The computational and neural basis of voluntary motor control and planning

Rapid Feedback Responses Parallel the Urgency of Voluntary Reaching Movements

Optimal feedback control is a prominent theory used to interpret human motor behaviour. The theory posits that skilled actions emerge from control policies that link voluntary motor control (feedforward) with flexible feedback corrections (feedback control). It is clear the nervous system can generate flexible motor corrections (reflexes) when performing actions with different goals. We know little, however, about shared features of voluntary actions and feedback control in human movement. Here we reveal a link between the timing demands of voluntary actions and flexible responses to mechanical perturbations. In two experiments, 40 human participants (21 females) made reaching movements with different timing demands. We disturbed the arm with mechanical perturbations at movement onset (Experiment 1) and at locations ranging from movement onset to completion (Experiment 2). We used the resulting muscle responses and limb displacements as a proxy for the control policies that support voluntary reaching movements. We observed an increase in the sensitivity of elbow and shoulder muscle responses and a reduction in limb motion when the task imposed greater urgency to respond to the same perturbations. The results reveal a relationship between voluntary actions and feedback control as the limb was displaced less when moving faster in perturbation trials. Muscle responses scaled with changes in the displacement of the limb in perturbation trials within each timing condition. Across both experiments, human behaviour was captured by simulations based on stochastic optimal feedback control. Taken together, the results highlight flexible control that links sensory processing with features of human reaching movements.

Second-order motor planning difficulties in children with developmental coordination disorder

The second-order motor planning ability of children with developmental coordination disorder (DCD) has often been studied using tasks that require judgements of end-state comfort (ESC). In these studies, children may have chosen to prioritize other aspects of performance (e.g., a comfortable start-posture) over ESC while still being able to complete the goal of the task. This is a limitation that is inherent to previously used ESC paradigms. To avoid this in the present study, 52 children with and without DCD (aged 5-12 years) completed a task that requires second-order motor planning for its successful completion. In the hexagonal knob task, children were instructed to grasp and rotate a hexagonal knob. The rotation angle varied in size: 60°, 120°, 180°, and 240° rotations. Both the 180° and 240° rotation conditions required an uncomfortable starting posture for successful task completion. Results showed that children with DCD were less likely to adjust their initial grip in anticipation of the required rotation angle, resulting in more task failures compared with typically developing (TD) children. Based on this finding we conclude that children with DCD experience genuine second-order motor planning difficulties. Analysis of temporal outcomes, showed that initial reaction time increased with rotation angle, but this was less pronounced for children with DCD than for TD children. There were no between group differences in timing of subsequent events. These results suggest that the difficulties of children with DCD are related to the initial planning process, that is, before the start of the movement.

Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision

Human movements with or without vision exhibit timing (i.e. speed and duration) and variability characteristics which are not well captured by existing computational models. Here, we introduce a stochastic optimal feedforward-feedback control (SFFC) model that can predict the nominal timing and trial-by-trial variability of self-paced arm reaching movements carried out with or without online visual feedback of the hand. In SFFC, movement timing results from the minimization of the intrinsic factors of effort and variance due to constant and signal-dependent motor noise, and movement variability depends on the integration of visual feedback. Reaching arm movements data are used to examine the effect of online vision on movement timing and variability, and test the model. This modelling suggests that the central nervous system predicts the effects of sensorimotor noise to generate an optimal feedforward motor command, and triggers optimal feedback corrections to task-related errors based on the available limb state estimate.

On the encoding capacity of human motor adaptation

Primitive-based models of motor learning suggest that adaptation occurs by tuning the responses of motor primitives. Based on this idea, we consider motor learning as an information encoding procedure, that is, a procedure of encoding a motor skill into primitives. The capacity of encoding is determined by the number of recruited primitives, which depends on how many primitives are "visited" by the movement, and this leads to a rather counterintuitive prediction that faster movement, where a larger number of motor primitives are involved, allows learning more complicated motor skills. Here, we provide a set of experimental results that support this hypothesis. First, we show that learning occurs only with movement, that is, only with nonzero encoding capacity. When participants were asked to counteract a rotating force applied to a robotic handle, they were unable to do so when maintaining a static posture but were able to adapt when making small circular movements. Our second experiment further investigated how adaptation is affected by movement speed. When adapting to a simple (low-information-content) force field, fast (high-capacity) movement did not have an advantage over slow (low-capacity) movement. However, for a complex (high-information-content) force field, the fast movement showed a significant advantage over slow movement. Our final experiment confirmed that the observed benefit of high-speed movement is only weakly affected by mechanical factors. Taken together, our results suggest that the encoding capacity is a genuine limiting factor of human motor adaptation.NEW & NOTEWORTHY We propose a novel concept called "encoding capacity" of motor adaptation, which describes an inherent limiting-factor of our brain's ability to learn new motor skills, just like any other storage system. By reinterpreting the existing primitive-based models of motor learning, we hypothesize that the encoding capacity is determined by the size of the movement, and present a set of experimental evidence suggesting that such limiting effect of encoding capacity does exist in human motor adaptation.

Motor Planning Modulates Neural Activity Patterns in Early Human Auditory Cortex

It is well established that movement planning recruits motor-related cortical brain areas in preparation for the forthcoming action. Given that an integral component to the control of action is the processing of sensory information throughout movement, we predicted that movement planning might also modulate early sensory cortical areas, readying them for sensory processing during the unfolding action. To test this hypothesis, we performed 2 human functional magnetic resonance imaging studies involving separate delayed movement tasks and focused on premovement neural activity in early auditory cortex, given the area's direct connections to the motor system and evidence that it is modulated by motor cortex during movement in rodents. We show that effector-specific information (i.e., movements of the left vs. right hand in Experiment 1 and movements of the hand vs. eye in Experiment 2) can be decoded, well before movement, from neural activity in early auditory cortex. We find that this motor-related information is encoded in a separate subregion of auditory cortex than sensory-related information and is present even when movements are cued visually instead of auditorily. These findings suggest that action planning, in addition to preparing the motor system for movement, involves selectively modulating primary sensory areas based on the intended action.

Optimal anticipatory control as a theory of motor preparation: A thalamo-cortical circuit model

Across a range of motor and cognitive tasks, cortical activity can be accurately described by low-dimensional dynamics unfolding from specific initial conditions on every trial. These "preparatory states" largely determine the subsequent evolution of both neural activity and behavior, and their importance raises questions regarding how they are, or ought to be, set. Here, we formulate motor preparation as optimal anticipatory control of future movements and show that the solution requires a form of internal feedback control of cortical circuit dynamics. In contrast to a simple feedforward strategy, feedback control enables fast movement preparation by selectively controlling the cortical state in the small subspace that matters for the upcoming movement. Feedback but not feedforward control explains the orthogonality between preparatory and movement activity observed in reaching monkeys. We propose a circuit model in which optimal preparatory control is implemented as a thalamo-cortical loop gated by the basal ganglia.

