The computational and neural basis of voluntary motor control and planning

Distributed task-specific processing of somatosensory feedback for voluntary motor control

Corrective responses to limb disturbances are surprisingly complex, but the neural basis of these goal-directed responses is poorly understood. Here we show that somatosensory feedback is transmitted to many sensory and motor cortical regions within 25 ms of a mechanical disturbance applied to the monkey’s arm. When limb feedback was salient to an ongoing motor action (task engagement), neurons in parietal area 5 immediately (~25 ms) increased their response to limb disturbances, whereas neurons in other regions did not alter their response until 15 to 40 ms later. In contrast, initiation of a motor action elicited by a limb disturbance (target selection) altered neural responses in primary motor cortex ~65 ms after the limb disturbance, and then in dorsal premotor cortex, with no effect in parietal regions until 150 ms post-perturbation. Our findings highlight broad parietofrontal circuits that provide the neural substrate for goal-directed corrections, an essential aspect of highly skilled motor behaviors.

DOI:http://dx.doi.org/10.7554/eLife.13141.001

Humans and other animals can change a movement they are making in a split second, such as when a basketball player has to move around an unexpected opponent to shoot a ball through the hoop. These on-the-fly corrections rely on information about the movement that comes in from the senses. However, it was unclear how the brain and spinal cord process this sensory information to guide movement.

Omrani et al. have now recorded electrical activity from the brains of monkeys while the animals tried to keep their hand at a target. Each monkey wore a robotic exoskeleton that would occasionally move the monkey’s arm. Even if the monkey was not engaged in a motor task, a small nudge of the limb by the robot caused neural activity to spread rapidly throughout the sensory and motor regions of the cerebral cortex (the outer layer of the brain).

In some trials, when the monkey was actively trying to keep its hand at a target, the robot would again nudge the monkey’s arm slightly. Omrani et al. observed that within 25 milliseconds of this nudge, the activity in an area of the cortex called parietal area 5 responded even more, suggesting that this area was using information from the senses to actively deal with the change in arm position. This change in activity then spread to other parts of the brain.

In another set of trials, the monkey was trained to move to a second target if the robot nudged its arm. In this case, the activity in an area called the primary motor cortex increased even more, likely supporting the monkey’s ability to rapidly move to this second target. Overall, the study by Omrani et al. highlights the complex way that sensory feedback is processed in the cerebral cortex, supporting our ability to perform highly skilled motor actions.

DOI:http://dx.doi.org/10.7554/eLife.13141.002

Long-Latency Feedback Coordinates Upper-Limb and Hand Muscles during Object Manipulation Tasks

Suppose that someone bumps into your arm at a party while you are holding a glass of wine. Motion of the disturbed arm will engage rapid and goal-directed feedback responses in the upper-limb. Although such responses can rapidly counter the perturbation, it is also clearly desirable not to destabilize your grasp and/or spill the wine. Here we investigated how healthy humans maintain a stable grasp following perturbations by using a paradigm that requires spatial tuning of the motor response dependent on the location of a virtual target. Our results highlight a synchronized expression of target-directed feedback in shoulder and hand muscles occurring at ∼60 ms. Considering that conduction delays are longer for the more distal hand muscles, these results suggest that target-directed responses in hand muscles were initiated before those for the shoulder muscles. These results show that long-latency feedback can coordinate upper limb and hand muscles during object manipulation tasks.

The importance of task design and behavioral control for understanding the neural basis of cognitive functions

The success of systems neuroscience depends on the ability to forge quantitative links between neural activity and behavior. Traditionally, this process has benefited from the rigorous development and testing of hypotheses using tools derived from classical psychophysics and computational motor control. As our capacity for measuring neural activity improves, accompanied by powerful new analysis strategies, it seems prudent to remember what these traditional approaches have to offer. Here I present a perspective on the merits of principled task design and tight behavioral control, along with some words of caution about interpretation in unguided, large-scale neural recording studies. I argue that a judicious combination of new and old approaches is the best way to advance our understanding of higher brain function in health and disease.

Primary motor cortex of the parkinsonian monkey: altered encoding of active movement

Abnormalities in the movement-related activation of the primary motor cortex (M1) are thought to be a major contributor to the motor signs of Parkinson's disease. The existing evidence, however, variably indicates that M1 is under-activated with movement, overactivated (due to a loss of functional specificity) or activated with abnormal timing. In addition, few models consider the possibility that distinct cortical neuron subtypes may be affected differently. Those gaps in knowledge were addressed by studying the extracellular activity of antidromically-identified lamina 5b pyramidal-tract type neurons (n = 153) and intratelencephalic-type corticostriatal neurons (n = 126) in the M1 of two monkeys as they performed a step-tracking arm movement task. We compared movement-related discharge before and after the induction of parkinsonism by administration of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) and quantified the spike rate encoding of specific kinematic parameters of movement using a generalized linear model. The fraction of M1 neurons with movement-related activity declined following MPTP but only marginally. The strength of neuronal encoding of parameters of movement was reduced markedly (mean 29% reduction in the coefficients from the generalized linear model). This relative decoupling of M1 activity from kinematics was attributable to reductions in the coefficients that estimated the spike rate encoding of movement direction (-22%), speed (-40%), acceleration (-49%) and hand position (-33%). After controlling for MPTP-induced changes in motor performance, M1 activity related to movement itself was reduced markedly (mean 36% hypoactivation). This reduced activation was strong in pyramidal tract-type neurons (-50%) but essentially absent in corticostriatal neurons. The timing of M1 activation was also abnormal, with earlier onset times, prolonged response durations, and a 43% reduction in the prevalence of movement-related changes beginning in the 150-ms period that immediately preceded movement. Overall, the results are consistent with proposals that under-activation and abnormal timing of movement-related activity in M1 contribute to parkinsonian motor signs but are not consistent with the idea that a loss of functional specificity plays an important role. Given that pyramidal tract-type neurons form the primary efferent pathway that conveys motor commands to the spinal cord, the dysfunction of movement-related activity in pyramidal tract-type neurons is likely to be a central factor in the pathophysiology of parkinsonian motor signs.

