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Background: In multiple sclerosis (MS), multiple periventricular lesions are commonly the first findings on MRI. However, most of these MS lesions are clinically silent. The brain atrophy rate has shown better correlation to physical disability, but it is not clear how atrophy develops over decades. Corpus callosum forms the roof of the third and lateral ventricles. The corpus callosum area (CCA) in a midsagittal image is age independent in a normal adult population up to the seventh decade; therefore it can be used as a marker for non-age-related, pathological brain atrophy.Objectives: To investigate whether and how CCA decreases in size over time in patients with MS.Methods: In a clinical observational study, 37 patients with MS with a wide range of disease duration at baseline (1-33 years) were followed. Three different MS courses were represented. The mean of individual MRI follow-up was 9 years. Multiple sclerosis severity score (MSSS) was also applied to evaluate disability at baseline and after 9 years of follow-up.Results: A significant decrease in CCA over 9 years (p<0.001) and a persisting association between CCA and the disability status were found. The atrophy rate was similar ever four decades of MS for all MS courses. The mean annual CCA decrease was 9.25 mm2 (1.8%). Surprisingly, atrophy rate did not correlate with sex, disease duration, age at MS onset or MS course.Conclusions: Serial evaluations of CCA might be a robust method in monitoring a non-age-related decrease in CCA, reflecting progression of irreversible destructive changes in MS. [ABSTRACT FROM AUTHOR]

Background: Treatment switching is a common challenge and opportunity in real-world clinical practice. Increasing diversity in disease-modifying treatments (DMTs) has generated interest in the identification of reliable and robust predictors of treatment switching across different countries, DMTs, and time periods., Objective: The objective of this retrospective, observational study was to identify independent predictors of treatment switching in a population of relapsing-remitting MS (RRMS) patients in the Big Multiple Sclerosis Data Network of national clinical registries, including the Italian MS registry, the OFSEP of France, the Danish MS registry, the Swedish national MS registry, and the international MSBase Registry., Methods: In this cohort study, we merged information on 269,822 treatment episodes in 110,326 patients from 1997 to 2018 from five clinical registries. Patients were included in the final pooled analysis set if they had initiated at least one DMT during the relapsing-remitting MS (RRMS) stage. Patients not diagnosed with RRMS or RRMS patients not initiating DMT therapy during the RRMS phase were excluded from the analysis. The primary study outcome was treatment switching. A multilevel mixed-effects shared frailty time-to-event model was used to identify independent predictors of treatment switching. The contributing MS registry was included in the pooled analysis as a random effect., Results: Every one-point increase in the Expanded Disability Status Scale (EDSS) score at treatment start was associated with 1.08 times the rate of subsequent switching, adjusting for age, sex, and calendar year (adjusted hazard ratio [aHR] 1.08; 95% CI 1.07-1.08). Women were associated with 1.11 times the rate of switching relative to men (95% CI 1.08-1.14), whilst older age was also associated with an increased rate of treatment switching. DMTs started between 2007 and 2012 were associated with 2.48 times the rate of switching relative to DMTs that began between 1996 and 2006 (aHR 2.48; 95% CI 2.48-2.56). DMTs started from 2013 onwards were more likely to switch relative to the earlier treatment epoch (aHR 8.09; 95% CI 7.79-8.41; reference = 1996-2006)., Conclusion: Switching between DMTs is associated with female sex, age, and disability at baseline and has increased in frequency considerably in recent years as more treatment options have become available. Consideration of a patient's individual risk and tolerance profile needs to be taken into account when selecting the most appropriate switch therapy from an expanding array of treatment choices., Competing Interests: TSp received compensation for serving on scientific advisory boards, honoraria for consultancy and funding for travel from Biogen; and speaker honoraria from Novartis. MM has served on the scientific advisory