Muscle networks: Connectivity analysis of EMG activity during postural control

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.

Network structure of the human musculoskeletal system shapes neural interactions on multiple timescales

“…Four distinct frequency components (0-3, 3-11, 11-21, and 21-60 Hz) were extracted using NNMF. These frequency bands closely match those found previously [30], demonstrating the robustness of this finding. An interesting possibility is that these frequency components reflect the spectral fingerprints of different pathways that project onto the spinal motor neurons.…”

“…In contrast, the bilateral module of lower leg muscles revealed strong coupling at multiple frequency bands, consistent with previous analyses on functional muscle networks [30], and showed the strongest long-range connections observed in the present study (Fig. 3C).…”

“…EMG data was then high-pass filtered (20 Hz) and EMG envelopes were extracted by means of the Hilbert amplitude [33]. We followed the procedure described in Boonstra et al [30] to extract functional muscle networks from surface EMG. First, complex-valued coherency was estimated and averaged over trials within each condition for each participant.…”

“…It has recently been applied to investigate the structure and function of the musculoskeletal system: The anatomical network can be constructed by mapping the origin and insertion of muscles [28,29]. Previously, we have shown how functional muscle networks can be constructed by assessing intermuscular coherence from surface electromyography (EMG) recorded from different muscles [30]. These functional networks reveal functional connectivity between groups of muscles at multiple frequency bands.…”

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

“…We previously showed that intermuscular coherence between postural muscles at different frequencies revealed distinct network topologies, which indicate the functioning of a multiplex network organization (Kerkman et al, 2018). Multivariate frequencydomain analysis of coupled processes can be used to assess frequency-dependent causal interactions in physiological time series (Faes and Nollo, 2011) and we have applied to assess frequency-dependent directed muscle networks (Boonstra et al, 2015). Nevertheless, network information measures have the advantage that they can be conveyed in a framework that identifies the basic components of information processing (i.e., storage, transfer and modification) into which network dynamics are dissected (Lizier, 2012), a perspective which is not available in the frequency domain.…”