Structured Variability of Muscle Activations Supports the Minimal Intervention Principle of Motor Control

Abstract

Numerous observations of structured motor variability indicate that the sensorimotor system preferentially controls task-relevant parameters while allowing task-irrelevant ones to fluctuate. Optimality models show that controlling a redundant musculo-skeletal system in this manner meets task demands while minimizing control effort. Although this line of inquiry has been very productive, the data are mostly behavioral with no direct physiological evidence on the level of muscle or neural activity. Furthermore, biomechanical coupling, signal-dependent noise, and alternative causes of trial-to-trial variability confound behavioral studies. Here we address those confounds and present evidence that the nervous system preferentially controls task-relevant parameters on the muscle level. We asked subjects to produce vertical fingertip force vectors of prescribed constant or time-varying magnitudes while maintaining a constant finger posture. We recorded intramuscular electromyograms (EMGs) simultaneously from all seven index finger muscles during this task. The experiment design and selective fine-wire muscle recordings allowed us to account for a median of 91% of the variance of fingertip forces given the EMG signals. By analyzing muscle coordination in the seven-dimensional EMG signal space, we find that variance-per-dimension is consistently smaller in the task-relevant subspace than in the task-irrelevant subspace. This first direct physiological evidence on the muscle level for preferential control of task-relevant parameters strongly suggest the use of a neural control strategy compatible with the principle of minimal intervention. Additionally, variance is nonnegligible in all seven dimensions, which is at odds with the view that muscle activation patterns are composed from a small number of synergies.

Anticipatory Control of Motion-to-Force Transitions With the Fingertips Adapts Optimally to Task Difficulty

“…In short, the upregulation of the sensorimotor corrections of the transition of coordination pattern alignment has the functional consequence of improving the accuracy of the force task. This idea of on-line corrections resulting in higher EMG variability and improved performance is compatible with our prior work (Valero-Cuevas et al 2009b). We refrain from commenting further on the details of force production beyond ϩ65 ms after contact for several reasons.…”

“…Namely, if on-line corrections are upregulated in the more difficult task, then initiating the transition sooner allows more time to complete on-line corrections in the presence of neuromuscular delays and noise. We have recently shown that such rich, taskoptimal, on-line corrections are not only possible but actually present in the control of accurate fingertip forces (Valero-Cuevas et al 2009b).…”

“…First the focus of this work is to explore the trends in muscle coordination during the motion-to-force transition, which saturates and is 90% complete before contact. Second, as stated in the preceding text, any changes in force magnitude and direction beyond ϩ65 ms can be attributed to a large number of possible voluntary and involuntary mechanismsand are thus best studied under specifically designed conditions as in, for example, (Valero-Cuevas et al 1998, 2009b.…”

Fatigue-related adaptations in muscle coordination during a cyclic exercise in humans

“…Recently, a technique has emerged that is capable of decomposing large EMG datasets into the summed activation of just a few muscle synergies or modules (Ting and McKay, 2007;Tresch et al, 2006). Although some studies suggested that muscle synergies better reflect task constraints rather than neural control strategies (Kutch et al, 2008;Valero-Cuevas et al, 2009), others proposed that the central nervous system produces movement through the flexible combination of muscle synergies (Torres-Oviedo et al, 2006;Torres-Oviedo and Ting, 2007;Tresch et al, 1999;Bizzi et al, 2008;d'Avella et al, 2003). Indeed, they provide an attractive simplified strategy for the control of complex movements because they reduce the number of output patterns that the nervous system must specify for a large number of muscles (Bizzi et al, 2008).…”

The effect of timing electrical stimulation to robotic-assisted stepping on neuromuscular activity and associated kinematics

“…Thus, we used EMG measures as one indicator of plasticity, in particular at the level of the motor pool. EMG patterns recorded from hind-limb muscles have been shown to reflect training-enhanced plasticity [57], and EMG behavior has been accepted as a representation of damage to or recovery of the neuromotor control that underlies muscle output [58][59][60][61]. The EMG activation profiles of the FES+RTT group showed more consistent timing of muscle activation to the swing phase of the gait cycle than those of the RS group.…”