Optical coherence tomography in secondary progressive multiple sclerosis: cross-sectional and longitudinal exploratory analysis from the MS-SMART randomised controlled trial.

Background: Optical coherence tomography (OCT) inner retinal metrics reflect neurodegeneration in multiple sclerosis (MS). We explored OCT measures as biomarkers of disease severity in secondary progressive MS (SPMS).

Methods: We investigated people with SPMS from the Multiple Sclerosis-Secondary Progressive Multi-Arm Randomisation Trial OCT substudy, analysing brain MRIs, clinical assessments and OCT at baseline and 96 weeks.

Aging is associated with reduced inflammatory disease activity independent of disease duration in relapsing multiple sclerosis trial populations.

Background: Higher age is associated with less inflammatory disease activity in relapsing-remitting multiple sclerosis (RRMS). It is unknown whether age itself or disease duration underlies this association.

Objectives: This study investigated the effects of age, disease duration, and inflammatory disease activity in people with RRMS.

Using the Progression Independent of Relapse Activity Framework to Unveil the Pathobiological Foundations of Multiple Sclerosis.

Progression independent of relapse activity (PIRA), a recent concept to formalize disability accrual in multiple sclerosis (MS) independent of relapses, has gained popularity as a potential clinical trial outcome. We discuss its shortcomings and appraise the challenges of implementing it in clinical settings, experimental trials, and research. The current definition of PIRA assumes that acute inflammation, which can manifest as a relapse, and neurodegeneration, manifesting as progressive disability accrual, can be disentangled by introducing specific time windows between the onset of relapses and the observed increase in disability.

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training.

Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly rely on data-hungry methods.

The temporal event-based model: Learning event timelines in progressive diseases.

Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change.

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).

influences long-term disability progression and tissue damage on MRI in relapse-onset multiple sclerosis.

Background: Whether genetic factors influence the long-term course of multiple sclerosis (MS) is unresolved.

Objective: To determine the influence of on long-term disease course in a homogeneous cohort of clinically isolated syndrome (CIS) patients.

Methods: One hundred seven patients underwent clinical and MRI assessment at the time of CIS and after 1, 3, 5 and 15 years.

Serum neurofilament light and MRI predictors of cognitive decline in patients with secondary progressive multiple sclerosis: Analysis from the MS-STAT randomised controlled trial.

Background: Cognitive impairment affects 50%-75% of people with secondary progressive multiple sclerosis (PwSPMS). Improving our ability to predict cognitive decline may facilitate earlier intervention.

Objective: The main aim of this study was to assess the relationship between longitudinal changes in cognition and baseline serum neurofilament light chain (sNfL) in PwSPMS.

Designing Multi-arm Multistage Adaptive Trials for Neuroprotection in Progressive Multiple Sclerosis.

There are few treatments shown to slow disability progression in progressive multiple sclerosis (PMS). One challenge has been efficiently testing the pipeline of candidate therapies from preclinical studies in clinical trials. Multi-arm multistage (MAMS) platform trials may accelerate evaluation of new therapies compared to traditional sequential clinical trials.

Assessing Neurofilaments as Biomarkers of Neuroprotection in Progressive Multiple Sclerosis: From the MS-STAT Randomized Controlled Trial.

Background And Objectives: Improved biomarkers of neuroprotective treatment are needed in progressive multiple sclerosis (PMS) to facilitate more efficient phase 2 trial design. The MS-STAT randomized controlled trial supported the neuroprotective potential of high-dose simvastatin in secondary progressive MS (SPMS). Here, we analyze serum from the MS-STAT trial to assess the extent to which neurofilament light (NfL) and neurofilament heavy (NfH), both promising biomarkers of neuroaxonal injury, may act as biomarkers of simvastatin treatment in SPMS.

pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm.

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression.

Spatial patterns of brain lesions assessed through covariance estimations of lesional voxels in multiple Sclerosis: The SPACE-MS technique.

Predicting disability in progressive multiple sclerosis (MS) is extremely challenging. Although there is some evidence that the spatial distribution of white matter (WM) lesions may play a role in disability accumulation, the lack of well-established quantitative metrics that characterise these aspects of MS pathology makes it difficult to assess their relevance for clinical progression. This study introduces a novel approach, called SPACE-MS, to quantitatively characterise spatial distributional features of brain MS lesions, so that these can be assessed as predictors of disability accumulation.

B Cells in the CNS at Postmortem Are Associated With Worse Outcome and Cell Types in Multiple Sclerosis.

Background And Objectives: To define the clinical and pathologic correlations of compartmentalized perivascular B cells in postmortem progressive multiple sclerosis (MS) brains.

Methods: Brain slices were acquired from 11 people with secondary progressive (SP) MS, 5 people with primary progressive (PP) MS, and 4 controls. Brain slices were immunostained for B lymphocytes (CD20), T lymphocytes (CD3), cytotoxic T lymphocytes (CD8), neuronal neurofilaments (NF200), myelin (SMI94), macrophages/microglia (CD68 and IBA1), astrocytes (glial fibrillary acidic protein [GFAP]), and mitochondria (voltage-dependent anion channel and cytochrome c oxidase subunit 4).

Linking immune-mediated damage to neurodegeneration in multiple sclerosis: could network-based MRI help?

Inflammatory demyelination characterizes the initial stages of multiple sclerosis, while progressive axonal and neuronal loss are coexisting and significantly contribute to the long-term physical and cognitive impairment. There is an unmet need for a conceptual shift from a dualistic view of multiple sclerosis pathology, involving either inflammatory demyelination or neurodegeneration, to integrative dynamic models of brain reorganization, where, glia-neuron interactions, synaptic alterations and grey matter pathology are longitudinally envisaged at the whole-brain level. Functional and structural MRI can delineate network hallmarks for relapses, remissions or disease progression, which can be linked to the pathophysiology behind inflammatory attacks, repair and neurodegeneration.

Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data.

Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings.