Combining magnetization transfer ratio mri and quantitative measures of walking improves the identification of fallers in MS

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

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Article number822
Pages (from-to)1-12
Number of pages12
JournalBrain Sciences
Volume10
Issue number11
DOIs
StatePublished - Nov 2020

Keywords

  • Accidental falls
  • Magnetic resonance imaging
  • Multiple sclerosis
  • Walking

ASJC Scopus subject areas

  • General Neuroscience

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