Muscle networks: Connectivity analysis of EMG activity during postural control

TLDR

Muscle synergies simplify control by reducing dimensionality, yet how they achieve this remains unclear. The study uses network theory to assess muscle coordination and elucidate the neural implementation of muscle synergies. Connectivity analysis of surface EMG from ten leg muscles during upright standing in four conditions was used to extract muscle networks. Widespread, frequency‑ and condition‑dependent connectivity revealed that muscle networks form a multiplex system enabling parallel and hierarchical motor control, demonstrating their utility for probing neural circuitry.

Abstract

Abstract Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.