Imaging chronic active lesions in multiple sclerosis: a consensus statement.

Chronic active lesions (CAL) are an important manifestation of chronic inflammation in multiple sclerosis (MS) and have implications for non-relapsing biological progression. In recent years, the discovery of innovative magnetic resonance imaging (MRI) and PET derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with MS, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted (T1-w) and T2-w scans, and 18-kDa translocator protein-positive lesions on PET are promising candidate biomarkers of CAL. While partially overlapping, these biomarkers do not have equivalent sensitivity and specificity to histopathological CAL. Standardization in the use of available imaging measures for CAL identification, quantification, and monitoring is lacking. To fast-forward clinical translation of CAL, the North American Imaging in Multiple Sclerosis Cooperative developed a Consensus Statement, which provides guidance for the radiological definition and measurement of CAL. The proposed manuscript presents this Consensus Statement, summarizes the multistep process leading to it, and identifies the remaining major gaps in knowledge.

Choroid plexus volume differentiates MS from its mimics.

This study aimed to determine whether choroid plexus volume (CPV) could differentiate multiple sclerosis (MS) from its mimics. A secondary analysis of two previously enrolled studies, 50 participants with MS and 64 with alternative diagnoses were included. CPV was automatically segmented from 3T magnetic resonance imaging (MRI), followed by manual review to remove misclassified tissue. Mean normalized choroid plexus volume (nCPV) to intracranial volume demonstrated relatively high specificity for MS participants in each cohort (0.80 and 0.76) with an area under the receiver-operator characteristic curve of 0.71 (95% confidence interval (CI) = 0.55-0.87) and 0.65 (95% CI = 0.52-0.77). In this preliminary study, nCPV differentiated MS from its mimics.

A multicenter pilot study evaluating simplified central vein assessment for the diagnosis of multiple sclerosis.

BACKGROUND: The central vein sign (CVS) is a proposed magnetic resonance imaging (MRI) biomarker for multiple sclerosis (MS); the optimal method for abbreviated CVS scoring is not yet established. OBJECTIVE: The aim of this study was to evaluate the performance of a simplified approach to CVS assessment in a multicenter study of patients being evaluated for suspected MS. METHODS: Adults referred for possible MS to 10 sites were recruited. A post-Gd 3D T2*-weighted MRI sequence (FLAIR*) was obtained in each subject. Trained raters at each site identified up to six CVS-positive lesions per FLAIR* scan. Diagnostic performance of CVS was evaluated for a diagnosis of MS which had been confirmed using the 2017 McDonald criteria at thresholds including three positive lesions (Select-3*) and six positive lesions (Select-6*). Inter-rater reliability assessments were performed. RESULTS: Overall, 78 participants were analyzed; 37 (47%) were diagnosed with MS, and 41 (53%) were not. The mean age of participants was 45 (range: 19-64) years, and most were female (n = 55, 71%). The area under the receiver operating characteristic curve (AUROC) for the simplified counting method was 0.83 (95% CI: 0.73-0.93). Select-3* and Select-6* had sensitivity of 81% and 65% and specificity of 68% and 98%, respectively. Inter-rater agreement was 78% for Select-3* and 83% for Select-6*. CONCLUSION: A simplified method for CVS assessment in patients referred for suspected MS demonstrated good diagnostic performance and inter-rater agreement.

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.

PURPOSE: To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors. METHODS: Eighteen subjects were imaged using a whole-brain quantitative T1 -T2 -T1ρ MR multitasking sequence. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 gradient echo, and T2 fluid-attenuated inversion recovery were acquired as target images. A 2D U-Net-based neural network was trained to synthesize conventional weighted images from MR multitasking spatial factors. Quantitative assessment and image quality rating by two radiologists were performed to evaluate the quality of deep-learning-based synthesis, in comparison with Bloch-equation-based synthesis from MR multitasking quantitative maps. RESULTS: The deep-learning synthetic images showed comparable contrasts of brain tissues with the reference images from true acquisitions and were substantially better than the Bloch-equation-based synthesis results. Averaging on the three contrasts, the deep learning synthesis achieved normalized root mean square error = 0.184 ± 0.075, peak SNR = 28.14 ± 2.51, and structural-similarity index = 0.918 ± 0.034, which were significantly better than Bloch-equation-based synthesis (p < 0.05). Radiologists' rating results show that compared with true acquisitions, deep learning synthesis had no notable quality degradation and was better than Bloch-equation-based synthesis. CONCLUSION: A deep learning technique was developed to synthesize conventional weighted images from MR multitasking spatial factors in the brain, enabling the simultaneous acquisition of multiparametric quantitative maps and clinical contrast-weighted images in a single scan.

