Network Reconfiguration Underlies Compensatory Muscle Control Across Force Gradients: Parallel Functional Network...

Understanding how the neuromuscular system adapts to increasing force demands is essential for characterizing compensatory motor control. This study investigated force-dependent reconfiguration of muscle and cortical functional networks during isometric upper-limb tasks.

Twelve healthy participants performed isometric elbow flexion at 30%, 50%, and 70% of maximal voluntary contraction (MVC). Surface electromyography (sEMG) from eight upper-limb muscles and electroencephalography (EEG) from 21 scalp electrodes were recorded concurrently. Directed functional connectivity was estimated using generalized partial directed coherence (GPDC), and graph-theoretical metrics-average global efficiency (AGE), average clustering coefficient (ACC), and average path length (APL)-were computed separately for muscle and cortical networks.

In the muscle network, a significant main effect of force level was observed. Compared with 30% MVC, AGE increased by 12.24% ( $P=0.043$ ) and APL decreased by 17.14% ( $P=0.031$ ) at 70% MVC, while ACC increased by 44.64% ( $P=0.018$ ). In the EEG beta band, AGE increased by 8.12% ( $P=0.048$ ) and APL decreased by 12.34% ( $P=0.036$ ) at 70% MVC relative to 30% MVC. Gamma band changes were limited or non-significant across conditions.

These results demonstrate systematic, force-dependent reconfiguration of both muscle and cortical functional networks during isometric force production. Rather than indicating improved performance or neural plasticity, the observed network changes suggest shifts in coordination strategies as force demands increase. The present framework provides quantitative network metrics that can be extended to clinical and longitudinal studies in future work.