Apparent and Actual Trajectory Control Depend on the Behavioral Context in Upper Limb Motor Tasks

A central problem in motor neuroscience is to understand how we select, plan, and control motor actions. An influential idea is that the motor system computes and implements a desired limb trajectory, an intermediary control process between the behavioral goal (reach a spatial goal) and motor commands to move the limb. The most compelling evidence for trajectory control is that corrective responses are directed back toward the unperturbed trajectory when the limb is disturbed during movement. However, the idea of trajectory control conflicts with optimal control theories that emphasize goal-directed motor corrections. Here we show that corrective responses in human subjects can deviate back toward the unperturbed trajectory, but these reversals were only present when there were explicit limits on movement time. Our second experiment asked whether trajectory control could be generated if the trajectory was made an explicit goal of the task. Participants countered unexpected loads while reaching to a static goal, tracking a moving target, or maintaining their hand within a visually constrained path to a static goal. Corrective responses were directed back toward the constrained path or to intercept the moving target. However, corrections back to the unperturbed path disappeared when reaching to the static target. Long-latency muscle responses paralleled changes in the behavioral goal in both sets of experiments, but goal-directed responses were delayed by 15-25 ms when tracking the moving goal. Our results show the motor system can behave like a trajectory controller but only if a "desired trajectory" is the goal of the task. Significance statement: One of the most influential ideas in motor control is that the motor system computes a "desired trajectory" when reaching to a spatial goal. Here we revisit the experimental paradigm from seminal papers supporting trajectory control to illustrate that corrective responses appear to return to the original trajectory of the limb, but only if there is an imposed timing constraint. We then provide direct evidence that the human motor system can behave like a trajectory controller, and return the limb to its original trajectory when a specified trajectory is the goal of the task. Our results show that the motor system is capable of a spectrum of corrective responses that depend on the behavioral goal of the motor task.

Practice reduces task relevant variance modulation and forms nominal trajectory

Humans are capable of achieving complex tasks with redundant degrees of freedom. Much attention has been paid to task relevant variance modulation as an indication of online feedback control strategies to cope with motor variability. Meanwhile, it has been discussed that the brain learns internal models of environments to realize feedforward control with nominal trajectories. Here we examined trajectory variance in both spatial and temporal domains to elucidate the relative contribution of these control schemas. We asked subjects to learn reaching movements with multiple via-points, and found that hand trajectories converged to stereotyped trajectories with the reduction of task relevant variance modulation as learning proceeded. Furthermore, variance reduction was not always associated with task constraints but was highly correlated with the velocity profile. A model assuming noise both on the nominal trajectory and motor command was able to reproduce the observed variance modulation, supporting an expression of nominal trajectories in the brain. The learning-related decrease in task-relevant modulation revealed a reduction in the influence of optimal feedback around the task constraints. After practice, the major part of computation seems to be taken over by the feedforward controller around the nominal trajectory with feedback added only when it becomes necessary.

Reach Trajectories Characterize Tactile Localization for Sensorimotor Decision Making

Spatial target information for movement planning appears to be coded in a gaze-centered reference frame. In touch, however, location is initially coded with reference to the skin. Therefore, the tactile spatial location must be derived by integrating skin location and posture. It has been suggested that this recoding is impaired when the limb is placed in the opposite hemispace, for example, by limb crossing. Here, human participants reached toward visual and tactile targets located at uncrossed and crossed feet in a sensorimotor decision task. We characterized stimulus recoding by analyzing the timing and spatial profile of hand reaches. For tactile targets at crossed feet, skin-based information implicates the incorrect side, and only recoded information points to the correct location. Participants initiated straight reaches and redirected the hand toward a target presented in midflight. Trajectories to visual targets were unaffected by foot crossing. In contrast, trajectories to tactile targets were redirected later with crossed than uncrossed feet. Reaches to crossed feet usually continued straight until they were directed toward the correct tactile target and were not biased toward the skin-based target location. Occasional, far deflections toward the incorrect target were most likely when this target was implicated by trial history. These results are inconsistent with the suggestion that spatial transformations in touch are impaired by limb crossing, but are consistent with tactile location being recoded rapidly and efficiently, followed by integration of skin-based and external information to specify the reach target. This process may be implemented in a bounded integrator framework.

SIGNIFICANCE STATEMENT

How do you touch yourself, for instance, to scratch an itch? The place you need to reach is defined by a sensation on the skin, but our bodies are flexible, so this skin location could be anywhere in 3D space. The movement toward the tactile sensation must therefore be specified by merging skin location and body posture. By investigating human hand reach trajectories toward tactile stimuli on the feet, we provide experimental evidence that this transformation process is quick and efficient, and that its output is integrated with the original skin location in a fashion consistent with bounded integrator decision-making frameworks.

Sensorimotor organization of a sustained involuntary movement

Involuntary movements share much of the motor control circuitry used for voluntary movement, yet the two can be easily distinguished. The Kohnstamm phenomenon (where a sustained, hard push produces subsequent involuntary arm raising) is a useful experimental model for exploring differences between voluntary and involuntary movement. Both central and peripheral accounts have been proposed, but little is known regarding how the putative Kohnstamm generator responds to afferent input. We addressed this by obstructing the involuntary upward movement of the arm. Obstruction prevented the rising EMG pattern that characterizes the Kohnstamm. Importantly, once the obstruction was removed, the EMG signal resumed its former increase, suggesting a generator that persists despite peripheral input. When only one arm was obstructed during bilateral involuntary movements, only the EMG signal from the obstructed arm showed the effect. Upon release of the obstacle, the obstructed arm reached the same position and EMG level as the unobstructed arm. Comparison to matched voluntary movements revealed a preserved stretch response when a Kohnstamm movement first contacts an obstacle, and also an overestimation of the perceived contact force. Our findings support a hybrid central and peripheral account of the Kohnstamm phenomenon. The strange subjective experience of this involuntary movement is consistent with the view that movement awareness depends strongly on efference copies, but that the Kohnstamm generator does not produces efference copies.