A New Advanced MRI Biomarker for Remyelinated Lesions in Multiple Sclerosis.

OBJECTIVES: Neuropathological studies have shown that multiple sclerosis (MS) lesions are heterogeneous in terms of myelin/axon damage and repair as well as iron content. However, it remains a challenge to identify specific chronic lesion types, especially remyelinated lesions, in vivo in patients with MS. METHODS: We performed 3 studies: (1) a cross-sectional study in a prospective cohort of 115 patients with MS and 76 healthy controls, who underwent 3 T magnetic resonance imaging (MRI) for quantitative susceptibility mapping (QSM), myelin water fraction (MWF), and neurite density index (NDI) maps. White matter (WM) lesions in QSM were classified into 5 QSM lesion types (iso-intense, hypo-intense, hyperintense, lesions with hypo-intense rims, and lesions with paramagnetic rim legions [PRLs]); (2) a longitudinal study of 40 patients with MS to study the evolution of lesions over 2 years; (3) a postmortem histopathology-QSM validation study in 3 brains of patients with MS to assess the accuracy of QSM classification to identify neuropathological lesion types in 63 WM lesions. RESULTS: At baseline, hypo- and isointense lesions showed higher mean MWF and NDI values compared to other QSM lesion types (p < 0.0001). Further, at 2-year follow-up, hypo-/iso-intense lesions showed an increase in MWF. Postmortem analyses revealed that QSM highly accurately identifies (1) fully remyelinated areas as hypo-/iso-intense (sensitivity = 88.89% and specificity = 100%), (2) chronic inactive lesions as hyperintense (sensitivity = 71.43% and specificity = 92.00%), and (3) chronic active/smoldering lesions as PRLs (sensitivity = 92.86% and specificity = 86.36%). INTERPRETATION: These results provide the first evidence that it is possible to distinguish chronic MS lesions in a clinical setting, hereby supporting with new biomarkers to develop and assess remyelinating treatments. ANN NEUROL 2022;92:486-502.

Susceptibility-based imaging aids accurate distinction of pediatric-onset MS from myelin oligodendrocyte glycoprotein antibody-associated disease.

BACKGROUND: Myelin oligodendrocyte glycoprotein (MOG) antibody-associated disease (MOGAD) and pediatric-onset multiple sclerosis (POMS) share clinical and magnetic resonance imaging (MRI) features but differ in prognosis and management. Early POMS diagnosis is essential to avoid disability accumulation. Central vein sign (CVS), paramagnetic rim lesions (PRLs), and central core lesions (CCLs) are susceptibility-based imaging (SbI)-related signs understudied in pediatric populations that may help discerning POMS from MOGAD. METHODS: T2-FLAIR and SbI (three-dimensional echoplanar imaging (3D-EPI)/susceptibility-weighted imaging (SWI) or similar) were acquired on 1.5T/3T scanners. Two readers assessed CVS-positive rate (%CVS+), and their average score was used to build a receiver operator curve (ROC) assessing the ability to discriminate disease type. PRLs and CCLs were identified using a consensual approach. RESULTS: The %CVS+ distinguished 26 POMS cases (mean age 13.7 years, 63% females, median EDSS 1.5) from 14 MOGAD cases (10.8 years, 35% females, EDSS 1.0) with ROC = 1, p < 0.0001, (cutoff 41%). PRLs were only detectable in POMS participants (mean 2.1±2.3, range 1-10), discriminating the two conditions with a sensitivity of 69% and a specificity of 100%. CCLs were more sensitive (81%) but less specific (71.43%). CONCLUSION: The %CVS+ and PRLs are highly specific markers of POMS. After proper validation on larger multicenter cohorts, consideration should be given to including such imaging markers for diagnosing POMS at disease onset.

Effect of GBCA Use on Detection and Diagnostic Performance of the Central Vein Sign: Evaluation Using a 3-T FLAIR* Sequence in Patients With Suspected Multiple Sclerosis.

