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Objective: This study aims to (1) build and validate model-based case definitions for multiple sclerosis (MS) that use trends (ie, trend-based case definitions) and (2) to apply dynamic classification to identify the average number of data years needed for classification (ie, average trend needed)., Design: Retrospective cohort study design., Participants: 608 MS cases and 59 620 MS non-cases., Setting: Data from 1 April 2004 to 31 March 2022 were obtained from the Manitoba Population Research Data Repository. MS case status was ascertained from homecare records and linked to health data. Trend-based case definitions were constructed using multivariate generalised linear mixed models applied to annual numbers of general and specialist physician visits, hospitalisations and MS healthcare contacts or medication dispensations. Dynamic classification, which ascertains cases and non-cases annually, was used to estimate mean classification time. Classification accuracy performance measures, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), proportion correctly classified (PCC) and F1-scores, were compared for trend-based case definitions and a deterministic case definition of 3+MS healthcare contacts or medication dispensations., Results: When applied to the full study period, classification accuracy performance measure estimates for all case definitions exceeded 0.90, except sensitivity and PPV for the trend-based dynamic case definition (0.88, 0.64, respectively). PCC was high for all case definitions (0.94-0.99); F1-scores were lower for the trend-based case definitions compared with the deterministic case definition (0.74-0.93 vs 0.96). Dynamic classification identified 5 years as the average trend needed. When applied to the average trend windows, accuracy estimates for trend-based case definitions were lower than the estimates from the full study period (sensitivity: 0.77-0.89; specificity: 0.90-0.97; PPV: 0.54-0.81; NPV: 0.97-0.99; F1-score: 0.64-0.84). Accuracy estimates for the deterministic case definition remained high, except sensitivity (0.42-0.80). F1-score was variable (0.59-0.89)., Conclusions: Trend-based and deterministic case definitions classifications were similar to a population-based clinician assessment reference standard for multiple measures of classification accuracy. However, accuracy estimates for both trend-based and deterministic case definitions varied as the years of data used for classification were reduced. Dynamic classification appears to be a viable option for identifying the average trend needed for trend-based case definitions., Competing Interests: Competing interests: RAM receives research funding from: CIHR, Research Manitoba, Multiple Sclerosis Society of Canada, Multiple Sclerosis Scientific Foundation, Crohn’s and Colitis Canada, National Multiple Sclerosis Society, CMSC and the US Department of Defense, and is a co-investigator on studies receiving funding from Biogen Idec and Roche Canada. LL receives research funding from CIHR, Canada Research Chairs Programme, Natural Sciences and Engineering Research Council of Canada, National Institutes of Health, and the Canadian Agency for Drugs and Technologies in Health., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies., Objective: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal., Methods: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance., Results: The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ -1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008)., Conclusion: ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need., (© 2024. The Author(s).)

Purpose: This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes., Materials and Methods: We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment., Results: The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between., Conclusions: Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

In this study, a survey of multiple sclerosis (MS) classification and segmentation process is presented, which is based on magnetic resonance imaging. Knowledge of MS lesions is gained by determining the number of sample lesions in order that the lesion development level can be followed precisely; therefore, the effects of pharmaceuticals in medical tests can be accurately assessed. Accurate recognition of MS lesions in magnetic resonance images is an additionally complex process because of their changing shapes and sizes which can be very difficult to identify based on anatomical positions in various subjects. This can be determined by precise segmentation; manual segmentation would be very difficult to perform as it requires high level knowledge which takes additional time. Inter‐ and intra‐expert variability need to be determined in order to perform the automated segmentation of lesions. The principal aim of this survey effort is to provide an analysis of the different categorization and segmentation methods and their techniques. This survey work will be valuable for researchers working in MS by considering and carefully evaluating the past work. The benefits and drawbacks of existing techniques are reviewed and the issue of MS lesion segmentation and classification is elucidated. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to determine classification criteria for multiple sclerosis-associated intermediate uveitis., Design: Machine learning of cases with multiple sclerosis-associated intermediate uveitis and 4 other intermediate uveitides., Methods: Cases of intermediate uveitides were collected in an informatics-designed preliminary database, and a final database was constructed of cases achieving supermajority agreement on the diagnosis, using formal consensus techniques. Cases were split into a training set and a validation set. Machine learning using multinomial logistic regression was used in the training set to determine a parsimonious set of criteria that minimized the misclassification rate among the intermediate uveitides. The resulting criteria were evaluated in the validation set., Results: A total of 589 cases of intermediate uveitides, including 112 cases of multiple sclerosis-associated intermediate uveitis, were evaluated by machine learning. The overall accuracy for intermediate uveitides was 99.8% in the training set and 99.3% in the validation set (95% confidence interval: 96.1-99.9). Key criteria for multiple sclerosis-associated intermediate uveitis included unilateral or bilateral intermediate uveitis and multiple sclerosis diagnosed by the McDonald criteria. Key exclusions included syphilis and sarcoidosis. The misclassification rates for multiple sclerosis-associated intermediate uveitis were 0 % in the training set and 0% in the validation set., Conclusions: The criteria for multiple sclerosis-associated intermediate uveitis had a low misclassification rate and appeared to perform sufficiently well enough for use in clinical and translational research., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Despite the availability of a lot of effective disease-modifying drugs, multiple sclerosis (MS) (in particular the progressive forms) still represents an important unmet medical need, because of issues in terms of effectiveness, duration of response, safety, and patient compliance. An increasing body of evidence from randomized clinical trials and real-world data suggest that rituximab is a highly effective alternative in both relapsing and progressive MS, with a low discontinuation rate, related to a good benefit/risk profile, and a good compliance. To date, the use of rituximab in patients with multiple sclerosis is not in accordance with the authorized product information (off-label use). However, the use of this medicine is widespread in several countries, and in some cases, it is the most commonly used disease-modifying drug for MS subtypes. This use could be officially recognized by national regulatory authorities, according to specific procedures, to ensure equal access for patients to a safe and effective option., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Brancati, Gozzo, Longo, Vitale and Drago.)

There is evidence that multiple sclerosis (MS) pathology leads to distinct patterns of volume loss over time (VLOT) in different central nervous system (CNS) structures. We aimed to use such patterns to identify patient subgroups. MS patients of all classical disease phenotypes underwent annual clinical, blood, and MRI examinations over 6 years. Spinal, striatal, pallidal, thalamic, cortical, white matter, and T2-weighted lesion volumes as well as serum neurofilament light chain (sNfL) were quantified. CNS VLOT patterns were identified using principal component analysis and patients were classified using hierarchical cluster analysis. 225 MS patients were classified into four distinct Groups A, B, C, and D including 14, 59, 141, and 11 patients, respectively). These groups did not differ in baseline demographics, disease duration, disease phenotype distribution, and lesion-load expansion. Interestingly, Group A showed pronounced spinothalamic VLOT, Group B marked pallidal VLOT, Group C small between-structure VLOT differences, and Group D myelocortical volume increase and pronounced white matter VLOT. Neurologic deficits were more severe and progressed faster in Group A that also had higher mean sNfL levels than all other groups. Group B experienced more frequent relapses than Group C. In conclusion, there are distinct patterns of VLOT across the CNS in MS patients, which do not overlap with clinical MS subtypes and are independent of disease duration and lesion-load but are partially associated to sNfL levels, relapse rates, and clinical worsening. Our findings support the need for a more biologic classification of MS subtypes including volumetric and body-fluid markers., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Alterations in the regulatory mechanisms that control the process of myelination in the nervous system, may lead to the impaired myelination in the Multiple sclerosis. The Hippo pathway is an important mediator of myelination in the nervous system and might contribute to the pathophysiology of MS. This study examined via qPCR the RNA expression of YAP1, TAZ, and CRB3 as the key effectors of the Hippo pathway and also, VDR in the peripheral blood of 35 sporadic, 37 familial MS patients; and also 34 healthy first-degree relatives of the familial MS patients (HFR) and 40 healthy individuals without a family history of the disease (control). The results showed the increased expression of VDR in the sporadic group, as compared to other groups. There was also an increased expression of TAZ in the familial and HFR groups, as compared to the control group. The familial and sporadic patients displayed a significantly lower level of expression of YAP1 in comparison to the HFR group. The increased expression level in the sporadic patients and control group, as compared to the HFR group, was seen in CRB3. We also assessed different clinical parameters and MRI characteristics of the patients. Overall, these findings suggest that Hippo pathway effectors and also VDR gene may play a potential role in the pathophysiology of the sporadic and familial forms of MS. Confirmation of different gene expression patterns in sporadic and familial MS groups may have obvious implications for the personalization of therapies in the disease.

