ArticleBrain communications2026
Disability profiles in progressive multiple sclerosis reflect pathology distribution, independent of clinical phenotype.
Article in Brain communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03523858 (An Open-Label, Single-Arm 4-Year Study to Evaluate Effectiveness and Safety of Ocrelizumab Treatment in Patients With Progressive Multiple Sclerosis), which is not on this map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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An Open-Label, Single-Arm 4-Year Study to Evaluate Effectiveness and Safety of Ocrelizumab Treatment in Patients With Progressive Multiple Sclerosis
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13 authors.
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Abstract
Multiple sclerosis (MS) is a clinically heterogeneous disease affecting both neurological and cognitive functions. In this study, we defined clinical profiles based on combined cognitive and motor assessments in progressive MS patients, examined their overlap with classical progressive phenotypes and linked each profile to MRI measures of network disconnection and grey matter atrophy. We analyzed baseline data from 580 participants [277 primary progressive (PP)MS, 303 secondary progressive (SP)MS] in the phase 3b CONSONANCE (NCT03523858) study. Clinical assessments included Expanded Disability Status Scale, 9-Hole Peg Test, timed 25-Foot Walk Test, Symbol Digit Modalities Test and Brief Visuospatial Memory Test Revised. MRI acquisition comprised Fluid Attenuated Inversion Recovery for lesion segmentation and 3D-T1 weighted images for disconnectome reconstruction and volumetry. Independent component analysis was applied to regional disconnection and volumes to, respectively, identify patterns (ICs) of disconnection and grey matter atrophy. Latent profile analysis (LPA) identified disability profiles from multimodal clinical data. To identify the variables most strongly associated with each LPA-defined profile, we trained one-versus-all eXtreme Gradient Boosting classifiers and computed SHapley Additive exPlanations values. Based on prevailing clinical manifestation, LPA delineated three profiles: motor disability (
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