Dynamic corticomuscular multi-regional modulations during finger movement revealed by time-varying network analysis

Objective. A body movement involves the complicated information exchange between the central and peripheral systems, which is characterized by the dynamical coupling patterns between the multiple brain areas and multiple muscle units. How the central and peripheral nerves coordinate multiple internal brain regions and muscle groups is very important when accomplishing the action. Approach. In this study, we extend the adaptive directed transfer function to construct the time-varying networks between multiple corticomuscular regions, and divided the movement duration into different stages by the time-varying corticomuscular network patterns. Main results. The inter dynamical corticomuscular network demonstrated the different interaction patterns between the central and peripheral systems during the different hand movement stages. The muscles transmit bottom-up movement information in the preparation stage, but the brain issues top-down control commands and dominates in the execution stage, and finally the brain's dominant advantage gradually weakens in the relaxation stage. When classifying the different movement stages based on time-varying corticomuscular network indicators, an average accuracy above 74% could be reliably achieved. Significance. The findings of this study help deepen our knowledge of central-peripheral nerve pathways and coordination mechanisms, and also provide opportunities for monitoring and regulating movement disorders.

Automated summary

This study investigates the interaction patterns between the central and peripheral systems during different stages of movement using time-varying corticomuscular networks. Results show that muscles transmit bottom-up movement information in the preparation stage, while the brain issues top-down control commands and dominates in the execution stage. Classifying different movement stages based on time-varying corticomuscular network indicators achieves an average accuracy above 74%.