Trunk robot rehabilitation training with active stepping reorganizes and enriches trunk motor cortex representations in spinal transected rats

Trunk motor control is crucial for postural stability and propulsion after low thoracic spinal cord injury (SCI) in animals and humans. Robotic rehabilitation aimed at trunk shows promise in SCI animal models and patients. However, little is known about the effect of SCI and robot rehabilitation of trunk on cortical motor representations. We previously showed reorganization of trunk motor cortex after adult SCI. Non-stepping training also exacerbated some SCI-driven plastic changes. Here we examine effects of robot rehabilitation that promotes recovery of hindlimb weight support functions on trunk motor cortex representations. Adult rats spinal transected as neonates (NTX rats) at the T9/10 level significantly improve function with our robot rehabilitation paradigm, whereas treadmill-only trained do not. We used intracortical microstimulation to map motor cortex in two NTX groups: (1) treadmill trained (control group); and (2) robot-assisted treadmill trained (improved function group). We found significant robot rehabilitation-driven changes in motor cortex: (1) caudal trunk motor areas expanded; (2) trunk coactivation at cortex sites increased; (3) richness of trunk cortex motor representations, as examined by cumulative entropy and mutual information for different trunk representations, increased; (4) trunk motor representations in the cortex moved toward more normal topography; and (5) trunk and forelimb motor representations that SCI-driven plasticity and compensations had caused to overlap were segregated. We conclude that effective robot rehabilitation training induces significant reorganization of trunk motor cortex and partially reverses some plastic changes that may be adaptive in non-stepping paraplegia after SCI.

The optimal neural strategy for a stable motor task requires a compromise between level of muscle cocontraction and synaptic gain of afferent feedback

Increasing joint stiffness by cocontraction of antagonist muscles and compensatory reflexes are neural strategies to minimize the impact of unexpected perturbations on movement. Combining these strategies, however, may compromise steadiness, as elements of the afferent input to motor pools innervating antagonist muscles are inherently negatively correlated. Consequently, a high afferent gain and active contractions of both muscles may imply negatively correlated neural drives to the muscles and thus an unstable limb position. This hypothesis was systematically explored with a novel computational model of the peripheral nervous system and the mechanics of one limb. Two populations of motor neurons received synaptic input from descending drive, spinal interneurons, and afferent feedback. Muscle force, simulated based on motor unit activity, determined limb movement that gave rise to afferent feedback from muscle spindles and Golgi tendon organs. The results indicated that optimal steadiness was achieved with low synaptic gain of the afferent feedback. High afferent gains during cocontraction implied increased levels of common drive in the motor neuron outputs, which were negatively correlated across the two populations, constraining instability of the limb. Increasing the force acting on the joint and the afferent gain both effectively minimized the impact of an external perturbation, and suboptimal adjustment of the afferent gain could be compensated by muscle cocontraction. These observations show that selection of the strategy for a given contraction implies a compromise between steadiness and effectiveness of compensations to perturbations. This indicates that a task-dependent selection of neural strategy for steadiness is necessary when acting in different environments.

Deafferented controllers: a fundamental failure mechanism in cortical neuroprosthetic systems

Brain-machine interface (BMI) research assumes that patients with disconnected neural pathways could naturally control a prosthetic device by volitionally modulating sensorimotor cortical activity usually responsible for movement coordination. However, computational approaches to motor control challenge this view. This article examines the predictions of optimal feedback control (OFC) theory on the effects that loss of motor output and sensory feedback have on the normal generation of motor commands. Example simulations of unimpaired, totally disconnected, and deafferented controllers illustrate that by neglecting the dynamic interplay between motor commands, state estimation, feedback and behavior, current BMI systems face translational challenges rooted in a debatable assumption and experimental models of limited validity.

Simultaneous Brain-Cervical Cord fMRI Reveals Intrinsic Spinal Cord Plasticity during Motor Sequence Learning

The spinal cord participates in the execution of skilled movements by translating high-level cerebral motor representations into musculotopic commands. Yet, the extent to which motor skill acquisition relies on intrinsic spinal cord processes remains unknown. To date, attempts to address this question were limited by difficulties in separating spinal local effects from supraspinal influences through traditional electrophysiological and neuroimaging methods. Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6–C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain–spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. These data suggest that the spinal cord not only constitutes an active functional component of the human motor learning network but also contributes distinctively from the brain to the learning process. The present findings open new avenues for rehabilitation of patients with spinal cord injuries, as they demonstrate that this part of the central nervous system is much more plastic than assumed before. Yet, the neurophysiological mechanisms underlying this intrinsic functional plasticity in the spinal cord warrant further investigations.

Simultaneous neuroimaging of brain and spinal cord reveals intrinsic plasticity in the spinal cord during motor sequence learning in humans, independent from that of related sensorimotor structures in the brain.

