The computational and neural basis of voluntary motor control and planning

Asymmetrical Relationship between Prediction and Control during Visuomotor Adaptation

Current theories suggest that the ability to control the body and to predict its associated sensory consequences is key for skilled motor behavior. It is also suggested that these abilities need to be updated when the mapping between motor commands and sensory consequences is altered. Here we challenge this view by investigating the transfer of adaptation to rotated visual feedback between one task in which human participants had to control a cursor with their hand in order to track a moving target, and another in which they had to predict with their eyes the visual consequences of their hand movement on the cursor. Hand and eye tracking performances were evaluated respectively through cursor–target and eye–cursor distance. Results reveal a striking dissociation: although prior adaptation of hand tracking greatly facilitates eye tracking, the adaptation of eye tracking does not transfer to hand tracking. We conclude that although the update of control is associated with the update of prediction, prediction can be updated independently of control. To account for this pattern of results, we propose that task demands mediate the update of prediction and control. Although a joint update of prediction and control seemed mandatory for success in our hand tracking task, the update of control was only facultative for success in our eye tracking task. More generally, those results promote the view that prediction and control are mediated by separate neural processes and suggest that people can learn to predict movement consequences without necessarily promoting their ability to control these movements.

Continuously updating one’s predictions underlies successful interception

This paper reviews our understanding of the interception of moving objects. Interception is a demanding task that requires both spatial and temporal precision. The required precision must be achieved on the basis of imprecise and sometimes biased sensory information. We argue that people make precise interceptive movements by continuously adjusting their movements. Initial estimates of how the movement should progress can be quite inaccurate. As the movement evolves, the estimate of how the rest of the movement should progress gradually becomes more reliable as prediction is replaced by sensory information about the progress of the movement. The improvement is particularly important when things do not progress as anticipated. Constantly adjusting one’s estimate of how the movement should progress combines the opportunity to move in a way that one anticipates will best meet the task demands with correcting for any errors in such anticipation. The fact that the ongoing movement might have to be adjusted can be considered when determining how to move, and any systematic anticipation errors can be corrected on the basis of the outcome of earlier actions.

The center of pressure and ankle muscle co-contraction in response to anterior-posterior perturbations

Though both contraction of agonist muscles and co-contraction of antagonistic muscle pairs across the ankle joint are essential to postural stability, they are perceived to operate independently of each other, In an antagonistic setup, agonist muscles contract generating moment about the joint, while antagonist muscles contract generating stiffness across the joint. While both work together in maintaining robustness in the face of external perturbations, contractions of agonist muscles and co-contractions of antagonistic muscle pairs across the ankle joint play different roles in responding to and adapting to external perturbations. To determine their respective roles, we exposed participants to repeated perturbations in both large and small magnitudes. The center of pressure (COP) and a co-contraction index (CCI) were used to quantify the activation of agonist muscles and antagonistic muscle pairs across the ankle joint. Our results found that participants generated moment of a large magnitude across the ankle joint—a large deviation in the COP curve—in response to perturbations of a large magnitude (p <0.05), whereas the same participants generated higher stiffness about the ankle—a larger value in CCI—in response to perturbations of a small magnitude (p <0.05). These results indicate that participants use different postural strategies pertaining to circumstances. Further, the moment across the ankle decreased with repetitions of the same perturbation (p <0.05), and CCI tended to remain unchanged even in response to a different perturbation following repetition of the same perturbation (p <0.05). These findings suggest that ankle muscle contraction and co-contraction play different roles in regaining and maintaining postural stability. This study demonstrates that ankle moment and stiffness are not correlated in response to external perturbations.

Toward mechanistic models of action-oriented and detached cognition

To be successful, the research agenda for a novel control view of cognition should foresee more detailed, computationally specified process models of cognitive operations including higher cognition. These models should cover all domains of cognition, including those cognitive abilities that can be characterized as online interactive loops and detached forms of cognition that depend on internally generated neuronal processing.