BACKGROUND. The central vein sign (CVS) is a proposed MRI biomarker of multiple sclerosis (MS). The impact of gadolinium-based contrast agent (GBCA) administration on CVS evaluation remains poorly investigated. OBJECTIVE. The purpose of this study was to assess the effect of GBCA use on CVS detection and on the diagnostic performance of the CVS for MS using a 3-T FLAIR* sequence. METHODS. This study was a secondary analysis of data from the pilot study for the prospective multicenter Central Vein Sign: A Diagnostic Biomarker in Multiple Sclerosis (CAVS-MS), which recruited adults with suspected MS from April 2018 to February 2020. Participants underwent 3-T brain MRI including FLAIR and precontrast and post-contrast echo-planar imaging T2*-weighted acquisitions. Postprocessing was used to generate combined FLAIR and T2*-weighted images (hereafter, FLAIR*). MS diagnoses were established using the 2017 McDonald criteria. Thirty participants (23 women, seven men; mean age, 45 years) were randomly selected from the CAVS-MS pilot study cohort. White matter lesions (WMLs) were marked using FLAIR* images. A single observer, blinded to clinical data and GBCA use, reviewed marked WMLs on FLAIR* images for the presence of the CVS. RESULTS. Thirteen of 30 participants had MS. Across participants, on precontrast FLAIR* imaging, 218 CVS-positive and 517 CVS-negative WMLs were identified; on post-contrast FLAIR* imaging, 269 CVS-positive and 459 CVS-negative WMLs were identified. The fraction of WMLs that were CVS-positive on precontrast and postcontrast images was 48% and 58% in participants with MS and 7% and 10% in participants without MS, respectively. The median patient-level CVS-positivity rate on precontrast and postcontrast images was 43% and 67% for participants with MS and 4% and 8% for participants without MS, respectively. In a binomial model adjusting for MS diagnoses, GBCA use was associated with an increased likelihood of at least one CVS-positive WML (odds ratio, 1.6; p < .001). At a 40% CVS-positivity threshold, the sensitivity of the CVS for MS increased from 62% on precontrast images to 92% on postcontrast images (p = .046). Specificity was not significantly different between precontrast (88%) and postcontrast (82%) images (p = .32). CONCLUSION. GBCA use increased CVS detection on FLAIR* images, thereby increasing the sensitivity of the CVS for MS diagnoses. CLINICAL IMPACT. The postcontrast FLAIR* sequence should be considered for CVS evaluation in future investigational trials and clinical practice.

7T MRI Differentiates Remyelinated from Demyelinated Multiple Sclerosis Lesions.

OBJECTIVE: To noninvasively assess myelin status in chronic white matter lesions of multiple sclerosis (MS), we developed and evaluated a simple classification scheme based on T1 relaxation time maps derived from 7-tesla postmortem and in vivo MRI. METHODS: Using the MP2RAGE MRI sequence, we classified 36 lesions from 4 postmortem MS brains as "long-T1," "short-T1," and "mixed-T1" by visual comparison to neocortex. Within these groups, we compared T1 times to histologically derived measures of myelin and axons. We performed similar analysis of 235 chronic lesions with known date of onset in 25 MS cases in vivo and in a validation cohort of 222 lesions from 66 MS cases, investigating associations with clinical and radiological outcomes. RESULTS: Postmortem, lesions classified qualitatively as long-T1, short-T1, and mixed-T1 corresponded to fully demyelinated, fully remyelinated, and mixed demyelinated/remyelinated lesions, respectively (p ≤ 0.001). Demyelination (rather than axon loss) dominantly contributed to initial T1 prolongation. We observed lesions with similar characteristics in vivo, allowing manual classification with substantial interrater and excellent intrarater reliability. Short-T1 lesions were most common in the deep white matter, whereas long-T1 and mixed-T1 lesions were prevalent in the juxtacortical and periventricular white matter (p = 0.02) and were much more likely to have paramagnetic rims suggesting chronic inflammation (p < 0.001). Older age at the time of lesion formation portended less remyelination (p = 0.007). INTERPRETATION: 7-tesla T1 mapping with MP2RAGE, a clinically available MRI method, allows qualitative and quantitative classification of chronic MS lesions according to myelin content, rendering straightforward the tracking of lesional myelination changes over time. ANN NEUROL 2021;90:612-626.

