Combining Magnetization Transfer Ratio MRI and Quantitative Measures of Walking Improves the Identification of Fallers in MS

Cerebellar Volume Measures Differentiate Multiple Sclerosis Fallers from Non-Fallers

INTRODUCTION

The cerebellum is a common lesion site in persons with multiple sclerosis (PwMS). Physiologic and anatomic studies have identified a topographic organization of the cerebellum including functionally distinct motor and cognitive areas. In this study, a recent parcellation algorithm was applied to a sample of PwMS and healthy controls to examine the relationships among specific cerebellar regions, fall status, and common clinical measures of motor and cognitive functions.

METHODS

Thirty-one PwMS and twenty-nine age- and sex-matched controls underwent an MRI scan and motor and cognitive testing. The parcellation algorithm was applied to all images and divided the cerebellum into 28 regions. Mann-Whitney U tests were used to compare cerebellar volumes among PwMS and controls, and MS fallers and MS non-fallers. Relationships between cerebellar volumes and motor and cognitive function were evaluated using Spearman correlations.

RESULTS

PwMS performed significantly worse on functional measures compared to controls. We found significant differences in volumetric measures between PwMS and controls in the corpus medullare, lobules I-III, and lobule V. Volumetric differences seen between the PwMS and controls were primarily driven by the MS fallers. Finally, functional performance on motor and cognitive tasks was associated with cerebellar volumes.

CONCLUSIONS

Using the parcellation tool, our results showed that the volumes of motor and cognitive lobules impact both motor and cognitive performance, and that functional performance and cerebellar volumes distinguishes the MS fallers from non-fallers. Future studies should explore the potential of cerebellar imaging to predict falls in PwMS.

Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review

Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinical presentation and course of progression. Disease-modifying therapies are the only available treatment, as there is no known cure for the disease. Careful selection of suitable therapies is necessary, as they can be accompanied by serious risks and adverse effects such as infection. Magnetic resonance imaging (MRI) plays a central role in the diagnosis and management of MS, though MRI lesions have displayed only moderate associations with MS clinical outcomes, known as the clinico-radiological paradox. With the advent of machine learning (ML) in healthcare, the predictive power of MRI can be improved by leveraging both traditional and advanced ML algorithms capable of analyzing increasingly complex patterns within neuroimaging data. The purpose of this review was to examine the application of MRI-based ML for prediction of MS disease progression. Studies were divided into five main categories: predicting the conversion of clinically isolated syndrome to MS, cognitive outcome, EDSS-related disability, motor disability and disease activity. The performance of ML models is discussed along with highlighting the influential MRI-derived biomarkers. Overall, MRI-based ML presents a promising avenue for MS prognosis. However, integration of imaging biomarkers with other multimodal patient data shows great potential for advancing personalized healthcare approaches in MS.

Cerebellar volume measures may differentiate multiple sclerosis fallers from non-fallers

Introduction

The cerebellum is a common lesion site in persons with multiple sclerosis (PwMS). Physiologic and anatomic studies have identified a topographic organization of the cerebellum including functionally distinct motor and cognitive areas. This study implemented a recent parcellation algorithm developed by Han et al., 2020 to a sample of PwMS and healthy controls to examine relationships among specific cerebellar regions, fall status, and common clinical measures of motor and cognitive functions.

Methods

Thirty-one PwMS and 29 age and sex-matched controls underwent an MRI scan and motor and cognitive testing. The parcellation algorithm was applied to all images and divided the cerebellum into 28 regions. Mann-Whitney U tests were used to compare cerebellar volumes among PwMS and controls, and MS fallers and MS non-fallers. Relationships between cerebellar volumes and motor and cognitive function was evaluated using Spearman correlations.

Results

PwMS performed significantly worse on functional measures compared to controls. We found significant differences in volumetric measures between PwMS and controls in the corpus medullare, lobules I-III, and lobule V. Volumetric differences seen between PwMS and controls were primarily driven by the MS fallers. Finally, functional performance on motor and cognitive tasks was associated with cerebellar volumes.

Conclusions

Using the parcellation tool, our results showed that volumes of motor and cognitive lobules impact both motor and cognitive performance, and that functional performance and cerebellar volumes distinguishes MS fallers from non-fallers. Future studies should explore the potential of cerebellar imaging to predict falls in PwMS.

