Multi-muscle control during bipedal stance: an EMG–EMG analysis approach
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.
KeywordsSynergy Posture Muscle mode Balance Electromyogram Intermuscular coherence Human
- Bernstein NA (1967) The co-ordination and regulation of movements. Pergamon Press, OxfordGoogle Scholar
- Danna-dos-Santos A, Shapkova EY, Shapkova AL, Degani AM, Latash ML (2009) Postural control during upper body locomotor-like movements: similar synergies based on dissimilar muscle modes. Exp Brani Res 193:568–579Google Scholar
- Gelfand IM, Latash ML (2002) On the problem of adequate language in biology. In: Latash ML (ed) Progress in motor control: structure-function relations in voluntary movement, vol 2. Human Kinetics, Urbana, pp 209–228Google Scholar
- Gelfand IM, Tsetlin ML (1966) On mathematical modeling of the mechanisms of the central nervous system. In: Gelfan IM, Gurfinkel VS, Fomin SV, Tsetlin ML (eds) Models of the structural-functional organization organization of certain biological systems. Nauka, Moscow, pp 9–26Google Scholar
- Latash ML, Levin MF, Scholz JP, Schöner G (2010) Motor control theories and their applications. Medicine (Kaunas) 46(6):382–392Google Scholar