Quantitative 7-Tesla Imaging of Cortical Myelin Changes in Early Multiple Sclerosis.

Cortical demyelination occurs early in multiple sclerosis (MS) and relates to disease outcome. The brain cortex has endogenous propensity for remyelination as proven from histopathology study. In this study, we aimed at characterizing cortical microstructural abnormalities related to myelin content by applying a novel quantitative MRI technique in early MS. A combined myelin estimation (CME) cortical map was obtained from quantitative 7-Tesla (7T) T 2 * and T1 acquisitions in 25 patients with early MS and 19 healthy volunteers. Cortical lesions in MS patients were classified based on their myelin content by comparison with CME values in healthy controls as demyelinated, partially demyelinated, or non-demyelinated. At follow-up, we registered changes in cortical lesions as increased, decreased, or stable CME. Vertex-wise analysis compared cortical CME in the normal-appearing cortex in 25 MS patients vs. 19 healthy controls at baseline and investigated longitudinal changes at 1 year in 10 MS patients. Measurements from the neurite orientation dispersion and density imaging (NODDI) diffusion model were obtained to account for cortical neurite/dendrite loss at baseline and follow-up. Finally, CME maps were correlated with clinical metrics. CME was overall low in cortical lesions (p = 0.03) and several normal-appearing cortical areas (p < 0.05) in the absence of NODDI abnormalities. Individual cortical lesion analysis revealed, however, heterogeneous CME patterns from extensive to partial or absent demyelination. At follow-up, CME overall decreased in cortical lesions and non-lesioned cortex, with few areas showing an increase (p < 0.05). Cortical CME maps correlated with processing speed in several areas across the cortex. In conclusion, CME allows detection of cortical microstructural changes related to coexisting demyelination and remyelination since the early phases of MS, and shows to be more sensitive than NODDI and relates to cognitive performance.

Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis.

BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. METHODS: Here, we present a machine learning method based on quantitative MRI that can achieve a robust classification of HDLS versus MS. Four HDLS and 14 age-matched MS patients underwent a quantitative brain MRI protocol (synthetic MRI) at 3 Tesla (T) (scan time <7 minutes). We also performed a repeatability analysis of the predicting features to assess their generalizability by scanning a healthy control with five scan-rescans at 3T and 1.5T. RESULTS: Our predicting features were measured with an average confidence interval of 1.7% (P = .01), at 3T and 2.3% (P = .01) at 1.5T. The model gave a 100% correct classification of the cross-validation data when using 5-11 predicting features. When the maximum measurement noise was inserted in the model, the true positive rate of HDLS was 97.2%, while the true positive rate of MS was 99.6%. CONCLUSIONS: This study suggests that computer-assistance in combination with quantitative MRI may be helpful in aiding the challenging differential diagnosis of HDLS versus MS.

Validation of Rapid Magnetic Resonance Myelin Imaging in Multiple Sclerosis.

OBJECTIVE: Magnetic resonance imaging (MRI) is essential for multiple sclerosis diagnostics but is conventionally not specific to demyelination. Myelin imaging is often hampered by long scanning times, complex postprocessing, or lack of clinical approval. This study aimed to assess the specificity, robustness, and clinical value of Rapid Estimation of Myelin for Diagnostic Imaging, a new myelin imaging technique based on time-efficient simultaneous T1 /T2 relaxometry and proton density mapping in multiple sclerosis. METHODS: Rapid myelin imaging was applied using 3T MRI ex vivo in 3 multiple sclerosis brain samples and in vivo in a prospective cohort of 71 multiple sclerosis patients and 21 age/sex-matched healthy controls, with scan-rescan repeatability in a subcohort. Disability in patients was assessed by the Expanded Disability Status Scale and the Symbol Digit Modalities Test at baseline and 2-year follow-up. RESULTS: Rapid myelin imaging correlated with myelin-related stains (proteolipid protein immunostaining and Luxol fast blue) and demonstrated good precision. Multiple sclerosis patients had, relative to controls, lower normalized whole-brain and normal-appearing white matter myelin fractions, which correlated with baseline cognitive and physical disability. Longitudinally, these myelin fractions correlated with follow-up physical disability, even with correction for baseline disability. INTERPRETATION: Rapid Estimation of Myelin for Diagnostic Imaging provides robust myelin quantification that detects diffuse demyelination in normal-appearing tissue in multiple sclerosis, which is associated with both cognitive and clinical disability. Because the technique is fast, with automatic postprocessing and US Food and Drug Administration/CE clinical approval, it can be a clinically feasible biomarker that may be suitable to monitor myelin dynamics and evaluate treatments aiming at remyelination. ANN NEUROL 2020;87:710-724.

