Search

Here we review recent studies of the cortical circuits subserving the control of posture and movement. This topic is addressed from neurophysiological and evolutionary perspectives describing recent advancements achieved through experimental studies in humans and non-human primates. We also describe current debates and controversies concerning motor mapping within the motor cortex and the different computational approaches aimed at resolving the mystery around motor representations and computations. In recent years there is growing interest in the possibly modular organization of motor representations and dynamical processes and the potential of such studies to provide new clues into motor information processing. Hence this review focuses on motor modularity, highlighting the new research directions inspired by empirical findings and theoretical models developed within the last several years., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Muscle synergies have been proposed as a mechanism to simplify movement control. Whether these coactivation patterns have any physiological reality within the nervous system remains unknown. Here we applied electrical microstimulation to motor cortical areas of rhesus macaques to evoke hand movements. Movements tended to converge toward particular postures, driven by synchronous bursts of muscle activity. Across stimulation sites, the muscle activations were reducible to linear sums of a few basic patterns-each corresponding to a muscle synergy evident in voluntary reach, grasp, and transport movements made by the animal. These synergies were represented nonuniformly over the cortical surface. We argue that the brain exploits these properties of synergies-postural equivalence, low dimensionality, and topographical representation-to simplify motor planning, even for complex hand movements., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Neurons in the primary motor cortex (M1) have been shown to have persistent, memory-like activity following adaptation to altered movement dynamics. However, the techniques used to study these memory traces limited recordings to only single sessions lasting no more than a few hours. Here, chronically implanted microelectrode arrays were used to study the long-term neuronal responses to repeated experience with perturbing, velocity-dependent force fields. Force-field-related neuronal activity within each session was similar to that found previously. That is, the directional tuning curves of the M1 neurons shifted in a manner appropriate to compensate for the forces. Next, the across-session behavior was examined. Long-term learning was evident in the performance improvements across multiple force-field sessions. Correlated with this change, the neuronal population had smaller within-session spike rate changes as experience with the force field increased. The smaller within-session changes were a result of persistent across-session shifts in directional tuning. The results extend the observation of memory traces of newly learned dynamics and provide further evidence for the role of M1 in early motor memory formation.

Production of voluntary movements relies critically on the functional integration of several motor cortical areas, such as the primary motor cortex, and the spinal circuitries. Surprisingly, after almost 40 years of research, how the motor cortices specify descending neural signals destined for the downstream interneurons and motoneurons has remained elusive. In light of the many recent experimental demonstrations that the motor system may coordinate muscle activations through a linear combination of muscle synergies, we hypothesize that the motor cortices may function to select and activate fixed muscle synergies specified by the spinal or brainstem networks. To test this hypothesis, we recorded electromyograms (EMGs) from 12-16 upper arm and shoulder muscles from both the unaffected and the stroke-affected arms of stroke patients having moderate-to-severe unilateral ischemic lesions in the frontal motor cortical areas. Analyses of EMGs using a nonnegative matrix factorization algorithm revealed that in seven of eight patients the muscular compositions of the synergies for both the unaffected and the affected arms were strikingly similar to each other despite differences in motor performance between the arms, and differences in cerebral lesion sizes and locations between patients. This robustness of muscle synergies that we observed supports the notion that descending cortical signals represent neuronal drives that select, activate, and flexibly combine muscle synergies specified by networks in the spinal cord and/or brainstem. Our conclusion also suggests an approach to stroke rehabilitation by focusing on those synergies with altered activations after stroke.

The basic hypothesis of producing a range of behaviors using a small set of motor commands has been proposed in various forms to explain motor behaviors ranging from basic reflexes to complex voluntary movements. Yet many fundamental questions regarding this long-standing hypothesis remain unanswered. Indeed, given the prominent nonlinearities and high dimensionality inherent in the control of biological limbs, the basic feasibility of a low-dimensional controller and an underlying principle for its creation has remained elusive. We propose a principle for the design of such a controller, that it endeavors to control the natural dynamics of the limb, taking into account the nature of the task being performed. Using this principle, we obtained a low-dimensional model of the hindlimb and a set of muscle synergies to command it. We demonstrate that this set of synergies was capable of producing effective control, establishing the viability of this muscle synergy hypothesis. Finally, by combining the low-dimensional model and the muscle synergies we were able to build a relatively simple controller whose overall performance was close to that of the system's full-dimensional nonlinear controller. Taken together, the results of this study establish that a low-dimensional controller is capable of simplifying control without degrading performance.