Multiple sclerosis cortical lesion detection with deep learning at ultra-high-field MRI.

Manually segmenting multiple sclerosis (MS) cortical lesions (CLs) is extremely time consuming, and past studies have shown only moderate inter-rater reliability. To accelerate this task, we developed a deep-learning-based framework (CLAIMS: Cortical Lesion AI-Based Assessment in Multiple Sclerosis) for the automated detection and classification of MS CLs with 7 T MRI. Two 7 T datasets, acquired at different sites, were considered. The first consisted of 60 scans that include 0.5 mm isotropic MP2RAGE acquired four times (MP2RAGE×4), 0.7 mm MP2RAGE, 0.5 mm T2 *-weighted GRE, and 0.5 mm T2 *-weighted EPI. The second dataset consisted of 20 scans including only 0.75 × 0.75 × 0.9 mm3 MP2RAGE. CLAIMS was first evaluated using sixfold cross-validation with single and multi-contrast 0.5 mm MRI input. Second, the performance of the model was tested on 0.7 mm MP2RAGE images after training with either 0.5 mm MP2RAGE×4, 0.7 mm MP2RAGE, or alternating the two. Third, its generalizability was evaluated on the second external dataset and compared with a state-of-the-art technique based on partial volume estimation and topological constraints (MSLAST). CLAIMS trained only with MP2RAGE×4 achieved results comparable to those of the multi-contrast model, reaching a CL true positive rate of 74% with a false positive rate of 30%. Detection rate was excellent for leukocortical and subpial lesions (83%, and 70%, respectively), whereas it reached 53% for intracortical lesions. The correlation between disability measures and CL count was similar for manual and CLAIMS lesion counts. Applying a domain-scanner adaptation approach and testing CLAIMS on the second dataset, the performance was superior to MSLAST when considering a minimum lesion volume of 6 µL (lesion-wise detection rate of 71% versus 48%). The proposed framework outperforms previous state-of-the-art methods for automated CL detection across scanners and protocols. In the future, CLAIMS may be useful to support clinical decisions at 7 T MRI, especially in the field of diagnosis and differential diagnosis of MS patients.

Lesion size and shape in central vein sign assessment for multiple sclerosis diagnosis: An in vivo and postmortem MRI study.

BACKGROUND: The "central vein sign" (CVS), a linear hypointensity on T2*-weighted imaging corresponding to a central vein/venule, is associated with multiple sclerosis (MS) lesions. The effect of lesion-size exclusion criteria on MS diagnostic accuracy has not been extensively studied. OBJECTIVE: Investigate the optimal lesion-size exclusion criteria for CVS use in MS diagnosis. METHODS: Cross-sectional study of 163 MS and 51 non-MS, and radiological/histopathological correlation of 5 MS and 1 control autopsy cases. The effects of lesion-size exclusion on MS diagnosis using the CVS, and intralesional vein detection on histopathology were evaluated. RESULTS: CVS+ lesions were larger compared to CVS- lesions, with effect modification by MS diagnosis (mean difference +7.7 mm3, p = 0.004). CVS percentage-based criteria with no lesion-size exclusion showed the highest diagnostic accuracy in differentiating MS cases. However, a simple count of three or more CVS+ lesions greater than 3.5 mm is highly accurate and can be rapidly implemented (sensitivity 93%; specificity 88%). On magnetic resonance imaging (MRI)-histopathological correlation, the CVS had high specificity for identifying intralesional veins (0/7 false positives). CONCLUSION: Lesion-size measures add important information when using CVS+ lesion counts for MS diagnosis. The CVS is a specific biomarker corresponding to intralesional veins on histopathology.

Cortical lesion hotspots and association of subpial lesions with disability in multiple sclerosis.