Peripheral blood mononuclear cells (PBMCs) bear specific dysregulations in genes and pathways at distinct stages of multiple sclerosis (MS) that may help with classifying MS and non-MS subjects, specifying the early stage of disease, or discriminating among MS courses. Here we describe an unbiased machine learning workflow to build MS stage-specific classifiers based on PBMC transcriptomics profiles from more than 300 individuals, including healthy subjects and patients with clinically isolated syndromes, relapsing-remitting MS, primary or secondary progressive MS, or other neurological disorders. The pipeline, designed to optimize and compare the performance of distinct machine learning algorithms in the training cohort, generates predictive models not influenced by demographic features, such as age and gender, and displays high accuracy in the independent validation cohort. Proper application of machine learning to transcriptional profiles of circulating blood cells may allow identification of disease state and stage in MS., Competing Interests: F.S. and L.M. report personal fees from Merck Serono, Novartis, Biogen, Sanofi-Genzyme, Teva, and Roche outside of the submitted work. M.R. reports personal fees from Merck-Serono and Genzyme and non-financial support from Novartis, Teva Pharmaceutical Industries, and Almirall outside of the submitted work. V.M. reports honoraria for speaking and/or for consultancy and support for travel expenses and participation in congresses from Biogen, Merck, Novartis, Roche, Genzyme, Almirall, and Teva. G.C. reports personal fees from Novartis, Teva Pharmaceutical Industries Ltd., Teva Italia Srl, Sanofi Genzyme, Genzyme Corporation, Genzyme Europe, Merck KGaA, Merck Serono SpA, Celgene Group, Biogen Idec, Biogen Italia Srl, F. Hoffman-La Roche, Roche SpA, Almirall SpA, Forward Pharma, Medday, and Excemed outside of the submitted work. C.F. reports grants from Merck-Serono and the Italian Ministry of Health during the conduct of the study and grants from Teva, Novartis, and Fondazione Italiana Sclerosi Multipla outside of the submitted work., (© 2020 The Author(s).)

Background: Manipulative dexterity impairments affect 76% of individuals with multiple sclerosis (MS). Patients with MS can experience reduced skill when performing both basic activities of daily living and instrumental activities of daily living. Many studies consider that physical rehabilitation leads to a decrease in the level of disability, especially at the mild and moderate stages of the disease. However, most studies fail to distinguish between the different MS subtypes., Purpose: Our aims were (1) to describe the manipulative skills of people according to the different subtypes of MS, (2) to analyze the correlation between dexterity and self-perception variables, and (3) to identify possible predictors of functionality. Study Design . A descriptive cross-sectional study., Methods: 30 individuals with MS. The measurement tools used were the ABILHAND, the Purdue Pegboard Test (PPT), the Nine-Hole Peg Test, and the Box and Block Test., Results: All subtypes of MS obtained lower scores for manipulative dexterity compared to normal skill levels, with individuals with primary progressive MS displaying the lowest values. However, the analysis of differences between the different subtypes did not reveal statistically significant intragroup differences. In addition, differences were found regarding the effect size of practically all the variables analyzed for both manipulative dexterity (PPT, NHPT, and BBT) and the self-perception of ADLs (ABILHAND), for which high values ( d r = 0.72) and very high values ( d r = 1.46) were obtained., Conclusions: Although no significant differences were found between the different types of MS, the assessment of manual dexterity and perceived efficacy of daily activities must be considered as prognostic factors in the progression of the disease. These findings may help support further research on targeted interventions to improve dexterity deficiencies, as well as promote an improved quality of therapeutic interventions., Competing Interests: The authors declare no conflict of interest., (Copyright © 2020 Elisabet Huertas-Hoyas et al.)

Background and objectives : multiple sclerosis (MS) is a chronic demyelinating disorder of the CNS with a variable course and disability progression. The latter may be prevented with disease-modifying therapy (DMT). Initial misdiagnosis may postpone the use of DMT. There are no studies to explore whether initial misdiagnosis is indeed associated with a higher rate of reaching disability in MS patients. We aimed to investigate the association between initial misdiagnosis and reaching disability milestones in relapsing-remitting MS (RR-MS) patients. Materials and methods : Data from 128 RR-MS patients were retrospectively reviewed. EDSS 4 and EDSS 6 were chosen as disability milestones as those associated with a significant decrease in ambulation. Survival analysis was used, and Kaplan-Meier curves were generated to investigate how initial misdiagnosis affects reaching the defined milestones. Results : 53 patients (41.4%, 31 females, 22 males) were initially misdiagnosed. Initially misdiagnosed patients had a lesser risk of reaching EDSS 4 up to 11 years and EDSS 6 up to 22 years from the onset than non-misdiagnosed patients ( p = 0.22 and p = 0.25 correspondingly). Median time to reaching EDSS 4 and 6 was eight years (95% CI 0.0-17.6) and 10 years (95% CI 4.25-20.75) in misdiagnosed and three years (95% CI 0.0-20.0 years) and five years (95% CI 0.0-13.73 years) in non-misdiagnosed patients correspondingly. Conclusions: Initially misdiagnosed RR-MS patients tended to reach disability milestones later than non-misdiagnosed ones, which might reflect an intrinsically milder disease. Individuals presenting with mild or non-specific symptoms suspicious of MS, must be deliberately managed.

Multiple sclerosis (MS) is associated with changes in the metabolome. Numerous studies employing varying metabolomics platforms have examined a range of biological material ranging from brain tissue to urine and demonstrated consistently alterations in multiple metabolic pathways in MS. We review not only the studies that describe the ability of metabolomics to differentiate MS patients from healthy controls and other neurological disease but also discuss the potential of metabolomics-based methods to build predictive models that are able to stage disease, monitor progression, and select the most appropriate therapy. The increasing number of impressive claims for the capacity of metabolomics to distinguish between different types of demyelinating disease suggests that the provision of such tests may be close at hand. Besides the ability to provide potential diagnostic and prognostic biomarkers, metabolomics also provides us with unique insights into the pathophysiology of the disease and helps identify metabolic pathways that may be potential therapeutic targets. Future studies will integrate metabolomics data with other omics techniques to provide further insight into the source of these metabolic abnormalities and help with identification of the most promising targets for therapeutic intervention.

Severity score represents disease duration-adjusted mean rank of disability in multiple sclerosis (MS) patients from the reference population. This measure allows one to compare the relative rates of disease progression among patients, patient subgroups, and across epochs, which opens up new question of what accounts for the observed differences in severity, and can be used to assess correlation between disease severity and clinical, radiologic, immunologic, genetic, and environmental variables of interest. Severity score can also prove useful for developing prognostic tools in MS. This article discusses the diverse applications of severity score concept in MS research, and (re)introduces Herbert's proposal of severity-based MS classification in the context of variability of MS severity.

