An update on the role of magnetic resonance imaging in predicting and monitoring multiple sclerosis progression

INTRODUCTION

While magnetic resonance imaging (MRI) is established in diagnosing and monitoring disease activity in multiple sclerosis (MS), its utility in predicting and monitoring disease progression is less clear.

AREAS COVERED

The authors consider changing concepts in the phenotypic classification of MS, including progression independent of relapses; pathological processes underpinning progression; advances in MRI measures to assess them; how well MRI features explain and predict clinical outcomes, including models that assess disease effects on neural networks, and the potential role for machine learning.

EXPERT OPINION

Relapsing-remitting and progressive MS have evolved from being viewed as mutually exclusive to having considerable overlap. Progression is likely the consequence of several pathological elements, each important in building more holistic prognostic models beyond conventional phenotypes. MRI is well placed to assess pathogenic processes underpinning progression, but we need to bridge the gap between MRI measures and clinical outcomes. Mapping pathological effects on specific neural networks may help and machine learning methods may be able to optimize predictive markers while identifying new, or previously overlooked, clinically relevant features. The ever-increasing ability to measure features on MRI raises the dilemma of what to measure and when, and the challenge of translating research methods into clinically useable tools.

Toward identifying key mechanisms of progression in multiple sclerosis

A major therapeutic goal in the treatment of multiple sclerosis (MS) is to prevent the accumulation of disability over an often decades-long disease course. Disability progression can result from acute relapses as well as from CNS intrinsic parenchymal disintegration without de novo CNS lesion formation. Research focus has shifted to progression not associated with acute inflammation, as it is not sufficiently controlled by currently available treatments. This review outlines how recent advances in the understanding of the pathogenesis of progressive MS have been facilitated by the development of more precise, less static pathogenetic concepts of progressive MS, as well as by new techniques for the analysis of region-specific proteomic and transcriptomic signatures in the human CNS. We highlight key drivers of MS disease progression and potential targets in its treatment.

Odour discrimination and identification as a biomarker of long-term disability worsening in multiple sclerosis

BACKGROUND

Odour discrimination and identification (DI) are markers associated with disability worsening and neuroaxonal damage in multiple sclerosis (MS).

OBJECTIVE

The main objective of this research is to investigate whether longitudinal change of DI predicts long-term MS disease course.

METHODS

This is a 6-year prospective longitudinal study on MS patients at the MS Clinic Innsbruck. Clinical, bi-annual visits assessed patients' history and Expanded Disability Status Scale (EDSS) score. DI and cognitive function were assessed at baseline (BL), Year 1 (Y1), Year 2 (Y2) and Year 6 (Y6) by the 'Sniffin' Sticks'/Symbol Digit Modalities Test.

RESULTS

Around 92 of 139 patients were available for Y6 follow-up. Mean DI scores significantly decreased over time (BL = 27.8, Y1 = 27.5, Y2 = 26.3 and Y6 = 26.3; p < 0.001) and negatively correlated with patients' age (rs = -0.120, p = 0.032) and disease duration (rs = -0.103, p = 0.041). Multivariable regression analyses revealed that lower absolute DI scores and larger DI score loss over time were associated with higher probability of EDSS worsening (per -1 point: hazard ratio (HR) = 1.40 (1.16-1.68) and 2.34 (1.27-4.21)), progression independent of relapse activity (PIRA) (HR = 1.49 (1.20-1.85) and 2.22 (1.33-3.31)) and cognitive deterioration (HR = 1.75 (1.35-2.27) and 4.29 (1.26-2.84)) at Y6, but not with time to first relapse.

CONCLUSION

Odour DI is an irreversible marker of neuroaxonal damage, associated with PIRA, cognitive deterioration and EDSS worsening.

Women's connectivity in extreme networks

A popular stereotype is that women will play more minor roles than men as environments become more dangerous and aggressive. Our analysis of new longitudinal data sets from offline and online operational networks [for example, ISIS (Islamic State)] shows that although men dominate numerically, women emerge with superior network connectivity that can benefit the underlying system's robustness and survival. Our observations suggest new female-centric approaches that could be used to affect such networks. They also raise questions about how individual contributions in high-pressure systems are evaluated.