It has been suggested that the motor system may circumvent the difficulty of controlling many degrees of freedom in the musculoskeletal apparatus by generating motor outputs through a combination of discrete muscle synergies. How a discretely organized motor system compensates for diverse perturbations has remained elusive. Here, we investigate whether motor responses observed after an inertial-load perturbation can be generated by altering the recruitment of synergies normally used for constructing unperturbed movements. Electromyographic (EMG, 13 muscles) data were collected from the bullfrog hindlimb during natural behaviors before, during, and after the same limb was loaded by a weight attached to the calf. Kinematic analysis reveals the absence of aftereffect on load removal, suggesting that load-related EMG changes were results of immediate motor pattern adjustments. We then extracted synergies from EMGs using the nonnegative matrix factorization algorithm and developed a procedure for assessing the extent of synergy sharing across different loading conditions. Most synergies extracted were found to be activated in all loaded and unloaded conditions. However, for certain synergies, the amplitude, duration, and/or onset time of their activation bursts were up- or down-modulated during loading. Behavioral parameterizations reveal that load-related modulation of synergy activations depended on the behavioral variety (e.g., kick direction and amplitude) and the movement phase performed. Our results suggest that muscle synergies are robust across different dynamic conditions and immediate motor adjustments can be accomplished by modulating synergy activations. An appendix describes the novel procedure we developed, useful for discovering shared and specific features from multiple data sets.

In this chapter we investigate the role of motor cortex in adapting movements to novel dynamic environments. We present results from two experiments in which monkey or human subjects learned to make two-dimensional reaching movements while holding a robotic manipulandum that applied a predictable pattern of forces (a curl field) to their hand. In the first study, we analyzed electrophysiological data recorded in motor cortex while monkeys adapted or readapted to the novel forces on each day of the experiment. In the second study, we perturbed the excitability of motor cortex using repetitive transcranial magnetic stimulation (rTMS) as human participants adapted to the forces. From the first experiment, we present qualitative evidence that a network of cortical areas including the supplementary motor area, premotor cortex, and primary motor cortex (M1) not only encodes kinematic and dynamic parameters of motor execution, but also registers changes in encoding that could provide a substrate for motor memory. Based on the second experiment, we qualify the role of M1 in motor memory, by showing that its disruption by rTMS does not interfere with the process of initial motor adaptation, but rather with offline improvement as measured at retest on the following day.

We review experiments supporting the hypothesis that the vertebrate motor system produces movements by combining a small number of units of motor output. Using a variety of approaches such as microstimulation of the spinal cord, NMDA iontophoresis, and an examination of natural behaviors in intact and deafferented animals we have provided evidence for a modular organization of the spinal cord. A module is a functional unit in the spinal cord that generates a specific motor output by imposing a specific pattern of muscle activation. Such an organization might help to simplify the production of movements by reducing the degrees of freedom that need to be specified.

Sensorimotor adaptation and sequence learning have often been treated as distinct forms of motor learning. But frequently the motor system must acquire both types of experience simultaneously. Here, we investigated the interaction of these two forms of motor learning by having subjects adapt to predictable forces imposed by a robotic manipulandum while simultaneously reaching to an implicit sequence of targets. We show that adaptation to novel dynamics and learning of a sequence of movements can occur simultaneously and without significant interference or facilitation. When both conditions were presented simultaneously to subjects, their trajectory error and reaction time decreased to the same extent as those of subjects who experienced the force field or sequence independently.

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.

Humans adaptively control reaching movements to maintain good performance in the presence of novel forces acting on the arm. A recent study suggested that motor memories of different force conditions are not transformed from fragile to stable states, but rather are always vulnerable to interference from newly learned conditions (Caithness et al., 2004). This is contrary to the results of previous studies (Brashers-Krug et al., 1996; Shadmehr and Brashers-Krug, 1997), although all of these studies followed similar methods. Here, we show that a seemingly insignificant and inconsistently applied methodological detail may reconcile this discrepancy. Catch trials, in which the novel forces are removed, may be randomly interspersed among the more frequent force trials to assess how a subject is learning to predict the pattern of forces. In the absence of an interfering condition, subjects retained their learning until retest a day later regardless of whether they experienced catch trials. But in the presence of an interfering condition, only the subjects who had experienced forces intermittently retained their learning and thereby showed resistance to the interference. Thus, intermittent rather than constant practice conditions appear to be critical for dynamic motor memory stabilization.

We investigated how neurons in the different motor areas of the frontal lobe reflect the movement dynamics, and how their neuronal activity undergoes plastic changes when monkeys adapt to perturbing forces (they learn new dynamics). Here we describe the results obtained in the dorsal premotor area (PMd) and ventral premotor area (PMv). Monkeys performed visually instructed, delayed reaching movements before, during and after exposure and adaptation to a viscous, curl force field. During movement planning (i.e., during an instructed delay that followed the cue and preceded the go signal), we found dynamics-related activity in PMd but not in PMv. A closer analysis revealed that the population of PMd reflected the dynamics of the upcoming movement increasingly over the course of the delay, starting from a kinematics-related signal. During movement execution, dynamics-related activity was present in both PMd and PMv. In this respect, the results for PMd were similar to that previously found for the supplementary motor area (SMA) whereas the results for PMv were more similar to that previously found for the primary motor cortex (M1). Plastic changes associated with the acquisition of new dynamics found in PMd and PMv were qualitatively similar to those previously observed in M1 and SMA. The ensemble of our experiments suggest a broader picture of the cortical control of movements, whereby multiple areas all contribute to the various sensorimotor processes, including "low" computations such as the movement dynamics, but also express a degree of specialization.

The mechanical stability properties of hindlimb-hindlimb wiping movements of the spinalized frog were examined. One hindlimb, the wiping limb, was implanted with 12 electromyographic (EMG) electrodes and attached to a robot that both recorded its trajectory and applied brief force perturbations. Cutaneous electrical stimulation was applied to the other hindlimb, the target limb, to evoke the hindlimb-hindlimb wiping reflex. Kinematic and EMG data from both unperturbed trials and trials in which a phasic perturbation was applied were collected from each spinalized frog. In the perturbed behaviors, we found that the initially large displacement attributable to the perturbation was compensated such that the final position was statistically indistinguishable from the unperturbed final position in all of the frogs, thus indicating the dynamic stability of these movements. This stability was robust to the range of perturbation amplitudes and nominal kinematic variation observed in this study. In addition, we investigated the extent to which intrinsic viscoelastic properties of the limb and proprioceptive feedback play a role in stabilizing the movements. No significant changes were seen in the EMGs after the perturbation. Furthermore, deafferentation of the wiping limb did not significantly affect the stability of the wiping reflex. Thus, we found that the intrinsic viscoelastic properties of the hindlimb conferred robust stability properties to the hindlimb-hindlimb wiping behavior. This stability mechanism may simplify the control required by the frog spinal motor systems to produce successful movements in an unpredictable and varying environment.

Selecting the appropriate muscle pattern to achieve a given goal is an extremely complex task because of the dimensionality of the search space and because of the nonlinear and dynamical nature of the transformation between muscle activity and movement. To investigate whether the central nervous system uses a modular architecture to achieve motor coordination we characterized the motor output over a large set of movements. We recorded electromyographic activity from 13 muscles of the hind limb of intact and freely moving frogs during jumping, swimming, and walking in naturalistic conditions. We used multidimensional factorization techniques to extract invariant amplitude and timing relationships among the muscle activations. A decomposition of the instantaneous muscle activations as combinations of nonnegative vectors, synchronous muscle synergies, revealed a spatial organization in the muscle patterns. A decomposition of the same activations as a combination of temporal sequences of nonnegative vectors, time-varying muscle synergies, further uncovered specific characteristics in the muscle activation waveforms. A mixture of synergies shared across behaviors and synergies for specific behaviors captured the invariances across the entire dataset. These results support the hypothesis that the motor controller has a modular organization.

It is widely acknowledged that movements are planned at the level of the kinematics. However, the central nervous system must ultimately transform kinematic plans into dynamics-related commands. How, when, and where the kinematics-to-dynamics (KD) transformation is processed represent fundamental and unanswered questions. We recorded from the supplementary motor area (SMA) of two monkeys as they executed visually instructed reaching movements. We specifically analyzed a delay period following the instruction but prior to the go signal (motor planning). During the delay, a group of neurons in the SMA progressively came to reflect the dynamics rather than the desired kinematics of the upcoming movement. This finding suggests that some neurons in the SMA participate in the KD transformation.

The vertebrate nervous system produces a wide range of movement flexibly and efficiently, even though the simplest of these movements is potentially highly complex. The strategies by which the nervous system overcomes these complexities have therefore been of interest to motor physiologists for decades. In this review, the authors present a number of recent experiments that propose one strategy by which the nervous system might simplify the production of movement. These experiments suggest that spinal motor systems are organized in terms of a small number of distinct motor responses, or "modules." These distinct modules can be combined together simply to produce a wide range of different movements. Such a modular organization of spinal motor systems can potentially allow the nervous system to produce a wide range of natural behaviors in a simple and flexible manner.

