Modeling the human myofascial network structure based on graph neural networks and its clinical relevance

doi:10.16098/j.issn.0529-1356.2026.03.007

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Acta Anatomica Sinica ›› 2026, Vol. 57 ›› Issue (3) : 323-329. DOI: 10.16098/j.issn.0529-1356.2026.03.007

Anatomy

Modeling the human myofascial network structure based on graph neural networks and its clinical relevance

WANG Chao-ya^1* , MENG Chao², PAN Chun¹, CHENG Jin¹, LI Zhi-fan¹

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Abstract

Objective To model and analyze the complex connectivity of the human myofascial system using Graph Neural Networks (GNNs) and explore their clinical potential. Methods Fifty-two healthy volunteers of varying ages and body types, along with 30 patients with chronic low back pain (>3 months), were recruited. Anatomical and biomechanical data of the myofascial system were obtained from high-resolution MRI, ultrasound elastography, and biomechanical testing to construct a whole-body myofascial network. An improved Graph Attention Network (GAT) model was applied to quantify connection strength, stress transmission, and pathological network alterations. Results The myofascial system showed distinct small-world network properties with key hub connections. Certain myofascial chains played crucial roles in movement and force transfer. Abnormal connectivity patterns were closely linked to chronic low back pain and cervicobrachial syndrome. The model achieved 85.7% accuracy in predicting therapeutic response. Conclusion GNN-based modeling provides a new perspective for understanding human integrative function and supports the development of personalized diagnosis and treatment strategies based on myofascial network features.

Key words

/ "> Graph Neural Network│Myofascial system│Biomechanics│Network topology│Small-World Network│Chronic Pain│Anatomy│Human

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WANG Chao-ya, MENG Chao, PAN Chun, CHENG Jin, LI Zhi-fan. Modeling the human myofascial network structure based on graph neural networks and its clinical relevance[J]. Acta Anatomica Sinica. 2026, 57(3): 323-329 https://doi.org/10.16098/j.issn.0529-1356.2026.03.007

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