When we acquire a new motor skill—for example, learning how to play a musical instrument—new synaptic connections are induced in a distributed network of brain areas. There is ample evidence from human neuroimaging studies for this high plasticity of the brain, but what about the spinal cord, the main link between the brain and the peripheral nervous system? Literature on animal models has recently hinted that spinal cord neurons can learn during various conditioning paradigms. However, human learning models by tradition assume that the spinal cord acts as a passive relay of information from the cortex to the muscles. In this study, we simultaneously acquired functional images of both the brain and the cervical spinal cord through functional magnetic resonance imaging, and we provide evidence for local spinal cord plasticity during a well-studied motor learning task in humans. We also demonstrate a dynamic change in the interaction of the brain and spinal cord regions over the course of motor learning. The present findings have important clinical implications for rehabilitation of patients with spinal cord injuries, as they demonstrate that this part of the central nervous system is much more plastic than it was assumed before.

Major remaining gaps in models of sensorimotor systems

Experimental descriptions of the anatomy and physiology of individual components of sensorimotor systems have revealed substantial complexity, making it difficult to intuit how complete systems might work. This has led to increasing efforts to develop and employ mathematical models to study the emergent properties of such systems. Conversely, the development of such models tends to reveal shortcomings in the experimental database upon which models must be constructed and validated. In both cases models are most useful when they point up discrepancies between what we think we know and possibilities that we may have overlooked. This overview considers those components of complete sensorimotor systems that currently appear to be potentially important but poorly understood. These are generally omitted completely from modeled systems or buried in implicit assumptions that underlie the design of the model.

The cortical computations underlying feedback control in vocal production

Recent neurophysiological studies of speaking are beginning to elucidate the neural mechanisms underlying auditory feedback processing during vocalizations. Here we review how research findings impact our state feedback control (SFC) model of speech motor control. We will discuss the evidence for cortical computations that compare incoming feedback with predictions derived from motor efference copy. We will also review observations from auditory feedback perturbation studies that demonstrate clear evidence for a state estimate correction process, which drives compensatory motor behavioral responses. While there is compelling support for cortical computations in the SFC model, there are still several outstanding questions that await resolution by future neural investigations.

Neurons in red nucleus and primary motor cortex exhibit similar responses to mechanical perturbations applied to the upper-limb during posture

Primary motor cortex (M1) and red nucleus (RN) are brain regions involved in limb motor control. Both structures are highly interconnected with the cerebellum and project directly to the spinal cord, although the contribution of RN is smaller than M1. It remains uncertain whether RN and M1 serve similar or distinct roles during posture and movement. Many neurons in M1 respond rapidly to mechanical disturbances of the limb, but it remains unclear whether RN neurons also respond to such limb perturbations. We have compared discharges of single neurons in RN (n = 49) and M1 (n = 109) of one monkey during a postural perturbation task. Neural responses to whole-limb perturbations were examined by transiently applying (300 ms) flexor or extensor torques to the shoulder and/or elbow while the monkeys attempted to maintain a static hand posture. Relative to baseline discharges before perturbation onset, perturbations evoked rapid (<100 ms) changes of neural discharges in many RN (28 of 49, 57%) and M1 (43 of 109, 39%) neurons. In addition to exhibiting a greater proportion of perturbation-related neurons, RN neurons also tended to exhibit higher peak discharge frequencies in response to perturbations than M1 neurons. Importantly, neurons in both structures exhibited similar response latencies and tuning properties (preferred torque directions and tuning widths) in joint-torque space. Proximal arm muscles also displayed similar tuning properties in joint-torque space. These results suggest that RN is more sensitive than M1 to mechanical perturbations applied during postural control but both structures may play a similar role in feedback control of posture.

Computations in Sensorimotor Learning

Our cognitive abilities can only be expressed on the world through our actions. Here we review the computations underlying the way that the sensorimotor system converts both low-level sensory signals and high-level decisions into action, focusing on the behavioral evidence for the theoretical frameworks. We review recent work that determines how motor memories underlying sensorimotor learning are activated and protected from interference, the role of Bayesian decision theory in sensorimotor control including sources of suboptimality, the role of risk sensitivity in guiding action, and how rapid motor responses may underlie the robustness of the motor system to the vagaries of the world.

Short-term motor learning of dynamic balance control in children with probable Developmental Coordination Disorder

PURPOSE

To explore the differences in learning a dynamic balance task between children with and without probable Developmental Coordination Disorder (p-DCD) from different cultural backgrounds.

PARTICIPANTS

Twenty-eight Dutch children with DCD (p-DCD-NL), a similar group of 17 South African children (p-DCD-SA) and 21 Dutch typically developing children (TD-NL) participated in the study.

METHODS

All children performed the Wii Fit protocol. The slope of the learning curve was used to estimate motor learning for each group. The protocol was repeated after six weeks. Level of motor skill was assessed with the Movement ABC-2.

RESULTS

No significant difference in motor learning rate was found between p-DCD-NL and p-DCD-SA, but the learning rate of children with p-DCD was slower than the learning rate of TD children. Speed-accuracy trade off, as a way to improve performance by slowing down in the beginning was only seen in the TD children, indicating that TD children and p-DCD children used different strategies. Retention of the level of learned control of the game after six weeks was found in all three groups after six weeks. The learning slope was associated with the level of balance skill for all children. This study provides evidence that children with p-DCD have limitations in motor learning on a complex balance task. In addition, the data do not support the contention that learning in DCD differs depending on cultural background.

Impaired corrective responses to postural perturbations of the arm in individuals with subacute stroke

Background

Stroke is known to alter muscle stretch responses following a perturbation, but little is known about the behavioural consequences of these altered feedback responses. Characterizing impairments in people with stroke in their interactions with the external environment may lead to better long term outcomes. This information can inform therapists about rehabilitation targets and help subjects with stroke avoid injury when moving in the world.

Methods

In this study, we developed a postural perturbation task to quantity upper limb function of subjects with subacute stroke (n = 38) and non-disabled controls (n = 74) to make rapid corrective responses with the arm. Subjects were instructed to maintain their hand at a target before and after a mechanical load was applied to the limb. Visual feedback of the hand was removed for half of the trials at perturbation onset. A number of parameters quantified subject performance, and impairment in performance was defined as outside the 95th percentile performance of control subjects.