We review here experiments examining the hypothesis that vertebrate spinal motor systems produce movement through the flexible combination of a small number of units of motor output. Using a variety of preparations and techniques, these experiments provide evidence for such spinally generated units and for the localization of the networks responsible for producing them within different regions of the spinal cord. Such an organization might help to simplify the production of movement, reducing the degrees of freedom that need to be specified by providing a set of units involved in regulating features common to a range of behaviors.

We used a computational analysis to identify the basic elements with which the vertebrate spinal cord constructs one complex behavior. This analysis extracted a small set of muscle synergies from the range of muscle activations generated by cutaneous stimulation of the frog hindlimb. The flexible combination of these synergies was able to account for the large number of different motor patterns produced by different animals. These results therefore demonstrate one strategy used by the vertebrate nervous system to produce movement in a computationally simple manner.

Psychophysical evidence shows that the planning of an arm trajectory is specified by the central nervous system in extrinsic coordinates. The complex issue of translating the planning of arm movements into muscle forces is discussed in relation to the recent discovery of structures in the brainstem and in the spinal cord. These structures represent discrete maps of motor behavior. Remarkably, the force outputs, produced by activating different zones of the map, sum vectorially. This vectorial combination of motor outputs is a mechanism for producing a vast repertoire of motor behaviors in a simple fashion.

When a perturbation displaces the human hand from equilibrium, arm muscles respond by producing restoring forces. When a set of displacements are given at various directions from the same equilibrium position, the resulting restoring forces form a "postural force field." It is not known whether these postural forces are related to those generated when a reaching movement is executed. However, if a movement is a consequence of a shift of the equilibrium position of the hand toward the target, then, from the postural force field, predictions can be made regarding the nature of the elastic forces acting on the hand during the movement. We have taken the first steps in testing this hypothesis by measuring the postural force field of a subject's arm over relatively large distances, and comparing these forces with the static forces generated at the hand while the subject attempted a reaching movement. Using a robot manipulandum, the hand was displaced at various directions from an equilibrium position. The measured restoring forces were fitted to a nonlinear model to define a postural force field for that equilibrium position. This field was used to predict elastic forces generated when the subject attempted to move the manipulandum from a point on the circumference of a circle to a target at its center--the center corresponded to the equilibrium position at which the postural field was measured. In some of the movement trials, the manipulandum was locked during approximately the first 120 msec of the program for motion and the resulting static "evoked" forces measured. We found that (1) the evoked forces did not point to the target, but were a function of the configuration of the arm and rotated with the shoulder joint, and (2) the magnitude of the evoked forces varied systematically, even though the movements were of the same magnitude. These patterns were remarkably similar to those observed in the postural forces. Our results provide experimental evidence linking maintenance of posture in a multijoint system to that of generating a movement. The evidence is consistent with the hypothesis that the CNS programs a reaching movement by shifting the equilibrium position of the hand toward the target.

To execute voluntary movements, the central nervous system must transform the neural representation of the direction, amplitude, and velocity of the limb, represented by the activity of cortical and subcortical neurons, into signals that activate the muscles that move the limb. This task is equivalent to solving an "ill-posed" computational problem because the number of degrees of freedom of the musculoskeletal apparatus is much larger than that specified in the plan of action. Some of the mechanisms and circuitry underlying the transformation of motor plans into motor commands are described. A central feature of this transformation is a coarse map of limb postures in the premotor areas of the spinal cord. Vectorial combination of motor outputs among different areas of the spinal map may produce a large repertoire of motor behaviors.

Eye and head movements during tracking of a smoothly moving visual target were recorded in trained monkeys. The head movement clearly followed the target, although with considerable variability from cycle to cycle. The eye stayed relatively near the primary position and moved in an apparently irregular fashion; however, the sum of eye and head, or gaze, remained accurately on target despite the irregularity of the individual eye and head movements. When compared with tracking with head fixed, head free tracking was not measurably different in accuracy. Further experiments were performed which demonstrated a role for the vestibular system in coordinating eye and head during smooth pursuit. The results of these experiments can be best explained by postulating an internal smooth pursuit command driving both eye and head movements. In the case of the eye movement, this smooth pursuit command is combined with vestibular feedback from head movement before being forwarded to eye movement centers.

One hypothesis for the generation of spatially oriented arm movements by the central nervous system is that a desired joint position is determined by the ratio of the tensions of agonist and antagonist muscles. According to this hypothesis, the transition between equilibrium states should be solely a function of the contraction time of the motor units and the mechanical properties of the arm. We tested this hypothesis in intact and deafferented rhesus monkeys by holding the forearm and measuring the accelerative transient after release of the forearm and by directly measuring the time course of the increase in torque during the movement. Both methods indicated an average time of 400 msec for attaining peak torque in a movement with a duration of 700 msec. In addition, by displacing the arm from its normal trajectory during the movement, we observed that the arm returned neither to the initial nor to the final equilibrium positions, but to points intermediate between them. We conclude that the processes underlying trajectory formation must be more complex than a simple switch between one equilibrium position and another.