board for Sanofi, Novartis, and Merck and has received honoraria for lecturing from Biogen, Merck, Novartis, Roche, Genzyme, and Bristol Myers Squibb. HB is an employee of Monash University and has accepted travel compensation from Merck; his institution receives honoraria for talks, steering committee activities, and research grants from Roche, Merck, Biogen, Novartis, UCB Pharma, Medical Research Future Fund Australia, NHMRC Australia, Trish MS Foundation, MS Australia, and the Pennycook Foundation. He receives personal compensation for steering group activities for the Brain Health Initiative from the Oxford Health Policy Forum and is funded by an NHMRC Australia Investigator Grant. SV received consulting and lecturing fees, travel grants, and research support from Biogen, Celgene, Genentech, Genzyme, Medday Pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi Aventis, and Teva Pharma. MT has served on scientific advisory boards for Biogen, Novartis, Roche, and Genzyme; has received speaker honoraria and travel support from Biogen Idec, Sanofi Aventis, Merck Serono, Teva, Genzyme, and Novartis; and has received research grants for her institution from Biogen Idec, Merck Serono, and Novartis. PI has served on scientific advisory boards for Biogen Idec, Bayer, Teva, Roche, Merck Serono, Novartis, and Genzyme and has received funding for travel and/or speaker honoraria from Sanofi Aventis, Genzyme, Biogen Idec, Teva, Merck Serono, and Novartis. DH was supported by the Charles University Cooperation Program in Neuroscience, the project National Institute for Neurological Research (Programme EXCELES, ID Project No. LX22NPO5107) funded by the European Union (Next Generation EU), and by the General University Hospital in Prague project MH CZ-DRO-VFN64165. She also received compensation for travel, speaker honoraria, and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche, and Teva, as well as support for research activities from Biogen Idec. FP is an employee of Biogen. RH is an employee of Biogen and holds stock. PD served on editorial boards and has been supported to attend meetings by EMD, Biogen, Novartis, Genzyme, and TEVA Neuroscience. He holds grants from the CIHR and the MS Society of Canada and has received funding for investigator-initiated trials from Biogen, Novartis, and Genzyme. JL-S received travel compensation from Novartis, Biogen, Roche, and Merck. Her institution receives honoraria for talks and advisory board commitments, as well as research grants from Biogen, Merck, Roche, TEVA, and Novartis. SS declared no competing interests. PL received honoraria for speaking and/or travel expenses from Biogen, Merck, Novartis, Roche; consulting fees from Biogen, GeNeuro, Merck, Novartis, Roche; and research support from Biogen, Merck, Novartis. None were related to this work. SE received speaker honoraria and consultant fees from Biogen Idec, Novartis, Merck, Bayer, Sanofi Genzyme, Roche, and Teva. RAI received honoraria as a speaker and for serving on scientific advisory boards from Bayer, Biogen, GSK, Merck, Novartis, Roche, and Sanofi-Genzyme. MT received travel grants from Novartis, Bayer-Schering, Merck, and Teva; and has participated in clinical trials by Sanofi Aventis, Roche, and Novartis. MG received consulting fees from Teva Canada Innovation, Biogen, Novartis, and Genzyme Sanofi; and lecture payments from Teva Canada Innovation, Novartis, and EMD. He has also received a research grant from the Canadian Institutes of Health Research. TK served on scientific advisory boards for MS International Federation and World Health Organization, BMS, Roche, Janssen, Sanofi Genzyme, Novartis, Merck, and Biogen; on the steering committee for Brain Atrophy Initiative by Sanofi Genzyme, received conference travel support and/or speaker honoraria from WebMD Global, Eisai, Novartis, Biogen, Roche, Sanofi-Genzyme, Teva, BioCSL, and Merck and received research or educational event support from Biogen, Novartis, Genzyme, Roche, Celgene, and Merck. FG received honoraria or research funding from Biogen, Genzyme, Novartis, Teva Neurosciences, and ATARA Pharmaceuticals. OS received honoraria and consulting fees from Bayer-Schering, Novartis, Merck, Biogen, and Genzyme. SK received compensation for scientific advisory board activity from Merck and Roche. BY received honoraria as a speaker and member of scientific advisory boards from Sanofi, Bayer, Biogen, Merck, Janssen, Novartis, Roche, and Aspen. MJ received consulting fees, speaker honoraria, and/or travel expenses for scientific meetings from Alexion, Bayer Healthcare, Biogen, Bristol Myers Squibb, Celgene, Janssen, Merck Serono, Novartis, Roche, Sanofi, and Teva. YB received speaker honoraria/consulting fees from Merck, Biogen, Roche, Bristol Myers Squibb, Novartis, Sanofi, and Sandoz. CO-G received honoraria as a consultant on scientific advisory boards from Biogen, Celgene, Merck, Novartis, Roche, Sanofi-Genzyme, and TEVA. AA received speaker honoraria from Novartis and Alexion. SH has received unrestricted educational grants or speaking honoraria from Biogen, Merck Serono, Novartis, Roche, and Sanofi Genzyme. PM received speaker fees and travel grants from Novartis, Biogen, T'évalua, and Sanofi. RAm received conference travel support from Novartis, Teva, Biogen, Bayer, and Merck and has participated in clinical trials by Biogen, Novartis, Teva, and Actelion. KdG served on scientific advisory boards for Roche, Janssen, Sanofi-Genzyme, Novartis, and Merck, received conference fees and travel support from Novartis, Biogen, Sanofi-Genzyme, Teva, AbbVie, and Merck, and received educational event support from Novartis. CM received honoraria as a consultant on scientific advisory boards for Genzyme, BMS, Janssen, Biogen, Merck, Roche, and Novartis; has received travel grants from Roche and Novartis. JP accepted travel compensation from Novartis, Biogen, Genzyme, Teva, and speaking honoraria from Biogen, Novartis, Genzyme, and Teva. NJ is a local principal investigator on commercial studies funded by Novartis, Biogen, Amicus, and Sanofi. JI declared no competing interests. FS has served on scientific advisory boards for, served as a consultant for, received support for congress participation, or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, and Sanofi Genzyme. His laboratory has received research support from Biogen, Merck, Novartis, Roche, and Sanofi Genzyme. HJ declared no competing interests. PR has served on scientific advisory boards for, served as consultant for, received support for congress participation, or received speaker honoraria from Alexion, Biogen, Bristol Myers Squibb, Merck, Novartis, Roche, and Sanofi Genzyme. TSe received and has served on scientific advisory boards for, served as a consultant for, received support for congress participation, or received speaker honoraria from Biogen, Merck, Novartis, Roche, and Sanofi. T. Sejbaeks received unrestricted research grants to his research institution from Biogen, Merck, and Roche and is currently engaged in sponsor-initiated research projects by Eisai, Lundbeck, Roche, and Sanofi. MP declared no competing interests. JC has received speaker honoraria from Biogen. MS has served on scientific advisory boards for, served as a consultant for, received support for congress participation, participated in industrial trials with, or received speaker honoraria from Bayer, Biogen, Merck, Novartis, Roche, and Sanofi Genzyme. JH has received honoraria for serving on advisory boards for Biogen, Sanofi-Genzyme, and Novartis and speaker's fees from Biogen, Novartis, Merck Serono, Bayer-Schering, Teva, and Sanofi-Genzyme. He has served as P.I. for projects or received unrestricted research support from BiogenIdec, Merck Serono, TEVA, Sanofi-Genzyme, and Bayer-Schering. His MS research is funded by the Swedish Research Council and the Swedish Brain Foundation. The authors declare that this study received funding from Biogen. The funder had the following involvement with the study: study design and manuscript review. The funder was not involved in the collection of data, analysis, writing of the article, or the decision to submit it for publication. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 Spelman, Magyari, Butzkueven, Van Der Walt, Vukusic, Trojano, Iaffaldano, Horáková, Drahota, Pellegrini, Hyde, Duquette, Lechner-Scott, Sajedi, Lalive, Shaygannejad, Ozakbas, Eichau, Alroughani, Terzi, Girard, Kalincik, Grand'Maison, Skibina, Khoury, Yamout, Sa, Gerlach, Blanco, Karabudak, Oreja-Guevara, Altintas, Hughes, McCombe, Ampapa, de Gans, McGuigan, Soysal, Prevost, John, Inshasi, Stawiarz, Manouchehrinia, Forsberg, Sellebjerg, Glaser, Pontieri, Joensen, Rasmussen, Sejbaek, Poulsen, Christensen, Kant, Stilund, Mathiesen, Hillert and the Big MS Data Network: a collaboration of the Czech MS Registry, the Danish MS Registry, Italian MS Registry, Swedish MS Registry, MSBase Study Group, and OFSEP.)