The role of eye movements in manual interception: A mini-review

When we catch a moving object in mid-flight, our eyes and hands are directed toward the object. Yet, the functional role of eye movements in guiding interceptive hand movements is not yet well understood. This review synthesizes emergent views on the importance of eye movements during manual interception with an emphasis on laboratory studies published since 2015. We discuss the role of eye movements in forming visual predictions about a moving object, and for enhancing the accuracy of interceptive hand movements through feedforward (extraretinal) and feedback (retinal) signals. We conclude by proposing a framework that defines the role of human eye movements for manual interception accuracy as a function of visual certainty and object motion predictability.

Transient deactivation of dorsal premotor cortex or parietal area 5 impairs feedback control of the limb in macaques

We can generate goal-directed motor corrections with surprising speed, but their neural basis is poorly understood. Here, we show that temporary cooling of dorsal premotor cortex (PMd) impaired both spatial accuracy and the speed of corrective responses, whereas cooling parietal area 5 (A5) impaired only spatial accuracy. Simulations based on optimal feedback control (OFC) models demonstrated that "deactivation" of the control policy (reduction in feedback gain) and state estimation (reduction in Kalman gain) caused impairments similar to that observed for PMd and A5 cooling, respectively. Furthermore, combined deactivation of both cortical regions led to additive impairments of individual deactivations, whereas reducing the amount of cooling to PMd led to impairments in response speed but not spatial accuracy, both also predicted by OFC models. These results provide causal support that frontoparietal circuits beyond primary somatosensory and motor cortices are involved in generating goal-directed motor corrections.

Goal-dependent tuning of muscle spindle receptors during movement preparation

Voluntary movements are believed to undergo preparation before they are executed. Preparatory activity can benefit reaction time and the quality of planned movements, but the neural mechanisms at work during preparation are unclear. For example, there are no overt changes in muscle force during preparation. Here, using an instructed-delay manual task, we demonstrate a decrease in human muscle afferent activity (primary spindles) when preparing to reach targets in directions associated with stretch of the spindle-bearing muscle. This goal-dependent modulation of proprioceptors began early after target onset but was markedly stronger at the latter parts of the preparatory period. Moreover, whole-arm perturbations during reach preparation revealed a modulation of stretch reflex gains (shoulder and upper arm muscles) that reflected the observed changes in spindle activity. We suggest that one function of central preparatory activity is to tune muscle stiffness according to task goals via the independent control of muscle spindle sensors.

Vestibular contributions to online reach execution are processed via mechanisms with knowledge about limb biomechanics

Studies of reach control with the body stationary have shown that proprioceptive and visual feedback signals contributing to rapid corrections during reaching are processed by neural circuits that incorporate knowledge about the physical properties of the limb (an internal model). However, among the most common spatial and mechanical perturbations to the limb are those caused by our body's own motion, suggesting that processing of vestibular signals for online reach control may reflect a similar level of sophistication. We investigated this hypothesis using galvanic vestibular stimulation (GVS) to selectively activate the vestibular sensors, simulating body rotation, as human subjects reached to remembered targets in different directions (forward, leftward, rightward). If vestibular signals contribute to purely kinematic/spatial corrections for body motion, GVS should evoke reach trajectory deviations of similar size in all directions. In contrast, biomechanical modeling predicts that if vestibular processing for online reach control takes into account knowledge of the physical properties of the limb and the forces applied on it by body motion, then GVS should evoke trajectory deviations that are significantly larger during forward and leftward reaches as compared with rightward reaches. When GVS was applied during reaching, the observed deviations were on average consistent with this prediction. In contrast, when GVS was instead applied before reaching, evoked deviations were similar across directions, as predicted for a purely spatial correction mechanism. These results suggest that vestibular signals, like proprioceptive and visual feedback, are processed for online reach control via sophisticated neural mechanisms that incorporate knowledge of limb biomechanics.NEW & NOTEWORTHY Studies examining proprioceptive and visual contributions to rapid corrections for externally applied mechanical and spatial perturbations during reaching have provided evidence for flexible processing of sensory feedback that accounts for musculoskeletal system dynamics. Notably, however, such perturbations commonly arise from our body's own motion. In line with this, we provide compelling evidence that, similar to proprioceptive and visual signals, vestibular signals are processed for online reach control via sophisticated mechanisms that incorporate knowledge of limb biomechanics.

Dynamic temporal modulation of somatosensory processing during reaching

Sensorimotor control of human action integrates feedforward policies that predict future body states with online sensory feedback. These predictions lead to a suppression of the associated feedback signals. Here, we examine whether somatosensory processing throughout a goal-directed movement is constantly suppressed or dynamically tuned so that online feedback processing is enhanced at critical moments of the movement. Participants reached towards their other hand in the absence of visual input and detected a probing tactile stimulus on their moving or static hand. Somatosensory processing on the moving hand was dynamically tuned over the time course of reaching, being hampered in early and late stages of the movement, but, interestingly, recovering around the time of maximal speed. This novel finding of temporal somatosensory tuning was further corroborated in a second experiment, in which larger movement amplitudes shifted the absolute time of maximal speed later in the movement. We further show that the release from suppression on the moving limb was temporally coupled with enhanced somatosensory processing on the target hand. We discuss these results in the context of optimal feedforward control and suggest that somatosensory processing is dynamically tuned during the time course of reaching by enhancing sensory processing at critical moments of the movement.

The impact of predictability on dual-task performance and implications for resource-sharing accounts

The aim of this study was to examine the impact of predictability on dual-task performance by systematically manipulating predictability in either one of two tasks, as well as between tasks. According to capacity-sharing accounts of multitasking, assuming a general pool of resources two tasks can draw upon, predictability should reduce the need for resources and allow more resources to be used by the other task. However, it is currently not well understood what drives resource-allocation policy in dual tasks and which resource allocation policies participants pursue. We used a continuous tracking task together with an audiomotor task and manipulated advance visual information about the tracking path in the first experiment and a sound sequence in the second experiments (2a/b). Results show that performance predominantly improved in the predictable task but not in the unpredictable task, suggesting that participants did not invest more resources into the unpredictable task. One possible explanation was that the re-investment of resources into another task requires some relationship between the tasks. Therefore, in the third experiment, we covaried the two tasks by having sounds 250 ms before turning points in the tracking curve. This enabled participants to improve performance in both tasks, suggesting that resources were shared better between tasks.

Sensorimotor signals underlying space perception: An investigation based on self-touch

Perception of space has puzzled scientists since antiquity, and is among the foundational questions of scientific psychology. Classical "local sign" theories assert that perception of spatial extent ultimately derives from efferent signals specifying the intensity of motor commands. Everyday cases of self-touch, such as stroking the left forearm with the right index fingertip, provide an important platform for studying spatial perception, because of the tight correlation between motor and tactile extents. Nevertheless, if the motor and sensory information in self-touch were artificially decoupled, these classical theories would clearly predict that motor signals - especially if self-generated rather than passive - should influence spatial perceptual judgements, but not vice versa. We tested this hypothesis by quantifying the contribution of tactile, kinaesthetic, and motor information to judgements of spatial extent. In a self-touch paradigm involving two coupled robots in master-slave configuration, voluntary movements of the right-hand produced simultaneous tactile stroking on the left forearm. Crucially, the coupling between robots was manipulated so that tactile stimulation could be shorter, equal, or longer in extent than the movement that caused it. Participants judged either the extent of the movement, or the extent of the tactile stroke. By controlling sensorimotor gains in this way, we quantified how motor signals influence tactile spatial perception, and vice versa. Perception of tactile extent was strongly biased by the amplitude of the movement performed. Importantly, touch also affected the perceived extent of movement. Finally, the effect of movement on touch was significantly stronger when movements were actively-generated compared to when the participant's right hand was passively moved by the experimenter. Overall, these results suggest that motor signals indeed dominate the construction of spatial percepts, at least when the normal tight correlation between motor and sensory signals is broken. Importantly, however, this dominance is not total, as classical theory might suggest.

