Proton MR spectroscopy of lesion evolution in multiple sclerosis: Steady‐state metabolism and its relationship to conventional imaging

Kirov, Ivan I.; Liu, Shu; Tal, Assaf; Wu, W. D.; Davitz, Matthew S.; Babb, James S.; Rusinek, Henry; Herbert, Joseph; Gonen, Oded

doi:10.1002/hbm.23647

Cited by 20 publications

(21 citation statements)

References 60 publications

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“…Region‐specific T 2 vivo values at 3T were available for NAA, Cr, and Cho (average T 2 vivo = 374, 185, 238), while for mI, T 2 vivo = 200 msec, was used for all ROIs. The NAA, Cr, Cho, and mI relaxation values in the phantom were T 1 vitro = 605, 336, 235, 319 msec and T 2 vitro = 483, 288, 200, 233 msec …”

Section: Methodsmentioning

confidence: 96%

Quantitative multivoxel proton MR spectroscopy for the identification of white matter abnormalities in mild traumatic brain injury: Comparison between regional and global analysis

Davitz

Gonen

Tal

et al. 2019

Magnetic Resonance Imaging

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Background 3D brain proton MR spectroscopic imaging (1H MRSI) facilitates simultaneous metabolic profiling of multiple loci, at higher, sub‐1 cm3, spatial resolution than single‐voxel 1H MRS with the ability to separate tissue‐type partial volume contribution(s). Purpose To determine if: 1) white matter (WM) damage in mild traumatic brain injury (mTBI) is homogeneously diffuse, or if specific regions are more affected; 2) partial‐volume‐corrected, structure‐specific 1H MRSI voxel averaging is sensitive to regional WM metabolic abnormalities. Study Type Retrospective cross‐sectional cohort study. Population Twenty‐seven subjects: 15 symptomatic mTBI patients, 12 matched controls. Field Strength/Sequence 3T using 3D 1H MRSI over a 360‐cm3 volume of interest (VOI) centered over the corpus callosum, partitioned into 480 voxels, each 0.75 cm3. Assessment N‐acetyl‐aspartate (NAA), creatine, choline, and myo‐inositol concentrations estimated in predominantly WM regions: body, genu, and splenium of the corpus callosum, corona radiata, frontal, and occipital WM. Statistical Tests Analysis of covariance (ANCOVA) to compare patients with controls in terms of regional concentrations. The effect sizes (Cohen's d) of the mean differences were compared across regions and with previously published global data obtained with linear regression of the WM over the entire VOI in the same dataset. Results Despite patients' global VOI WM NAA being significantly lower than the controls', no regional differences were observed for any metabolite. Regional NAA comparisons, however, were all unidirectional (patients' NAA concentrations < controls') within a narrow range: 0.3 ≤ Cohen's d ≤ 0.6. Data Conclusion Since the patient group was symptomatic and exhibiting global WM NAA deficits, these findings suggest: 1) diffuse axonal mTBI damage; that is 2) below the 1H MRSI detection threshold in small regions. Therefore, larger, ie, more sensitive, single‐voxel 1H MRS, placed anywhere in WM regions, may be well suited for mTBI 1H MRS studies, given that these results are confirmed in other cohorts. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1424–1432.

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Section: Methodsmentioning

confidence: 96%

Quantitative multivoxel proton MR spectroscopy for the identification of white matter abnormalities in mild traumatic brain injury: Comparison between regional and global analysis

Davitz

Gonen

Tal

et al. 2019

Magnetic Resonance Imaging

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“…The ratios between Ins and other metabolic markers such as N-acetylaspartate (tNAA), a surrogate for axonal integrity, or creatine (tCr), a surrogate for cell proliferation, are used to define astrocyte reactivity and disease evolution in MS. Increased Ins levels can be observed in the normal appearing white matter compared to control white matter [39,83], indicating that alterations associated with astroglial reactivity may be assessed early in disease and may play a role in its development. Elevated Ins/tNAA in the normal appearing white matter is a reliable predictor of brain atrophy, thus of disease progression and neurological disability evolution [99,130].…”

