Muscle modes and synergies during voluntary body sway

Abstract

We studied the coordination of muscle activity during voluntary body sway performed by human subjects at different frequencies. Subjects stood on the force platform and performed cyclic shifts of the center of pressure (COP) while being paced by the metronome. A major question was: does the makeup of muscle synergies and their ability to assure reproducible sway trajectory vary with the speed of the sway? Principal component analysis was used to identify three muscle groups (M-modes) within the space of integrated indices of muscle activity. M-mode vectors were similar across both subjects and sway frequencies. There were also similar relations between changes in the magnitudes of all three M-modes and COP shifts (the Jacobians) across the sway frequencies. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect the average value of COP shift ("good variance") and the other that did. An index (DeltaV) was computed reflecting the relative amount of the "good variance"; this index has been interpreted as reflecting a multi-M-mode synergy stabilizing the COP trajectory. The average value of DeltaV was similar across all sway frequencies; DeltaV showed a within-a-cycle modulation at low but not at high sway frequencies. The modulation was mostly due to variations in the "good variance". We conclude that muscle modes and their mapping on COP shifts are robust across a wide range of rates of COP shifts. Multi-M-mode synergies stabilize COP shifts (assure its reproducibility) within a wide range of its speeds, but only during cyclic COP changes. Taken together with earlier studies that showed weak or absent multi-M-mode synergies during fast discrete COP shifts, the results suggest a basic difference between the neural control assuring stability of steady-state processes (postural or oscillatory) and transient processes (such as discrete actions). Current results provide the most comprehensive support for the notion of multi-M-mode synergies stabilizing time profiles of important performance variables in motor tasks involving large muscle groups.

Multi-muscle synergies in an unusual postural task: quick shear force production

“…However, as in earlier studies (Krishnamoorthy et al 2003b;Wang et al 2005;Danna-Dos-Santos et al 2007), this simple model led to statistically significant relations between changes in the M-mode magnitudes and performance variables (see Results).…”

“…At the upper level, the M-modes are considered as the elemental variables, and their co-variation is organized in a task-specific manner to stabilize an important performance variable. The idea of a two-level hierarchy has been supported by studies that showed similar M-modes but different multi-Mmode co-variation indices across different tasks and subjects (Krishnamoorthy et al 2003a;Danna-Dos-Santos et al 2007) and also by reports of changes in the M-mode composition in unusual conditions (Krishnamoorthy et al 2004) and with practice (Asaka et al 2007). …”

“…The notion of multi-muscle synergies has been invoked in many studies of postural tasks (Krishnamoorthy et al 2003a(Krishnamoorthy et al ,b, 2004Ivanenko et al 2005Ivanenko et al , 2006 ; Ting and Macpherson 2005;Wang et al 2005Wang et al , 2006Torres-Oviedo et al 2006;Danna-Dos-Santos et al 2007) as a means of reducing (but not eliminating) the notorious redundancy of the multi-muscle system (cf. Bernstein 1967).…”

“…Bernstein 1967). In a series of recent studies, the framework of the uncontrolled manifold (UCM) hypothesis has been used to quantify multi-muscle synergies involved in various tasks such as preparation to a self-inflicted postural perturbation (Krishnamoorthy et al 2003b), preparation to making a step (Wang et al 2005(Wang et al , 2006, and voluntary sway (Danna-Dos-Santos et al 2007). The UCM hypothesis (Scholz and Schüner 1999;reviewed in Latash et al 2002breviewed in Latash et al , 2007 assumes that the neural controller acts in a space of elemental variables, forms in that space a sub-space (UCM) corresponding to a desired value (time profile) of an important performance variable, and then confines most of the variability to that sub-space.…”

“…We normalized the index by the total amount of variance similarly to how this was done in earlier studies (Krishnamoorthy et al 2003b;Danna-Dos-Santos et al 2007):…”