Cortico-cerebellar interactions during goal-directed behavior

Preparatory activity is observed across multiple interconnected brain regions before goal-directed movement. Preparatory activity reflects discrete activity states representing specific future actions. It is unclear how this activity is mediated by multi-regional interactions. Recent evidence suggests that the cerebellum, classically associated with fine motor control, contributes to preparatory activity in the neocortex. We review recent advances and offer perspective on the function of cortico-cerebellar interactions during goal-directed behavior. We propose that the cerebellum learns to facilitate transitions between neocortical activity states. Transitions between activity states enable flexible and appropriately timed behavioral responses.

Cerebellar adaptive mechanisms explain the optimal control of saccadic eye movements

Cerebellar synaptic plasticity is vital for adaptability and fine tuning of goal-directed movements. The perceived sensory errors between desired and actual movement outcomes are commonly considered to induce plasticity in the cerebellar synapses, with an objective to improve desirability of the executed movements. In rapid goal-directed eye movements called saccades, the only available sensory feedback is the direction of reaching error information received only at end of the movement. Moreover, this sensory error dependent plasticity can only improve the accuracy of the movements, while ignoring other essential characteristics such as reaching in minimum-time. In this work we propose a rate based, cerebellum inspired adaptive filter model to address refinement of both accuracy and movement-time of saccades. We use optimal control approach in conjunction with information constraints posed by the cerebellum to derive bio-plausible supervised plasticity rules. We implement and validate this bio-inspired scheme on a humanoid robot. We found out that, separate plasticity mechanisms in the model cerebellum separately control accuracy and movement-time. These plasticity mechanisms ensure that optimal saccades are produced by just receiving the direction of end reaching error as an evaluative signal. Furthermore, the model emulates encoding in the cerebellum of movement kinematics as observed in biological experiments.

Spinal Reflex Recovery after Dorsal Rhizotomy and Repair with Platelet-Rich Plasma (PRP) Gel Combined with Bioengineered Human Embryonic Stem Cells (hESCs)

Dorsal root rhizotomy (DRZ) is currently considered an untreatable injury, resulting in the loss of sensitive function and usually leading to neuropathic pain. In this context, we recently proposed a new surgical approach to treat DRZ that uses platelet-rich plasma (PRP) gel to restore the spinal reflex. Success was correlated with the reentry of primary afferents into the spinal cord. Here, aiming to enhance previous results, cell therapy with bioengineered human embryonic stem cells (hESCs) to overexpress fibroblast growth factor 2 (FGF2) was combined with PRP. For these experiments, adult female rats were submitted to a unilateral rhizotomy of the lumbar spinal dorsal roots, which was followed by root repair with PRP gel with or without bioengineered hESCs. One week after DRZ, the spinal cords were processed to evaluate changes in the glial response (GFAP and Iba-1) and excitatory synaptic circuits (VGLUT1) by immunofluorescence. Eight weeks postsurgery, the lumbar intumescences were processed for analysis of the repaired microenvironment by transmission electron microscopy. Spinal reflex recovery was evaluated by the electronic Von Frey method for eight weeks. The transcript levels for human FGF2 were over 37-fold higher in the induced hESCs than in the noninduced and the wildtype counterparts. Altogether, the results indicate that the combination of hESCs with PRP gel promoted substantial and prominent axonal regeneration processes after DRZ. Thus, the repair of dorsal roots, if done appropriately, may be considered an approach to regain sensory-motor function after dorsal root axotomy.

Cortical responses to whole-body balance perturbations index perturbation magnitude and predict reactive stepping behavior

The goal of this study was to determine whether the cortical responses elicited by whole‐body balance perturbations were similar to established cortical markers of action monitoring. Postural changes imposed by balance perturbations elicit a robust negative potential (N1) and a brisk increase of theta activity in the electroencephalogram recorded over midfrontal scalp areas. Because action monitoring is a cognitive function proposed to detect errors and initiate corrective adjustments, we hypothesized that the possible cortical markers of action monitoring during balance control (N1 potential and theta rhythm) scale with perturbation intensity and the eventual execution of reactive stepping responses (as opposed to feet‐in‐place responses). We recorded high‐density electroencephalogram from eleven young individuals, who participated in an experimental balance assessment. The participants were asked to recover balance following anteroposterior translations of the support surface at various intensities, while attempting to maintain both feet in place. We estimated source‐resolved cortical activity using independent component analysis. Combining time‐frequency decomposition and group‐level general linear modeling of single‐trial responses, we found a significant relation of the interaction between perturbation intensity and stepping responses with multiple cortical features from the midfrontal cortex, including the N1 potential, and theta, alpha, and beta rhythms. Our findings suggest that the cortical responses to balance perturbations index the magnitude of a deviation from a stable postural state to predict the need for reactive stepping responses. We propose that the cortical control of balance may involve cognitive control mechanisms (i.e., action monitoring) that facilitate postural adjustments to maintain postural stability.

Motor memories in manipulation tasks are linked to contact goals between objects

Skillful manipulation requires forming memories of object dynamics, linking applied force to motion. Although it has been assumed that such memories are linked to objects, a recent study showed that people can form separate memories when these are linked to different controlled points on an object (Heald JB, Ingram JN, Flanagan JR, Wolpert DM. Nat Hum Behav 2: 300-311, 2018). In that study, participants controlled the handle of a robotic device to move a virtual bar with circles (control points) on the left and right sides. Participants were instructed to move either the left or right control point to a target on the left or right, respectively, such that the required movement was constant. When these control points were paired with opposing force fields, adaptation was observed. In this previous study, both the controlled point and the target changed between contexts. To assess which of these factors is critical for learning, here, we used a similar paradigm but with a bar that automatically rotated as it was moved. In the first experiment, the bar rotated, such that the left and right control points moved to a common target. In the second experiment, the bar rotated such that a single control point moved to a target located on either the left or right. In both experiments, participants were able to learn opposing force fields applied in the two contexts. We conclude that separate memories of dynamics can be formed for different "contact goals," involving a unique combination of the controlled point on an object and the target location this point "contacts."NEW & NOTEWORTHY Skilled manipulation requires forming memories of object dynamics, previously assumed to be associated with entire objects. However, we recently demonstrated that people can form multiple motor memories when explicitly instructed to move different locations on an object to different targets. Here, we show that separate motor memories can be learned for different contact goals, which involve a unique combination of a control point and target.