Results

Individual subjects with stroke showed increased postural instability (44%), delayed motor responses (79%), delayed returns towards the spatial target (79%), and greater endpoint errors (74%). Several subjects also showed impairments in the temporal coordination of the elbow and shoulder joints when responding to the perturbation (47%). Interestingly, impairments in task parameters were often found for both arms of individual subjects with stroke (up to 58% for return time). Visual feedback did not improve performance on task parameters except for decreasing endpoint error for all subjects. Significant correlations between task performance and clinical measures were dependent on the arm assessed.

Conclusions

This study used a simple postural perturbation task to highlight that subjects with stroke commonly have difficulties responding to mechanical disturbances that may have important implications for their ability to perform daily activities.

Motor cortical function and the precision grip

While task‐dependent changes in motor cortical outputs have been previously reported, the issue of whether such changes are specific for complex hand tasks remains unresolved. The aim of the present study was to determine whether cortical inhibitory tone and cortical output were greater during precision grip and power grip. Motor cortex excitability was undertaken by using the transcranial magnetic stimulation threshold tracking technique in 15 healthy subjects. The motor‐evoked potential (MEP) responses were recorded over the abductor pollicis brevis (APB), with the hand in the following positions: (1) rest, (2) precision grip and (3) power grip. The MEP amplitude (MEP amplitude _REST 23.6 ± 3.3%; MEP amplitude _{PRECISIONGRIP} 35.2 ± 5.6%; MEP amplitude _POWERGRIP 19.6 ± 3.4%, F = 2.4, P < 0.001) and stimulus‐response gradient (SLOPE_REST 0.06 ± 0.01; SLOPE_PRCISIONGRIP 0.15 ± 0.04; SLOPE _POWERGRIP 0.07 ± 0.01, P < 0.05) were significantly increased during precision grip. Short interval intracortical inhibition (SICI) was significantly reduced during the precision grip (SICI _REST 15.0 ± 2.3%; SICI _{PRECISIONGRIP} 9.7 ± 1.5%, SICI _POWERGRIP 15.9 ± 2.7%, F = 2.6, P < 0.05). The present study suggests that changes in motor cortex excitability are specific for precision grip, with functional coupling of descending corticospinal pathways controlling thumb and finger movements potentially forming the basis of these cortical changes.

This manuscript establishes that specific cortical mechanisms underlie the maintenance of the precision grip. The mechanisms appear distinct to the processes maintaining the power grip.

Online gain update for manual following response accompanied by gaze shift during arm reaching

To capture objects by hand, online motor corrections are required to compensate for self-body movements. Recent studies have shown that background visual motion, usually caused by body movement, plays a significant role in such online corrections. Visual motion applied during a reaching movement induces a rapid and automatic manual following response (MFR) in the direction of the visual motion. Importantly, the MFR amplitude is modulated by the gaze direction relative to the reach target location (i.e., foveal or peripheral reaching). That is, the brain specifies the adequate visuomotor gain for an online controller based on gaze-reach coordination. However, the time or state point at which the brain specifies this visuomotor gain remains unclear. More specifically, does the gain change occur even during the execution of reaching? In the present study, we measured MFR amplitudes during a task in which the participant performed a saccadic eye movement that altered the gaze-reach coordination during reaching. The results indicate that the MFR amplitude immediately after the saccade termination changed according to the new gaze-reach coordination, suggesting a flexible online updating of the MFR gain during reaching. An additional experiment showed that this gain updating mostly started before the saccade terminated. Therefore, the MFR gain updating process would be triggered by an ocular command related to saccade planning or execution based on forthcoming changes in the gaze-reach coordination. Our findings suggest that the brain flexibly updates the visuomotor gain for an online controller even during reaching movements based on continuous monitoring of the gaze-reach coordination.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Degraded EEG decoding of wrist movements in absence of kinaesthetic feedback

A major assumption of brain–machine interface research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here, we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in the absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control. Hum Brain Mapp 36:643–654, 2015. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.

Beyond muscles stiffness: importance of state-estimation to account for very fast motor corrections

Feedback delays are a major challenge for any controlled process, and yet we are able to easily control limb movements with speed and grace. A popular hypothesis suggests that the brain largely mitigates the impact of feedback delays (∼50 ms) by regulating the limb intrinsic visco-elastic properties (or impedance) with muscle co-contraction, which generates forces proportional to changes in joint angle and velocity with zero delay. Although attractive, this hypothesis is often based on estimates of limb impedance that include neural feedback, and therefore describe the entire motor system. In addition, this approach does not systematically take into account that muscles exhibit high intrinsic impedance only for small perturbations (short-range impedance). As a consequence, it remains unclear how the nervous system handles large perturbations, as well as disturbances encountered during movement when short-range impedance cannot contribute. We address this issue by comparing feedback responses to load pulses applied to the elbow of human subjects with theoretical simulations. After validating the model parameters, we show that the ability of humans to generate fast and accurate corrective movements is compatible with a control strategy based on state estimation. We also highlight the merits of delay-uncompensated robust control, which can mitigate the impact of internal model errors, but at the cost of slowing feedback corrections. We speculate that the puzzling observation of presynaptic inhibition of peripheral afferents in the spinal cord at movement onset helps to counter the destabilizing transition from high muscle impedance during posture to low muscle impedance during movement.

