Brain Connectivity During Walking and Obstacle Avoidance in Persons With Multiple Sclerosis and Healthy Controls: A Pilot EEG Study

This study investigated effective connectivity and hemispheric asymmetry in persons with multiple sclerosis (pwMS) compared to healthy controls (HC) during two walking conditions: walking alone and walking while avoiding unpredictable obstacles. Cognitive-motor interference (CMI) was analyzed using electroencephalography (EEG) across beta, alpha, and theta frequency bands. Directed functional connectivity was estimated using partial directed coherence (PDC) to assess differences in connectivity patterns between conditions and groups. In healthy controls, obstacle avoidance increased connectivity in motor and cognitive regions including left central (LC), left temporal (LT), and right frontal (RF) regions, p<0.0014. In contrast, pwMS demonstrated weaker and more localized connectivity, primarily in the left central regions (sensorimotor cortices) p<0.0013, suggesting reduced efficiency in brain networks and compensatory mechanisms to maintain task performance. Further, pwMS showed left laterality toward the central region during both walking conditions compared to HC, p<0.05. Correlational analysis revealed that connectivity during obstacle avoidance in HC positively correlated with comfortable walking speed (r =0.57), indicating efficient neural pathways. In pwMS, connectivity showed a negative correlation with walking speed (r $= -0.65$ ), indicating compensatory but inefficient neural engagement. These findings highlight disruptions in brain connectivity during motor-cognitive tasks in pwMS, with potential implications for designing targeted rehabilitation strategies to improve gait and neural efficiency.

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Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tnsre.2025.3592492
Citations
1
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
1.43

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article

Brain Connectivity During Walking and Obstacle Avoidance in Persons With Multiple Sclerosis and Healthy Controls: A Pilot EEG Study

Fares Al-Shargie, John DeLuca, Michael Glassen, Soha Saleh
1 citations
IEEE Transactions on Neural Systems and Rehabilitation Engineering
EEG and Brain-Computer Interfaces
1.43
article

Brain Connectivity During Walking and Obstacle Avoidance in Persons With Multiple Sclerosis and Healthy Controls: A Pilot EEG Study

Fares Al-Shargie, John DeLuca, Michael Glassen, Soha Saleh
article en
1 citations

Abstract

This study investigated effective connectivity and hemispheric asymmetry in persons with multiple sclerosis (pwMS) compared to healthy controls (HC) during two walking conditions: walking alone and walking while avoiding unpredictable obstacles. Cognitive-motor interference (CMI) was analyzed using electroencephalography (EEG) across beta, alpha, and theta frequency bands. Directed functional connectivity was estimated using partial directed coherence (PDC) to assess differences in connectivity patterns between conditions and groups. In healthy controls, obstacle avoidance increased connectivity in motor and cognitive regions including left central (LC), left temporal (LT), and right frontal (RF) regions, p<0.0014. In contrast, pwMS demonstrated weaker and more localized connectivity, primarily in the left central regions (sensorimotor cortices) p<0.0013, suggesting reduced efficiency in brain networks and compensatory mechanisms to maintain task performance. Further, pwMS showed left laterality toward the central region during both walking conditions compared to HC, p<0.05. Correlational analysis revealed that connectivity during obstacle avoidance in HC positively correlated with comfortable walking speed (r =0.57), indicating efficient neural pathways. In pwMS, connectivity showed a negative correlation with walking speed (r $= -0.65$ ), indicating compensatory but inefficient neural engagement. These findings highlight disruptions in brain connectivity during motor-cognitive tasks in pwMS, with potential implications for designing targeted rehabilitation strategies to improve gait and neural efficiency.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
Rutgers, The State University of New Jersey (US), MGH Institute of Health Professions (US), Kessler Foundation (US), Johnson University (US)
National Institutes of Health
Openalex Percentile: Top 17%
EEG and Brain-Computer Interfaces
1.43
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