Optimal Control Perspective on Parkinson's Disease: Increased Delay Between State Estimator and Controller Produces Tremor

Parkinson's disease produces tremor in a large subset of patients despite generally inhibiting movement. The pathophysiology of parkinsonian tremor is unclear, leading to uncertainty in how and why treatments reduce tremor with varying effectiveness. Models for parkinsonian tremor attempt to explain the underlying principles of tremor generation in the central nervous system, often focusing on neural activity of specific substructures. In contrast, control system approaches to modeling the human motor system provide qualitative results that help inform conclusions from clinical studies. This article uses an optimal control approach to investigate the hypothesis that an increased delay in the central nervous system-unaccounted by delay compensation mechanisms-produces parkinsonian tremor. This hypothesis is motivated by the excessive inhibition projected from the basal ganglia to the thalamus in Parkinson's disease. The thalamus relays signals from the cerebellum to the primary motor cortex: previous mapping of optimal control components indicates this prospective delay exists between the estimator (cerebellum) and controller (primary motor cortex). Simulations demonstrate realistic tremor in a neuromuscular model of the wrist. In addition, changes to effort sensitivity in the optimal controller may account for some clinical features of parkinsonian tremor, including the characteristics of re-emergent tremor and the time-varying amplitude and frequency of tremor. Contextualization of the optimal control model with physiological models and clinical observations provides insight into the potential role of the basal ganglia and cerebello-thalamo-cortical circuit and how treatments like dopaminergic medications and deep brain stimulation reduce tremor.

Impact of Experimental Tonic Pain on Corrective Motor Responses to Mechanical Perturbations

Movement is altered by pain, but the underlying mechanisms remain unclear. Assessing corrective muscle responses following mechanical perturbations can help clarify these underlying mechanisms, as these responses involve spinal (short-latency response, 20-50 ms), transcortical (long-latency response, 50-100 ms), and cortical (early voluntary response, 100-150 ms) mechanisms. Pairing mechanical (proprioceptive) perturbations with different conditions of visual feedback can also offer insight into how pain impacts on sensorimotor integration. The general aim of this study was to examine the impact of experimental tonic pain on corrective muscle responses evoked by mechanical and/or visual perturbations in healthy adults. Two sessions (Pain (induced with capsaicin) and No pain) were performed using a robotic exoskeleton combined with a 2D virtual environment. Participants were instructed to maintain their index in a target despite the application of perturbations under four conditions of sensory feedback: (1) proprioceptive only, (2) visuoproprioceptive congruent, (3) visuoproprioceptive incongruent, and (4) visual only. Perturbations were induced in either flexion or extension, with an amplitude of 2 or 3 Nm. Surface electromyography was recorded from Biceps and Triceps muscles. Results demonstrated no significant effect of the type of sensory feedback on corrective muscle responses, no matter whether pain was present or not. When looking at the effect of pain on corrective responses across muscles, a significant interaction was found, but for the early voluntary responses only. These results suggest that the effect of cutaneous tonic pain on motor control arises mainly at the cortical (rather than spinal) level and that proprioception dominates vision for responses to perturbations, even in the presence of pain. The observation of a muscle-specific modulation using a cutaneous pain model highlights the fact that the impacts of pain on the motor system are not only driven by the need to unload structures from which the nociceptive signal is arising.

Bayesian State Estimation in Sensorimotor Systems With Particle Filtering

In sensorimotor control, sensory feedback integrates with forward models to alleviate the impacts of sensory noise and delay on state estimation. The sensorimotor integration is subject to Bayesian inference and has been formulated by the Kalman filter in computational neuroscience. However, the Kalman filter, as an artificial optimal estimator to address the abstract characteristics of spatial perception, is inadequate to present the neural computation in the cerebellum. Besides, the nonlinear neuromuscular dynamics with tightly coupled state variables also substantially impedes the implementation of Kalman filter in realistic sensorimotor systems. Here we address the sensorimotor state estimate by using the particle filter, a nonlinear Bayesian estimator that can be implemented in arbitrary dynamic systems with the neurocomputational compatibility. Particle filtering is explicitly implemented in a biophysically realistic sensorimotor model of an upper limb integrating Hill-type muscles, tendons, skeleton, and primary afferents. By involving the command noises, the constructed neuromusculoskeletal model qualitatively represents the experimental variability in center-out reaching movements. Despite the initial estimation uncertainty and sensorimotor noises, the particle filter is able to approximate the actual states in forward-reaching movements. Furthermore, the simulated hand-position estimate is consistent with the experimental results, in the presence of forward model errors, neural noises, and sensory delays. The particle filter is demonstrated to effectively implement the Bayesian state estimation in biophysically realistic sensorimotor systems and provide better compatibility with neuronal computation than the Kalman filter.

Time course of changes in the long-latency feedback response parallels the fast process of short-term motor adaptation

Adapting to novel dynamics involves modifying both feedforward and feedback control. We investigated whether the motor system alters feedback responses during adaptation to a novel force field in a manner similar to adjustments in feedforward control. We simultaneously tracked the time course of both feedforward and feedback systems via independent probes during a force field adaptation task. Participants (n = 35) grasped the handle of a robotic manipulandum and performed reaches to a visual target while the hand and arm were occluded. We introduced an abrupt counterclockwise velocity-dependent force field during a block of reaching trials. We measured movement kinematics and shoulder and elbow muscle activity with surface EMG electrodes. We tracked the feedback stretch response throughout the task. Using force channel trials, we measured overall learning, which was later decomposed into a fast and slow process. We found that the long-latency feedback response (LLFR) was upregulated in the early stages of learning and was correlated with the fast component of feedforward adaptation. The change in feedback response was specific to the long-latency epoch (50-100 ms after muscle stretch) and was observed only in the triceps muscle, which was the muscle required to counter the force field during adaptation. The similarity in time course for the LLFR and the estimated time course of the fast process suggests both are supported by common neural circuits. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.NEW & NOTEWORTHY We investigated whether changes in the feedback stretch response were related to the proposed fast and slow processes of motor adaptation. We found that the long-latency component of the feedback stretch response was upregulated in the early stages of learning and the time course was correlated with the fast process. While some propose that the fast process reflects an explicit strategy, we argue instead that it may be a proxy for the feedback controller.