Profiles of cortical inflammation in multiple sclerosis by 11C-PBR28 MR-PET and 7 Tesla imaging.

BACKGROUND: Neuroinflammation with microglia activation is thought to be closely related to cortical multiple sclerosis (MS) lesion pathogenesis. OBJECTIVE: Using 11C-PBR28 and 7 Tesla (7T) imaging, we assessed in 9 relapsing-remitting multiple sclerosis (RRMS) and 10 secondary progressive multiple sclerosis (SPMS) patients the following: (1) microglia activation in lesioned and normal-appearing cortex, (2) cortical lesion inflammatory profiles, and (3) the relationship between neuroinflammation and cortical integrity. METHODS: Mean 11C-PBR28 uptake was measured in focal cortical lesions, cortical areas with 7T quantitative T2* (q-T2*) abnormalities, and normal-appearing cortex. The relative difference in cortical 11C-PBR28 uptake between patients and 14 controls was used to classify cortical lesions as either active or inactive. Disease burden was investigated according to cortical lesion inflammatory profiles. The relation between q-T2* and 11C-PBR28 uptake along the cortex was assessed. RESULTS: 11C-PBR28 uptake was abnormally high in cortical lesions in RRMS and SPMS; in SPMS, tracer uptake was significantly increased also in normal-appearing cortex. 11C-PBR28 uptake and q-T2* correlated positively in many cortical areas, negatively in some regions. Patients with high cortical lesion inflammation had worse clinical outcome and higher intracortical lesion burden than patients with low inflammation. CONCLUSION: 11C-PBR28 and 7T imaging reveal distinct profiles of cortical inflammation in MS, which are related to disease burden.

Changes in structural network are associated with cortical demyelination in early multiple sclerosis.

The aim of this study was to investigate the interplay between structural connectivity and cortical demyelination in early multiple sclerosis. About 27 multiple sclerosis patients and 18 age-matched controls underwent two MRI scanning sessions. The first was done at 7T and involved acquiring quantitative T1 and T2 * high-resolution maps to estimate cortical myelination. The second was done on a Connectom scanner and consisted of acquiring high angular resolution diffusion-weighted images to compute white matter structural connectivity metrics: strength, clustering and local efficiency. To further investigate the interplay between structural connectivity and cortical demyelination, patients were divided into four groups according to disease-duration: 0-1 year, 1-2 years, 2-3 years, and >3 years. ANOVA and Spearman's correlations were used to highlight relations between metrics. ANOVA detected a significant effect between disease duration and both cortical myelin (p = 2 × 10-8 ) and connectivity metrics (p < 10-4 ). We observed significant cortical myelin loss in the shorter disease-duration cohorts (0-1 year, p = .0015), and an increase in connectivity in the longer disease-duration cohort (2-3 years, strength: p = .01, local efficiency: p = .002, clustering: p = .001). Moreover, significant covariations between myelin estimation and white matter connectivity metrics were observed: Spearman's Rho correlation coefficients of 0.52 (p = .0003), 0.55 (p = .0001), and 0.53 (p = .0001) for strength, local efficiency, and clustering, respectively. An association between cortical myelin loss and changes in white matter connectivity in early multiple sclerosis was detected. These changes in network organization might be the result of compensatory mechanisms in response to the ongoing cortical diffuse damage in the early stages of multiple sclerosis.

In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis.

Neuroaxonal pathology is a main determinant of disease progression in multiple sclerosis; however, its underlying pathophysiological mechanisms, including its link to inflammatory demyelination and temporal occurrence in the disease course are still unknown. We used ultra-high field (7 T), ultra-high gradient strength diffusion and T1/T2-weighted myelin-sensitive magnetic resonance imaging to characterize microstructural changes in myelin and neuroaxonal integrity in the cortex and white matter in early stage multiple sclerosis, their distribution in lesional and normal-appearing tissue, and their correlations with neurological disability. Twenty-six early stage multiple sclerosis subjects (disease duration ≤5 years) and 24 age-matched healthy controls underwent 7 T T2*-weighted imaging for cortical lesion segmentation and 3 T T1/T2-weighted myelin-sensitive imaging and neurite orientation dispersion and density imaging for assessing microstructural myelin, axonal and dendrite integrity in lesional and normal-appearing tissue of the cortex and the white matter. Conventional mean diffusivity and fractional anisotropy metrics were also assessed for comparison. Cortical lesions were identified in 92% of early multiple sclerosis subjects and they were characterized by lower intracellular volume fraction (P = 0.015 by paired t-test), lower myelin-sensitive contrast (P = 0.030 by related-samples Wilcoxon signed-rank test) and higher mean diffusivity (P = 0.022 by related-samples Wilcoxon signed-rank test) relative to the contralateral normal-appearing cortex. Similar findings were observed in white matter lesions relative to normal-appearing white matter (all P < 0.001), accompanied by an increased orientation dispersion (P < 0.001 by paired t-test) and lower fractional anisotropy (P < 0.001 by related-samples Wilcoxon signed-rank test) suggestive of less coherent underlying fibre orientation. Additionally, the normal-appearing white matter in multiple sclerosis subjects had diffusely lower intracellular volume fractions than the white matter in controls (P = 0.029 by unpaired t-test). Cortical thickness did not differ significantly between multiple sclerosis subjects and controls. Higher orientation dispersion in the left primary motor-somatosensory cortex was associated with increased Expanded Disability Status Scale scores in surface-based general linear modelling (P < 0.05). Microstructural pathology was frequent in early multiple sclerosis, and present mainly focally in cortical lesions, whereas more diffusely in white matter. These results suggest early demyelination with loss of cells and/or cell volumes in cortical and white matter lesions, with additional axonal dispersion in white matter lesions. In the cortex, focal lesion changes might precede diffuse atrophy with cortical thinning. Findings in the normal-appearing white matter reveal early axonal pathology outside inflammatory demyelinating lesions.

