Reciprocal and coactivation commands for fast wrist movements

Reciprocal and coactivation commands at the level of individual motor units in an extrinsic finger flexor-extensor muscle pair

We explored the synergic organization of motor units in extrinsic finger muscles, flexor digitorum superficialis (FDS), and extensor digitorum communis (EDC). Healthy subjects produced accurate cyclical force by pressing with the middle phalanges of one of the three fingers (Index, Middle, and Ring) and all three together. Two wireless sensor arrays were used to record and identify motor unit action potentials in FDS and EDC. Stable motor unit groups were identified within each muscle and across both muscles. Analysis of motor units combined over the two muscles showed one of the first two motor unit groups with consistently opposite signs of the loading factors for the FDS and EDC motor units, and the other group with consistently same signs of the loading factors for the two muscles. We interpret the two motor unit groups as reflections of the reciprocal and co-activation commands within the theory of control with spatial referent coordinates. Force changes within the cycle were primarily associated with the modulation of the co-activation motor unit group. Analysis of inter-cycle variance within the spaces of motor unit groups defined for FDS and EDC separately showed force-stabilizing synergies across both single-finger and three-finger tasks. In contrast, analysis within the motor unit groups defined across both muscles failed to show force-stabilizing synergies. We interpret these results as a reflection of the trade-off across levels within a hierarchical control system.

Central pattern generator and human locomotion in the context of referent control of motor actions

Unperturbed human locomotion presumably results from feedforward shifts in stable body equilibrium in the environment, thus avoiding falling and subsequent catching considered in alternative theories of locomotion. Such shifts are achieved by relocation of the referent body configuration at which multiple muscle recruitment begins. Rather than being directly specified by a central pattern generator, multiple muscles are activated depending on the extent to which the body is deflected from the referent, threshold body configuration, as confirmed in previous studies. Based on the referent control theory of action and perception, solutions to classical problems in motor control are offered, including the previously unresolved problem of the integration of central and reflex influences on motoneurons and the problem of how posture and movement are related. The speed of locomotion depends on the rate of shifts in the referent body configuration. The transition from walking to running results from increasing the rate of referent shifts. It is emphasised that there is a certain hierarchy between reciprocal and co-activation of agonist and antagonist muscles during locomotion and other motor actions, which is also essential for the understanding of how locomotor speed is regulated. The analysis opens a new avenue in neurophysiological approaches to human locomotion with clinical implications.

Effects of body weight support and guidance force settings on muscle synergy during Lokomat walking

BACKGROUND

The Lokomat is a robotic device that has been suggested to make gait therapy easier, more comfortable, and more efficient. In this study, we asked whether the Lokomat promotes physiological muscle activation patterns, a fundamental question when considering motor learning and adaptation.

METHODS

We investigated lower limb muscles coordination in terms of muscle activity level, muscle activity pattern similarity, and muscle synergy in 15 healthy participants walking at 3 km/h on either a treadmill or in a Lokomat at various guidance forces (GF: 30, 50 or 70%) and body weight supports (BWS: 30, 50 or 70% of participant's body weight).

RESULTS

Walking in the Lokomat was associated with a greater activation level of the rectus femoris and vastus medialis (×2-3) compared to treadmill walking. The level of activity tended to be diminished in gastrocnemius and semi-tendinosus, which particularly affected the similarity with treadmill walking (normalized scalar product NSP = 0.7-0.8). GF and BWS independently altered the muscle activation pattern in terms of amplitude and shape. Increasing BWS decreased the level of activity in all but one muscle (the soleus). Increasing GF slightly improved the similarity with treadmill walking for the tibialis anterior and vastus medialis muscles. The muscle synergies (N = 4) were similar (NSP = 0.93-0.97), but a cross-validation procedure revealed an alteration by the Lokomat. The activation of these synergies differed (NSP = 0.74-0.82).

CONCLUSION

The effects of GF and BWS are modest compared to the effect of the Lokomat itself, suggesting that Lokomat design should be improved to promote more typical muscle activity patterns.

Differential neural correlates of reciprocal activation and cocontraction control in dorsal and ventral premotor cortices

Efficient control of reciprocal activation and cocontraction of the muscles are critical to perform skillful actions with suitable force and impedance. However, it remains unclear how the brain controls force and impedance while recruiting the same set of muscles as actuators. Does control take place at the single muscle level leading to force and impedance, or are there higher-order centers dedicated to controlling force and impedance? We addressed this question using functional MRI during voluntary isometric wrist contractions with online electromyogram feedback. Comparison of the brain activity between the conditions requiring control of either wrist torque or cocontraction demonstrates that blood oxygen level-dependent activity in the caudo-dorsal premotor cortex (PMd) correlates well with torque, whereas the activity in the ventral premotor cortex (PMv) correlates well with the level of cocontraction. This suggests distinct roles of the PMd and PMv in the voluntary control of reciprocal activation and cocontraction of muscles, respectively.