Associations between myelin water imaging and measures of fall risk and functional mobility in multiple sclerosis

BACKGROUND AND PURPOSE

Myelin water fraction (MWF) deficits as measured by myelin water imaging (MWI) have been related to worse motor function in persons with multiple sclerosis (PwMS). However, it is unknown if measures from MWI metrics in motor areas relate to fall risk measures in PwMS. The objective of this study was to examine the relationship between MWI measures in motor areas to performance on clinical measures of fall risk and disability in PwMS.

METHODS

Sixteen individuals with relapsing-remitting MS participated (1 male, 15 female; age 47.1 years [12.3]; Expanded Disability Status Scale 4.0 [range 0-6.5]) and completed measures of walking and fall risk (Timed 25 Foot Walk [T25FW] and Timed Up and Go). MWF and the geometric mean of the intra-/extracellular water T₂ (geomT_2IEW ) values reflecting myelin content and contribution of large-diameter axons/density, respectively, were assessed in three motor-related regions.

RESULTS

The geomT_2IEW of the corticospinal tract (r = -.599; p = .018) and superior cerebellar peduncles (r = -.613; p = .015) demonstrated significant inverse relationships with T25FW, suggesting that decreased geomT_2IEW was related to slower walking. Though not significant, MWF in the corticospinal tract and superior cerebellar peduncles also demonstrated fair relationships with the T25FW, suggesting that worse performance on the T25FW was associated with lower MWF values.

CONCLUSIONS

MWI of key motor regions was associated with walking performance in PwMS. Further MWI studies are needed to identify relationships between pathology and clinical function in PwMS to guide targeted rehabilitation therapies aimed at preventing falls.

Using myelin water imaging to link underlying pathology to clinical function in multiple sclerosis: A scoping review

BACKGROUND

Multiple sclerosis (MS) symptoms and pathology are heterogenous and complex. Identifying links between MS-related pathology (i.e., myelin damage) and associated clinical symptoms is critical for developing targeted therapeutics. Conventional MRI, commonly used for MS diagnosis and disease monitoring, lacks specificity with functional performance. Myelin water imaging (MWI) demonstrates increased specificity to myelin and is viewed as the gold standard for imaging myelin content in vivo. Yet, there is a paucity of MWI studies in MS and only a limited number also examine clinical function. Thus, it remains unknown whether MWI corresponds to functional performance in MS. This scoping review aimed to examine relations between MWI and functional domains relevant to MS to inform and guide future research.

METHODS

Seven databases were searched from their inception to September 1, 2021. Studies of adults with MS that included both brain MWI and either a measure of physical function, a measure of cognitive function, or a measure of disease severity were included. Thirteen studies (11 observational, 2 intervention) met the inclusion criteria.

RESULTS

The most commonly investigated MWI metric is the myelin water fraction (MWF). Persons with MS demonstrated markedly decreased MWF compared to healthy controls globally and across brain regions of interest (ROIs). Decreased MWF was associated with higher disability, worse motor and cognitive performance and decreased intervention response. Only five studies examined structure-function relationships in brain areas related to walking and cognitive function and only six studies extracted MWI metrics from explicit brain ROIs.

CONCLUSIONS

MWI is a neuroimaging technique with increased specificity to myelin and offers greater insight to MS-driven pathology and its clinical manifestations, including motor and cognitive dysfunction and rehabilitation response. This scoping review identified critical gaps in MWI research in MS to offer future perspectives including ROI-based studies, inclusion of multi-domain functional assessment and examining MWI to provide evidence of neuroplasticity following training.

Prioritizing progressive MS rehabilitation research: A call from the International Progressive MS Alliance

Background:

People with multiple sclerosis (MS) experience myriad symptoms that negatively affect their quality of life. Despite significant progress in rehabilitation strategies for people living with relapsing-remitting MS (RRMS), the development of similar strategies for people with progressive MS has received little attention.

Objective:

To highlight key symptoms of importance to people with progressive MS and stimulate the design and implementation of high-quality studies focused on symptom management and rehabilitation.

Methods:

A group of international research experts, representatives from industry, and people affected by progressive MS was convened by the International Progressive MS Alliance to devise research priorities for addressing symptoms in progressive MS.

Results:

Based on information from the MS community, we outline a rationale for highlighting four symptoms of particular interest: fatigue, mobility and upper extremity impairment, pain, and cognitive impairment. Factors such as depression, resilience, comorbidities, and psychosocial support are described, as they affect treatment efficacy.

Conclusions:

This coordinated call to action—to the research community to prioritize investigation of effective symptom management strategies, and to funders to support them—is an important step in addressing gaps in rehabilitation research for people affected by progressive MS.