Slowly expanding lesions relate to persisting black-holes and clinical outcomes in relapse-onset multiple sclerosis

Black holes; Chronic active lesions; Volumetric MRIAgujeros negros; Lesiones activas crónicas; Resonancia magnética volumétricaForats negres; Lesions actives cròniques; Ressonància magnètica volumètricaBackground Slowly expanding lesions (SELs) are MRI markers of chronic active lesions in multiple sclerosis (MS). T1-hypointense black holes, and reductions in magnetization transfer ratio (MTR) are pathologically correlated with myelin and axonal loss. While all associated with progressive MS, the relationship between these lesion’s metrics and clinical outcomes in relapse-onset MS has not been widely investigated. Objectives To explore the relationship of SELs with T1-hypointense black holes, and longitudinal T1 intensity contrast ratio and MTR, their correlation to brain volume, and their contribution to MS disability in relapse-onset patients. Methods 135 patients with relapsing-remitting MS (RRMS) were studied with clinical assessments and brain MRI (T2/FLAIR and T1-weighted scans at 1.5/3 T) at baseline and two subsequent follow-ups; a subset of 83 patients also had MTR acquisitions. Early-onset patients were defined when the baseline disease duration was ≤ 5 years (n = 85). SELs were identified using deformation field maps from the manually segmented baseline T2 lesions and differentiated from the non-SELs. Persisting black holes (PBHs) were defined as a subset of T2 lesions with a signal below a patient-specific grey matter T1 intensity in a semi-quantitative manner. SELs, PBH counts, and brain volume were computed, and their associations were assessed through Spearman and Pearson correlation. Clusters of patients according to low (up to 2), intermediate (3 to 10), or high (more than 10) SEL counts were determined with a Gaussian generalised mixture model. Mixed-effects and logistic regression models assessed volumes, T1 and MTR within SELs, and their correlation with Expanded Disability Status Scale (EDSS) and confirmed disability progression (CDP). Results Mean age at study onset was 35.5 years (73% female), disease duration 5.5 years and mean time to last follow-up 6.5 years (range 1 to 12.5); median baseline EDSS 1.5 (range 0 to 5.5) and a mean EDSS change of 0.31 units at final follow-up. Among 4007 T2 lesions, 27% were classified as SELs and 10% as PBHs. Most patients (n = 65) belonged to the cluster with an intermediate SEL count (3 to 10 SELs). The percentage of PBHs was higher in SELs than non-SELs (up to 61% vs 44%, p < 0.001) and within-patient SEL volumes positively correlated with PBH volumes (r = 0.53, p < 0.001). SELs showed a decrease in T1 intensity over time (beta = -0.004, 95%CI −0.005 to −0.003, p < 0.001), accompanied by lower cross-sectional baseline and follow-up MTR. In mixed-effects models, EDSS worsening was predicted by the SEL log-volumes increase over time (beta = 0.11, 95%CI 0.03 to 0.20, p = 0.01), which was confirmed in the sub-cohort of patients with early onset MS (beta = 0.14, 95%CI 0.04 to 0.25, p = 0.008). In logistic regressions, a higher risk for CDP was associated with SEL volumes (OR = 5.15, 95%CI 1.60 to 16.60, p = 0.006). Conclusions SELs are associated with accumulation of more destructive pathology as indicated by an association with PBH volume, longitudinal reduction in T1 intensity and MTR. Higher SEL volumes are associated with clinical progression, while lower ones are associated with stability in relapse-onset MS

Immunotherapy for people with clinically isolated syndrome or relapsing-remitting multiple sclerosis: treatment response by demographic, clinical, and biomarker subgroups (PROMISE)—a systematic review protocol