Early active multiple sclerosis (MS) lesions can be classified histologically into three main immunopathological patterns of demyelination (patterns I-III), which suggest pathogenic heterogeneity and may predict therapy response. Patterns I and II show signs of immune-mediated demyelination, but only pattern II is associated with antibody/complement deposition. In pattern III lesions, which include Baló's concentric sclerosis, primary oligodendrocyte damage was proposed. Serum antibody reactivities could reflect disease pathogenesis and thus distinguish histopathologically defined MS patterns. We established a customized microarray with more than 700 peptides that represent human and viral antigens potentially relevant for inflammatory demyelinating CNS diseases, and tested sera from 66 patients (pattern I n = 12; II n = 29; III n = 25, including 8 with Baló's), healthy controls, patients with Sjögren's syndrome and stroke patients. Cell-based assays were performed for aquaporin 1 (AQP1) and AQP4 antibody detection. No single peptide showed differential binding among study cohorts. Because antibodies can react with different peptides from one protein, we also analyzed groups of peptides. Patients with pattern II showed significantly higher reactivities to Nogo-A peptides as compared to patterns I (p = 0.02) and III (p = 0.02). Pattern III patients showed higher reactivities to AQP1 (compared to pattern I p = 0.002, pattern II p = 0.001) and varicella zoster virus (VZV, compared to pattern II p = 0.05). In patients with Baló's, AQP1 reactivity was also significantly higher compared to patients without Baló's (p = 0.04), and the former revealed distinct antibody signatures. Histologically, Baló's patients showed loss of AQP1 and AQP4 in demyelinating lesions, but no antibodies binding conformational AQP1 or AQP4 were detected. In summary, higher reactivities to Nogo-A peptides in pattern II patients could be relevant for enhanced axonal repair and remyelination. Higher reactivities to AQP1 peptides in pattern III patients and its subgroup of Baló's patients possibly reflect astrocytic damage. Finally, latent VZV infection may cause peripheral immune activation.

Background and Purpose: Multiple sclerosis (MS) is a progressive neurological disorder, characterized by exacerbations and remissions, often resulting in disability affecting multiple neurological functions. The purpose of this article was (1) to describe the frequencies of self-reported symptoms in a natural environment and (2) to determine characteristics and associations between self-reported symptoms and home gait parameters (speed, stride time, and stride length) at baseline and at 3 months in patients with MS., Methods: Participants completed the self-report MS-Related Symptom Scale to measure symptoms. A three-dimensional depth imaging system (Foresite Healthcare) was used to measure gait parameters in the home environment., Results: These data show significant correlations between the following symptoms: knee locking or collapsing, difficulty sleeping, depression, and anxiety with decreased number of average walks per day; however, the symptoms including trouble-making toilet: day and difficulty in starting urine were positively correlated with average walks per day. The symptom numbness was significantly correlated with decreased speed and decreased stride length., Discussion and Conclusions: Our findings suggest that certain groups of symptoms were more frequently reported with certain gait parameters (stride time/speed) in persons with MS. Rehabilitation nurses can provide optimal care to prevent future decline in symptoms and gait.

Aims: The aim of this study was to quantify how multiple sclerosis (MS) phenotypes differ from each other in respect of costs and quality-of-life. Materials and methods: The study is based on survey data from Finnish patients with MS ( n  = 553). The information contained disease type, disease severity according to self-reported Expanded Disease Severity Scale (EDSS), healthcare resource use, and medication use. In addition, information related to employment and early retirement was collected. EQ-5D-VAS and Multiple Sclerosis Impact Scale-29 (MSIS-29) instruments were used to collect quality-of-life data, and Fatigue Severity Scale (FSS) instrument for evaluating fatigue. Patients were stratified based on their disease type (relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), primary progressive MS (PPMS)) and disease severity. The data were primarily analyzed using summary statistics. Results: SPMS had the highest annual total cost (71,177€) followed by PPMS (51,082€) and RRMS (36,492€). Early retirement covered the greatest share of costs in RRMS (39%) and SPMS (43%). In PPMS, early retirement and professional care were the two most equally important cost drivers, contributing together 56% of the total costs. Direct healthcare costs were responsible for 33%, 19%, and 18% of total costs in RRMS, SPMS, and PPMS. The mean EDSS in RRMS, SPMS, and PPMS were 2.5, 5.5, and 5.9, respectively. Differences in the quality-of-life were observed with both disease specific (MSIS-29) and generic (EQ-5D-VAS) instruments. The mean utility value from EQ-5D among patients with RRMS, SPMS, and PPMS was 0.76, 0.52, and 0.49, respectively. In addition, patients with SPMS and PPMS were more likely to report fatigue than patients with RRMS. Conclusions: MS phenotype has an impact on costs and quality-of-life of the patients. Early retirement seems to be one of the most important contributors to MS-related costs.

Introduction: Diffusion tensor imaging (DTI) has shown abnormalities of the fornix and other limbic white matter tracts in multiple sclerosis (MS), mainly focusing on relapsing-remitting MS., Methods: The goal here was to evaluate the fornix, cingulum, and uncinate fasciculus with DTI tractography at 1.7 mm isotropic resolution in three MS subgroups (11 relapsing-remitting (RRMS), nine secondary progressive (SPMS), eight primary progressive (PPMS)) versus 11 controls, and assess correlations with cognitive and clinical scores., Results: The MS group overall showed extensive diffusion abnormalities of the fornix with less volume, lower fractional anisotropy (FA), and higher mean and radial diffusivities, which were similarly affected in all three MS subgroups. The uncinate fasciculus had lower FA only in the secondary progressive subgroup, and the cingulum had no DTI differences in any MS subgroup. The FA and/or volumes of these tracts correlated negatively with larger total lesion volume. The only DTI-cognitive correlation was lower right cingulum FA and greater depression over the entire MS cohort., Conclusions: Diffusion tractography identified abnormalities in the fornix that appears to be affected early and consistently across all three primary MS phenotypes of RRMS, SPMS, and PPMS regardless of Expanded Disability Status Scale, time since diagnosis, or cognitive scores., (© 2019 The Authors. Brain and Behavior published by Wiley Periodicals, Inc.)

The potential of antidepressant medication to have a neuroprotective effect for people with multiple sclerosis (MS) has received increased interest in recent years. The possibility of antidepressants, particularly fluoxetine, for potential repurposing to treat primary progressive and secondary progressive MS is of interest as a result of the relative lack of disease-modifying medications for these subtypes. A number of animal studies have found positive results for a neuroprotective effect of antidepressant use in MS, with human studies showing mixed results. These human studies all have a significant limitation: they exclude people with moderate to severe depressive symptoms, a core symptom of MS beyond that of reactive depression. It is likely that reregulation of the common mechanisms in depression and MS, such as inflammation, serotonin, norepinephrine, glutamate and brain-derived neurotropic factor disruption, and hypothalamic-pituitary-thalamic axis dysregulation, are important to the neuroprotective value of antidepressant medication. Given that MS is known for its heterogeneity, the question might be less about whether antidepressant medication provides neuroprotective benefits to people with multiple sclerosis but for whom they provide benefits and whether we are designing studies that will detect a benefit. To answer these questions, studies must include people with MS and depressive symptoms as well as people with relapsing remitting and chronic subtypes.