Sensorimotor control and linear visuomotor gains

In everyday life, we often use graphical interfaces where the visual space is mapped to the motor space with a visuomotor gain called the control display gain. One of the key objectives in the field of Human Computer Interaction is to design this control display gain so as to enhance users' performance. Although the control display gain involved in operating systems has been found to improve users' pointing performance, the reasons for this improvement have not yet been fully elucidated, especially because the control display gains on operating systems are both non-constant and non-linear. Here, we tested non-constant but linear velocity-based control display gains to determine which parameters were responsible for pointing performance changes based on analyses of the movement kinematics. Using a Fitts' paradigm, constant gains of 1 and 3 were compared with a linearly increasing gain (i.e., the control display gain increases with the motor velocity) and a decreasing gain (i.e., the control display gain decreases with the motor velocity). Three movements with various indexes of difficulty (ID) were tested (3, 5 and 7 bits). The increasing gain was expected to increase the velocity of the initial impulse phase and decrease that of the correction phase, thus decreasing the movement time (MT), and the contrary in the case of the decreasing gain. Although the decreasing gain increased MT at ID3, the increasing gain was found to be less efficient than the constant gain of 3, probably because a non-constant gain between the motion and its visual consequences disrupted the sensorimotor control. In addition, the kinematic analyses of the movements suggested that the motion profile was planned by the central nervous system based on the visuomotor gain at maximum motor velocity, as common features were observed between the constant gain of 1 and the decreasing gain, and between the constant gain of 3 and the increasing gain. By contrast, the amplitude of the velocity profile seemed to be specific to each particular visuomotor mapping process.

Both reaching and grasping are impacted by temporarily induced paresthesia

Along with visual feedback, somatosensory feedback provides the nervous system with information regarding movement performance. Somatosensory system damage disrupts the normal feedback process, which can lead to a pins and needles sensation, or paresthaesia, and impaired movement control. The present study assessed the impact of temporarily induced median nerve paresthaesia, in individuals with otherwise intact sensorimotor function, on goal-directed reaching and grasping movements. Healthy, right-handed participants performed reach and grasp movements to five wooden Efron shapes, of which three were selected for analysis. Participants performed the task without online visual feedback and in two somatosensory conditions: 1) normal; and 2) disrupted somatosensory feedback. Disrupted somatosensory feedback was induced temporarily using a Digitimer (DS7AH) constant current stimulator. Participants' movements to shapes 15 or 30 cm to the right of the hand's start position were recorded using a 3 D motion analysis system at 300 Hz (Optotrak 3 D Investigator). Analyses revealed no significant differences for reaction time. Main effects for paresthaesia were observed for temporal and spatial aspects of the both the reach and grasp components of the movements. Although participants scaled their grip aperture to shape size under paresthaesia, the movements were smaller and more variable. Overall participants behaved as though they perceived they were performing larger and faster movements than they actually were. We suggest the presence of temporally induced paresthaesia affected online control by disrupting somatosensory feedback of the reach and grasp movements, ultimately leading to smaller forces and fewer corrective movements.

Distinct sensorimotor feedback loops for dynamic and static control of primate precision grip

Volitional limb motor control involves dynamic and static muscle actions. It remains elusive how such distinct actions are controlled through separated or shared neural circuits. Here we explored the potential separation for dynamic and static controls in primate hand actions, by investigating the neuronal coherence between local field potentials (LFPs) of the spinal cord and the forelimb electromyographic activity (EMGs), and LFPs of the motor cortex and the EMGs during the performance of a precision grip in macaque monkeys. We observed the emergence of beta-range coherence with EMGs at spinal cord and motor cortex in the separated phases; spinal coherence during the grip phase and cortical coherence during the hold phase. Further, both of the coherences were influenced by bidirectional interactions with reasonable latencies as beta oscillatory cycles. These results indicate that dedicated feedback circuits comprising spinal and cortical structures underlie dynamic and static controls of dexterous hand actions.

Computational Evidence for Underweighting of Current Error and Overestimation of Future Error in Anxious Individuals

BACKGROUND

Real-time control of goal-directed actions requires continuous adjustments in response to both current error (i.e., distance from goal state) and predicted future error. Proportion-integral-derivative control models, which are extensively used in the automated control of industrial processes, formalize this intuition. Previous computational approaches to anxiety have separately addressed behavioral inhibition and exaggerated error processing, but a proportion-integral-derivative control approach that decomposes error processing into current and anticipated error could integrate these accounts and extend them to a real-time sensorimotor control domain.

METHODS

We applied a simplified proportion-derivative control model to a virtual driving task in a transdiagnostic psychiatric sample of 317 individuals and computed a drive parameter (weighting of current error) and a damping parameter (weighting of the rate of change of error, enabling adjustment based on future error).

RESULTS

Self-reported fear, but not negative affect, was selectively associated with lower drive and lower damping. Those individuals that were characterized by lower drive and damping also exhibited lower caudal anterior cingulate cortex, but not insula, volume in a structural magnetic resonance imaging analysis.

CONCLUSIONS

The proportion-derivative control approach reveals that fear is specifically associated with reduced weighting of current error and overestimation of future error, resulting in both approach inhibition and overcorrecting overshoots around a goal state.

Force accuracy rather than high stiffness is associated with faster learning and reduced falls in human balance

Balance requires the centre of mass to be maintained within the base of support. This can be achieved by minimising position sway (stiffness control: SC) or minimising force error (force accuracy control: FAC). Minimising sway reduces exploration of system properties, whereas minimising force error maximizes accurate mapping of the force vs position. We hypothesise that (i) FAC is associated with faster learning and fewer falls whereas (ii) SC is not. Fifteen participants used myoelectric signals from their legs to maintain balance of an actuated, inverted pendulum, to which they were strapped. Using challenging perturbations, participants were trained to maintain balance without falling within five sessions and tested before (PRE) and after (POST) training. We quantified FAC as 'change (POST-PRE) in correlation of force with position' and SC as 'change in sway'. PRE training, five measures (sway, acceleration, co-contraction, effort, falls) showed no correlation with either FAC or SC. POST training, reduced fall rate, effort and acceleration correlated with FAC metric. SC correlated only with reduced sway. Unlike sway minimisation, development of force accuracy was associated with learning and reduced falls. These results support that accurate force estimation allowing movement is more relevant than stiffness to improve balance and prevent falls.

Using machine learning to reveal the population vector from EEG signals

OBJECTIVE

Since the discovery of the population vector that directly relates neural spiking activity with arm movement direction, it has become feasible to control robotic arms and neuroprostheses using invasively recorded brain signals. For non-invasive approaches, a direct relation between human brain signals and arm movement direction is yet to be established.

APPROACH

Here, we investigated electroencephalographic (EEG) signals in temporal and spectral domains in a continuous, circular arm movement task. Using machine learning methods that respect the linear mixture of brain activity within EEG signals, we show that directional information is represented in the temporal domain in amplitude modulations of the same frequency as the arm movement, and in the spectral domain in power modulations of the 20-24 Hz frequency band.

MAIN RESULTS

In the temporal domain, the directional information was mainly expressed in primary sensorimotor cortex (SM1) and posterior parietal cortex (PPC) contralateral to the moving arm, while in the spectral domain SM1 and PPC of both hemispheres predicted arm movement direction. The different cortical representations suggest distinct neural representations in both domains.

SIGNIFICANCE

This direct relation between neural activity and arm movement direction in both domains demonstrates the potential of machine learning to reveal neuroscientific insights about the dynamics of human arm movements.