Immunotherapy; Multiple sclerosis; Treatment responseInmunoterapia; Esclerosis múltiple; Respuesta al tratamientoImmunoteràpia; Esclerosi múltiple; Resposta al tractamentBackground Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system with an increasing worldwide prevalence. Since 1993, more than 15 disease-modifying immunotherapies (DMTs) have been licenced and have shown moderate efficacy in clinical trials. Based on the heterogeneity of the disease and the partial effectiveness of therapies, a personalised medicine approach would be valuable taking individual prognosis and suitability of a chosen therapy into account to gain the best possible treatment effect. The primary objective of this review is to assess the differential treatment effects of all approved DMTs in subgroups of adults with clinically isolated syndrome or relapsing forms of MS. We will analyse possible treatment effect modifiers (TEM) defined by baseline demographic characteristics (gender, age), and diagnostic (i.e. MRI measures) and clinical (i.e. relapses, disability level) measures of MS disease activity. Methods We will include all published and accessible unpublished primary and secondary analyses of randomised controlled trials (RCTs) with a follow-up of at least 12 months investigating the efficacy of at least one approved DMT, with placebo or other approved DMTs as control intervention(s) in subgroups of trial participants. As the primary outcome, we will address disability as defined by the Expanded Disability Status Scale or multiple sclerosis functional composite scores followed by relapse frequency, quality of life measures, and side effects. MRI data will be analysed as secondary outcomes. MEDLINE, EMBASE, CINAHL, LILACS, CENTRAL and major trial registers will be searched for suitable studies. Titles and abstracts and full texts will be screened by two persons independently using Covidence. The risk of bias will be analysed based on the Cochrane “Risk of Bias 2” tool, and the certainty of evidence will be assessed using GRADE. Treatment effects will be reported as rate ratio or odds ratio. Primary analyses will follow the intention-to-treat principle. Meta-analyses will be carried out using random-effects models. Discussion Given that individual patient data from clinical studies are often not available, the review will allow to analyse the evidence on TEM in MS immunotherapy and thus support clinical decision making in individual cases.Open Access funding enabled and organized by Projekt DEAL. Federal Ministry of Education and Research (BMBF), Germany (grant 01KG1804). The funding body had no influence on the design of the protocol

The relation of sarcopenia and disability in multiple sclerosis

Background: The relation of sarcopenia and disability in MS is unknown. Objective: To investigate the relation of temporal muscle thickness (TMT) and disability. Methods: A cohort of 132 people who presented with a clinically isolated syndrome (CIS) suggestive of MS at a mean age of 30.0 years, were prospectively followed clinically and with MRI over 30-years. TMT and expanded disability status scale (EDSS) were assessed at baseline, one- five- ten- fourteen- twenty- and thirty-year follow-up. Results: At 30-years, 27 participants remained classified as having had a CIS, 34 converted to relapsing remitting MS, 26 to secondary progressive MS, and 16 had died due to MS. Using linear mixed effect models with subject nested in time, greater annualized TMT-thinning was seen in individuals who developed MS (-0.04 mm/a, 95%CI: -0.07 to -0.01, p = 0.023). In those who converted to MS, a thinner TMT was reached at 14- (p = 0.008), 20- (p = 0.002) and 30-years (p< 0.001). TMT was negatively correlated with EDSS at 20-years (R=-0.18, p = 0.032) and 30-years (R-0.244, p = 0.005). Longitudinally, TMT at earlier timepoints was not predictive for 30-year clinical outcomes. Conclusion: TMT thinning is accelerated in MS and correlated with disability in later disease stages, but is not predictive of future disability

Understanding Magnetic Resonance Imaging in Multiple Sclerosis (UMIMS): Development and Piloting of an Online Education Program About Magnetic Resonance Imaging for People With Multiple Sclerosis

Background: People with multiple sclerosis (pwMS) lack sufficient magnetic resonance imaging (MRI) knowledge to truly participate in frequently occurring MRI-related therapy decisions. An evidence-based patient information (EBPI) about MRI is currently lacking. Objective: The aim of this study was to develop an evidence-based online education program about limitations and benefits of MRI for pwMS. Ultimately, our goal was to improve MRI risk-knowledge, empower pwMS, and promote shared decision-making. Methods: The program's contents were based on literature research and a previous pilot study. It was revised following 2 evaluation rounds with pwMS, MRI experts and expert patients. In a pilot study, n = 92 pwMS received access to the program for 4 weeks. User experiences and acceptance, MRI knowledge (MRI-RIKNO 2.0 questionnaire) and emotions and attitudes toward MRI (MRI-EMA questionnaire) were assessed. Results were compared to a previous survey population of n = 508 pwMS without access to the program. Results: Participants rated the program as easy to understand, interesting, relevant, recommendable, and encouraging. In comparison to pwMS without access to the program, MRI risk-knowledge and perceived MRI competence were higher. Conclusion: Satisfaction with the program and good MRI-risk knowledge after usage demonstrates the need and applicability of EBPI about MRI in MS

SVM recursive feature elimination analyses of structural brain MRI predicts near-term relapses in patients with clinically isolated syndromes suggestive of multiple sclerosis