Context-dependent inhibition of unloaded muscles during the long-latency epoch

A number of studies have highlighted the sophistication of corrective responses in lengthened muscles during the long-latency epoch. However, in various contexts, unloading can occur, which requires corrective actions from a shortened muscle. Here, we investigate the sophistication of inhibitory responses in shortened muscles due to unloading. Our first experiment quantified the inhibitory responses following an unloading torque that displaced the hand either into or away from a peripheral target. We observed larger long-latency inhibitory responses when perturbed into the peripheral target compared with away from the target. In our second experiment, we characterized the degree of inhibition following unloading with respect to different levels of preperturbation muscle activity. We initially observed that the inhibitory activity during the short-latency epoch scaled with increased levels of preperturbation muscle activity. However, this scaling peaked early in the R2 epoch (∼ 50 ms) but then quickly diminished through the rest of the long-latency epoch. Finally, in experiment 3, we investigated whether inhibitory perturbation responses consider intersegmental dynamics of the limb. We quantified unloading responses for either pure shoulder or pure elbow torques that evoked similar motion at the shoulder but different elbow motion. The long-latency inhibitory response in the shoulder, unlike the short-latency, was greater for the shoulder torque compared with the response following an elbow torque, as previously observed for a loading response. Taken together, these results illustrate that the long-latency unloading response is capable of a similar level of complexity as observed when loads are applied to the limb.

Primary motor cortex and fast feedback responses to mechanical perturbations: a primer on what we know now and some suggestions on what we should find out next

Many researchers have drawn a clear distinction between fast feedback responses to mechanical perturbations (e.g., stretch responses) and voluntary control processes. But this simple distinction is difficult to reconcile with growing evidence that long-latency stretch responses share most of the defining capabilities of voluntary control. My general view—and I believe a growing consensus—is that the functional similarities between long-latency stretch responses and voluntary control processes can be readily understood based on their shared neural circuitry, especially a transcortical pathway through primary motor cortex. Here I provide a very brief and selective account of the human and monkey studies linking a transcortical pathway through primary motor cortex to the generation and functional sophistication of the long-latency stretch response. I then lay out some of the notable issues that are ready to be answered.

Synaptic plasticity and sensory-motor improvement following fibrin sealant dorsal root reimplantation and mononuclear cell therapy

Root lesions may affect both dorsal and ventral roots. However, due to the possibility of generating further inflammation and neuropathic pain, surgical procedures do not prioritize the repair of the afferent component. The loss of such sensorial input directly disturbs the spinal circuits thus affecting the functionality of the injuried limb. The present study evaluated the motor and sensory improvement following dorsal root reimplantation with fibrin sealant (FS) plus bone marrow mononuclear cells (MC) after dorsal rhizotomy. MC were used to enhance the repair process. We also analyzed changes in the glial response and synaptic circuits within the spinal cord. Female Lewis rats (6–8 weeks old) were divided in three groups: rhizotomy (RZ group), rhizotomy repaired with FS (RZ+FS group) and rhizotomy repaired with FS and MC (RZ+FS+MC group). The behavioral tests electronic von-Frey and Walking track test were carried out. For immunohistochemistry we used markers to detect different synapse profiles as well as glial reaction. The behavioral results showed a significant decrease in sensory and motor function after lesion. The reimplantation decreased glial reaction and improved synaptic plasticity of afferent inputs. Cell therapy further enhanced the rewiring process. In addition, both reimplanted groups presented twice as much motor control compared to the non-treated group. In conclusion, the reimplantation with FS and MC is efficient and may be considered an approach to improve sensory-motor recovery following dorsal rhizotomy.

The influence of visual target information on the online control of movements

The continuously changing properties of our environment require constant monitoring of our actions and updating of our motor commands based on the task goals. Such updating relies upon our predictions about the sensory consequences of our movement commands, as well as sensory feedback received during movement execution. Here we focus on how visual information about target location is used to update and guide ongoing actions so that the task goal is successfully achieved. We review several studies that have manipulated vision of the target in a variety of ways, ranging from complete removal of visual target information to changes in visual target properties after movement onset to examine how such changes are accounted for during motor execution. We also examined the specific role of a critical neural structure, the parietal cortex, and argue that a fundamental challenge for the future is to understand how visual information about target location is integrated with other streams of information, during movement execution, to estimate the state of the body and the environment in order to ensure optimal motor performance.

Two distinct layer-specific dynamics of cortical ensembles during learning of a motor task

The primary motor cortex (M1) possesses two intermediate layers upstream of the motor-output layer: layer 2/3 (L2/3) and layer 5a (L5a). Although repetitive training often improves motor performance and movement coding by M1 neuronal ensembles, it is unclear how neuronal activities in L2/3 and L5a are reorganized during motor task learning. We conducted two-photon calcium imaging in mouse M1 during 14 training sessions of a self-initiated lever-pull task. In L2/3, the accuracy of neuronal ensemble prediction of lever trajectory remained unchanged globally, with a subset of individual neurons retaining high prediction accuracy throughout the training period. However, in L5a, the ensemble prediction accuracy steadily improved, and one-third of neurons, including subcortical projection neurons, evolved to contribute substantially to ensemble prediction in the late stage of learning. The L2/3 network may represent coordination of signals from other areas throughout learning, whereas L5a may participate in the evolving network representing well-learned movements.

Goal-dependent modulation of fast feedback responses in primary motor cortex

Many human studies have demonstrated that rapid motor responses (i.e., muscle-stretch reflexes) to mechanical perturbations can be modified by a participant's intended response. Here, we used a novel experimental paradigm to investigate the neural mechanisms that underlie such goal-dependent modulation. Two monkeys positioned their hand in a central area against a constant load and responded to mechanical perturbations by quickly placing their hand into visually defined spatial targets. The perturbation was chosen to excite a particular proximal arm muscle or isolated neuron in primary motor cortex and two targets were placed so that the hand was pushed away from one target (OUT target) and toward the other (IN target). We chose these targets because they produced behavioral responses analogous to the classical verbal instructions used in human studies. A third centrally located target was used to examine responses with a constant goal. Arm muscles and neurons robustly responded to the perturbation and showed clear goal-dependent responses ∼35 and 70 ms after perturbation onset, respectively. Most M1 neurons and all muscles displayed larger perturbation-related responses for the OUT target than the IN target. However, a substantial number of M1 neurons showed more complex patterns of target-dependent modulation not seen in muscles, including greater activity for the IN target than the OUT target, and changes in target preference over time. Together, our results reveal complex goal-dependent modulation of fast feedback responses in M1 that are present early enough to account for goal-dependent stretch responses in arm muscles.