Background: Multiple sclerosis is the most common demyelinating disease of the central nervous system with serious social and economic burden. Siponimod is a sphingosine-1-phosphate receptor agonist, and clinical trials in the past decade have shown good prospects for the treatment of multiple sclerosis. But there is a lack of comprehensive understanding of the dose-effect relationship and safety in different subtypes of multiple sclerosis at present., Methods: We will perform a systematic review and meta-analysis of clinical randomized controlled trials to evaluate the efficacy and safety of siponimod in multiple sclerosis. We will search PubMed, EMBASE, Cochrane Library, Clinical Trials, Cochrane Central Register of Controlled Trials (CENTRAL) using a comprehensive strategy. The reference lists of the articles we select for inclusion will be checked to identify additional studies for potential inclusion. Two reviewers will review all literature independently. Upon inclusion of articles, another 2 reviewers will extract available data using a standardized form and assess the potential bias. Review Manager will be used to conduct data synthesis. There is no requirement of ethical approval and informed consent., Result: This is the first systematic assessment of siponimod for the treatment of multiple sclerosis. We predict it will provide high-quality synthesis of existing evidence for the efficacy and safety of siponimod for multiple sclerosis and a relatively comprehensive reference for clinical practice and clinical trials about siponimod to be conducted., Conclusion: The results of the systematic review and meta-analysis will provide updated evidence for the use of siponimod for multiple sclerosis., Registration: The systematic review and meta-analysis is registered in the PROSPERO international prospective register of systematic review (PROSPERO#CRD42018112721).

Objective: To measure McArdle sign (rapidly reversible weakness induced by neck flexion) both qualitatively and quantitatively and to evaluate its specificity and clinical utility for diagnosis of multiple sclerosis (MS)., Patients and Methods: In this prospective study, McArdle sign was evaluated by a technician blinded to diagnosis by measuring changes in finger extensor strength in successive trials of neck extension and flexion, first clinically and then with a torque measurement device. We studied 25 healthy controls and 81 patients with finger extensor weakness. Patients were not selected for having McArdle sign. Fifty-two patients had MS, 24 had other myelopathies, and 5 had peripheral nerve lesions accounting for their weakness. The study was conducted between February 1, 2016, and June 30, 2017., Results: The median clinical McArdle sign and the 2 quantitative measures of neck flexion-induced strength reduction were greater in patients with MS than in the other groups (P<.001). Baseline strength did not confound the difference. The area under the receiver operating characteristic curve was 0.84 (95% CI, 0.75-0.93) comparing patients with MS vs healthy controls and 0.84 (95% CI, 0.75-0.93) comparing MS vs patients with other myelopathies. The 2 quantitative and 1 clinical measurement of McArdle sign by the technician who performed the quantitative testing were correlated (r=.57 and r=.58; P<.001), and in turn, the technician's and unblinded referring physician's clinical assessments were correlated (r=.58; P<.001). McArdle sign was evident in some patients who had minor disability and who were in early phases of MS., Conclusion: McArdle sign, when defined as greater than 10% neck flexion-induced reduction in strength, is entirely specific and 65% sensitive for a diagnosis of MS when compared with other conditions that mimic MS-associated myelopathy. It may facilitate diagnosis in certain clinical situations., Trial Registration: clinicaltrials.gov Identifier: NCT03122873., (Copyright © 2019 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

We identified five female patients retrospectively with relapsing short-segment partial myelitis whose clinical and paraclinical features were suggestive of cord involvement of multiple sclerosis (MS)-type albeit not rigidly fulfilling the 2017 McDonald criteria. Notably, these patients had not developed any typical MS-like brain lesions despite repeated neuroimaging assessments over years. Comprehensive work-up for differential diagnoses of MS and other causes of transverse myelitis particularly neuromyelitis optica spectrum disorders had been consistently negative on longitudinal follow-up. Thus, we postulate a possible entity of pure spinal MS which may represent a novel forme fruste within the MS disease spectrum.

Background and Objectives: Determining the clinical course of multiple sclerosis (MS) and prediction of long-term disability can be a big challenge. To determine early clinical features of MS, their influence on long-term disability progression, and time to transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS), a cohort of Polish patients was studied. Materials and Methods: We retrospectively evaluated 375 Polish MS patients based on data from available medical records. We assessed early clinical MS features and the relationship between demographics and time from disease onset to attainment of 4 and 6 points on the Expanded Disability Status Scale (EDSS), as well as time to conversion from RRMS to SPMS. Results: The differences between initial MS variants were significantly associated with gender, age at disease onset, number and type of the first symptoms, and rate of the disability accrual. Mean times from disease onset to attainment of EDSS 4 and 6 were significantly influenced by the disease variant, age at onset, gender, degree of recovery from the initial symptoms, and first inter-bouts interval. The mean time to secondary progression was significantly influenced by the number and type of the first symptoms of RRMS. Conclusions: Early clinical features of MS are important in determining the disease variant, the time to transition from RRMS to SPMS, as well as predicting the disability accumulation of patients. Despite the small differences regarding the first MS symptoms, the disability outcomes in the cohort of Polish patients are similar to other regions of the world.

Multiple sclerosis (MS) is the commonest non-traumatic disabling disease to affect young adults. The incidence of MS is increasing worldwide, together with the socioeconomic impact of the disease. The underlying cause of MS and mechanisms behind this increase remain opaque, although complex gene-environment interactions almost certainly play a significant role. The epidemiology of MS indicates that low serum levels of vitamin D, smoking, childhood obesity and infection with the Epstein-Barr virus are likely to play a role in disease development. Changes in diagnostic methods and criteria mean that people with MS can be diagnosed increasingly early in their disease trajectory. Alongside this, treatments for MS have increased exponentially in number, efficacy and risk. There is now the possibility of a diagnosis of 'pre-symptomatic MS' being made; as a result potentially preventive strategies could be studied. In this comprehensive review, MS epidemiology, potential aetiological factors and pathology are discussed, before moving on to clinical aspects of MS diagnosis and management., (© 2018 EAN.)

Purpose of Review: Multiple sclerosis is a chronic, predominantly immune-mediated disease of the central nervous system, and one of the most common causes of neurological disability in young adults globally. This review will discuss the epidemiology, diagnosis, disease course, and prognosis of multiple sclerosis and will focus on recent evidence and advances in these aspects of the disease., Recent Findings: Multiple sclerosis is increasing in incidence and prevalence globally, even in traditionally low-prevalence regions of the world. Recent revisions have been proposed to the existing multiple sclerosis diagnostic criteria, which will facilitate earlier diagnosis and treatment in appropriate patients. Classifying multiple sclerosis into distinct disease phenotypes can be challenging, and recent refinements have been proposed to clarify existing definitions. The prognosis of multiple sclerosis varies substantially across individual patients, and a combination of clinical, imaging, and laboratory markers can be useful in predicting clinical course and optimizing treatment in individual patients., Summary: A number of recent advances have been made in the clinical diagnosis and prognostication of multiple sclerosis patients. Future research will enable the development of more accurate biomarkers of disease categorization and prognosis, which will enable timely personalized treatment in individual multiple sclerosis patients.