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

Understanding the underpinnings of biological motor control is an important issue in movement neuroscience. Optimal control theory is a leading framework to rationalize this problem in computational terms. Previously, optimal control models have been devised either in deterministic or in stochastic settings to account for different aspects of motor control (e.g. average behavior versus trial-to-trial variability). While these approaches have yielded valuable insights about motor control, they typically fail in explaining muscle co-contraction. Co-contraction of a group of muscles associated to a motor function (e.g. agonist and antagonist muscles spanning a joint) contributes to modulate the mechanical impedance of the neuromusculoskeletal system (e.g. joint viscoelasticity) and is thought to be mainly under the influence of descending signals from the brain. Here we present a theory suggesting that one primary goal of motor planning may be to issue feedforward (open-loop) motor commands that optimally specify both force and impedance, according to noisy neuromusculoskeletal dynamics and to optimality criteria based on effort and variance. We show that the proposed framework naturally accounts for several previous experimental findings regarding the regulation of force and impedance via muscle co-contraction in the upper-limb. Stochastic optimal (closed-loop) control, preprogramming feedback gains but requiring on-line state estimation processes through long-latency sensory feedback loops, may then complement this nominal feedforward motor command to fully determine the limb’s mechanical impedance. The proposed stochastic optimal open-loop control theory may provide new insights about the general articulation of feedforward/feedback control mechanisms and justify the occurrence of muscle co-contraction in the neural control of movement.

This study presents a novel computational theory to explain the planning of force and impedance (e.g. viscoelasticity) in the neural control of movement. It assumes that one main goal of motor planning is to elaborate feedforward motor commands that determine both the force and the impedance required for the task at hand. These feedforward motor commands (i.e. that are defined prior to movement execution) are designed to minimize effort and variance costs considering the uncertainty arising from sensorimotor or environmental noise. A major outcome of this mathematical framework is the explanation of muscle co-contraction (i.e. the concurrent contraction of a group of muscles involved in a motor function). Muscle co-contraction has been shown to occur in many situations but previous modeling works struggled to account for it. Although effortful, co-contraction contributes to increase the robustness of motor behavior (e.g. small variance) upstream of sophisticated optimal closed-loop control processes that require state estimation from delayed sensory feedback to function. This work may have implications regarding our understanding of the neural control of movement in computational terms. It also provides a theoretical ground to explain how to optimally plan force and impedance within a general and versatile framework.

Brain mechanisms in motor control during reaching movements: Transition of functional connectivity according to movement states

Understanding how the brain controls movements is a critical issue in neuroscience. The role of brain changes rapidly according to movement states. To elucidate the motor control mechanism of brain, it is essential to investigate the changes in brain network in motor-related regions according to movement states. Therefore, the objective of this study was to investigate the brain network transitions according to movement states. We measured whole brain magnetoencephalography (MEG) signals and extracted source signals in 24 motor-related areas. Functional connectivity and centralities were calculated according to time flow. Our results showed that brain networks differed between states of motor planning and movement. Connectivities between most motor-related areas were increased in the motor-planning state. In contrast, only connectivities with cerebellum and basal ganglia were increased while those of other motor-related areas were decreased during movement. Our results indicate that most processes involved in motor control are completed before movement. Further, brain developed network related to feedback rather than motor decision during movements. Our findings also suggest that neural signals during motor planning might be more predictive than neural signals during movement. They facilitate accurate prediction of movement for brain-machine interfaces and provide insight into brain mechanisms in motor control.

Hierarchical motor control in mammals and machines

Advances in artificial intelligence are stimulating interest in neuroscience. However, most attention is given to discrete tasks with simple action spaces, such as board games and classic video games. Less discussed in neuroscience are parallel advances in "synthetic motor control". While motor neuroscience has recently focused on optimization of single, simple movements, AI has progressed to the generation of rich, diverse motor behaviors across multiple tasks, at humanoid scale. It is becoming clear that specific, well-motivated hierarchical design elements repeatedly arise when engineering these flexible control systems. We review these core principles of hierarchical control, relate them to hierarchy in the nervous system, and highlight research themes that we anticipate will be critical in solving challenges at this disciplinary intersection.

Neural correlates of proprioceptive upper limb position matching

Proprioceptive information allows humans to perform smooth coordinated movements by constantly updating one's mind with knowledge of the position of one's limbs in space. How this information is combined with other sensory modalities and centrally processed to form conscious perceptions of limb position remains relatively unknown. What has proven even more elusive is pinpointing the contribution of proprioception in cortical activity related to motion. This study addresses these gaps by examining electrocortical dynamics while participants performed an upper limb position matching task in two conditions, namely with proprioceptive feedback or with both visual and proprioceptive feedback. Specifically, we evaluated the reduction of the electroencephalographic power (desynchronization) in the μ frequency band (8-12 Hz), which is known to characterize the neural activation associated with motor control and behavior. We observed a stronger desynchronization in the left motor and somatosensory areas, contralateral to the moving limb while, parietal and occipital regions, identifying association and visual areas, respectively, exhibited a similar activation level in the two hemispheres. Pertaining to the influence of the two experimental conditions it affected only movement's offset, and precisely we found that when matching movements are performed relying only on proprioceptive information, a lower cortical activity is entailed. This effect was strongest in the visual and association areas, while there was a minor effect in the hand motor and somatosensory areas.

Robust Control in Human Reaching Movements: A Model-Free Strategy to Compensate for Unpredictable Disturbances

Current models of motor learning suggest that multiple timescales support adaptation to changes in visual or mechanical properties of the environment. These models capture patterns of learning and memory across a broad range of tasks, yet do not consider the possibility that rapid changes in behavior may occur without adaptation. Such changes in behavior may be desirable when facing transient disturbances, or when unpredictable changes in visual or mechanical properties of the task make it difficult to form an accurate model of the perturbation. Whether humans can modulate control strategies without an accurate model of the perturbation remains unknown. Here we frame this question in the context of robust control (H _∞-control), a control strategy that specifically considers unpredictable disturbances by increasing initial movement speed and feedback gains. Correspondingly, we demonstrate in two human reaching experiments including males and females that the occurrence of a single unpredictable disturbance led to an increase in movement speed and in the gain of rapid feedback responses to mechanical disturbances on subsequent movements. This strategy reduced perturbation-related motion regardless of the direction of the perturbation. Furthermore, we found that changes in the control strategy were associated with co-contraction, which amplified the gain of muscle responses to both lengthening and shortening perturbations. These results have important implications for studies on motor adaptation because they highlight that trial-by-trial changes in limb motion also reflected changes in control strategies dissociable from error-based adaptation.SIGNIFICANCE STATEMENT Humans and animals use internal representations of movement dynamics to anticipate the impact of predictable disturbances. However, we are often confronted with transient or unpredictable disturbances, and it remains unknown whether and how the nervous system handles these disturbances over fast time scales. Here we hypothesized that humans can modulate their control strategy to make reaching movements less sensitive to perturbations. We tested this hypothesis in the framework of robust control, and found changes in movement speed and feedback gains consistent with the model predictions. These changes impacted participants' behavior on a trial-by-trial basis. We conclude that compensation for disturbances over fast time scales involves a robust control strategy, which potentially plays a key role in motor planning and execution.