Topologically preserving straightening of spinal cord MRI.

PURPOSE: To propose a robust and accurate method for straightening magnetic resonance (MR) images of the spinal cord, based on spinal cord segmentation, that preserves spinal cord topology and that works for any MRI contrast, in a context of spinal cord template-based analysis. MATERIALS AND METHODS: The spinal cord curvature was computed using an iterative Non-Uniform Rational B-Spline (NURBS) approximation. Forward and inverse deformation fields for straightening were computed by solving analytically the straightening equations for each image voxel. Computational speed-up was accomplished by solving all voxel equation systems as one single system. Straightening accuracy (mean and maximum distance from straight line), computational time, and robustness to spinal cord length was evaluated using the proposed and the standard straightening method (label-based spline deformation) on 3T T2 - and T1 -weighted images from 57 healthy subjects and 33 patients with spinal cord compression due to degenerative cervical myelopathy (DCM). RESULTS: The proposed algorithm was more accurate, more robust, and faster than the standard method (mean distance = 0.80 vs. 0.83 mm, maximum distance = 1.49 vs. 1.78 mm, time = 71 vs. 174 sec for the healthy population and mean distance = 0.65 vs. 0.68 mm, maximum distance = 1.28 vs. 1.55 mm, time = 32 vs. 60 sec for the DCM population). CONCLUSION: A novel image straightening method that enables template-based analysis of quantitative spinal cord MRI data is introduced. This algorithm works for any MRI contrast and was validated on healthy and patient populations. The presented method is implemented in the Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1209-1219.

Neuroinflammatory component of gray matter pathology in multiple sclerosis.

OBJECTIVE: In multiple sclerosis (MS), using simultaneous magnetic resonance-positron emission tomography (MR-PET) imaging with 11 C-PBR28, we quantified expression of the 18kDa translocator protein (TSPO), a marker of activated microglia/macrophages, in cortex, cortical lesions, deep gray matter (GM), white matter (WM) lesions, and normal-appearing WM (NAWM) to investigate the in vivo pathological and clinical relevance of neuroinflammation. METHODS: Fifteen secondary-progressive MS (SPMS) patients, 12 relapsing-remitting MS (RRMS) patients, and 14 matched healthy controls underwent 11 C-PBR28 MR-PET. MS subjects underwent 7T T2*-weighted imaging for cortical lesion segmentation, and neurological and cognitive evaluation. 11 C-PBR28 binding was measured using normalized 60- to 90-minute standardized uptake values and volume of distribution ratios. RESULTS: Relative to controls, MS subjects exhibited abnormally high 11 C-PBR28 binding across the brain, the greatest increases being in cortex and cortical lesions, thalamus, hippocampus, and NAWM. MS WM lesions showed relatively modest TSPO increases. With the exception of cortical lesions, where TSPO expression was similar, 11 C-PBR28 uptake across the brain was greater in SPMS than in RRMS. In MS, increased 11 C-PBR28 binding in cortex, deep GM, and NAWM correlated with neurological disability and impaired cognitive performance; cortical thinning correlated with increased thalamic TSPO levels. INTERPRETATION: In MS, neuroinflammation is present in the cortex, cortical lesions, deep GM, and NAWM, is closely linked to poor clinical outcome, and is at least partly linked to neurodegeneration. Distinct inflammatory-mediated factors may underlie accumulation of cortical and WM lesions. Quantification of TSPO levels in MS could prove to be a sensitive tool for evaluating in vivo the inflammatory component of GM pathology, particularly in cortical lesions. Ann Neurol 2016;80:776-790.