Background: Most previous multiple sclerosis (MS) brain atrophy studies using MS impact scale 29 (MSIS-29) or symbol digit modalities test (SDMT) have been cross-sectional with limited sets of clinical outcomes., Objectives: To investigate which brain and lesion volume metrics show the strongest long-term associations with the expanded disability status scale (EDSS), SDMT, and MSIS-29, and whether MRI-clinical associations vary with age., Methods: We acquired MRI and clinical data from a real-world Swedish MS cohort. FreeSurfer and SPM Lesion Segmentation Tool were used to obtain brain parenchymal, cortical and subcortical grey matter, thalamic and white matter fractions as well as T 1 - and T 2 -lesion volumes. Mixed-effects and rolling regression models were used in the statistical analyses., Results: We included 989 persons with MS followed for a median of 9.3 (EDSS), 10.1 (SDMT), and 9.3 (MSIS-29) years, respectively. In a cross-sectional analysis, the strength of the associations of the MRI metrics with the EDSS and MSIS-29 was found to drastically increase after 40-50 years of age. Low baseline regional grey matter fractions were associated with longitudinal increase of EDSS and physical MSIS-29 scores and decrease in SDMT scores and these atrophy measures were stronger predictors than the lesion volumes., Conclusions: The strength of MRI-clinical associations increase with age. Grey matter volume fractions are stronger predictors of long-term disability measures than lesion volumes., Competing Interests: T. Moridi has received funding from ALF Medicine Region Stockholm and EU Horizon 2020. L. Stawiarz reports no disclosures relevant to the manuscript. K. A. McKay reports no disclosures relevant to the manuscript. B. V. Ineichen reports no disclosures relevant to the manuscript. R. Ouellette reports no disclosures relevant to the manuscript D. Ferreira reports no disclosures relevant to the manuscript. J-S. Muehlboeck reports no disclosures relevant to the manuscript. E. Westman reports no disclosures relevant to the manuscript. I. Kockum has received funding from ALF Medicine Region Stockholm, EU Horizon2020, and the Swedish Brain foundation; and received payment for a lecture held in November 2020 for Northern Connections, Scotland. T. Olsson has received funding from the Swedish Research council and the Swedish Brain Foundation; and received honoraria from Biogen, Merck, Novartis, Roche and Sanofi for lectures. F. Piehl has received funding from the Swedish Research council, Merck KGaA and UCB; served as DMC chairman for phase III trials in NMO (Chugai/Parexel); and served as unpaid chairman in the research committee for the Swedish national patient organization for persons with neurological diseases. J. Hillert has received funding from Merck, Biogen, Novartis and Roche; served on the advisory boards of Merck KGaA, Biogen, Novartis, Sandoz, Celgene and Sanofi-Genzyme; and received speaker's fee from Merck KGaA, Biogen, Novartis, Sanofi-Genzyme and Teva. A. Manouchehrinia has received funding from the Margaretha af Ugglas Foundation. T. Granberg is a recipient of the Grant for MS Innovation Award funded by Merck., (© The Author(s), 2022.)

