Structure, function, and control of the human musculoskeletal network

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.Author summary: While network science is frequently used to characterize networks from genomics, proteomics, and connectomics, its utility in understanding biomechanics, orthopedics, and physical therapy has remained largely unexplored. Indeed, current clinical practice and knowledge regarding the musculoskeletal system largely focuses on single areas of the body, single muscles, or single injuries and therefore remains agnostic to mesoscale or global features of the body’s architecture that may have critical implications for injury and recovery. We addressed this gap by representing the musculoskeletal system as a graph or network, in which we considered bones and the muscular connections between them. By modeling muscles as springs and bones as point masses, we developed a perturbative approach to interrogate the function of this network. Employing this model, we calculated the network level effects of perturbing individual muscles. Using this formalism, we are able to draw new parallels between this system and the primary motor cortex that controls it, and illustrate clinical connections between network structure and muscular injury.