The FACTS model of speech motor control: Fusing state estimation and task-based control

We present a new computational model of speech motor control: the Feedback-Aware Control of Tasks in Speech or FACTS model. FACTS employs a hierarchical state feedback control architecture to control simulated vocal tract and produce intelligible speech. The model includes higher-level control of speech tasks and lower-level control of speech articulators. The task controller is modeled as a dynamical system governing the creation of desired constrictions in the vocal tract, after Task Dynamics. Both the task and articulatory controllers rely on an internal estimate of the current state of the vocal tract to generate motor commands. This estimate is derived, based on efference copy of applied controls, from a forward model that predicts both the next vocal tract state as well as expected auditory and somatosensory feedback. A comparison between predicted feedback and actual feedback is then used to update the internal state prediction. FACTS is able to qualitatively replicate many characteristics of the human speech system: the model is robust to noise in both the sensory and motor pathways, is relatively unaffected by a loss of auditory feedback but is more significantly impacted by the loss of somatosensory feedback, and responds appropriately to externally-imposed alterations of auditory and somatosensory feedback. The model also replicates previously hypothesized trade-offs between reliance on auditory and somatosensory feedback and shows for the first time how this relationship may be mediated by acuity in each sensory domain. These results have important implications for our understanding of the speech motor control system in humans.

Modeling the Role of Sensory Feedback in Speech Motor Control and Learning

Purpose While the speech motor system is sensitive to feedback perturbations, sensory feedback does not seem to be critical to speech motor production. How the speech motor system is able to be so flexible in its use of sensory feedback remains an open question. Method We draw on evidence from a variety of disciplines to summarize current understanding of the sensory systems' role in speech motor control, including both online control and motor learning. We focus particularly on computational models of speech motor control that incorporate sensory feedback, as these models provide clear encapsulations of different theories of sensory systems' function in speech production. These computational models include the well-established directions into velocities of articulators model and computational models that we have been developing in our labs based on the domain-general theory of state feedback control (feedback aware control of tasks in speech model). Results After establishing the architecture of the models, we show that both the directions into velocities of articulators and state feedback control/feedback aware control of tasks models can replicate key behaviors related to sensory feedback in the speech motor system. Although the models agree on many points, the underlying architecture of the 2 models differs in a few key ways, leading to different predictions in certain areas. We cover key disagreements between the models to show the limits of our current understanding and point toward areas where future experimental studies can resolve these questions. Conclusions Understanding the role of sensory information in the speech motor system is critical to understanding speech motor production and sensorimotor learning in healthy speakers as well as in disordered populations. Computational models, with their concrete implementations and testable predictions, are an important tool to understand this process. Comparison of different models can highlight areas of agreement and disagreement in the field and point toward future experiments to resolve important outstanding questions about the speech motor control system.

Decision-making in sensorimotor control

Skilled sensorimotor interactions with the world result from a series of decision-making processes that determine, on the basis of information extracted during the unfolding sequence of events, which movements to make and when and how to make them. Despite this inherent link between decision-making and sensorimotor control, research into each of these two areas has largely evolved in isolation, and it is only fairly recently that researchers have begun investigating how they interact and, together, influence behaviour. Here, we review recent behavioural, neurophysiological and computational research that highlights the role of decision-making processes in the selection, planning and control of goal-directed movements in humans and nonhuman primates.

Reflex arc recovery after spinal cord dorsal root repair with platelet rich plasma (PRP)

Spinal dorsal roots can be affected by a wide range of lesions, leading to a significant loss of proprioceptive information transmission and greatly affecting motor behavior. In this context, the reimplantation of lesioned roots with platelet-rich plasma (PRP) may allow nerve regeneration. Therefore, the present study evaluated sensorimotor improvement following dorsal root rhizotomy and repair with PRP. For this purpose, female Lewis rats were subjected to unilateral rhizotomy (RZ) of the L4-L6 dorsal roots and divided into the following groups: (1) the unlesioned control group; (2) the group that underwent rhizotomy (RZ) without repair; and (3) the group that underwent RZ followed by root repair with PRP. PRP was obtained from human blood and characterized regarding platelet concentration, integrity, and viability. Reflex arc recovery was evaluated weekly for eight weeks by the electronic von Frey method. The spinal cords were processed 1 week postlesion to evaluate the in vivo gene expression of TNFα, TGF-β, BDNF, GDNF, VEGF, NGF, IL-4, IL-6, IL-13 by qRT-PCR and eight weeks postlesion to evaluate changes in the glial response (GFAP and Iba-1) and excitatory synaptic circuits (VGLUT1) by immunofluorescence. The results indicated that PRP therapy partially restores the paw withdrawal reflex over time, indicating the reentry of primary afferents from the dorsal root ganglia into the spinal cord without exacerbating glial reactivity. Additionally, the analysis of mRNA levels showed that PRP therapy has immunomodulatory properties. Overall, the present data suggest that the repair of dorsal roots with PRP may be considered a promising approach to improve sensorimotor recovery following dorsal rhizotomy.

Older adults exhibit a more pronounced modulation of beta oscillations when performing sustained and dynamic handgrips

Muscle contractions are associated with a decrease in beta oscillatory activity, known as movement-related beta desynchronization (MRBD). Older adults exhibit a MRBD of greater amplitude compared to their younger counterparts, even though their beta power remains higher both at rest and during muscle contractions. Further, a modulation in MRBD has been observed during sustained and dynamic pinch contractions, whereby beta activity during periods of steady contraction following a dynamic contraction is elevated. However, how the modulation of MRBD is affected by aging has remained an open question. In the present work, we investigated the effect of aging on the modulation of beta oscillations and their putative link with motor performance. We collected magnetoencephalography (MEG) data from younger and older adults during a resting-state period and motor handgrip paradigms, which included sustained and dynamic contractions, to quantify spontaneous and motor-related beta oscillatory activity. Beta power at rest was found to be significantly increased in the motor cortex of older adults. During dynamic hand contractions, MRBD was more pronounced in older participants in frontal, premotor and motor brain regions. These brain areas also exhibited age-related decreases in cortical thickness; however, the magnitude of MRBD and cortical thickness were not found to be associated after controlling for age. During sustained hand contractions, MRBD exhibited a decrease in magnitude compared to dynamic contraction periods in both groups and did not show age-related differences. This suggests that the amplitude change in MRBD between dynamic and sustained contractions is larger in older compared to younger adults. We further probed for a relationship between beta oscillations and motor behaviour and found that greater MRBD in primary motor cortices was related to degraded motor performance beyond age, but our results suggested that age-related differences in beta oscillations were not predictive of motor performance.

Handedness Matters for Motor Control But Not for Prediction

Skilled motor behavior relies on the ability to control the body and to predict the sensory consequences of this control. Although there is ample evidence that manual dexterity depends on handedness, it remains unclear whether control and prediction are similarly impacted. To address this issue, right-handed human participants performed two tasks with either the right or the left hand. In the first task, participants had to move a cursor with their hand so as to track a target that followed a quasi-random trajectory. This hand-tracking task allowed testing the ability to control the hand along an imposed trajectory. In the second task, participants had to track with their eyes a target that was self-moved through voluntary hand motion. This eye-tracking task allowed testing the ability to predict the visual consequences of hand movements. As expected, results showed that hand tracking was more accurate with the right hand than with the left hand. In contrast, eye tracking was similar in terms of spatial and temporal gaze attributes whether the target was moved by the right or the left hand. Although these results extend previous evidence for different levels of control by the two hands, they show that the ability to predict the visual consequences of self-generated actions does not depend on handedness. We propose that the greater dexterity exhibited by the dominant hand in many motor tasks stems from advantages in control, not in prediction. Finally, these findings support the notion that prediction and control are distinct processes.