BACKGROUND: Dramatic improvements in visualization of cortical (especially subpial) multiple sclerosis (MS) lesions allow assessment of impact on clinical course. OBJECTIVE: Characterize cortical lesions by 7 tesla (T) T2*-/T1-weighted magnetic resonance imaging (MRI); determine relationship with other MS pathology and contribution to disability. METHODS: Sixty-four adults with MS (45 relapsing-remitting/19 progressive) underwent 3 T brain/spine MRI, 7 T brain MRI, and clinical testing. RESULTS: Cortical lesions were found in 94% (progressive: median 56/range 2-203; relapsing-remitting: 15/0-168; p = 0.004). Lesion distribution across 50 cortical regions was nonuniform (p = 0.006), with highest lesion burden in supplementary motor cortex and highest prevalence in superior frontal gyrus. Leukocortical and white matter lesion volumes were strongly correlated (r = 0.58, p < 0.0001), while subpial and white matter lesion volumes were moderately correlated (r = 0.30, p = 0.002). Leukocortical (p = 0.02) but not subpial lesions (p = 0.40) were correlated with paramagnetic rim lesions; both were correlated with spinal cord lesions (p = 0.01). Cortical lesion volumes (total and subtypes) were correlated with expanded disability status scale, 25-foot timed walk, nine-hole peg test, and symbol digit modality test scores. CONCLUSION: Cortical lesions are highly prevalent and are associated with disability and progressive disease. Subpial lesion burden is not strongly correlated with white matter lesions, suggesting differences in inflammation and repair mechanisms.

Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging.

Damage to the myelin sheath and the neuroaxonal unit is a cardinal feature of multiple sclerosis; however, a detailed characterization of the interaction between myelin and axon damage in vivo remains challenging. We applied myelin water and multi-shell diffusion imaging to quantify the relative damage to myelin and axons (i) among different lesion types; (ii) in normal-appearing tissue; and (iii) across multiple sclerosis clinical subtypes and healthy controls. We also assessed the relation of focal myelin/axon damage with disability and serum neurofilament light chain as a global biological measure of neuroaxonal damage. Ninety-one multiple sclerosis patients (62 relapsing-remitting, 29 progressive) and 72 healthy controls were enrolled in the study. Differences in myelin water fraction and neurite density index were substantial when lesions were compared to healthy control subjects and normal-appearing multiple sclerosis tissue: both white matter and cortical lesions exhibited a decreased myelin water fraction and neurite density index compared with healthy (P < 0.0001) and peri-plaque white matter (P < 0.0001). Periventricular lesions showed decreased myelin water fraction and neurite density index compared with lesions in the juxtacortical region (P < 0.0001 and P < 0.05). Similarly, lesions with paramagnetic rims showed decreased myelin water fraction and neurite density index relative to lesions without a rim (P < 0.0001). Also, in 75% of white matter lesions, the reduction in neurite density index was higher than the reduction in the myelin water fraction. Besides, normal-appearing white and grey matter revealed diffuse reduction of myelin water fraction and neurite density index in multiple sclerosis compared to healthy controls (P < 0.01). Further, a more extensive reduction in myelin water fraction and neurite density index in normal-appearing cortex was observed in progressive versus relapsing-remitting participants. Neurite density index in white matter lesions correlated with disability in patients with clinical deficits (P < 0.01, beta = -10.00); and neurite density index and myelin water fraction in white matter lesions were associated to serum neurofilament light chain in the entire patient cohort (P < 0.01, beta = -3.60 and P < 0.01, beta = 0.13, respectively). These findings suggest that (i) myelin and axon pathology in multiple sclerosis is extensive in both lesions and normal-appearing tissue; (ii) particular types of lesions exhibit more damage to myelin and axons than others; (iii) progressive patients differ from relapsing-remitting patients because of more extensive axon/myelin damage in the cortex; and (iv) myelin and axon pathology in lesions is related to disability in patients with clinical deficits and global measures of neuroaxonal damage.

Quantitative magnetic resonance imaging towards clinical application in multiple sclerosis.

Quantitative MRI provides biophysical measures of the microstructural integrity of the CNS, which can be compared across CNS regions, patients, and centres. In patients with multiple sclerosis, quantitative MRI techniques such as relaxometry, myelin imaging, magnetization transfer, diffusion MRI, quantitative susceptibility mapping, and perfusion MRI, complement conventional MRI techniques by providing insight into disease mechanisms. These include: (i) presence and extent of diffuse damage in CNS tissue outside lesions (normal-appearing tissue); (ii) heterogeneity of damage and repair in focal lesions; and (iii) specific damage to CNS tissue components. This review summarizes recent technical advances in quantitative MRI, existing pathological validation of quantitative MRI techniques, and emerging applications of quantitative MRI to patients with multiple sclerosis in both research and clinical settings. The current level of clinical maturity of each quantitative MRI technique, especially regarding its integration into clinical routine, is discussed. We aim to provide a better understanding of how quantitative MRI may help clinical practice by improving stratification of patients with multiple sclerosis, and assessment of disease progression, and evaluation of treatment response.