Objective: The purpose of this study was to explore the longitudinal relationship between multiple sclerosis (MS) relapses and information processing efficiency among persons with relapsing-remitting MS., Methods: We conducted a Swedish nationwide cohort study of persons with incident relapsing-remitting MS (2001-2019). Relapse information and symbol digit modalities test (SDMT) scores were obtained from the Swedish MS Registry. Follow-up was categorized into 2 periods based on relapse status: "relapse" (90 days pre-relapse to 730 days post-relapse, subdivided into 10 periods) and "remission." Linear mixed models compared SDMT scores during the relapse periods to SDMT scores recorded during remission (reference) with results reported as β-coefficients and 95% confidence intervals (CIs), adjusted for age, sex, SDMT type (written vs oral), time-varying, disease-modifying therapy exposure and sequence of SDMT., Results: Over a mean (SD) follow-up of 10.7 (4.3) years, 31,529 distinct SDMTs were recorded among 3,877 persons with MS. There was a significant decline in information processing efficiency that lasted from 30 days pre-relapse up to 550 days post-relapse, with the largest decline occurring 0 to 30 days post-relapse (β-coefficient: -4.00 (95% CI = -4.61 to -3.39), relative to the period of remission., Interpretation: We found evidence of cognitive change up to 1 month prior to relapse onset. The reduction in SDMT lasted 1.5 years and was clinically significant up to 3 months post-relapse. These results suggest that the effects of a relapse on cognition are longer than previously thought and highlight the importance of reducing relapse rates as a potential means of preserving cognitive function. ANN NEUROL 2022;91:417-423., (© 2022 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Background: Although over a dozen disease modifying treatments (DMTs) are available for relapsing forms of multiple sclerosis (MS), treatment interruption, switching and discontinuation are common challenges. The objective of this study was to describe treatment interruption and discontinuation in the Big MS data network. Methods: We merged information on 269,822 treatment episodes in 110,326 patients from 1997 to 2016 from five clinical registries in this cohort study. Treatment stop was defined as a clinician recorded DMT end for any reason and included treatment interruptions, switching to alternate DMTs and long-term or permanent discontinuations. Results: The incidence of DMT stopping cross the full observation period was lowest in FTY (19.7 per 100 person-years (PY) of treatment; 95% CI 19.2-20.1), followed by NAT (22.6/100 PY; 95% CI 22.2-23.0), IFNβ (23.3/100 PY; 95% CI 23.2-23.5). Of the 184,013 observed DMT stops, 159,309 (86.6%) switched to an alternate DMT within 6 months. Reasons for stopping a drug were stable during the observation period with lack of efficacy being the most common reason followed by lack of tolerance and side effects. The proportion of patients continuing on most DMTs were similarly stable until 2014 and 2015 when drop from 83 to 75% was noted. Conclusions: DMT stopping reasons and rates were mostly stable over time with a slight increase in recent years, with the availability of more DMTs. The overall results suggest that discontinuation of MS DMTs is mostly due to DMT properties and to a lesser extent to risk management and a competitive market., Competing Interests: TS received compensation for serving on scientific advisory boards, honoraria for consultancy and funding for travel from Biogen; speaker honoraria from Novartis. MM has served on scientific advisory board for Biogen Idec and Teva and has received honoraria for lecturing from Biogen Idec, Merck Serono, Sanofi-Aventis and Teva. MM has received support for congress participation from Biogen Idec, Merck Serono, Novartis and Genzyme. PS has served on scientific advisory boards for Merck Serono, Teva, Novartis, Sanofi-Aventis and Biogen Idec and has received research support from Biogen Idec, Novartis and Sanofi-Aventis and received speaker honoraria from Merck Serono, Novartis, Teva, Sanofi-Aventis, Biogen Idec and Genzyme. HB received compensation for serving on scientific advisory boards and as a consultant for Biogen, Novartis; speaker honoraria from Biogen Australia, Merck Serono Australia, Novartis Australia; travel support from Biogen Australia, Merck Serono Australia; research support from the CASS Foundation (Australia), Merck Serono Australia, the Royal Melbourne Hospital. SV received consulting and lecturing fees, travel grants and research support from Biogen, Celgene, Genentech, Genzyme, Medday pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi Aventis, and Teva Pharma. MT has served on scientific Advisory Boards for Biogen, Novartis, Roche, and Genzyme; has received speaker honoraria and travel support from Biogen Idec, Sanofi-Aventis, Merck Serono, Teva, Genzyme and Novartis; and has received research grants for her Institution from Biogen Idec, Merck Serono, and Novartis. PI has served on scientific advisory boards for Biogen Idec, Bayer, Teva, Roche, Merck Serono, Novartis, and Genzyme and has received funding for travel and/or Speaker honoraria from Sanofi Aventis, Genzyme, Biogen Idec, Teva, Merck Serono, and Novartis. FP is an employee of Biogen. RH is an employee of Biogen and holds stock. JH has received honoraria for serving on advisory boards for Biogen, Sanofi-Genzyme, and Novartis and speaker's fees from Biogen, Novartis, Merck-Serono, Bayer-Schering, Teva, and Sanofi-Genzyme. JH has served as PI for projects, or received unrestricted research support from BiogenIdec, Merck-Serono, TEVA, Sanofi-Genzyme, and Bayer-Schering. JH MS research is funded by the Swedish Research Council and the Swedish Brain Foundation. The remaining 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 Hillert, Magyari, Soelberg Sørensen, Butzkueven, Van Der Welt, Vukusic, Trojano, Iaffaldano, Pellegrini, Hyde, Stawiarz, Manouchehrinia and Spelman.)

Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups., (© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T 1 -, T 2 -, and T 2 ∗ -weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Smoking is one of the most established risk factors for rheumatoid arthritis (RA). The aim of this study was to estimate how age at smoking debut, smoking cessation, duration, intensity, and cumulative dose of smoking influence the risk of developing anti-citrullinated peptide antibodies (ACPA) positive and ACPA negative RA. The present report is based on a Swedish population-based, case-control study with incident cases of RA (3655 cases, 5883 matched controls). Using logistic regression models, subjects with different smoking habits were compared regarding risk of developing the two variants of RA, by calculating odds ratios (OR) with 95% confidence intervals (CI). Smoking increased the risk of developing both ACPA positive (OR 1.9, 95% CI 1.7-2.1) and ACPA negative RA (OR 1.3, 95% CI 1.2-1.5). For both subsets of RA, there seemed to be a threshold (~ 2.5 pack years for ACPA positive RA and ~ 5 pack years for ACPA negative RA) below which no association between smoking and RA occurred. A dose-response association was observed between cumulative dose of smoking and risk of developing ACPA positive RA (p value for trend < 0.0001). Duration of smoking had a higher influence on the association between smoking and RA than did intensity of smoking. For both subsets of RA, the detrimental effect of smoking decreased after smoking cessation. Twenty years after smoking cessation, there was no longer an association between smoking and risk of ACPA negative RA, whereas the association between smoking and ACPA positive RA risk persisted and was dependent on the cumulative dose of smoking. Smoking increases the risk of both subsets of RA with a more pronounced influence on the risk of ACPA positive RA. Preventive measures in order to reduce smoking are essential and may result in a decline in RA incidence.

Objectives: The aim of this study was to identify factors influencing the long-term clinical progression of multiple sclerosis (MS). A special objective was to investigate whether early treatment decisions influence outcome., Methods: We included 639 patients diagnosed with MS from 2001 to 2007. The median follow-up time was 99 months (8.25 years). Cox regression models were applied to identify factors correlating with the outcome variable defined as time from treatment start to irreversible score 4 of the Expanded Disability Status Scale (EDSS)., Results: Patients initiated on treatment later had a greater risk of reaching EDSS 4 (hazard ratio of 1.074 (95% confidence interval (CI), 1.048-1.101)), increased by 7.4% for every year of delay in treatment start after MS onset. Patients who started treatment after 3 years from MS onset reached the outcome sooner with hazard ratio of 2.64 (95% CI, 1.71-4.08) compared with the patients who started treatment within 1 year from MS onset. Baseline EDSS and age at onset were found to be predictive factors of disability progression., Conclusion: Early treatment initiation was associated with a better clinical outcome. In addition, we confirmed the well-established prognostic factors of late age at onset and early disability.

Background: Several reports indicate changes to prevalence, incidence, female-to-male ratio in multiple sclerosis. Diagnostic criteria, course definitions and clinical management of the disease have also undergone change during the recent decades., Objective: To investigate temporal trends in the diagnosis of primary progressive multiple sclerosis (PPMS) in Sweden., Methods: Through the Swedish MS registry we investigated the proportion of PPMS diagnosis in birth, diagnosis and age period cohorts using Poisson regression., Results: A total of 16,915 patients were categorised into six birth-cohorts from 1946 to 1975 and seven date-of-diagnosis-cohorts from 1980 to 2014. We observed a decrease in the uncorrected analysis of diagnosis of PPMS from 19.2% to 2.2% and an average decrease of 23% (p < 0.001) per 5-year birth-cohort in the adjusted analysis. An average 21% (p < 0.001) decrease per diagnosis-cohort was seen. In the age-specific diagnosis period cohorts the same decreasing trend of PPMS diagnosis was observed in almost all groups., Conclusion: The diagnosis of PPMS has significantly decreased in Sweden specifically after introduction of disease-modifying treatments. Such decrease can have severe impacts on the future research on PPMS. Our data also suggest that the current trend to emphasise presence or absence of inflammatory activity is already reflected in clinical practice., (© The Author(s), 2016.)

