Multi-muscle control during bipedal stance: an EMG–EMG analysis approach

Abstract

Posture and postural reactions to mechanical perturbations require the harmonic modulation of the activity of multiple muscles. This precision can become suboptimal in the presence of neuromuscular disorders and result in higher fall risk and associated levels of comorbidity. This study was designed to investigate neurophysiological principles related to the generation and distribution of inputs to skeletal muscles previously recognized as a synergistic group. Specifically, we investigated the current hypothesis that correlated neural inputs, as measured by intermuscular coherence, are the mechanism used by the central nervous system to coordinate the formation of postural muscle synergies. This hypothesis was investigated by analyzing the strength and distribution of correlated neural inputs to postural muscles during the execution of a quiet stance task. Nine participants, 4 females and 5 males, mean age 29.2 years old (±6.1 SD), performed the task of standing while holding a 5-kg barbell in front of their bodies at chest level. Subjects were asked to maintain a standing position for 10 s while the activity of three postural muscles was recorded by surface electrodes: soleus (SOL), biceps femoris (BF), and lumbar erector spinae (ERE). EMG-EMG coherence was estimated for three muscle pairs (SOL/BF, SOL/ERE, and BF/ERE). Our choice of studying these muscles was made based on the fact that they have been reported as components of a functional (synergistic) muscle group that emerges during the execution of bipedal stance. In addition, an isometric contraction can be easily induced in this muscle group by simply adding a weight to the body's anterior aspect. The experimental condition elicited a significant increase in muscle activation levels for all three muscles (p < 0.01 for all muscles). EMG-EMG coherence analysis revealed significant coherence within two distinct frequency bands, 0-5 and 5-20 Hz. Significant coherence within the later frequency band was also found to be significantly uniformly distributed across the three muscle pairs. These findings are interpreted as corroborative with the idea of a hierarchic system of control where the controller may use the generation of common neural inputs to reduce the number of variables it manipulates.

The influence of visual information on multi-muscle control during quiet stance: a spectral analysis approach

“…Synchronization of EMG signals within this frequency interval (<10 Hz) is consistent with previous studies that reported similar findings during the execution of slow movements (Kamen and DeLuca 1992;Vallbo and Wessberg 1993;Farmer et al 1993;DeLuca and Erim 2002). In fact, similar results have been recently reported by us (Danna-dos-Santos et al 2014). In that study, we reported synchronizations that were not only concentrated between 1 and 10 Hz but also extended to frequencies up to 20 Hz.…”

“…Recently, we investigated the role of correlated neural inputs as a neural mechanism by which the CNS coordinates the activation of synergistic muscles forming only one of these M-modes (Danna-dos-Santos et al 2014). More specifically, we employed measures of intermuscular coherence between pairs of EMG signals composing the posterior M-mode (soleus, biceps femoris, and lumbar erector spinae muscles) and confirmed the presence of correlated neural inputs to all three muscles.…”

Muscle networks: Connectivity analysis of EMG activity during postural control

“…Intermuscular coherence at higher frequencies has also been reported by several studies. We observed 6–10 Hz intermuscular coherence between leg muscles within and across legs 30 31 . Coherence at 16 Hz has also been observed between leg muscles 30 , in particular in patients with orthostatic tremor in which 16-Hz coherence is very pronounced 32 .…”

Muscle synergies are associated with intermuscular coherence in an isometric upper limb task

“…This has relevance to the integration of theories of common drive and motor control supervision. On one hand, coherence reflects common excitatory drive originating from a neural structure (Danna-Dos- Santos et al 2014). At the same time, primary movers in a motor task are closely supervised by the motor control system (Krishnamoorthy et al 2003;Danna-dos-Santos et al 2007).…”

“…Synchronisation within the beta band during precise, steady force outputs of hand muscles have been proposed to be indicative of functional binding between the primary motor cortex and effector muscles (Kilner et al 2000;Boonstra 2009;Danna-Dos Santos et al 2010). Alpha band synchronisation across distinct muscles or even bilateral muscles, as IMC, suggests a common input from subcortical structures (Conway et al 1995;Baker et al 2003) and may specifically reflect the involvement of the reticulospinal pathway (Grosse and Brown 2003) Coherent activity has been described across muscles of an individual synergy during postural responses (Danna-Dos- Santos et al 2014) and a cycling task (Marchis et al 2015).…”