Through the eyes of a bird: modelling visually guided obstacle flight

Various flight navigation strategies for birds have been identified at the large spatial scales of migratory and homing behaviours. However, relatively little is known about close-range obstacle negotiation through cluttered environments. To examine obstacle flight guidance, we tracked pigeons (Columba livia) flying through an artificial forest of vertical poles. Interestingly, pigeons adjusted their flight path only approximately 1.5 m from the forest entry, suggesting a reactive mode of path planning. Combining flight trajectories with obstacle pole positions, we reconstructed the visual experience of the pigeons throughout obstacle flights. Assuming proportional-derivative control with a constant delay, we searched the relevant parameter space of steering gains and visuomotor delays that best explained the observed steering. We found that a pigeon's steering resembles proportional control driven by the error angle between the flight direction and the desired opening, or gap, between obstacles. Using this pigeon steering controller, we simulated obstacle flights and showed that pigeons do not simply steer to the nearest opening in the direction of flight or destination. Pigeons bias their flight direction towards larger visual gaps when making fast steering decisions. The proposed behavioural modelling method converts the obstacle avoidance behaviour into a (piecewise) target-aiming behaviour, which is better defined and understood. This study demonstrates how such an approach decomposes open-loop free-flight behaviours into components that can be independently evaluated.

Movement-based embodied contemplative practices: definitions and paradigms

Over the past decades, cognitive neuroscience has witnessed a shift from predominantly disembodied and computational views of the mind, to more embodied and situated views of the mind. These postulate that mental functions cannot be fully understood without reference to the physical body and the environment in which they are experienced. Within the field of contemplative science, the directing of attention to bodily sensations has so far mainly been studied in the context of seated meditation and mindfulness practices. However, the cultivation of interoceptive, proprioceptive and kinesthetic awareness is also said to lie at the core of many movement-based contemplative practices such as Yoga, Qigong, and Tai Chi. In addition, it likely plays a key role in the efficacy of modern somatic therapeutic techniques such as the Feldenkrais Method and the Alexander Technique. In the current paper we examine how these practices are grounded in the concepts of embodiment, movement and contemplation, as we look at them primarily through the lens of an enactive approach to cognition. Throughout, we point to a series of challenges that arise when Western scientists study practices that are based on a non-dualistic view of mind and body.

Limb dominance results from asymmetries in predictive and impedance control mechanisms

Handedness is a pronounced feature of human motor behavior, yet the underlying neural mechanisms remain unclear. We hypothesize that motor lateralization results from asymmetries in predictive control of task dynamics and in control of limb impedance. To test this hypothesis, we present an experiment with two different force field environments, a field with a predictable magnitude that varies with the square of velocity, and a field with a less predictable magnitude that varies linearly with velocity. These fields were designed to be compatible with controllers that are specialized in predicting limb and task dynamics, and modulating position and velocity dependent impedance, respectively. Because the velocity square field does not change the form of the equations of motion for the reaching arm, we reasoned that a forward dynamic-type controller should perform well in this field, while control of linear damping and stiffness terms should be less effective. In contrast, the unpredictable linear field should be most compatible with impedance control, but incompatible with predictive dynamics control. We measured steady state final position accuracy and 3 trajectory features during exposure to these fields: Mean squared jerk, Straightness, and Movement time. Our results confirmed that each arm made straighter, smoother, and quicker movements in its compatible field. Both arms showed similar final position accuracies, which were achieved using more extensive corrective sub-movements when either arm performed in its incompatible field. Finally, each arm showed limited adaptation to its incompatible field. Analysis of the dependence of trajectory errors on field magnitude suggested that dominant arm adaptation occurred by prediction of the mean field, thus exploiting predictive mechanisms for adaptation to the unpredictable field. Overall, our results support the hypothesis that motor lateralization reflects asymmetries in specific motor control mechanisms associated with predictive control of limb and task dynamics, and modulation of limb impedance.

Rapid online selection between multiple motor plans

Recent theories of voluntary control predict that multiple motor strategies can be precomputed and expressed throughout movement. We examined online decisional processing in humans by asking them to make reaching movements with obstacles located just to the sides of a direct path between start and end targets. On random trials, the limb was perturbed with one of four mechanical loads that varied in direction and amplitude. Notably, we observed two different strategies when we applied a perturbation (left medium-sized) that deviated the participants' hand directly toward an obstacle. In some trials, subjects directed their hand between the obstacles and in other trials to the left of the obstacles. Importantly, changes in the muscle stretch response between these two strategies were observed in <60 ms after perturbation, during the R2 long-latency epoch (~45-75 ms). As predicted, the selected strategy depended on the estimated position of the limb when it was perturbed. In our second experiment, we presented either one or three potential goal targets. Movements initially directed to the closest target could be quickly redirected to other potential targets after a perturbation. Differences in muscle stretch responses for redirected movements were observed ~75 ms after perturbation during the R3 long-latency epoch (~75-105 ms). The results show that decisional processes are rapidly implemented during movement execution. In addition, our data suggest a hierarchical process with corrective responses on "how" to attain a behavioral goal expressed during the R2 epoch and responses on "what" goal to attain during the R3 epoch.

Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning

Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208-3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.