Background: Demyelination of cerebral white matter is thought to drive neuronal degeneration and permanent neurological disability in individuals with multiple sclerosis. Findings from brain MRI studies, however, support the possibility that demyelination and neuronal degeneration can occur independently. We aimed to establish whether post-mortem brains from patients with multiple sclerosis show pathological evidence of cortical neuronal loss that is independent of cerebral white-matter demyelination., Methods: Brains and spinal cords were removed at autopsy from patients, who had died with multiple sclerosis, at the Cleveland Clinic in Cleveland, OH, USA. Visual examination of centimetre-thick slices of cerebral hemispheres was done to identify brains without areas of cerebral white-matter discoloration that were indicative of demyelinated lesions (referred to as myelocortical multiple sclerosis) and brains that had cerebral white-matter discolorations or demyelinated lesions (referred to as typical multiple sclerosis). These individuals with myelocortical multiple sclerosis were matched by age, sex, MRI protocol, multiple sclerosis disease subtype, disease duration, and Expanded Disability Status Scale, with individuals with typical multiple sclerosis. Demyelinated lesion area in tissue sections of cerebral white matter, spinal cord, and cerebral cortex from individuals classed as having myelocortical and typical multiple sclerosis were compared using myelin protein immunocytochemistry. Neuronal densities in cortical layers III, V, and VI from five cortical regions not directly connected to spinal cord (cingulate gyrus and inferior frontal cortex, superior temporal cortex, and superior insular cortex and inferior insular cortex) were also compared between the two groups and with aged-matched post-mortem brains from individuals without evidence of neurological disease., Findings: Brains and spinal cords were collected from 100 deceased patients between May, 1998, and November, 2012, and this retrospective study was done between Sept 6, 2011, and Feb 2, 2018. 12 individuals were identified as having myelocortical multiple sclerosis and were compared with 12 individuals identified as having typical multiple sclerosis. Demyelinated lesions were detected in spinal cord and cerebral cortex, but not in cerebral white matter, of people with myelocortical multiple sclerosis. Cortical demyelinated lesion area was similar between myelocortical and typical multiple sclerosis (median 4·45% [IQR 2·54-10·81] in myelocortical vs 9·74% [1·35-19·50] in typical multiple sclerosis; p=0·5512). Spinal cord demyelinated area was significantly greater in typical than in myelocortical multiple sclerosis (median 3·81% [IQR 1·72-7·42] in myelocortical vs 13·81% [6·51-29·01] in typical multiple sclerosis; p=0·0083). Despite the lack of cerebral white-matter demyelination in myelocortical multiple sclerosis, mean cortical neuronal densities were significantly decreased compared with control brains (349·8 neurons per mm 2 [SD 51·9] in myelocortical multiple sclerosis vs 419·0 [43·6] in controls in layer III [p=0·0104]; 355·6 [46·5] vs 454·2 [48·3] in layer V [p=0·0006]; 366·6 [50·9] vs 458·3 [48·4] in layer VI [p=0·0049]). By contrast, mean cortical neuronal densities were decreased in typical multiple sclerosis brains compared with those from controls in layer V (392·5 [59·0] vs 454·2 [48·3]; p=0·0182) but not layers III and VI., Interpretation: We propose that myelocortical multiple sclerosis is a subtype of multiple sclerosis that is characterised by demyelination of spinal cord and cerebral cortex but not of cerebral white matter. Cortical neuronal loss is not accompanied by cerebral white-matter demyelination and can be an independent pathological event in myelocortical multiple sclerosis. Compared with control brains, cortical neuronal loss was greater in myelocortical multiple sclerosis cortex than in typical multiple sclerosis cortex. The molecular mechanisms of primary neuronal degeneration and axonal pathology in myelocortical multiple sclerosis should be investigated in future studies., Funding: US National Institutes of Health and National Multiple Sclerosis Society., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The 1996 originally established multiple sclerosis (MS) subtypes, based solely on clinical impression and consensus, were revised in 2013 to review potential imaging and biological correlates and to reflect recently identified clinical aspects of MS. As a result, potential new disease phenotypes, radiologically isolated syndrome, and clinically isolated syndrome were considered along with the addition of two new descriptor subtypes: activity and progression applied to relapsing remitting and progressive MS phenotypes. In this way, the description of an individual patient's disease course is refined and provides temporal information about the ongoing disease process. There is still a lack of imaging and biological markers that would distinguish MS phenotypes and prognosticate the disease course on an individual patient's level, creating a pressing need for large collaborative research efforts in this field., (Copyright © 2018 Cold Spring Harbor Laboratory Press; all rights reserved.)

Atypical idiopathic inflammatory demyelinating disorders (IIDDs) of the brain have long been known to be disorders closely related to multiple sclerosis (MS), despite having distinctive clinical and radiological characteristics. Originally, they mostly corresponded to acute-onset variants of MS that classically had poor prognoses, such as Baló's concentric sclerosis, Marburg variant of MS and Schilder's disease, and their relationship with MS was based on their shared pathological findings and the co-occurrence of these variants in patients with typical MS. More recently, other atypical disorders, such as solitary sclerosis, have also been described as belonging to the MS spectrum, raising the question of their links with MS. Meanwhile, multiple MS mimics have been described and need to be considered in the differential diagnosis of MS. In addition, thorough characterization of these atypical entities, including advanced MRI and biological studies, is now warranted to further improve their management., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

Inflammasome-driven inflammation is postulated to play a role in multiple sclerosis (MS), but there is no direct evidence that the nod-like receptor protein 3 (NLRP3) inflammasome is involved in MS pathogenesis. Uric acid was shown to be one of the "danger" signals involved in the activation of NLRP3 inflammasome; notably, the concentration of uric acid is increased in the serum and in the cerebrospinal fluid of MS individuals. To better investigate the role of the NLRP3 inflammasome in MS-associated inflammation, we primed with lipopolysaccharide and stimulated with monosodium urate crystals PBMCs of 41 MS patients with different disease phenotypes. Eleven individuals with primary progressive MS (PPMS), 10 individuals with stable relapsing-remitting MS (SMS), 10 individuals with acute relapsing-remitting MS (AMS), 10 individuals with benign MS were analyzed; 10 healthy controls were enrolled as well in the study. The expression of the NLRP3, apoptosis-associated speck-like protein containing CARD (ASC), caspase-1, caspase-8, IL-1β, and IL-18 inflammasome genes was evaluated by RT-PCR. NLRP3 and ASC-speck protein expression was analyzed by FlowSight AMNIS, whereas production of the pro-inflammatory cytokines IL-1β and IL-18 and of caspase-1 and caspase-8 was measured by ELISA in supernatants. Results showed that uric acid serum concentration was significantly increased in PPMS; in these and in AMS patients, mRNA for NLRP3, ASC, and IL-18 was upregulated as well, but caspase-8 mRNA was upregulated only in PPMS. Expression of NLRP3 and ASC-speck protein was significantly increased in PPMS, SMS, and AMS patients, but IL-18 and caspase-8 production was significantly increased only in PPMS, in whom a direct correlation between hyperuricemia and caspase-8 was detected. The NLRP3/caspase-8 inflammasome pathway is activated in PPMS, possibly as a consequence of hyperuricemia. Therapeutic strategies reducing NLRP3 activation and/or lowering hyperuricemia could be useful in the therapy of PPMS.

