3D muscle networks based on vibrational mechanomyography

Objective. Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography, which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach. We introduce three-dimensional (3D) muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands. We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main results. Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction. No statistical decrease in signal strength was observed with higher contact force.Significance. Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification. Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue 3D models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

Body Weight Control Is a Key Element of Motor Control for Toddlers' Walking

New-borns can step when supported for about 70-80% of their own body weight. Gravity-related sensorimotor information might be an important factor in developing the ability to walk independently. We explored how body weight support alters motor control in toddlers during the first independent steps and in toddlers with about half a year of walking experience. Sixteen different typically developing children were assessed during (un)supported walking on a running treadmill. Electromyography of 18-24 bilateral leg and back muscles and vertical ground reaction forces were recorded. Strides were grouped into four levels of body weight support ranging from no (<10%), low (10-35%), medium (35-55%), and high (55-95%) support. We constructed muscle synergies and muscle networks and assessed differences between levels of support and between groups. In both groups, muscle activities could be described by four synergies. As expected, the mean activity decreased with body weight support around foot strikes. The younger first-steps group showed changes in the temporal pattern of the synergies when supported for more than 35% of their body weight. In this group, the muscle network was dense with several interlimb connections. Apparently, the ability to process gravity-related information is not fully developed at the onset of independent walking causing motor control to be fairly disperse. Synergy-specific sensitivity for unloading implies distinct neural mechanisms underlying (the emergence of) these synergies.

Muscle Synergies and Coherence Networks Reflect Different Modes of Coordination During Walking

When walking speed is increased, the frequency ratio between the arm and leg swing switches spontaneously from 2:1 to 1:1. We examined whether these switches are accompanied by changes in functional connectivity between multiple muscles. Subjects walked on a treadmill with their arms swinging along their body while kinematics and surface electromyography (EMG) of 26 bilateral muscles across the body were recorded. Walking speed was varied from very slow to normal. We decomposed EMG envelopes and intermuscular coherence spectra using non-negative matrix factorization (NMF), and the resulting modes were combined into multiplex networks and analyzed for their community structure. We found five relevant muscle synergies that significantly differed in activation patterns between 1:1 and 2:1 arm-leg coordination and the transition period between them. The corresponding multiplex network contained a single module indicating pronounced muscle co-activation patterns across the whole body during a gait cycle. NMF of the coherence spectra distinguished three EMG frequency bands: 4-8, 8-22, and 22-60 Hz. The community structure of the multiplex network revealed four modules, which clustered functional and anatomical linked muscles across modes of coordination. Intermuscular coherence at 4-22 Hz between upper and lower body and within the legs was particularly pronounced for 1:1 arm-leg coordination and was diminished when switching between modes of coordination. These findings suggest that the stability of arm-leg coordination is associated with modulations in long-distant neuromuscular connectivity.