Rapid feedback responses correlate with reach adaptation and properties of novel upper limb loads

A hallmark of voluntary motor control is the ability to adjust motor patterns for novel mechanical or visuomotor contexts. Recent work has also highlighted the importance of feedback for voluntary control, leading to the hypothesis that feedback responses should adapt when we learn new motor skills. We tested this prediction with a novel paradigm requiring that human subjects adapt to a viscous elbow load while reaching to three targets. Target 1 required combined shoulder and elbow motion, target 2 required only elbow motion, and target 3 (probe target) required shoulder but no elbow motion. This simple approach controlled muscle activity at the probe target before, during, and after the application of novel elbow loads. Our paradigm allowed us to perturb the elbow during reaching movements to the probe target and identify several key properties of adapted stretch responses. Adapted long-latency responses expressed (de-) adaptation similar to reaching errors observed when we introduced (removed) the elbow load. Moreover, reaching errors during learning correlated with changes in the long-latency response, showing subjects who adapted more to the elbow load displayed greater modulation of their stretch responses. These adapted responses were sensitive to the size and direction of the viscous training load. Our results highlight an important link between the adaptation of feedforward and feedback control and suggest a key part of motor adaptation is to adjust feedback responses to the requirements of novel motor skills.

Feedback responses rapidly scale with the urgency to correct for external perturbations

Healthy subjects can easily produce voluntary actions at different speeds and with varying accuracy requirements. It remains unknown whether rapid corrective responses to mechanical perturbations also possess this flexibility and, thereby, contribute to the capability expressed in voluntary control. Paralleling previous studies on self-initiated movements, we examined how muscle activity was impacted by either implicit or explicit criteria affecting the urgency to respond to the perturbation. Participants maintained their arm position against torque perturbations with unpredictable timing and direction. In the first experiment, the urgency to respond was explicitly altered by varying the time limit (300 ms vs. 700 ms) to return to a small target. A second experiment addresses implicit urgency criteria by varying the radius of the goal target, such that task accuracy could be achieved with less vigorous corrections for large targets than small target. We show that muscle responses at ∼60 ms scaled with the task demand. Moreover, in both experiments, we found a strong intertrial correlation between long-latency responses (∼50–100 ms) and the movement reversal times, which emphasizes that these rapid motor responses are directly linked to behavioral performance. The slopes of these linear regressions were sensitive to the experimental condition during the long-latency and early voluntary epochs. These findings suggest that feedback gains for very rapid responses are flexibly scaled according to task-related urgency.

Priors engaged in long-latency responses to mechanical perturbations suggest a rapid update in state estimation

In every motor task, our brain must handle external forces acting on the body. For example, riding a bike on cobblestones or skating on irregular surface requires us to appropriately respond to external perturbations. In these situations, motor predictions cannot help anticipate the motion of the body induced by external factors, and direct use of delayed sensory feedback will tend to generate instability. Here, we show that to solve this problem the motor system uses a rapid sensory prediction to correct the estimated state of the limb. We used a postural task with mechanical perturbations to address whether sensory predictions were engaged in upper-limb corrective movements. Subjects altered their initial motor response in ∼60 ms, depending on the expected perturbation profile, suggesting the use of an internal model, or prior, in this corrective process. Further, we found trial-to-trial changes in corrective responses indicating a rapid update of these perturbation priors. We used a computational model based on Kalman filtering to show that the response modulation was compatible with a rapid correction of the estimated state engaged in the feedback response. Such a process may allow us to handle external disturbances encountered in virtually every physical activity, which is likely an important feature of skilled motor behaviour.

Preference distributions of primary motor cortex neurons reflect control solutions optimized for limb biomechanics

Neurons in monkey primary motor cortex (M1) tend to be most active for certain directions of hand movement and joint-torque loads applied to the limb. The origin and function of these biases in preference distribution are unclear but may be key to understanding the causal role of M1 in limb control. We demonstrate that these distributions arise naturally in a network model that commands muscle activity and is optimized to control movements and counter applied forces. In the model, movement and load preference distributions matching those observed empirically are only evident when particular features of the musculoskeletal system were included: limb geometry, intersegmental dynamics, and the force-length/velocity properties of muscle were dominant factors in dictating movement preferences, and the presence of biarticular muscles dictated load preferences. Our results suggest a general principle: neural activity in M1 commands muscle activity and is optimized for the physics of the motor effector.

Refractoriness in sustained visuo-manual control: is the refractory duration intrinsic or does it depend on external system properties?

Researchers have previously adopted the double stimulus paradigm to study refractoriness in human neuromotor control. Currently, refractoriness, such as the Psychological Refractory Period (PRP) has only been quantified in discrete movement conditions. Whether refractoriness and the associated serial ballistic hypothesis generalises to sustained control tasks has remained open for more than sixty years. Recently, a method of analysis has been presented that quantifies refractoriness in sustained control tasks and discriminates intermittent (serial ballistic) from continuous control. Following our recent demonstration that continuous control of an unstable second order system (i.e. balancing a 'virtual' inverted pendulum through a joystick interface) is unnecessary, we ask whether refractoriness of substantial duration (~200 ms) is evident in sustained visual-manual control of external systems. We ask whether the refractory duration (i) is physiologically intrinsic, (ii) depends upon system properties like the order (0, 1(st), and 2(nd)) or stability, (iii) depends upon target jump direction (reversal, same direction). Thirteen participants used discrete movements (zero order system) as well as more sustained control activity (1(st) and 2(nd) order systems) to track unpredictable step-sequence targets. Results show a substantial refractory duration that depends upon system order (250, 350 and 550 ms for 0, 1(st) and 2(nd) order respectively, n=13, p<0.05), but not stability. In sustained control refractoriness was only found when the target reverses direction. In the presence of time varying actuators, systems and constraints, we propose that central refractoriness is an appropriate control mechanism for accommodating online optimization delays within the neural circuitry including the more variable processing times of higher order (complex) input-output relations.