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Analysis of muscle synergies and muscle network in sling exercise rehabilitation technique.
Li, Xin; Xu, Guixing; Li, Le; Hao, Zengming; Lo, Wai Leung Ambrose; Wang, Chuhuai.
Affiliation
  • Li X; Department of Rehabilitation Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Xu G; Institute of Medical Research, Northwestern Polytechnical University, Xi'an 710072, China.
  • Li L; Department of Neurosurgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Hao Z; Department of Rehabilitation Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: haozm3@mail.sysu.edu.cn.
  • Lo WLA; Department of Rehabilitation Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China; Guangdong Engineering and Technology Research Centre for Rehabilitation Medicine and Translation, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. Electronic
  • Wang C; Department of Rehabilitation Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: wangchuh@mail.sysu.edu.cn.
Comput Biol Med ; 183: 109166, 2024 Oct 09.
Article in En | MEDLINE | ID: mdl-39388842
ABSTRACT
The study assessed motor control strategies across the four sling exercises of supine sling exercise (SSE), prone sling exercise (PSE), left side-lying sling exercise (LLSE), and right side-lying sling exercise (RLSE) positions base on the muscle synergies and muscle network analyses. Muscle activities of bilateral transversus abdominis (TA), rectus abdominis, multifidus (MF), and erector spinae (ES) were captured via surface electromyography. Muscle synergies were extracted through principal components analysis (PCA) and non-negative matrix factorization (NNMF). Muscle synergies number, muscle synergies complexity, muscle synergies sparseness, muscle synergies clusters and muscle networks were calculated. PCA results indicated that SSE and PSE decomposed into 2.88 ± 0.20 and 2.82 ± 0.15 synergies respectively, while the LLSE and RLSE positions decomposed into 3.76 ± 0.14 and 3.71 ± 0.11 muscle synergies, respectively, which were more complex (P = 0.00) but less sparse (P = 0.01). Muscle synergies clusters analysis indicated common muscle synergies among different sling exercises. SSE position demonstrated specific muscle synergies with a strong contribution of the bilateral TA. LLSE-specific synergy has a strong contribution of the left erector spinae (ES). The RLSE-specific synergy has significant contributions from the right ES and multifidus. Muscle networks were functionally organized, with clustering coefficient (F(1.5, 24) = 6.041, P = 0.01) and global efficiency of the undirected network (F(1.5, 24) = 6.041, P = 0.01), and betweenness-centrality of the directed network (F(2.7, 44) = 6.453, P = 0.00). Our research highlights the importance of evaluating muscle synergies and network adaptation strategies in individuals with neuromuscular disorders and developing targeted therapeutic interventions accordingly.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Comput Biol Med / Comput. biol. med / Computers in biology and medicine Year: 2024 Document type: Article Affiliation country: China Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Comput Biol Med / Comput. biol. med / Computers in biology and medicine Year: 2024 Document type: Article Affiliation country: China Country of publication: United States