Ultrahigh-resolution MRI Reveals Extensive Cortical Demyelination in a Nonhuman Primate Model of Multiple Sclerosis.

Cortical lesions are a primary driver of disability in multiple sclerosis (MS). However, noninvasive detection of cortical lesions with in vivo magnetic resonance imaging (MRI) remains challenging. Experimental autoimmune encephalomyelitis (EAE) in the common marmoset is a relevant animal model of MS for investigating the pathophysiological mechanisms leading to brain damage. This study aimed to characterize cortical lesions in marmosets with EAE using ultrahigh-field (7 T) MRI and histological analysis. Tissue preparation was optimized to enable the acquisition of high-spatial resolution (50-µm isotropic) T2*-weighted images. A total of 14 animals were scanned in this study, and 70% of the diseased animals presented at least one cortical lesion on postmortem imaging. Cortical lesions identified on MRI were verified with myelin proteolipid protein immunostaining. An optimized T2*-weighted sequence was developed for in vivo imaging and shown to capture 65% of cortical lesions detected postmortem. Immunostaining confirmed extensive demyelination with preserved neuronal somata in several cortical areas of EAE animals. Overall, this study demonstrates the relevance and feasibility of the marmoset EAE model to study cortical lesions, among the most important yet least understood features of MS.

Paramagnetic Rim Lesions are Specific to Multiple Sclerosis: An International Multicenter 3T MRI Study.

In multiple sclerosis (MS), a subset of chronic active white matter lesions are identifiable on magnetic resonance imaging by their paramagnetic rims, and increasing evidence supports their association with severity of clinical disease. We studied their potential role in differential diagnosis, screening an international multicenter clinical research-based sample of 438 individuals affected by different neurological conditions (MS, other inflammatory, infectious, and non-inflammatory conditions). Paramagnetic rim lesions, rare in other neurological conditions (52% of MS vs 7% of non-MS cases), yielded high specificity (93%) in differentiating MS from non-MS. Future prospective multicenter studies should validate their role as a diagnostic biomarker. ANN NEUROL 2020;88:1034-1042.

Cognitive impairment, the central vein sign, and paramagnetic rim lesions in RIS.

OBJECTIVE: The central vein sign (CVS) and "paramagnetic rim lesions" (PRL) are emerging imaging biomarkers in multiple sclerosis (MS) reflecting perivenular demyelination and chronic, smoldering inflammation. The objective of this study was to assess relationships between cognitive impairment (CI) and the CVS and PRL in radiologically isolated syndrome (RIS). METHODS: Twenty-seven adults with RIS underwent 3.0 T MRI of the brain and cervical spinal cord (SC) and cognitive assessment using the minimal assessment of cognitive function in MS battery. The CVS and PRL were assessed in white-matter lesions (WMLs) on T2*-weighted segmented echo-planar magnitude and phase images. Multivariable linear regression evaluated relationships between CI and MRI measures. RESULTS: Global CI was present in 9 (33%) participants with processing speed and visual memory most frequently affected. Most participants (93%) had ⩾ 40% CVS + WML (a threshold distinguishing MS from other WM disorders); 63% demonstrated PRL. Linear regression revealed that CVS + WML predicted performance on verbal memory(ß =-0.024, p = 0.03) while PRL predicted performance on verbal memory (ß = -0.040, p = 0.04) and processing speed (ß = -0.039, p = 0.03). CONCLUSIONS: CI is common in RIS and is associated with markers of perivenular demyelination and chronic inflammation in WML, such as CVS + WML and PRL. A prospective follow-up of this cohort will ascertain the importance of CI, CVS, and PRL as risk factors for conversion from RIS to MS.

Herpesvirus trigger accelerates neuroinflammation in a nonhuman primate model of multiple sclerosis.