Background: Multiple sclerosis (MS) is a chronic disease of the central nervous system. One of the major questions concerning the clinical progression of MS, still insufficiently elaborated or confirmed, is if it can be slowed down or augmented by external factors. Immunomodulatory treatment is a disease modifiable factor shown to influence disease progression of various medical conditions., Objective: To investigate if treatment affects the long-term clinical progression of MS, measured as time from diagnosis to score of 4 or higher of Expanded Disability Status Scale (EDSS)., Methods: Longitudinal, prospective data concerning treatment status and EDSS were collected by health professionals in the Swedish MS Registry. Study cohort comprised new diagnosed MS patients at Karolinska Hospital between 2001 and 2005. Survival analysis adjusted for suspected confounders was used with the outcome variable time from diagnosis to EDSS ≥ 4., Results: Early treatment was correlated with longer time from diagnosis to EDSS ≥ 4 (HR: 1.77; 95 % CI: 1.15-2.73; p = 0.01). Additionally, the influence of the covariates-age at onset and the baseline EDSS, which were statistically significant with hazard ratios of 1.03 and 2.1, respectively, was found., Conclusion: Early treatment was associated with a better clinical outcome.

Background: An increasing women-to-men ratio in later birth cohorts of patients with multiple sclerosis (MS) has been observed in several populations and has been hypothesised to be due to one or several environmental factors of importance for disease aetiology. However, in a study based on data from the Swedish MS registry (SMSreg) this ratio was recently reported to be rather stable during the 20(th) century., Objective: The purpose of this study was to reinvestigate the women-to-men ratio in Sweden based on data from all available data sources, including deceased patients., Method: We combined data from the SMSreg with data from national patient registers., Results: In total we obtained information on 19,510 MS patients born 1931-1985, 13,321 women and 6189 men. The women-to-men ratio increased from 1.70 for patients born in the 1930s to 2.67 for patients born in the 1980s. When comparing the coverage of SMSreg to the full data set, a significantly higher proportion of women born 1931-1935 compared to men born in the same period were found in SMSreg, resulting in a sampling bias hiding the increasing sex ratio in the full material., Conclusion: The women-to-men ratio in MS has increased in Sweden during the 20(th) century similarly to observations in other western countries., (© The Author(s), 2014.)

To investigate whether multiple sclerosis (MS) patients with and without cerebrospinal fluid (CSF) oligoclonal immunoglobulin G bands (OCB) differ in brain atrophy. Twenty-eight OCB-negative and thirty-five OCB-positive patients were included. Larger volumes of total CSF and white matter (WM) lesions; smaller gray matter (GM) volume in the basal ganglia, diencephalon, cerebellum, and hippocampus; and smaller WM volume in corpus callosum, periventricular-deep WM, brainstem, and cerebellum, were observed in OCB-positives. OCB-negative patients, known to differ genetically from OCB-positives, are characterized by less global and regional brain atrophy. This finding supports the notion that OCB-negative MS patients may represent a clinically relevant MS subgroup., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Background: It is currently unknown whether early immunomodulatory treatment in relapsing-remitting MS (RRMS) can delay the transition to secondary progression (SP)., Objective: To compare the time interval from onset to SP in patients with RRMS between a contemporary cohort, treated with first generation disease modifying drugs (DMDs), and a historical control cohort., Methods: We included a cohort of contemporary RRMS patients treated with DMDs, obtained from the Swedish National MS Registry (disease onset between 1995-2004, n = 730) and a historical population-based incidence cohort (onset 1950-64, n = 186). We retrospectively analyzed the difference in time to SP, termed the "period effect" within a 12-year survival analysis, using Kaplan-Meier and Cox regression analysis., Results: We found that the "period" affected the entire severity spectrum. After adjusting for onset features, which were weaker in the contemporary material, as well as the therapy initiation time, the DMD-treated patients still exhibited a longer time to SP than the controls (hazard ratios: men, 0.32; women, 0.53)., Conclusion: Our results showed there was a longer time to SP in the contemporary subjects given DMD. Our analyses suggested that this effect was not solely driven by the inclusion of benign cases, and it was at least partly due to the long-term immunomodulating therapy given.