A network information theoretic framework to characterise muscle synergies in space and time

Coordinated movement is thought to be simplified by the nervous system through the activation of muscle synergies. Current approaches to muscle synergy extraction rely on dimensionality reduction algorithms that impose limiting constraints. To capture large-scale interactions between muscle activations, a more generalised approach that considers the complexity and nonlinearity of the motor system is required. Here we developed a novel framework for muscle synergy extraction that relaxes model assumptions by using a combination of information- and network theory and dimensionality reduction. This novel framework can capture spatial, temporal and spatiotemporal interactions, producing distinct spatial groupings and both tonic and phasic temporal patterns. Furthermore, our framework identifies submodular structures in the extracted synergies that exemplify the fractal modularity of the human motor system. To demonstrate the versatility of the methodology, we applied it to two benchmark datasets of arm and whole-body reaching movements. Readily interpretable muscle synergies spanning multiple spatial and temporal scales were identified that demonstrated significant task dependence, ability to capture trial-to-trial fluctuations, a scale-invariance with dataset complexity and a substantial concordance across participants. Finally, we position this framework as a bridge between existing models and recent network-theoretic endeavours by discussing the continuity and novelty of the presented findings.