Pathogens, particularly human herpesviruses (HHVs), are implicated as triggers of disease onset/progression in multiple sclerosis (MS) and other neuroinflammatory disorders. However, the time between viral acquisition in childhood and disease onset in adulthood complicates the study of this association. Using nonhuman primates, we demonstrate that intranasal inoculations with HHV-6A and HHV-6B accelerate an MS-like neuroinflammatory disease, experimental autoimmune encephalomyelitis (EAE). Although animals inoculated intranasally with HHV-6 (virus/EAE marmosets) were asymptomatic, they exhibited significantly accelerated clinical EAE compared with control animals. Expansion of a proinflammatory CD8 subset correlated with post-EAE survival in virus/EAE marmosets, suggesting that a peripheral (viral?) antigen-driven expansion may have occurred post-EAE induction. HHV-6 viral antigen in virus/EAE marmosets was markedly elevated and concentrated in brain lesions, similar to previously reported localizations of HHV-6 in MS brain lesions. Collectively, we demonstrate that asymptomatic intranasal viral acquisition accelerates subsequent neuroinflammation in a nonhuman primate model of MS.

The "central vein sign" in inflammatory demyelination: The role of fibrillar collagen type I.

Accumulating evidence corroborates the role of the "central vein sign" in the radiological diagnosis of multiple sclerosis (MS). Here, we report human magnetic resonance imaging (MRI) and corresponding pathological data that inflammation-dependent intracerebral remodeling of the vessel wall is directly associated with the prominence of intralesional veins on susceptibility-based MRI. In adult marmosets with experimental autoimmune encephalomyelitis, vessel-wall fibrosis was detected early in the demyelinating process, even in lesions <2 weeks old, though fibrosis was more evident after 6 weeks. Vascular remodeling consisted of both luminal enlargement and eccentric thickening of the perivascular space (fibrillar collagen type I deposition) and affected almost exclusively white matter, but not subpial cortical, lesions. The long-term effect of vessel remodeling in MS lesions is currently unknown, but it might potentially affect tissue repair. ANN NEUROL 2019;85:934-942.

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). Brain white matter lesions were manually segmented on FLAIR* images. Perivenular assessment was performed according to consensus guidelines and used as ground truth, yielding 539 CVS-positive (CVS+ ) and 448 CVS-negative (CVS- ) lesions. A 3D convolutional neural network ("CVSnet") was designed and trained on 47 datasets, keeping 33 for testing. FLAIR* lesion patches of CVS+ /CVS- lesions were used for training and validation (n = 375/298) and for testing (n = 164/150). Performance was evaluated lesion-wise and subject-wise and compared with a state-of-the-art vesselness filtering approach through McNemar's test. The proposed CVSnet approached human performance, with lesion-wise median balanced accuracy of 81%, and subject-wise balanced accuracy of 89% on the validation set, and 91% on the test set. The process of CVS assessment, in previously manually segmented lesions, was ~ 600-fold faster using the proposed CVSnet compared with human visual assessment (test set: 4 seconds vs. 40 minutes). On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.

The "central vein sign" in patients with diagnostic "red flags" for multiple sclerosis: A prospective multicenter 3T study.

BACKGROUND: The central vein sign (CVS) has been shown to help in the differential diagnosis of multiple sclerosis (MS), but most prior studies are retrospective. OBJECTIVES: To prospectively assess the diagnostic predictive value of the CVS in diagnostically difficult cases. METHODS: In this prospective multicenter study, 51 patients with suspected MS who had clinical, imaging, or laboratory "red flags" (i.e. features atypical for MS) underwent 3T fluid-attenuated inversion recovery (FLAIR*) magnetic resonance imaging (MRI) for CVS assessment. After the diagnostic work-up, expert clinicians blinded to the results of the CVS assessment came to a clinical diagnosis. The value of the CVS to prospectively predict an MS diagnosis was assessed. RESULTS: Of the 39 patients who received a clinical diagnosis by the end of the study, 27 had MS and 12 received a non-MS diagnosis that included systemic lupus erythematosus, sarcoidosis, migraine, Sjögren disease, SPG4-spastic-paraparesis, neuromyelitis optica, and Susac syndrome. The percentage of perivenular lesions was higher in MS (median = 86%) compared to non-MS (median = 21%; p < 0.0001) patients. A 40% perivenular lesion cutoff was associated with 97% accuracy and a 96% positive/100% negative predictive value. CONCLUSION: The CVS detected on 3T FLAIR* images can accurately predict an MS diagnosis in patients suspected to have MS, but with atypical clinical, laboratory, and imaging features.