Background: We report a dramatic clinical and radiological worsening within two months after rituximab initiation in a patient with NeuroMyelitis Optica/Multiple Sclerosis (NMO/MS) overlap syndrome., Methods: Case study., Results: A 45-year-old Caucasian woman with NMO/MS overlap syndrome experienced a severe myelitis nine weeks after first rituximab infusion, with extensive new gadolinium-enhanced spinal cord lesions., Conclusion: This case report illustrates the limits of MS and NMO-Spectrum Disorder classification and challenges the criteria of therapeutic failure within the 6 months after rituximab initiation., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: Fall rates among adults with multiple sclerosis are consistently greater than 50%, but near-falls (i.e. a trip or stumble) are often undocumented. Furthermore, little is known about the circumstances surrounding fall and near-fall events. The purpose of this study was to examine the similarities and differences among non-fallers, near-fallers and fallers with multiple sclerosis, including the circumstances that surround falls and near-falls., Methods: In a single visit, 135 multiple sclerosis participants completed the Hopkins Falls Grading Scale, a custom questionnaire investigating circumstances surrounding falls and near-falls, and performed the Timed Up and Go and Timed 25-Foot Walk tests. Mann-Whitney tests were used to examine differences between fallers, near-fallers and non-fallers. Multiple logistic regression with AIC criterion was used to examine associations of circumstances with the odds of falling vs. near-falling. Cumulative odds ordinal logistic regression was used to analyze the association between each of the walking tests and the susceptibility of the individual for falls or near-falls., Results: 30% of individuals reported falls, while 44% reported near-falls over a 1-year period. Non-fallers completed the walking tests more quickly than near-fallers (p < 0.0045), and fallers (p < 0.0001); near-fallers and fallers demonstrated similar motor profiles. Individuals were more likely to sustain a fall rather than a near-fall under the following circumstances: transferring outside the home (p = 0.015) and tripping over an obstacle (p = 0.025). Performing 1-second slower on the walking tests increased the odds of a history of a fall by 6-20%., Conclusion: Near-falls occur commonly in individuals with MS; near-fallers and fallers reported similar circumstances surrounding fall events and demonstrated similar performance on standard timed walking tests. Clinicians monitoring individuals with MS should consider evaluation of the circumstances surrounding falls in combination with quantitative walking measures to improve determination of fall risk and appropriate rehabilitation interventions., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Habitual physical activity (HPA) measurement addresses the impact of MS on real-world walking, yet its interpretation is confounded by the competing influences of MS-associated walking capacity and physical activity behaviors., Objective: To develop specific measures of MS-associated walking capacity through statistically sophisticated HPA analysis, thereby more precisely defining the real-world impact of disease., Methods: Eighty-eight MS and 38 control subjects completed timed walks and patient-reported outcomes in clinic, then wore an accelerometer for 7days. HPA was analyzed with several new statistics, including the maximum step rate (MSR) and habitual walking step rate (HWSR), along with conventional methods, including average daily steps. HPA statistics were validated using clinical walking outcomes., Results: The six-minute walk (6MW) step rate correlated most strongly with MSR (r=0.863, p<10 -25 ) and HWSR (r=0.815, p<10 -11 ) rather than average daily steps (r=0.676, p<10 -11 ). The combination of MSR and HWSR correlated more strongly with the 6MW step rate than either measure alone (r=0.884, p<10 -14 ). The MSR overestimated the 6MW step rate (μ=10.4, p<10 -7 ), whereas the HWSR underestimated it (μ=-18.2, p<10 -19 )., Conclusions: Conventional HPA statistics are poor measures of capacity due to variability in activity behaviors. The MSR and HWSR are valid, specific measures of real-world capacity which capture subjects' highest step rate and preferred step rate, respectively., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Motor speech disorders in multiple sclerosis (MS) are poorly understood and their quantitative, objective acoustic characterization remains limited. Additionally, little data regarding relationships between the severity of speech disorders and neurological involvement in MS, as well as the contribution of pyramidal and cerebellar functional systems on speech phenotypes, is available., Methods: Speech data were acquired from 141 MS patients with Expanded Disability Status Scale (EDSS) ranging from 1 to 6.5 and 70 matched healthy controls. Objective acoustic speech assessment including subtests on phonation, oral diadochokinesis, articulation and prosody was performed., Results: The prevalence of dysarthria in our MS cohort was 56% while the severity was generally mild and primarily consisted of a combination of spastic and ataxic components. Prosodic-articulatory disorder presenting with monopitch, articulatory decay, excess loudness variations and slow rate was the most salient. Speech disorders reflected subclinical motor impairment with 78% accuracy in discriminating between a subgroup of asymptomatic MS (EDSS < 2.0) and control speakers. Speech disorder severity was related to the severity of neurological involvement. Decreased articulation rate was moderately correlated to EDSS as well as all subtests of the multiple sclerosis functional composite. The strongest correlation was observed between irregular oral diadochokinesis and the 9-Hole Peg Test (r = - 0.65, p < 0.001). Irregular oral diadochokinesis and excess loudness variations significantly separated pure pyramidal and mixed pyramidal-cerebellar MS subgroups., Conclusions: Automated speech analyses may provide valuable biomarkers of disease progression in MS as dysarthria represents common and early manifestation that reflects disease disability and underlying pyramidal-cerebellar pathophysiology., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: To examine the effects of disease activity, disability, and disease-modifying therapies (DMTs) on serum neurofilament light (NFL) and the correlation between NFL concentrations in serum and CSF in multiple sclerosis (MS)., Methods: NFL concentrations were measured in paired serum and CSF samples (n = 521) from 373 participants: 286 had MS, 45 had other neurologic conditions, and 42 were healthy controls (HCs). In 138 patients with MS, the serum and CSF samples were obtained before and after DMT treatment with a median interval of 12 months. The CSF NFL concentration was measured with the UmanDiagnostics NF-light enzyme-linked immunosorbent assay. The serum NFL concentration was measured with an in-house ultrasensitive single-molecule array assay., Results: In MS, the correlation between serum and CSF NFL was r = 0.62 ( p < 0.001). Serum concentrations were significantly higher in patients with relapsing-remitting MS (16.9 ng/L) and in patients with progressive MS (23 ng/L) than in HCs (10.5 ng/L, p < 0.001 and p < 0.001, respectively). Treatment with DMT reduced median serum NFL levels from 18.6 (interquartile range [IQR] 12.6-32.7) ng/L to 15.7 (IQR 9.6-22.7) ng/L ( p < 0.001). Patients with relapse or with radiologic activity had significantly higher serum NFL levels than those in remission ( p < 0.001) or those without new lesions on MRI ( p < 0.001)., Conclusions: Serum and CSF NFL levels were highly correlated, indicating that blood sampling can replace CSF taps for this particular marker. Disease activity and DMT had similar effects on serum and CSF NFL concentrations. Repeated NFL determinations in peripheral blood for detecting axonal damage may represent new possibilities in MS monitoring., (Copyright © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.)

Multiple Sclerosis (MS) is an autoimmune disease driving inflammatory and degenerative processes that damage the central nervous system (CNS). However, it is not well understood how these events interact and evolve to evoke such a highly dynamic and heterogeneous disease. We established a hypothesis whereby the variability in the course of MS is driven by the very same pathogenic mechanisms responsible for the disease, the autoimmune attack on the CNS that leads to chronic inflammation, neuroaxonal degeneration and remyelination. We propose that each of these processes acts more or less severely and at different times in each of the clinical subgroups. To test this hypothesis, we developed a mathematical model that was constrained by experimental data (the expanded disability status scale [EDSS] time series) obtained from a retrospective longitudinal cohort of 66 MS patients with a long-term follow-up (up to 20 years). Moreover, we validated this model in a second prospective cohort of 120 MS patients with a three-year follow-up, for which EDSS data and brain volume time series were available. The clinical heterogeneity in the datasets was reduced by grouping the EDSS time series using an unsupervised clustering analysis. We found that by adjusting certain parameters, albeit within their biological range, the mathematical model reproduced the different disease courses, supporting the dynamic CNS damage hypothesis to explain MS heterogeneity. Our analysis suggests that the irreversible axon degeneration produced in the early stages of progressive MS is mainly due to the higher rate of myelinated axon degeneration, coupled to the lower capacity for remyelination. However, and in agreement with recent pathological studies, degeneration of chronically demyelinated axons is not a key feature that distinguishes this phenotype. Moreover, the model reveals that lower rates of axon degeneration and more rapid remyelination make relapsing MS more resilient than the progressive subtype. Therefore, our results support the hypothesis of a common pathogenesis for the different MS subtypes, even in the presence of genetic and environmental heterogeneity. Hence, MS can be considered as a single disease in which specific dynamics can provoke a variety of clinical outcomes in different patient groups. These results have important implications for the design of therapeutic interventions for MS at different stages of the disease.