Section: In Vivo Measurements Of Astroglial Reactivity: Diagnostic Anmentioning

confidence: 99%

The contribution of astrocytes to the neuroinflammatory response in multiple sclerosis and experimental autoimmune encephalomyelitis

2019

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Neuroinflammation is the coordinated response of the central nervous system (CNS) to threats to its integrity posed by a variety of conditions, including autoimmunity, pathogens and trauma. Activated astrocytes, in concert with other cellular elements of the CNS and immune system, are important players in the modulation of the neuroinflammatory response. During neurological disease, they produce and respond to cellular signals that often lead to dichotomous processes, which can promote further damage or contribute to repair. This occurs also in multiple sclerosis (MS), where astrocytes are now recognized as key components of its immunopathology. Evidence supporting this role has emerged not only from studies in MS patients, but also from animal models, among which the experimental autoimmune encephalomyelitis (EAE) model has proved especially instrumental. Based on this premise, the purpose of the present review is to summarize the current knowledge of astrocyte behavior in MS and EAE. Following a brief description of the pathological characteristics of the two diseases and the main functional roles of astrocytes in CNS physiology, we will delve into the specific responses of this cell population, analyzing MS and EAE in parallel. We will define the temporal and anatomical profile of astroglial activation, then focus on key processes they participate in. These include: 1) production and response to soluble mediators (e.g. cytokines and chemokines), 2) regulation of oxidative stress, and 3) maintenance of BBB integrity and function. Finally, we will review the state of the art on the available methods to measure astroglial activation in vivo in MS patients, and how this could be exploited to optimize diagnosis, prognosis and treatment decisions. Ultimately, we believe that integrating the knowledge obtained from studies in MS and EAE may help not only better understand the pathophysiology of MS, but also uncover new signals to be targeted for therapeutic intervention.

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“…Almost half of the surveyed papers employed magnetic resonance spectroscopic imaging (MRSI) instead of single-voxel acquisition schemes, including echo-planar spectroscopic imaging (EPSI) (85, 89, 124, 144, 145, 221–223), and other CSI or spectroscopic imaging sequences encoded in two (25, 28, 30, 34, 36, 41, 43, 45, 50, 52, 54, 57, 60–62, 64–66, 68, 72, 73, 80, 81, 83, 87, 93, 94, 115, 119, 130, 131, 133, 135, 141, 159, 162–165, 167, 168, 174, 188, 191, 194, 204, 224–231) or three (102, 121, 122, 137, 202, 232, 233) spatial dimensions. MRSI offers the obvious advantage of enabling metabolic profiling over a large area of multiple tissue types, thereby enabling averaging over gray matter, white matter, and lesions within the same individual, enabling more robust estimates for the metabolic patterns of hypothetically pure tissue (102, 104, 163, 164).…”

Section: Spectral Acquisition and Processingmentioning

confidence: 99%

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker

et al. 2019

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Proton magnetic resonance spectroscopy (1H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, 1H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of 1H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T1 and T2 relaxation; B1 field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of 1H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo 1H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by 1H-MRS.

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Proton MR spectroscopy of lesion evolution in multiple sclerosis: Steady‐state metabolism and its relationship to conventional imaging

Cited by 20 publications

References 60 publications

Quantitative multivoxel proton MR spectroscopy for the identification of white matter abnormalities in mild traumatic brain injury: Comparison between regional and global analysis

Quantitative multivoxel proton MR spectroscopy for the identification of white matter abnormalities in mild traumatic brain injury: Comparison between regional and global analysis

The contribution of astrocytes to the neuroinflammatory response in multiple sclerosis and experimental autoimmune encephalomyelitis

Quantifying the Metabolic Signature of Multiple Sclerosis by in vivo Proton Magnetic Resonance Spectroscopy: Current Challenges and Future Outlook in the Translation From Proton Signal to Diagnostic Biomarker

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