What does body configuration in microgravity tell us about the contribution of intra- and extrapersonal frames of reference for motor control?

The authors report that the reorganization of body configuration during weightlessness is based on an intrapersonal frame of reference such as the configuration of the support surface and the position of the body's center of gravity. These results stress the importance of “knowledge” of the state of internal geometric structures, which cannot be directly signalled by specific receptors responsible for direct dialogue with the physical external world.

Reciprocal and coactivation commands are not sufficient to describe muscle activation patterns

Recent results have shown that the relative activation of muscles is different for isometric contractions and for movements. These results exclude an explanation of muscle activation patterns by a combination ofreciprocal and coactivation commands. These results also indicate that joint stiffness is not uniquely determined and that it may be different for isometric contractions and movements.

Frames of reference interact and are task-dependent

The problem for the CNS in any particular movement task is to coordinate the various frames of reference appropriate to the task. Control variables are determined by this coordination. The coordination problem varies greatly from task to task, and so no single set of control variables is likely to account for a broad range of movement tasks.

The λ model for motor control: More than meets the eye

Understanding of the λ model has greatly increased in recent years as evidenced by most of the commentaries. Some commentators underscored the potential of the model to integrate aspects of different sensorimotor systems in the production of movement. Other commentators focused on not-yet-fully-developed parts of the model. A few persisted in misunderstanding some of its basic concepts, and on these grounds they reject it. In responding to commentaries we continue to elaborate on some fundamental points of the model, especially control variables, the idea of movement production by shifting the positional frame of reference and the hypothesis of biomechanical correspondence in motor control. We also continue to develop our ideas on the intrinsic generation of the frame of reference associated with external space and utilized for the control of arm movement and locomotion. The dynamic principles underlying the model are discussed in light of the dynamical systems approach.

Grip force adjustments during rapid hand movements suggest that detailed movement kinematics are predicted

The λ model suggests that detailed kinematics arise from changes in control variables and need not be explicitly planned. However, we have shown that when moving a grasped object, grip force is precisely modulated in phase with acceleration-dependent inertial load. This suggests that the motor system can predict detailed kinematics. This prediction may be based on a forward model of the dynamics of the loaded limb.

Let us accept a “controlled trade-off” model of motor control

The trade-off between force and length of muscle as adjusted by neural signals is a critical fact in the dynamics of motor control. Whether we call it “length-tension effect,” “feedback-like,” “invariant condition,” or “spring-like” is unimportant. We must not let semantics or details of representation obscure the basic physics of effects introduced by this trade-off in muscle.

Equilibrium-point control? Yes! Deterministic mechanisms of control? No!

The equilibrium-point hypothesis (the λ-model) is superior to all other models of single-joint control and provides deep insights into the mechanisms of control of multi-joint movements. Attempts at associating control variables with neurophysiological variables look confusing rather than promising. Probabilistic mechanisms may play an important role in movement generation in redundant systems.

The unobservability of central commands: Why testing hypotheses is so difficult

The experiments Feldman and Levin suggest do not definitively test their proposed solution to the problem of selecting muscle activations. Their test of the movement directions that elicit EMG activity can be interpreted without regard to the form of the central commands, and their fast elbow flexion test is based on a forward computation that obscures the insensitivity of the predicted trajectory to the details of the putative commands.

Biological variability and control of movements via δλ

Three issues related to Feldman and Levin's treatment of biological variability are discussed. We question the usefulness of the indirect component of δλ. We suggest that trade-offs between speed and accuracy in aimed movements support identification of δλ, rather than λ, as a control variable. We take issue with the authors' proposal for resolving redundancy in multi-joint movements, given recent data.

A few reasons why psychologlsts can adhere to Feldman and Levin's model

We emphasize the relevance to cognitive psychology of Feldman and Levin's theoretical position. Traditional views of motor control have failed to clearly separate “production control” at the level of motor command, based on task-independent CV (control variables), from intentional “product control” based on task-dependent parameters. Because F&L's approach concentrates on the first process (trajectory formation), it can distinguish the product control stage.

Levers to generate movement

The following questions are discussed: (1) Who determines the nature of “control variables”? (2) Is the “positional monopoly” healthy? (3) Does a descending command alter reflex threshold alone without eoncomitantly altering stiffness? (4) How does the CNS deal with history-dependent effects? (5) Should we abandon the idea that the CNS controls classical Newtonian variables such as muscle length?