Multiple Sclerosis (MS) is an autoimmune disease targeting the central nervous system (CNS) causing demyelination and neurodegeneration leading to accumulation of neurological disability. Here we present a minimal, computational model involving the immune system and CNS that generates the principal subtypes of the disease observed in patients. The model captures several key features of MS, especially those that distinguish the chronic progressive phase from that of the relapse-remitting. In addition, a rare subtype of the disease, progressive relapsing MS naturally emerges from the model. The model posits the existence of two key thresholds, one in the immune system and the other in the CNS, that separate dynamically distinct behavior of the model. Exploring the two-dimensional space of these thresholds, we obtain multiple phases of disease evolution and these shows greater variation than the clinical classification of MS, thus capturing the heterogeneity that is manifested in patients., (Copyright © 2017 KAUST, BESE, Saudi Arabia. Published by Elsevier Inc. All rights reserved.)

A structural or functional pattern of neuroplasticity that could systematically discriminate between people with impaired and preserved motor performance could help us to understand the brain networks contributing to preservation or compensation of behavior in multiple sclerosis (MS). This study aimed to (1) investigate whether a machine learning-based technique could accurately classify MS participants into groups defined by upper extremity function (i.e. motor function preserved (MP) vs. motor function impaired (MI)) based on their regional grey matter measures (GMM, cortical thickness and deep grey matter volume) and inter-regional functional connection (FC), (2) investigate which features (GMM, FC, or GMM + FC) could classify groups more accurately, and (3) identify the multivariate patterns of GMM and FCs that are most discriminative between MP and MI participants, and between each of these groups and the healthy controls (HCs). With 26 MP, 25 MI, and 21 HCs (age and sex matched) underwent T1-weighted and resting-state functional MRI at 3 T, we applied support vector machine (SVM) based classification to learn discriminant functions indicating regions in which GMM or between which FCs were most discriminative between groups. This study demonstrates that there exist structural and FC patterns sufficient for correct classification of upper limb motor ability of people with MS. The classifier with GMM + FC features yielded the highest accuracy of 85.61 % (p < 0.001) to distinguish between the MS groups using leave-one-out cross-validation. It suggests that a machine-learning approach combining structural and functional features is useful for identifying the specific neural substrates that are necessary and sufficient to preserve motor function among people with MS.

In a cross-sectional study involving 160 Multiple Sclerosis (MS) patients and 70 healthy controls we set out to determine the association of five blood biomarkers with MS risk and progression scores. High levels of Semaphorin3A (SEMA3A) in females, and low levels of prolactin and estradiol in males associated with MS risk. High MS disability correlated with higher SEMA3A levels in females. Our findings suggest the clinical applicability of SEMA3A, and prolactin as biomarkers for MS progression. However, these biomarkers had sex-specific associations with MS, and any therapeutic approaches utilizing them should take that into consideration., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Heterogeneity in disease course exists within multiple sclerosis (MS) subtypes., Objective: The objective was to estimate disease course heterogeneity over three distinct onset periods (pre-1995, 1995-2004, and 2005-present) for men and women., Methods: Group-based trajectory model (GBTM) was used to estimate clusters of patients following stable or unstable disease progression trajectories based on the Expanded Disability Status Scale (EDSS). Inception cohorts were generated from the Montreal Neurological Institute MS Clinic registry. Stable trajectories were defined as an EDSS ⩽3.0 and change ⩽1 point over the study period. Annualized relapse rate (ARR) based on the first 5 years of disease was an explanatory variable., Results: Proportion of women classified as stable was 0% for pre-1995, 69.0% for 1995-2004, and 83.9% post-2005; for men, these proportions were 18.4%, 41.4%, and 53.8%, respectively. Men had lower percentage of stable disease than women in both post-1995 cohorts (chi-square p < 0.0001). ARR was associated with higher disability trajectories in both post-1995 cohort (odds ratios >1.0) but not in the pre-1995 cohort., Conclusion: Large proportions of patients remain stable at their initial disability level for at least 15 years. Higher ARR increases the odds of patients being in a higher disability trajectory in the latter cohorts.

Background: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers., Methods: An MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients' notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients' notes than controls'. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification., Results: Classification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23-59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS., Conclusions: Diagnostic accuracy might be improved by mining patients' clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

Botulinum toxin (BT) therapy is an established treatment of spasticity due to stroke. For multiple sclerosis (MS) spasticity this is not the case. IAB-Interdisciplinary Working Group for Movement Disorders formed a task force to explore the use of BT therapy for treatment of MS spasticity. A formalised PubMed literature search produced 55 publications (3 randomised controlled trials, 3 interventional studies, 11 observational studies, 2 case studies, 35 reviews, 1 guideline) all unanimously favouring the use of BT therapy for MS spasticity. There is no reason to believe that BT should be less effective and safe in MS spasticity than it is in stroke spasticity. Recommendations include an update of the current prevalence of MS spasticity and its clinical features according to classifications used in movement disorders. Immunological data on MS patients already treated should be analysed with respect to frequencies of MS relapses and BT antibody formation. Registration authorities should expand registration of BT therapy for spasticity regardless of its aetiology. MS specialists should consider BT therapy for symptomatic treatment of spasticity.

Background: Falls are of great concern among persons with multiple sclerosis (MS)., Objective: To examine differences in metrics of mobility, postural control, and cognition in persons with MS with distinct fall risk status; and to investigate predictors of fall risk group membership using discriminant analysis., Methods: Forty-seven persons with MS completed the Activities-Balance Confidence (ABC) Scale and underwent a battery of assessments of mobility, balance, and cognition. Participants further wore an accelerometer for 7 days as an assessment of steps/day. Participants were allocated into fall risk groups based on ABC scale scores (increased fall risk (IFR); and normal fall risk (NFR)). We examined univariate differences between groups using ANOVA, and discriminant function analysis (DFA) identified the significant multivariate predictors of FR status., Results: After controlling for disability level, the IFR group had significantly (p < 0.05) worse scores on measures of mobility (i.e., MSWS-12, 6 MW, and steps/day) compared to the NFR group. DFA identified MSWS-12 and 6 MW scores as significant (p < 0.05) predictors of fall risk group membership. Those two variables collectively explained 55% of variance in fall risk grouping., Conclusions: The findings suggest that mobility should be the focus of rehabilitation programs in persons with MS, especially for those at IFR.

Multiple sclerosis is a complex and heterogeneous, most likely autoimmune, demyelinating disease of the central nervous system (CNS). Although a number of histological classification systems for CNS lesions have been used by different groups in recent years, no uniform classification exists. In this paper, we propose a simple and unifying classification of MS lesions incorporating many elements of earlier histological systems that aims to provide guidelines for neuropathologists and researchers studying MS lesions to allow for better comparison of different studies performed with MS tissue, and to aid in understanding the pathogenesis of the disease. Based on the presence/absence and distribution of macrophages/microglia (inflammatory activity) and the presence/absence of ongoing demyelination (demyelinating activity), we suggest differentiating between active, mixed active/inactive, and inactive lesions with or without ongoing demyelination. Active lesions are characterized by macrophages/microglia throughout the lesion area, whereas mixed active/inactive lesions have a hypocellular lesion center with macrophages/microglia limited to the lesion border. Inactive lesions are almost completely lacking macrophages/microglia. Active and mixed active/inactive lesions can be further subdivided into lesions with ongoing myelin destruction (demyelinating lesions) and lesions in which the destruction of myelin has ceased, but macrophages are still present (post-demyelinating lesions). This distinction is based on the presence or absence of myelin degradation products within the cytoplasm of macrophages/microglia. For this classification of MS lesions, identification of myelin with histological stains [such as luxol fast blue-PAS] or by immunohistochemistry using antibodies against myelin basic-protein (MBP) or proteolipid-protein (PLP), as well as, detection of macrophages/microglia by, e.g., anti-CD68 is sufficient. Active and demyelinating lesions may be further subdivided into the early and late demyelinating lesions. The former is defined by the presence in macrophages of major and small molecular weight myelin proteins, such as cyclic nucleotide diphosphoesterase (CNP), myelin oligodendrocyte glycoprotein (MOG), or myelin-associated protein (MAG), whereas macrophages in the latter demonstrate merely the presence of the major myelin proteins MBP or PLP. We discuss the histological features and staining techniques required to classify MS lesions, and, in addition, describe the histological hallmarks of cortical pathology and diffuse white matter changes, as well as of remyelination.