Reviewing the Variability-Overuse Injury Hypothesis: Does Movement Variability Relate to Landing Injuries?

PURPOSE

Overuse injuries are common in sport, but complete understanding of injury risk factors remains incomplete. Although biomechanical studies frequently examine musculoskeletal injury mechanisms, human movement variability studies aim to better understand neuromotor functioning, with proposed connections between overuse injury mechanisms and changes in motor variability.

METHOD

In a narrative review, we discuss the variability-overuse injury hypothesis, which suggests repeated load application leads to mechanical tissue breakdown and subsequent injury when exceeding the rate of physiological adaptation. Due to the multidisciplinary nature of this hypothesis, we incorporate concepts from motor control, neurophysiology, biomechanics, as well as research design and data analysis. We therefore summarize multiple perspectives while proposing theoretical relationships between movement variability and lower extremity overuse injuries.

RESULTS

Experimental data are presented and summarized from published experiments examining interactions between experimental task demands and movement variability in the context of drop landing movements, along with comparisons to previous movement variability studies.

CONCLUSION

We provide a conceptual framework for sports medicine researchers interested in predicting and preventing sports injuries. Under performance conditions with greater task demands, we predict reduced trial-to-trial movement variability that could increase the likelihood of overuse injuries.

Large-scale changes in cortical dynamics triggered by repetitive somatosensory electrical stimulation

Background

Repetitive somatosensory electrical stimulation (SES) of forelimb peripheral nerves is a promising therapy; studies have shown that SES can improve motor function in stroke subjects with chronic deficits. However, little is known about how SES can directly modulate neural dynamics. Past studies using SES have primarily used noninvasive methods in human subjects. Here we used electrophysiological recordings from the rodent primary motor cortex (M1) to assess how SES affects neural dynamics at the level of single neurons as well as at the level of mesoscale dynamics.

Methods

We performed acute extracellular recordings in 7 intact adult Long Evans rats under ketamine-xylazine anesthesia while they received transcutaneous SES. We recorded single unit spiking and local field potentials (LFP) in the M1 contralateral to the stimulated arm. We then compared neural firing rate, spike-field coherence (SFC), and power spectral density (PSD) before and after stimulation.

Results

Following SES, the firing rate of a majority of neurons changed significantly from their respective baseline values. There was, however, a diversity of responses; some neurons increased while others decreased their firing rates. Interestingly, SFC, a measure of how a neuron’s firing is coupled to mesoscale oscillatory dynamics, increased specifically in the δ-band, also known as the low frequency band (0.3- 4 Hz). This increase appeared to be driven by a change in the phase-locking of broad-spiking, putative pyramidal neurons. These changes in the low frequency range occurred without a significant change in the overall PSD.

Conclusions

Repetitive SES significantly and persistently altered the local cortical dynamics of M1 neurons, changing both firing rates as well as the SFC magnitude in the δ-band. Thus, SES altered the neural firing and coupling to ongoing mesoscale dynamics. Our study provides evidence that SES can directly modulate cortical dynamics.

Internal Models in Biological Control

Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

Reaction Time Improvements by Neural Bistability

The often reported reduction of Reaction Time (RT) by Vision Training) is successfully replicated by 81 athletes across sports. This enabled us to achieve a mean reduction of RTs for athletes eye-hand coordination of more than 10%, with high statistical significance. We explain how such an observed effect of Sensorimotor systems' plasticity causing reduced RT can last in practice for multiple days and even weeks in subjects, via a proof of principle. Its mathematical neural model can be forced outside a previous stable (but long) RT into a state leading to reduced eye-hand coordination RT, which is, again, in a stable neural state.

The Role of Motor Activity in Insight Problem Solving (the Case of the Nine-Dot Problem)

Attempts to estimate the contribution made by motor activity to insight problem solving is hindered by a lack of detailed description of motor behavior. The goal of this study was to develop and put to the test a novel method for studying the dynamics of insight problem solving based on a quantitative analysis of ongoing motor activity. As a proper problem model, we chose the nine-dot problem (Maier, 1930), in which solvers had to draw a sequence of connected line segments. Instead of using the traditional pen-and-paper way of solving the nine-dot problem we asked participants to use their index finger to draw line segments on the surface of a tablet computer. We are arguing that successful studying of the role of motor activity during problem solving requires the distinction between its instrumental and functional role. We considered the functional role on the motor activity as closely related to the on-line mode of motor planning. The goal of Experiment 1 was to explore the potential power of the method and, at the same time, to assay the patterns of motor activity related to on-line and off-line modes of motor planning. Experiments 2 and 3 were designed to uncover the potential impact of preliminary motor training on the motor output of successful and unsuccessful problem solvers. In these experiments, we tested hypotheses on how preliminary motor training, which presumably played a functional role in Experiment 2 and an instrumental role in Experiment 3, affects the motor activity of a problem solver and hence their effectiveness in solving the problem. The three experiments showed consistent results. They suggest that successful solving of the nine-dot problem relies upon the functional role of motor activity and requires both off-line and on-line modes of motor planning, with the latter helping to overcome the perceptual constraints imposed by a spatial arrangement of the nine dots. The method that we applied allows for systematic comparison between successful and unsuccessful problem solvers based on the quantitative parameters of their motor activity. Through it, we found new specific patterns of motor activity that differentiate successful and unsuccessful solvers.

A hypothetical neural network model for generation of human precision grip

Humans can stably hold and skillfully manipulate an object by coordinated control of a complex, redundant musculoskeletal system. However, how the human central nervous system actually accomplishes precision grip tasks by coordinated control of fingertip forces remains unclear. In the present study, we aimed to construct a hypothetical neural network model that can spontaneously generate humanlike precision grip. The nervous system was modeled as a recurrent neural network model prescribing kinematic and kinetic constraints that must be satisfied in precision grip tasks in the form of energy functions. The recurrent neural network autonomously behaves so as to decrease the energy functions; therefore, given the estimated mass and center-of-mass location of the target object, the nervous system model can spontaneously generate muscle activation signals that achieve stable precision grips due to dynamic relaxation of the energy functions embedded in the nervous system. Fingertip forces are modulated by sensory information about slip between the object and fingertips. A two-dimensional musculoskeletal model of the human hand with a thumb and an index finger was constructed. Forward dynamic simulation of the precision grip was performed using the proposed neural network model. Our results demonstrated that the proposed neural network model could stably pinch and successfully hold up the object in various conditions, including changes in friction, object shape, object mass, and center-of-mass location. The proposed hypothetical neuro-computational model may possibly explain some aspects of the control strategy humans use for precision grip.