging mean and radial diffusivities were accompanied by a significantly higher intracellular diffusion of total creatine (0.202 ± 0.032 μm(2)/ms, P = 0.018) and total choline (0.142 ± 0.031 μm(2)/ms, P = 0.044) compared to healthy controls (0.171 ± 0.024 μm(2)/ms, 0.124 ± 0.018 μm(2)/ms, respectively). Total N-acetylaspartate, total creatine and total choline diffusion values from all patients with systemic lupus erythematosus correlated positively with systemic lupus erythematosus disease activity index score (P = 0.033, P = 0.040, P = 0.008, respectively). Our results indicate that intracellular alterations, and in particular changes in glia, as evidenced by increase in the average diffusivities of total choline and total creatine, correlate with systemic lupus erythematosus activity. The higher diffusivity of total creatine and total choline in patients with NPSLE, as well as the positive correlation of these diffusivities with the systemic lupus erythematosus disease activity index are in line with cytomorphological changes in reactive glia, suggesting that the diffusivities of choline compounds and of total creatine are potentially unique markers for glial reactivity in response to inflammation.© The Author (2016). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Sensitive and specific in vivo measures of axonal damage, an important determinant of clinical status in multiple sclerosis (MS), might greatly benefit prognostication and therapy assessment. Diffusion tensor spectroscopy (DTS) combines features of diffusion tensor imaging and magnetic resonance spectroscopy, allowing measurement of the diffusion properties of intracellular, cell-type-specific metabolites. As such, it may be sensitive to disruption of tissue microstructure within neurons. In this cross-sectional pilot study, diffusion of the neuronal metabolite N-acetylaspartate (NAA) was measured in the human normal-appearing corpus callosum on a 7 tesla MRI scanner, comparing 15 MS patients and 14 healthy controls. We found that NAA parallel diffusivity is lower in MS (p = 0.030) and inversely correlated with both water parallel diffusivity (p = 0.020) and clinical severity (p = 0.015). Interpreted in the context of previous experiments, our findings provide preliminary evidence that DTS can distinguish axonopathy from other processes such as inflammation, edema, demyelination, and gliosis. By detecting reduced diffusion of NAA parallel to axons in white matter, DTS may thus be capable of distinguishing axonal disruption in MS in the setting of increased parallel diffusion of water, which is commonly observed in MS but pathologically nonspecific.