Challenges and New Approaches to Proving the Existence of Muscle Synergies of Neural Origin

Muscle coordination studies repeatedly show low-dimensionality of muscle activations for a wide variety of motor tasks. The basis vectors of this low-dimensional subspace, termed muscle synergies, are hypothesized to reflect neurally-established functional muscle groupings that simplify body control. However, the muscle synergy hypothesis has been notoriously difficult to prove or falsify. We use cadaveric experiments and computational models to perform a crucial thought experiment and develop an alternative explanation of how muscle synergies could be observed without the nervous system having controlled muscles in groups. We first show that the biomechanics of the limb constrains musculotendon length changes to a low-dimensional subspace across all possible movement directions. We then show that a modest assumption—that each muscle is independently instructed to resist length change—leads to the result that electromyographic (EMG) synergies will arise without the need to conclude that they are a product of neural coupling among muscles. Finally, we show that there are dimensionality-reducing constraints in the isometric production of force in a variety of directions, but that these constraints are more easily controlled for, suggesting new experimental directions. These counter-examples to current thinking clearly show how experimenters could adequately control for the constraints described here when designing experiments to test for muscle synergies—but, to the best of our knowledge, this has not yet been done

Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?

It is widely accepted that humans and animals minimize energetic cost while walking. While such principles predict average behavior, they do not explain the variability observed in walking. For robust performance, walking movements must adapt at each step, not just on average. Here, we propose an analytical framework that reconciles issues of optimality, redundancy, and stochasticity. For human treadmill walking, we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride. We recorded stride times and stride lengths from healthy subjects walking at five speeds. The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy. Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy, but far greater variability for goal-irrelevant fluctuations. More importantly, humans immediately corrected goal-relevant deviations at each successive stride, while allowing goal-irrelevant deviations to persist across multiple strides. To demonstrate that this was not the only strategy people could have used to successfully accomplish the task, we created three surrogate data sets. Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function. Humans did not adopt any of these viable alternative strategies. Finally, we developed a sequence of stochastic control models of stride-to-stride variability for walking, based on the Minimum Intervention Principle. We demonstrate that healthy humans are not precisely “optimal,” but instead consistently slightly over-correct small deviations in walking speed at each stride. Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of, but in parallel with, minimizing energetic cost. Thus, humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability

Optimization of Muscle Activity for Task-Level Goals Predicts Complex Changes in Limb Forces across Biomechanical Contexts

Optimality principles have been proposed as a general framework for understanding motor control in animals and humans largely based on their ability to predict general features movement in idealized motor tasks. However, generalizing these concepts past proof-of-principle to understand the neuromechanical transformation from task-level control to detailed execution-level muscle activity and forces during behaviorally-relevant motor tasks has proved difficult. In an unrestrained balance task in cats, we demonstrate that achieving task-level constraints center of mass forces and moments while minimizing control effort predicts detailed patterns of muscle activity and ground reaction forces in an anatomically-realistic musculoskeletal model. Whereas optimization is typically used to resolve redundancy at a single level of the motor hierarchy, we simultaneously resolved redundancy across both muscles and limbs and directly compared predictions to experimental measures across multiple perturbation directions that elicit different intra- and interlimb coordination patterns. Further, although some candidate task-level variables and cost functions generated indistinguishable predictions in a single biomechanical context, we identified a common optimization framework that could predict up to 48 experimental conditions per animal (n = 3) across both perturbation directions and different biomechanical contexts created by altering animals' postural configuration. Predictions were further improved by imposing experimentally-derived muscle synergy constraints, suggesting additional task variables or costs that may be relevant to the neural control of balance. These results suggested that reduced-dimension neural control mechanisms such as muscle synergies can achieve similar kinetics to the optimal solution, but with increased control effort (≈2×) compared to individual muscle control. Our results are consistent with the idea that hierarchical, task-level neural control mechanisms previously associated with voluntary tasks may also be used in automatic brainstem-mediated pathways for balance

Principles of sensorimotor learning.

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved

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

Moving our fingertips toward objects to produce well-directed forces immediately upon contact is fundamental to dexterous manipulation. This apparently simple motion-to-force transition in fact involves a time-critical, predictive switch in control strategy. Given that dexterous manipulation must accommodate multiple mechanical conditions, we investigated whether and how this transition adapts to task difficulty. Eight adults (19–39 yr) produced ramps of isometric vertical fingertip force against a rigid surface immediately following a tapping motion. By changing target surface friction and size, we defined an easier (sandpaper, 11 mm diam) versus a more difficult (polished steel, 5 mm diam) task. As in prior work, we assembled fine-wire electromyograms from all seven muscles of the index finger into a seven-dimensional vector defining the full muscle coordination pattern—and quantified its temporal evolution as its alignment with a reference coordination pattern vector for steady-state force production. As predicted by numerical optimizations to neuromuscular delays, our empirical and sigmoidal nonlinear regression analyses show that the coordination pattern transitions begin sooner for the more difficult tasks than for the easier tasks (∼120 ms, P < 0.02, and ∼115 ms, P < 0.015, respectively) and that the coordination pattern transition in alignment is well represented by a sigmoidal trend (R^2 > 0.7 in most cases). Importantly, the force vector following contact had smaller directional error (P < 0.02) for the more difficult task even though the transition in coordination pattern was less stereotypical and uniform than for the easier task. These adaptations of transition strategy to task difficulty are compatible with an optimization to counteract neuromuscular delays and noise to enable this fundamental element of dexterous manipulation

The fundamental thumb-tip force vectors produced by the muscles of the thumb

A rigorous description of the magnitude and direction of the 3D force vector each thumb muscle produces at the thumb-tip is necessary to understand the biomechanical consequences to pinch of a variety of paralyses and surgical procedures (such as tendon transfers). In this study, we characterized the 3D force vector each muscle produces at the thumb-tip, and investigated if these thumb-tip force vectors scaled linearly with tendon tension. In 13 cadaver specimens, we measured the output 3D thumb-tip force vector produced by each tendon acting on the thumb, plus two common tendon transfers, as a function of input tendon tension. After fixing the hand to a rigid frame, we mounted the thumb by configuring it in standardized key or opposition pinch posture and coupling the thumb-tip to a rigidly held 6 degree-of-freedom force/torque sensor. Linear actuators applied tension to the distal tendons of the four extrinsic thumb muscles, and to six Nylon cords reproducing the lines of action of (i) the four intrinsic thumb muscles and (ii) two alternative tendon transfers commonly used to restore thumb opposition following low median nerve palsy. Each computer-controlled linear actuator ramped tendon tension from zero to 1/3 of predicted maximal muscle force expected at each tendon, and back to zero, while we measured the 3D force vector at the thumb-tip. In test/re-test trials, we saw thumb-tip force vectors were quite sensitive to mounting procedure, but also sensitive to variations in the seating of joint surfaces. We found that: (i) some thumb-tip force vectors act in unexpected directions (e.g., the opponens force vector is parallel to the distal phalanx), (ii) the two tendon transfers produced patently different force vectors, and (iii) for most muscles, thumb-tip force vectors do not scale linearly with tendon tension - likely due to load-dependent viscoelastic tendon paths, joint seating and/or bone motion. Our 3D force vector data provide the first quantitative reference descriptions of the thumb-tip force vectors produced by all thumb muscles and two tendon transfers. We conclude that it may not be realistic to assume in biomechanical models that thumb-tip force vectors scale linearly with tendon tensions, and that our data suggest the thumb may act as a "floating digit" affected by load-dependent trapezium motion. © 2003 Orthopaedic Research Society. Published by Elsevier Ltd. All rights reserved