# Resting-state functional connectivity correlates of gait and turning performance in multiple sclerosis: a multivariate pattern analysis

## TL;DR

This study explores how brain connectivity patterns relate to walking and turning abilities in people with multiple sclerosis.

## Contribution

The study identifies resting-state functional connectivity differences in MS patients during turning tasks, suggesting turning as a sensitive measure for neural impairments.

## Key findings

- PwMS showed slower gait speed and impaired 360° turning compared to controls.
- MVPA revealed rs-FC differences in DMN, FPN, SN, and VIS networks in PwMS.
- 360° turn angle showed distinct within-group rs-FC associations involving VAN and DAN networks.

## Abstract

Multiple sclerosis (MS) often leads to mobility impairments, yet the neural mechanisms underlying these deficits remain poorly understood. This study examined whether resting-state functional connectivity (rs-FC) differs between people with MS (PwMS) and healthy controls in relation to spatiotemporal mobility performance. We hypothesized that group differences within the default mode (DMN), frontoparietal (FPN), somatomotor (SN), and visual (VIS) networks would be associated with gait and turning metrics. Twenty-nine PwMS and 28 matched controls completed a two-minute walk test, 180° walking turns, and 360° in-place turns at natural and fast speeds. fMRI data were analyzed using multivariate pattern analysis (MVPA) and post-hoc seed-to-voxel analyses for gait speed, cadence, double support time, stride length, turn duration, peak velocity, and turn angle. PwMS exhibited slower gait speed, shorter stride length, and impaired 360° turning, but no group differences in cadence, double support, or 180° turn metrics. MVPA revealed rs-FC differences across DMN, FPN, SN, and VIS networks. While rs-FC differences were evident for walking metrics, within-group associations were not significant. In contrast, 360° turn angle showed distinct within-group rs-FC associations, particularly involving VAN and DAN networks. These findings highlight turning as a sensitive task for capturing functional neural differences in MS.

The online version contains supplementary material available at 10.1038/s41598-025-21102-6.