Cerebellar Volume Measures Differentiate Multiple Sclerosis Fallers from Non-Fallers

Overview

Journal Brain Sci

Publisher MDPI

Date 2025 Jan 24

PMID 39851444

Authors

Taylor N Takla

Jennie Feldpausch

Erin M Edwards

Shuo Han

Peter A Calabresi

Jerry Prince

Kathleen M Zackowski

Nora E Fritz

Affiliations

Soon will be listed here.

Abstract

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.

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