Objectives: This study compared the sensitivity of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) as clinical endpoints in multiple sclerosis (MS) clinical trials., Methods: Medline (1946 through 12 September 2014) and Embase (1974 through 12 September 2014) databases searches were conducted using keywords and Medical Subject Heading (MeSH) terms related to MS, EDSS, and MSFC. Only studies that used the EDSS and MSFC as endpoints were assessed. All statistical analyses were conducted using comprehensive meta-analysis (CMA). The percentages of the overall changes in EDSS and MSFC were compared. The relative risks were calculated in randomized clinical trials (RCTs)., Results: A total of 123 studies were identified. There were nine studies (6 case series and 3 RCTs) included in the analysis. In the case series, the EDSS change rate in MS patients was 33.5% (95% CI: 12.9-63.2%) and the MSFC change rate was 30.3% (95% CI: 9.2-65.2%). In RCTs, patients who take the drug would be 22.9 times as likely as patients who did not take the drug to experience a change in the EDSS scale (RR = 22.9, 95% CI = 0.996-1.517, p = 0.055). Patients who take the drug would be 48.9 times as likely as patients who did not take the drug to experience a change in the MSFC scale (RR = 48.9, 95% CI = CI = 0.916-2.419, p = 0.108)., Limitations: This study focused only on MS patient improvement (positive changes) on the EDSS and MSFC. More studies are needed to include patient deterioration (negative changes) on EDSS and MSFC., Conclusions: There is controversy about the sensitivity of the EDSS and MSFC in detecting the progression of MS disease. The EDSS and MSFC are effective tools to assess the clinical severity and progression of MS disease. MSFC is more sensitive than EDSS in detecting the progression of MS disease.

Background: Multiple sclerosis (MS), a central nervous system disease in which nerve signals are disrupted by scarring and demyelination, is classified into phenotypes depending on the patterns of cognitive or physical impairment progression: relapsing-remitting MS (RRMS), primary-progressive MS (PPMS), secondary-progressive MS (SPMS), or progressive-relapsing MS (PRMS). The phenotype is important in managing the disease and determining appropriate treatment. The ICD-9-CM code 340.0 is uninformative about MS phenotype, which increases the difficulty of studying the effects of phenotype on disease., Objective: To identify MS phenotype using natural language processing (NLP) techniques on progress notes and other clinical text in the electronic medical record (EMR)., Methods: Patients with at least 2 ICD-9-CM codes for MS (340.0) from 1999 through 2010 were identified from nationwide EMR data in the Department of Veterans Affairs. Clinical experts were interviewed for possible keywords and phrases denoting MS phenotype in order to develop a data dictionary for NLP. For each patient, NLP was used to search EMR clinical notes, since the first MS diagnosis date for these keywords and phrases. Presence of phenotype-related keywords and phrases were analyzed in context to remove mentions that were negated (e.g., "not relapsing-remitting") or unrelated to MS (e.g., "RR" meaning "respiratory rate"). One thousand mentions of MS phenotype were validated, and all records of 150 patients were reviewed for missed mentions., Results: There were 7,756 MS patients identified by ICD-9-CM code 340.0. MS phenotype was identified for 2,854 (36.8%) patients, with 1,836 (64.3%) of those having just 1 phenotype mentioned in their EMR clinical notes: 1,118 (39.2%) RRMS, 325 (11.4%) PPMS, 374 (13.1%) SPMS, and 19 (0.7%) PRMS. A total of 747 patients (26.2%) had 2 phenotypes, the most common being 459 patients (16.1%) with RRMS and SPMS. A total of 213 patients (7.5%) had 3 phenotypes, and 58 patients (2.0%) had 4 phenotypes mentioned in their EMR clinical notes. Positive predictive value of phenotype identification was 93.8% with sensitivity of 94.0%., Conclusions: Phenotype was documented for slightly more than one third of MS patients, an important but disappointing finding that sets a limit on studying the effects of phenotype on MS in general. However, for cases where the phenotype was documented, NLP accurately identified the phenotypes. Having multiple phenotypes documented is consistent with disease progression. The most common misidentification was because of ambiguity while clinicians were trying to determine phenotype. This study brings attention to the need for care providers to document MS phenotype more consistently and provides a solution for capturing phenotype from clinical text., Disclosures: This study was funded by Anolinx and F. Hoffman-La Roche. Nelson serves as a consultant for Anolinx. Kamauu is owner of Anolinx, which has received multiple research grants from pharmaceutical and biotechnology companies. LaFleur has received a Novartis grant for ongoing work. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the U.S. government. Study concept and design were contributed by Butler, LaFleur, Kamauu, DuVall, and Nelson. DuVall collected the data, and interpretation was performed by Nelson, DuVall, and Kamauu, along with Butler, LaFleur, and Knippenberg. The manuscript was written primarily by Nelson, along with Knippenberg and assisted by the other authors, and revised by Knippenberg, Nelson, and DuVall, along with the other authors.

Background: Cognitive event-related potentials (ERPs) have been previously correlated with T2 lesion load (Τ2LL) in patients with multiple sclerosis (MS). It is currently unknown, however, whether ERPs also correlate with brain atrophy or the presence of T1 hypointense lesions ("black holes") which reflect tissue destruction and axonal loss. The primary aim of the current study is to explore the effect of neuroradiological parameters such as brain atrophy, T1 and T2 lesion load on auditory ERPs in MS patients. In addition, we correlated cognitive impairment with neurophysiological (ERP) and neuroradiological (MRI) variables and investigated whether a combination of ERP and MRI parameters is capable of distinguishing patients suffering from secondary progressive (SP), primary progressive (PP) and relapsing-remitting (RR) MS., Materials and Methods: The study sample consisted of fifty nine MS patients (mean age±SD: 37.82±1.38 years; average disease duration: 6.76±5.3 years) and twenty six age-matched controls (mean age±SD: 41.42±15.39 years). The patients' EDSS and NRS scores were 3.77±2.14 (range: 1-7.5) and 75.88±11.99 (range: 42-94) respectively. ERPs were recorded using the auditory "odd-ball" paradigm. T1 and T2 lesions were automatically segmented using an edge-finding tool and total lesion volumes were calculated. MRI measures of brain atrophy included third ventricle width (THIRDVW) and the ratio of mid-sagittal corpus callosum area to the mid-sagittal intracranial skull surface area (CC/MISS). Statistical analysis was performed using multiple regression, principal component (PCA) and discriminant analysis., Results: T1 lesion load emerged as the most significant predictor of P300 and N200 latency. The rest of the endogenous ERPs parameters (P300 amplitude, N200 amplitude) were not significantly correlated with the MRI variables. PCA of pooled neuroradiological and neurophysiological markers suggested that four components accounted for 64.6% of the total variability. Discriminant analysis based on ERP & MRI markers classified correctly 79.63% of patients in RR, PP and SP subgroups., Conclusion: T1 lesion load is the most significant MRI correlate of auditory ERPs in MS patients. Importantly, ERPs in combination with MRI variables can be usefully employed for distinguishing patients with different subtypes of MS., (Copyright © 2016 Elsevier B.V. All rights reserved.)