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DOI 10.1109/tnsre.2025.3557777

Медицинская библиотека · IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025

Neuro-Modulation Analysis Based on Muscle Synergy Graph Neural Network in Human Locomotion.

Yang Ningjia, Li Xuesi, An Qi, Li Jingsong, Shimoda Shingo

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

The coordination of muscles in human locomotion is commonly understood as the integration of motor modules known as muscle synergies. Recent research has delved into the adaptation of muscle synergies during the acquisition of new motor skills. However, the precise interplay between modulated muscle synergies during movement according to motion requirements remains unclear. Here, we aim to elucidate the alterations in locomotor synergies across various lower-limb motion strategies and motor tasks. Our findings reveal consistent weights of muscles in muscle synergies alongside varying timing activation aligned with specific motion requirements. It shows that spatial muscle synergies remain stable across different motor tasks, but humans adjusted the timing activation of these modules (temporal muscle synergies) to meet the motor requirements. To classify temporal muscle synergies and quantify connection weights for both self-connections and connections between muscle synergies, we employed a graph neural network. Our results demonstrate that muscle synergy 4, responsible for elevating the thigh to propel forward during the swing phase, experiences pronounced enhancement with changes in motion strategies. Furthermore, we observed a reduction in the self-connection of muscle synergy 2, implicated in stabilizing body posture, during motion tasks other than normal walking. Additionally, the connections between muscle synergy 2 and other synergies diminished, indicating more adaptation in muscle synergy 2 to achieve stabilization in more challenging motor tasks. The validity of these findings was verified through five-fold cross-validation, affirming the efficacy of our approach in elucidating neuro-modulation mechanisms in human locomotion. Our proposed methodology holds promising implications for the development of personalized training strategies, offering insights into the intricate interactions among different muscle synergies in accomplishing motor tasks.

Для цитирования: Yang Ningjia, Li Xuesi, An Qi, Li Jingsong, Shimoda Shingo. Neuro-Modulation Analysis Based on Muscle Synergy Graph Neural Network in Human Locomotion.. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society. 2025. DOI: 10.1109/tnsre.2025.3557777.

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