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RESEARCH PAPER

Muscle Network of Parkinson's Gait: A 12-Month Longitudinal Analysis Before and After Deep Brain Stimulation Surgery.

PMID
41955137
Journal
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Publication Date
2026-01-01
Grade
E

AI Summary

Graph-theory analysis of inter-muscular coherence from instrumented gait EMG shows PD patients have increased muscle network density and reduced modularity versus controls, which move toward normal levels 12 months after bilateral subthalamic DBS and correlate with UPDRS-III improvement.

Why It Matters

Offers a noninvasive, quantitative network-level biomarker for gait motor control and DBS response that could be used to monitor treatment efficacy or as an objective endpoint in clinical studies, though it provides limited new mechanistic or target-discovery insight.

Abstract

Bilateral Deep Brain Stimulation (DBS) of the subthalamic nucleus is commonly used for treating motor symptoms in patients with advanced Parkinson's Disease (PD). The aim of this study is to quantitatively and non-invasively evaluate motor control changes in PD patients following DBS through an approach based on the combination of graph theory and frequency-domain electromyography (EMG) analysis. Instrumented gait analysis was carried out on a group of 30 PD patients and 30 age-matched controls. PD patients were longitudinally followed up, with assessments pre-DBS implant ( $\mathrm{T}_{{0}}\text {)}$ , 3 months post-DBS implant ( $\mathrm{T}_{{1}}\text {)}$ , and 12 months post-DBS implant ( $\mathrm{T}_{{2}}\text {)}$ . EMG signals from 12 lower-limb and trunk muscles were acquired, calculating Inter-Muscular Coherence (IMC) for each muscle pair. Adjacency matrices derived from IMC were used to generate 3D muscle networks through a force-based algorithm. Two families of network parameters were extracted: global metrics (modularity and density) and local metrics (node strength and local clustering coefficient). Muscle network modularity of PD patients at T0 was significantly lower than that of controls ( $0.34{\,}\pm{\,}0.07$ vs. $0.41{\,}\pm{\,}0.07$ ; ${p} =0.003$ ) and this difference persisted at T1 ( $0.35{\,}\pm{\,}0.01$ ; ${p} =0.037$ ), but not at T2 ( $0.38{\,}\pm{\,}0.01$ ; ${p} =1.00$ ). Analogously, muscle network density was higher in PD patients at T0 (T0: $0.69{\,}\pm{\,}0.10$ vs. Controls: $0.56{\,}\pm{\,}0.10$ ; ${p} =0.004$ ), decreased at T1 ( $0.65{\,}\pm{\,}0.14$ ; ${p} =0.034$ ), and was comparable to that of controls at T2 ( $0.60{\,}\pm{\,}0.14$ ; ${p} =1.00$ ). Node-level analyses similarly showed that PD patients values moved toward control-group reference levels after DBS surgery, reflecting reduced individual muscle connectivity and a more structured pattern of muscle coordination. Global metrics showed a good agreement with respect to the clinical score UPDRS-III. Graph theory applied to EMG analysis opens new perspectives in the study of motor control strategies during gait and confirms the efficacy of DBS in alleviating motor symptoms of PD patients.

Score Breakdown

AI Score
45.0
Base Score
30.8
Rank Score
29.2
Narrative Velocity
-
AI Confidence
-
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