In these experiments we studied the organization of the neural pattern of activity underlying the achievement and maintenance of final limb position. The examination of the alpha motoneuronal inputs to flexors and extensors acting on the elbow and wrist joints revealed that a change from one posture to another required a modulation of both flexors and extensors. For a given position, the final EMG level of flexors and extensors is more variable than the ratio between the alpha neuronal activity of these two antagonistic muscles. Further, the ratio was not significantly affected by the direction, amplitude or velocity of the movement. These results indicate that the final position could be coded in the central nervous system (CNS) as the ratio of activity in antagonistic muscles acting on a joint.

The experiments identify some of the processes underlying arm movements in rhesus monkeys. Three monkeys were trained to point to a target with the hand and forearm and to hold that position for about 1 second to obtain a reward. Forearm movements were performed without sight of the arm before and after bilateral dorsal rhizotomy. In both intact and deafferented animals, we unexpectedly displaced the forearm prior to movement initiation and observed that the arm moved accurately to the target. These results are relevant to the question of what is being controlled by motor commands. The controlled variable appears to be an equilibrium point between agonist and antagonist muscles. The findings suggest that the feedback system plays a major role in updating and adjusting the central programs subserving the execution of learned motor patterns.

This paper deals with the problem of representing and generating unconstrained aiming movements of a limb by means of a neural network architecture. The network produced time trajectories of a limb from a starting posture toward targets specified by sensory stimuli. Thus the network performed a sensory-motor transformation. The experimenters trained the network using a bell-shaped velocity profile on the trajectories. This type of profile is characteristic of most movements performed by biological systems. We investigated the generalization capabilities of the network as well as its internal organization. Experiments performed during learning and on the trained network showed that: (i) the task could be learned by a three-layer sequential network; (ii) the network successfully generalized in trajectory space and adjusted the velocity profiles properly; (iii) the same task could not be learned by a linear network; (iv) after learning, the internal connections became organized into inhibitory and excitatory zones and encoded the main features of the training set; (v) the model was robust to noise on the input signals; (vi) the network exhibited attractor-dynamics properties; (vii) the network was able to solve the motor-equivalence problem. A key feature of this work is the fact that the neural network was coupled to a mechanical model of a limb in which muscles are represented as springs. With this representation the model solved the problem of motor redundancy.

1. The experiments described here are addressed at identifying some of the processes underlying arm movements in monkeys. 2. We used three adult monkeys that were trained to point to a target light with the forearm and hold at that position for about 1 s in order to obtain a reward. During the experimental sessions the monkey was seated in a primate chair and its forearm was fastened to an apparatus that permitted flexion and extension of the forearm about the elbow in the horizontal plane. 3. We tested their performance prior to and after bilateral dorsal rhizotomy (C2--T3). Forearm movements were performed without the sight of the arm both before and after the surgical intervention. In intact animals we unexpectedly displaced the arm prior to movement initiation (150--200 ms) and observed the outcome of this displacement on movement termination. Our results indicated that the arm moved accurately to the target. The same procedure was used in the deafferented monkeys, yielding qualitatively the same results; i.e., a displacement of the initial position did not affect the attainment of the intended final position. 4. These results are relevant to the question of what is being controlled by motor commands. It appears that the controlled variable is an equilibrium point resulting from the interaction of agonist and antagonist muscles. Consequently, a change in the equilibrium leads to movement and the attainment of a new posture. The fact that both intact and deafferent monkeys display essentially similar motor behavior in our highly practiced task should not obliterate the dramatic difference in motor performance that exists between intact and rhizotomized animals. In fact, the successful execution of the learned motor performance in the deafferented animal is contingent on the animal's body being in a fixed relation to the arm apparatus. Whenever we changed the usual spatial relationship between the monkey's body and the arm apparatus, the animal's pointing response to the target was inaccurate. All of our intact monkeys, in contrast, were able to compensate quickly for any variations in their accustomed position with respect to the arm apparatus. The dramatic inability of the deafferented monkey to execute accurate pointing responses in an unusual postural setting underscores the great importance of the afferent monkey to execute accurate pointing responses in an unusual postural settiing underscores the great importance of the afferent feedback. These findings suggest that, in the performance of visually evoked learned movements, one of the major functions of the afferent feedback is in the adaptive modifications of learned motor programs.