Esclerosi múltiple; Classificació d'aprenentatge automàtic; Selecció de funcionsEsclerosis múltiple; Clasificación de aprendizaje automático; Selección de característicasMultiple sclerosis; Machine learning classification; Feature selectionMachine learning classification is an attractive approach to automatically differentiate patients from healthy subjects, and to predict future disease outcomes. A clinically isolated syndrome (CIS) is often the first presentation of multiple sclerosis (MS), but it is difficult at onset to predict who will have a second relapse and hence convert to clinically definite MS. In this study, we thus aimed to distinguish CIS converters from non-converters at onset of a CIS, using recursive feature elimination and weight averaging with support vector machines. We also sought to assess the influence of cohort size and cross-validation methods on the accuracy estimate of the classification. We retrospectively collected 400 patients with CIS from six European MAGNIMS MS centres. Patients underwent brain MRI at onset of a CIS according to local standard-of-care protocols. The diagnosis of clinically definite MS at one-year follow-up was the standard against which the accuracy of the model was tested. For each patient, we derived MRI-based features, such as grey matter probability, white matter lesion load, cortical thickness, and volume of specific cortical and white matter regions. Features with little contribution to the classification model were removed iteratively through an interleaved sample bootstrapping and feature averaging approach. Classification of CIS outcome at one-year follow-up was performed with 2-fold, 5-fold, 10-fold and leave-one-out cross-validation for each centre cohort independently and in all patients together. The estimated classification accuracy across centres ranged from 64.9% to 88.1% using 2-fold cross-validation and from 73% to 92.9% using leave-one-out cross-validation. The classification accuracy estimate was higher in single-centre, smaller data sets than in combinations of data sets, being the lowest when all patients were merged together. Regional MRI features such as WM lesions, grey matter probability in the thalamus and the precuneus or cortical thickness in the cuneus and inferior temporal gyrus predicted the occurrence of a second relapse in patients at onset of a CIS using support vector machines. The increased accuracy estimate of the classification achieved with smaller and single-centre samples may indicate a model bias (overfitting) when data points were limited, but also more homogeneous. We provide an overview of classifier performance from a range of cross-validation schemes to give insight into the variability across schemes. The proposed recursive feature elimination approach with weight averaging can be used both in single- and multi-centre data sets in order to bridge the gap between group-level comparisons and making predictions for individual patients.This project received funding from the European Union's Horizon2020 Research and Innovation Program EuroPOND under grant agreement number 666992, and it was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre. We thank all participating partners of the MAGNIMS study group for sharing their data with us

Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis

In multiple sclerosis, grey matter pathology occurs mostly next to or near the outer surface of the brain. Using quantitative MRI, Liu et al. reveal that white matter abnormalities are also greatest near the surface of the brain, suggesting common elements in the genesis of grey and white matter patholog

ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple sclerosis lesion heterogeneity, as demonstrated by quantitative MRI.

BACKGROUND: There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans. OBJECTIVE: To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images. METHODS: We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML. RESULTS: Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively. CONCLUSION: ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished

Treatment reduces the incidence of newly appearing multiple sclerosis lesions evolving into chronic active, slowly expanding lesions: A retrospective analysis

Background and purpose: Newly appearing lesions in multiple sclerosis (MS) may evolve into chronically active, slowly expanding lesions (SELs), leading to sustained disability progression. The aim of this study was to evaluate the incidence of newly appearing lesions developing into SELs, and their correlation to clinical evolution and treatment. // Methods: A retrospective analysis of a fingolimod trial in primary progressive MS (PPMS; INFORMS, NCT 00731692) was undertaken. Data were available from 324 patients with magnetic resonance imaging scans up to 3 years after screening. New lesions at year 1 were identified with convolutional neural networks, and SELs obtained through a deformation-based method. Clinical disability was assessed annually by Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test, Timed 25-Foot Walk, and Paced Auditory Serial Addition Test. Linear, logistic, and mixed-effect models were used to assess the relationship between the Jacobian expansion in new lesions and SELs, disability scores, and treatment status. // Results: One hundred seventy patients had ≥1 new lesions at year 1 and had a higher lesion count at screening compared to patients with no new lesions (median = 27 vs. 22, p = 0.007). Among the new lesions (median = 2 per patient), 37% evolved into definite or possible SELs. Higher SEL volume and count were associated with EDSS worsening and confirmed disability progression. Treated patients had lower volume and count of definite SELs (β = −0.04, 95% confidence interval [CI] = −0.07 to −0.01, p = 0.015; β = −0.36, 95% CI = −0.67 to −0.06, p = 0.019, respectively). // Conclusions: Incident chronic active lesions are common in PPMS, and fingolimod treatment can reduce their number