Kinematic invariances and body schema

Generalizing the notion that muscles are positional frames of reference, a high-dimensional muscle space is defined for multi-muscle systems with an embedded low-dimensional motor manifold of functional articulators. A central representation of such a manifold is proposed as computational body schema. The example of the jaw-tongue system is presented, discussing the relation of functional articulators with kinematic invariances and control problems.

Tendon elasticity and positional control

The spring-like behaviour of a joint following a sudden change of torque is partly a result of the elastic properties of tendons. A large fall in a muscle with a long tendon may be accompanied by tendon recoil causing joint movements as large as 20°.

Command invariants and the frame of reference for human movement

We describe a solution to the redundancy problem related to that proposed in Feldman & Levin's target article. We suggest that the system may use a fixed mapping between commands organized at the level of degrees of freedom and commands to individual muscles. This proposal eliminates the need to maintain an explicit representation of musculoskeletalgeometry in planning movements.

Frameworks on shifting sands

Feldman and Levin present a model for movement control in which the system is said to seek equilibrium points, active movement being produced by shifting frames of reference in space. It is argued that whatever merit this model might have is limited to an understanding of “the how” and not “the why” we move. In this way the authors seem to be forced into a dualistic position leaving the upper level of the proposed control hierarchy “floating.”

Conservative or nonconservative control schemes

The conservative strategy proposed by the authors suggests a solution of the degrees-of-freedom problem of the controller. However, several simple motor control tasks cannot be explained by this strategy. A nonconservative strategy, in which more parameters of the control signal vary, can account for these simple motor tasks. However, the simplicity that distinguishes the proposed model from many others is lost.

The origin and use of positional frames of reference in motor control

A hypothesis about sensorimotor integration (the λ model) is described and applied to movement control and kinesthesia. The central idea is that the nervous system organizes positional frames of reference for the sensorimotor apparatus and produces active movements by shifting the frames in terms of spatial coordinates. Kinematic and electromyographic patterns are not programmed, but emerge from the dynamic interaction among the system s components, including external forces within the designated frame of reference. Motoneuronal threshold properties and proprioceptive inputs to motoneurons may be cardinal components of the physiological mechanism that produces positional frames of reference. The hypothesis that intentional movements are produced by shifting the frame of reference is extended to multi-muscle and multi-degrees-of-freedom systems with a solution of the redundancy problem that allows the control of a joint alone or in combination with other joints to produce any desired limb configuration and movement trajectory. The model also implies that for each motor behavior, the nervous system uses a strategy that minimizes the number of changeable control variables and keeps the parameters of these changes invariant. Examples are provided of simulated kinematic and electromyographic signals from single- and multi-joint arm movements produced by suggested patterns of control variables. Empirical support is provided and additional tests of the model are suggested. The model is contrasted with others based on the ideas of programming of motoneuronal activity, muscle forces, stiffness, or movement kinematics.

Origins of origins of motor control

Examination of infant spontaneous and goal-directed arm movements supports Feldman and Levin's hypothesis of a functional hierarchy. Early infant movements are dominated by biomechanical and dynamic factors without external frames of reference. Development involves not only learning to generate these frames of reference, but also protecting the higher-level goal of the movement from internal and external perturbations.

Spatial frames for motor control would be commensurate with spatial frames for vision and proprioception, but what about control of energy flows?

The model identifies a spatial coordinate frame within which the sensorimotor apparatus produces movement. Its spatial nature simplifies its coupling with spatial reference frames used concurrently by vision and proprioception. While the positional reference frame addresses the performance of spatial tasks, it seems to have little to say about movements involving energy expenditure as the principle component of the task.

Interneurons as backseat drivers and the elusive control variable

It is proposed here that the spinal network of proprioceptive feedback from length and force receptors constitutes the mechanism underlying the coordination of activation thresholds for muscles acting about the same and neighboring joints. For the most part, these circuits come between motoneurons and supraspinal signals, invalidating the idea that the activation thresholds constitute control variables for the motor system.

Two joints are more than twice one joint

An alternative multi-joint extension to the lambda model is proposed. According to this extension, the activity of a muscle depends not only on the difference between lambda and length of that muscle, but also on the difference between lambda and length of other muscles. This 2-D extension can describe more neurophysiological experiments than the extension proposed in the target article.

Control parameters, equilibria, and coordination dynamics

Important similarities exist between the dynamical concepts implicit in Feldman & Levin's extended λ model and those basic to a dynamical systems approach. We argue that careful application of the key concepts of control and order parameters, equilibria, and stability, can relate known facts of neuromuscular processes to the observables of functional, task-specific behavior.

