Hemostatic factors in the pathogenesis of neuroinflammation in multiple sclerosis.

BACKGROUND: A growing body of evidence has shed light on the role of the hemostatic pathway and its components in the pathogenesis of multiple sclerosis (MS), particularly in enhancing and sustaining neuroinflammation. OBJECTIVE: To review the clinical, experimental, and neuroimaging evidence supporting the role of different components of the hemostatic pathway in the pathogenesis of neuroinflammation in MS and discuss their translational potential as disease biomarkers and therapeutic targets. METHODS: A literature search for most relevant articles from 1956 to 2020 was conducted in PubMed and Scopus. RESULTS: Hemostasis components appear to be involved in different key events of neuroinflammation in MS including mononuclear cell diapedesis, microglia activation, and neuronal damage. CONCLUSION: The findings on the interplay between hemostatic and thrombotic molecular pathways in the pathogenesis of neuroinflammation in MS open new opportunities for developing novel biomarkers for disease monitoring and prognosis, as well as novel therapeutic targets.

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.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

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.

An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis.

To date, the relationship between central hallmarks of multiple sclerosis (MS), such as white matter (WM)/cortical demyelinated lesions and cortical gray matter atrophy, remains unclear. We investigated the interplay between cortical atrophy and individual lesion-type patterns that have recently emerged as new radiological markers of MS disease progression. We employed a machine learning model to predict mean cortical thinning in whole-brain and single hemispheres in 150 cortical regions using demographic and lesion-related characteristics, evaluated via an ultrahigh field (7 Tesla) MRI. We found that (i) volume and rimless (i.e., without a "rim" of iron-laden immune cells) WM lesions, patient age, and volume of intracortical lesions have the most predictive power; (ii) WM lesions are more important for prediction when their load is small, while cortical lesion load becomes more important as it increases; (iii) WM lesions play a greater role in the progression of atrophy during the latest stages of the disease. Our results highlight the intricacy of MS pathology across the whole brain. In turn, this calls for multivariate statistical analyses and mechanistic modeling techniques to understand the etiopathogenesis of lesions.

In vivo characterization of microglia and myelin relation in multiple sclerosis by combined 11C-PBR28 PET and synthetic MRI.

BACKGROUND: The in vivo relation between microglia activation and demyelination in multiple sclerosis is still unclear. OBJECTIVE: We combined 11C-PBR28 positron emission tomography and rapid estimation of myelin for diagnostic imaging (REMyDI) to characterize the relation between these pathological processes in a heterogeneous MS cohort. METHODS: 11C-PBR28 standardized uptake values normalized by a pseudo-reference region (SUVR) were used to measure activated microglia. A voxelwise analysis compared 11C-PBR28 SUVR in the white matter of 38 MS patients and 16 matched healthy controls. The relative difference in SUVR served as a threshold to classify patients' lesioned, perilesional and normal-appearing white matter as active or inactive. REMyDI was acquired in 27 MS patients for assessing myelin content in active and inactive white matter and its relationship with SUVR. Finally, we investigated the contribution of radiological metrics to clinical outcomes. RESULTS: 11C-PBR28 SUVR were abnormally higher in several white matter areas in MS. Myelin content was lower in active compared to inactive corresponding white matter regions. An inverse correlation between SUVR and myelin content was found. Radiological metrics correlated with both neurological and cognitive impairment. CONCLUSION: our data suggest an inverse relation of microglia activation and myelination, particularly in perilesional white matter tissue.

Cerebral lesions of multiple sclerosis: is gadolinium always irreplaceable in assessing lesion activity?

PURPOSE: We aimed to identify imaging characteristics on conventional magnetic resonance imaging that could predict multiple sclerosis (MS) brain lesion activity without contrast media administration. MATERIALS AND METHODS: Magnetic resonance data sets of forty-two patients with relapsing-remitting MS who presented symptoms or signs suggestive of new disease activity were retrospectively reviewed. We classified the MS lesions into three types according to different patterns present on T2-weighted images and evaluated their relationship with the contrast uptake. Evolving aspects of each type of lesion were observed in 18 patients during a follow-up period ranging from nine to 36 months. RESULTS: On T2-weighted images, only the pattern consisting of a thin border of decreased intensity compared with the lesion's center and perifocal edema (Type II) reached diagnostic accuracy in terms of its relationship with gadolinium enhancement (P = 0.006). The sensitivity was 0.461, and the specificity was 0.698. In contrast, enhancement was not significantly related to the pattern consisting of a lesion center that was homogeneously brighter than its periphery (Type I) or less-hyperintense T2 focal lesions with either homogeneous or inhomogeneous center (Type III) (P > 0.05 for both). CONCLUSION: The assessment of MS lesion activity should include a careful evaluation of T2-weighted images in addition to contrast enhancement assessment. The presence of an accompanying peripheral thin rim of hypointensity on T2-weighted images related best with contrast enhancement and subsequent lesion activity and may represent an additional pattern for disease activity assessment when gadolinium examination is contraindicated or influenced by prior therapy.