Networks of microstructural damage predict disability in multiple sclerosis

Background: Network-based measures are emerging MRI markers in multiple sclerosis (MS). We aimed to identify networks of white (WM) and grey matter (GM) damage that predict disability progression and cognitive worsening using data-driven methods. // Methods: We analysed data from 1836 participants with different MS phenotypes (843 in a discovery cohort and 842 in a replication cohort). We calculated standardised T1-weighted/T2-weighted (sT1w/T2w) ratio maps in brain GM and WM, and applied spatial independent component analysis to identify networks of covarying microstructural damage. Clinical outcomes were Expanded Disability Status Scale worsening confirmed at 24 weeks (24-week confirmed disability progression (CDP)) and time to cognitive worsening assessed by the Symbol Digit Modalities Test (SDMT). We used Cox proportional hazard models to calculate predictive value of network measures. // Results: We identified 8 WM and 7 GM sT1w/T2w networks (of regional covariation in sT1w/T2w measures) in both cohorts. Network loading represents the degree of covariation in regional T1/T2 ratio within a given network. The loading factor in the anterior corona radiata and temporo-parieto-frontal components were associated with higher risks of developing CDP both in the discovery (HR=0.85, p<0.05 and HR=0.83, p<0.05, respectively) and replication cohorts (HR=0.84, p<0.05 and HR=0.80, p<0.005, respectively). The decreasing or increasing loading factor in the arcuate fasciculus, corpus callosum, deep GM, cortico-cerebellar patterns and lesion load were associated with a higher risk of developing SDMT worsening both in the discovery (HR=0.82, p<0.01; HR=0.87, p<0.05; HR=0.75, p<0.001; HR=0.86, p<0.05 and HR=1.27, p<0.0001) and replication cohorts (HR=0.82, p<0.005; HR=0.73, p<0.0001; HR=0.80, p<0.005; HR=0.85, p<0.01 and HR=1.26, p<0.0001). // Conclusions: GM and WM networks of microstructural changes predict disability and cognitive worsening in MS. Our approach may be used to identify patients at greater risk of disability worsening and stratify cohorts in treatment trials

Default Mode Network Structural Integrity and Cerebellar Connectivity Predict Information Processing Speed Deficit in Multiple Sclerosis

Cognitive impairment affects about 50% of multiple sclerosis (MS) patients, but the mechanisms underlying this remain unclear. The default mode network (DMN) has been linked with cognition, but in MS its role is still poorly understood. Moreover, within an extended DMN network including the cerebellum (CBL-DMN), the contribution of cortico-cerebellar connectivity to MS cognitive performance remains unexplored. The present study investigated associations of DMN and CBL-DMN structural connectivity with cognitive processing speed in MS, in both cognitively impaired (CIMS) and cognitively preserved (CPMS) MS patients. 68 MS patients and 22 healthy controls (HCs) completed a symbol digit modalities test (SDMT) and had 3T brain magnetic resonance imaging (MRI) scans that included a diffusion weighted imaging protocol. DMN and CBL-DMN tracts were reconstructed with probabilistic tractography. These networks (DMN and CBL-DMN) and the cortico-cerebellar tracts alone were modeled using a graph theoretical approach with fractional anisotropy (FA) as the weighting factor. Brain parenchymal fraction (BPF) was also calculated. In CIMS SDMT scores strongly correlated with the FA-weighted global efficiency (GE) of the network [GE(CBL-DMN): ρ = 0.87, R2 = 0.76, p &lt; 0.001; GE(DMN): ρ = 0.82, R2 = 0.67, p &lt; 0.001; GE(CBL): ρ = 0.80, R2 = 0.64, p &lt; 0.001]. In CPMS the correlation between these measures was significantly lower [GE(CBL-DMN): ρ = 0.51, R2 = 0.26, p &lt; 0.001; GE(DMN): ρ = 0.48, R2 = 0.23, p = 0.001; GE(CBL): ρ = 0.52, R2 = 0.27, p &lt; 0.001] and SDMT scores correlated most with BPF (ρ = 0.57, R2 = 0.33, p &lt; 0.001). In a multivariable regression model where SDMT was the independent variable, FA-weighted GE was the only significant explanatory variable in CIMS, while in CPMS BPF and expanded disability status scale were significant. No significant correlation was found in HC between SDMT scores, MRI or network measures. DMN structural GE is related to cognitive performance in MS, and results of CBL-DMN suggest that the cerebellum structural connectivity to the DMN plays an important role in information processing speed decline