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

Nora E Fritz,Peter A Calabresi,Jennifer Keller,Kathleen M Zackowski,Erin M Edwards,Ani Eloyan

doi:10.3390/brainsci10110822

Abstract

Multiple sclerosis (MS) impacts balance and walking function, resulting in accidental falls. History of falls and clinical assessment are commonly used for fall prediction, yet these measures have limited predictive validity. Falls are multifactorial; consideration of disease-specific pathology may be critical for improving fall prediction in MS. The objective of this study was to examine the predictive value of clinical measures (i.e., walking, strength, sensation) and corticospinal tract (CST) MRI measures, both discretely and combined, to fall status in MS. Twenty-nine individuals with relapsing-remitting MS (mean ± SD age: 48.7 ± 11.5 years; 17 females; Expanded Disability Status Scale (EDSS): 4.0 (range 1–6.5); symptom duration: 11.9 ± 8.7 years; 14 fallers) participated in a 3T brain MRI including diffusion tensor imaging and magnetization transfer ratio (MTR) and clinical tests of walking, strength, sensation and falls history. Clinical measures of walking were significantly associated with CST fractional anisotropy and MTR. A model including CST MTR, walk velocity and vibration sensation explained >31% of the variance in fall status (R2 = 0.3181) and accurately distinguished 73.8% fallers, which was superior to stand-alone models that included only MRI or clinical measures. This study advances the field by combining clinical and MRI measures to improve fall prediction accuracy in MS.

Highlights

Multiple sclerosis (MS) is a complex neurogenerative disease targeting the central nervous system
Corticospinal Tract (CST) Fractional Anisotropy (FA) was significantly related to walking velocity (r = 0.4983; p = 0.0051), Timed Up and Go (TUG) (r = −0.3777; CST FA was significantly related to walking velocity (r = 0.4983; p = 0.0051), TUG
The critical finding of the current study is that the combination of clinical measures and MRI measures demonstrated 73.8% accuracy (Figure 2), which greatly improves upon the accuracy of stand-alone clinical measures (50%) and stand-alone MRI measures (44.8%) to predict fall status in our sample of MS participants

Summary

Introduction

Multiple sclerosis (MS) is a complex neurogenerative disease targeting the central nervous system. The pathophysiological hallmarks of MS include inflammatory lesions that result in varying degrees of neuronal demyelination, nerve damage and a vast spectrum of subsequent neurological dysfunctions [1]. It remains widely accepted that MS damages the myelin sheath, a protective covering that insulates axons to promote successful nerve signaling [2]. This varying degree of myelin damage can. Brain Sci. 2020, 10, 822; doi:10.3390/brainsci10110822 www.mdpi.com/journal/brainsci. Brain Sci. 2020, 10, 822 disrupt signal transmission across a large range of systems [3] and manifest as a myriad of pathological symptoms and comorbidities, including walking and balance impairments [4].

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