Quantifying the diverse contributions of hierarchical muscle interactions to motor function.

The muscle synergy concept suggests that the human motor system is organized into functional modules composed of muscles "" toward common task goals. This study offers a nuanced computational perspective to muscle synergies, where muscles interacting across multiple scales have functionally similar, complementary, and independent roles. Making this viewpoint implicit to a methodological approach applying Partial Information Decomposition to large-scale muscle activations, we unveiled nested networks of functionally diverse inter- and intramuscular interactions with distinct functional consequences on task performance. The effectiveness of this approach is demonstrated using simulations and by extracting generalizable muscle networks from benchmark datasets of muscle activity. Specific network components are shown to correlate with (1) balance performance and (2) differences in motor variability between young and older adults. By aligning muscle synergy analysis with leading theoretical insights on movement modularity, the mechanistic insights presented here suggest the proposed methodology offers enhanced research opportunities toward health and engineering applications.