Shifting frames of reference but the same old point of view

Models of central control variables (CVs) that are expressed in positional reference frames and rely on proprioception as the dominant specifier of muscle activation patterns have not yet been shown to be adequate for the description of fast, voluntary movement, even of single joints. An alternative model with illustrative data is proposed.

Inverse kinematic problem: Solutions by pseudoinversion, inversion and no-inversion

Kinematic properties of reaching movements reflect constraints imposed on the joint angles. Contemporary models present solutions to the redundancy problem by a pseudoinverse procedure (Whitney 1969) or without any inversion (Berkenblit et al. 1986). Feldman & Levin suggest a procedure based on a regular inversion. These procedures are considered as an outcome of a more general approach.

Natural unconstrained movements obey rules different from constrained elementary movements

The concept of a conservative control strategy minimizing the number of degrees of freedom used is criticised with reference to 3-D simple reaching and grasping experiments. The vector error in a redundant system would not be the prime controlled variable, but rather the posture for reaching, as exemplified by nearly straight displacements in joint space as opposed to curved ones in task space.

Is λ an appropriate control variable for locomotion?

The lambda model predicts that the command received by each motor nucleus during locomotion is specific for the joint at which its muscle acts and is independent of external conditions. However, investigation of the commands received by motor nuclei during fictive locomotion and of the sensitivity of these commands to feedback from the limb during locomotion indicates that neither condition is satisfied.

How far should we extend the equilibrium point (lambda) hypothesis?

A key feature of the lambda model is the hypothesis of a local spring-like muscle-reflex system defined by a central control variable that has units of position. This is intriguing, especially for a study of postural stability in large-scale systems, but it has limited direct application to skilled everyday movements. If movement is considered as a goal-directed, neuro-optimization problem, however, theavailabilityof lambda-like peripheral models (vs. conventional musculoskeletal models) deserves exploration.

How are internal models of unstable tasks formed?

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

Adaptation to Stable and Unstable Dynamics Achieved By Combined Impedance Control and Inverse Dynamics Model

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Different Mechanisms Involved in Adaptation to Stable and Unstable Dynamics

Recently, we demonstrated that humans can learn to make accurate movements in an unstable environment by controlling magnitude, shape, and orientation of the endpoint impedance. Although previous studies of human motor learning suggest that the brain acquires an inverse dynamics model of the novel environment, it is not known whether this control mechanism is operative in unstable environments. We compared learning of multijoint arm movements in a “velocity-dependent force field” (VF), which interacted with the arm in a stable manner, and learning in a “divergent force field” (DF), where the interaction was unstable. The characteristics of error evolution were markedly different in the 2 fields. The direction of trajectory error in the DF alternated to the left and right during the early stage of learning; that is, signed error was inconsistent from movement to movement and could not have guided learning of an inverse dynamics model. This contrasted sharply with trajectory error in the VF, which was initially biased and decayed in a manner that was consistent with rapid feedback error learning. EMG recorded before and after learning in the DF and VF are also consistent with different learning and control mechanisms for adapting to stable and unstable dynamics, that is, inverse dynamics model formation and impedance control. We also investigated adaptation to a rotated DF to examine the interplay between inverse dynamics model formation and impedance control. Our results suggest that an inverse dynamics model can function in parallel with an impedance controller to compensate for consistent perturbing force in unstable environments.

Coactivation in arm and shoulder muscles during voluntary fixation of a single joint

The objective of this study was to determine the organization of coactivation in the arm and shoulder muscles. Normal human subjects made alternate movements of a joint in the horizontal plane, either adduction-abduction of the second finger and shoulder, ulnar-radial deviation of the wrist, or extension-flexion of the elbow, during which they fixed a focal joint while decreasing the movement amplitude and increasing the fixation strength. They varied the fixation strength at four different levels up to the maximum. The focal-joint angle, and surface electromyograms (EMGs) from the intrinsic hand, antebrachial, upper-arm, and shoulder muscles were recorded. EMGs in the phase of fixation were quantified by integration after rectification. The degree of coactivation among the muscles was evaluated by calculating correlation coefficients across the integrated EMGs. There were correlations in the integrated EMGs among focal-joint muscles (FJMs), and also between one of the FJMs and the muscles distal and/or proximal to the FJMs: in the finger fixation between the hand and antebrachial muscles, in the wrist fixation between the antebrachial and hand/upper-arm muscles, in the elbow fixation between the upper-arm and antebrachial/shoulder muscles, and in the shoulder fixation between the shoulder and upper-arm muscles. Moderate or slight correlations were seen in muscles more distant from FJMs. Our results indicate that the longitudinal distance from FJMs in the shoulder and arm muscles is an important factor in determining levels of coactivation. This is discussed in relation to the fact that neighboring muscles share joints with FJMs.