Efference copy in kinesthetic perception: A copy of what is it?

A number of notions in the fields of motor control and kinesthetic perception have been used without clear definitions. In this review, we consider definitions for efference copy, percept, and sense of effort based on recent studies within the physical approach, which assumes that the neural control of movement is based on principles of parametric control and involves defining time-varying profiles of spatial referent coordinates for the effectors. The apparent redundancy in both motor and perceptual processes is reconsidered based on the principle of abundance. Abundance of efferent and afferent signals is viewed as the means of stabilizing both salient action characteristics and salient percepts formalized as stable manifolds in high-dimensional spaces of relevant elemental variables. This theoretical scheme has led recently to a number of novel predictions and findings. These include, in particular, lower accuracy in perception of variables produced by elements involved in a multi-element task compared to the same elements in single-element tasks, dissociation between motor and perceptual effects of muscle co-activation, force illusions induced by muscle vibration, and errors in perception of unintentional drifts in performance. Taken together, these results suggest that participation of efferent signals in perception frequently involves distorted copies of actual neural commands, particularly those to antagonist muscles. Sense of effort is associated with such distorted efferent signals. Distortions in efference copy happen spontaneously and can also be caused by changes in sensory signals, e.g., those produced by muscle vibration.

Distinct representations of finger movement and force in human motor and premotor cortices

The ability to grasp and manipulate objects requires controlling both finger movement kinematics and isometric force. Previous work suggests that these behavioral modes are controlled separately, but it is unknown whether the cerebral cortex represents them differently. Here, we investigated this question by recording high-density electrocorticography from the motor and premotor cortices of seven human subjects performing a sequential movement-force motor task. We decoded finger movement (0.7±0.3 fractional variance account for; FVAF) and force (0.7±0.2 FVAF) with high accuracy, yet found different spatial representations. We also found clear distinctions in electrocorticographic activity by using deep learning methods to uncover state-space representations, and by developing a new metric, the neural vector angle. Thus, state-space techniques can help to investigate broad cortical networks. Finally, we were able to classify the behavioral mode from neural signals with high accuracy (90±6%). Thus, finger movement and force have distinct representations in motor/premotor cortices. This will inform our understanding of the neural control of movement as well as the design of grasp brain-machine interfaces.

Stochastic optimal open-loop control as a theory of force and impedance planning via muscle co-contraction

Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail explain a common phenomenon known as muscle co-contraction. Co-contraction of agonist and antagonist muscles contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint stiffness) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to the noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Optimal feedback (closedloop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.Author summaryThis study presents a novel computational theory to explain the planning of force and impedance (e.g. stiffness) in the neural control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor noise. A major outcome of this mathematical framework is the explanation of a long-known phenomenon called muscle co-contraction (i.e. the concurrent contraction of opposing muscles). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal feedback control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.

On task: Considerations and future directions for studies of corticospinal excitability in exercise neuroscience and related disciplines

Over the last few decades, transcranial magnetic stimulation (TMS) has emerged as a conventional laboratory technique in human neurophysiological research. Exercise neuroscientists have used TMS to study central nervous system contributions to fatigue, training, and performance in health, injury, and disease. In such studies, corticospinal excitability is often assessed at rest or during simple isometric tasks with the implication that the results may be extrapolated to more functional and complex movement outside of the laboratory. However, the neural mechanisms that influence corticospinal excitability are both state- and task-dependent. Furthermore, there are many sites of modulation along the pathway from the motor cortex to the muscle; a fact that is somewhat obscured by the all-encompassing and poorly defined term “corticospinal excitability”. Therefore, the tasks we use to assess corticospinal excitability and the conclusions that we draw from such a global measure of the motor pathway must be taken into consideration. The overall objective of this review is to highlight the task-dependent nature of corticospinal excitability and the tools used to assess modulation at cortical and spinal sites of modulation. By weighing the advantages and constraints of conventional approaches to studying corticospinal excitability, and considering some new and novel approaches, we will continue to advance our understanding of the neural control of movement during exercise.