Neck muscle biomechanics and neural control

The mechanics, morphometry, and geometry of our joints, segments, and muscles are fundamental biomechanical properties intrinsic to human neural control. The goal of our study was to investigate whether the biomechanical actions of individual neck muscles predict their neural control. Specifically, we compared the moment direction and variability produced by electrical stimulation of a neck muscle (biomechanics) to the preferred activation direction and variability (neural control). Subjects sat upright with their head fixed to a six-axis load cell and their torso restrained. Indwelling wire electrodes were placed into the sternocleidomastoid (SCM), splenius capitis (SPL), and semispinalis capitis (SSC) muscles. The electrically stimulated direction was defined as the moment direction produced when a current (2-19 mA) was passed through each muscle's electrodes. Preferred activation direction was defined as the vector sum of the spatial tuning curve built from root mean squared electromyogram when subjects produced isometric moments at 7.5% and 15% of their maximum voluntary contraction (MVC) in 26 three-dimensional directions. The spatial tuning curves at 15% MVC were well defined (unimodal, P < 0.05), and their preferred directions were 23°, 39°, and 21° different from their electrically stimulated directions for the SCM, SPL, and SSC, respectively ( P < 0.05). Intrasubject variability was smaller in electrically stimulated moment directions compared with voluntary preferred directions, and intrasubject variability decreased with increased activation levels. Our findings show that the neural control of neck muscles is not based solely on optimizing individual muscle biomechanics but, as activation increases, biomechanical constraints in part dictate the activation of synergistic neck muscles. NEW & NOTEWORTHY Biomechanics are an intrinsic part of human neural control. In this study, we found that the biomechanics of individual neck muscles cannot fully predict their neural control. Consequently, physiologically based computational neck muscle controllers cannot calculate muscle activation schemes based on the isolated biomechanics of muscles. Furthermore, by measuring biomechanics we showed that the intrasubject variability of the neural control was lower for electrical vs. voluntary activation of the neck muscles.

Detailed somatotopy in primary motor and somatosensory cortex revealed by Gaussian population receptive fields

The relevance of human primary motor cortex (M1) for motor actions has long been established. However, it is still unknown how motor actions are represented, and whether M1 contains an ordered somatotopy at the mesoscopic level. In the current study we show that a detailed within-limb somatotopy can be obtained in M1 during finger movements using Gaussian population Receptive Field (pRF) models. Similar organizations were also obtained for primary somatosensory cortex (S1), showing that individual finger representations are interconnected throughout sensorimotor cortex. The current study additionally estimates receptive field sizes of neuronal populations, showing differences between finger digit representations, between M1 and S1, and additionally between finger digit flexion and extension. Using the Gaussian pRF approach, the detailed somatotopic organization of M1 can be obtained including underlying characteristics, allowing for the in-depth investigation of cortical motor representation and sensorimotor integration.

Humans control stride-to-stride stepping movements differently for walking and running, independent of speed

As humans walk or run, external (environmental) and internal (physiological) disturbances induce variability. How humans regulate this variability from stride-to-stride can be critical to maintaining balance. One cannot infer what is "controlled" based on analyses of variability alone. Assessing control requires quantifying how deviations are corrected across consecutive movements. Here, we assessed walking and running, each at two speeds. We hypothesized differences in speed would drive changes in variability, while adopting different gaits would drive changes in how people regulated stepping. Ten healthy adults walked/ran on a treadmill under four conditions: walk or run at comfortable speed, and walk or run at their predicted walk-to-run transition speed. Time series of relevant stride parameters were analyzed to quantify variability and stride-to-stride error-correction dynamics within a Goal-Equivalent Manifold (GEM) framework. In all conditions, participants' stride-to-stride control respected a constant-speed GEM strategy. At each consecutively faster speed, variability tangent to the GEM increased (p ≤ 0.031), while variability perpendicular to the GEM decreased (p ≤ 0.044). There were no differences (p ≥ 0.999) between gaits at the transition speed. Differences in speed determined how stepping variability was structured, independent of gait, confirming our first hypothesis. For running versus walking, measures of GEM-relevant statistical persistence were significantly less (p ≤ 0.004), but showed minimal-to-no speed differences (0.069 ≤ p ≤ 0.718). When running, people corrected deviations both more quickly and more directly, each indicating tighter control. Thus, differences in gait determined how stride-to-stride fluctuations were regulated, independent of speed, confirming our second hypothesis.

Low Gain Servo Control During the Kohnstamm Phenomenon Reveals Dissociation Between Low-Level Control Mechanisms for Involuntary vs. Voluntary Arm Movements

The Kohnstamm phenomenon is a prolonged involuntary aftercontraction following a sustained voluntary isometric muscle contraction. The control principles of the Kohnstamm have been investigated using mechanical perturbations, but previous studies could not dissociate sensorimotor responses to perturbation from effects of gravity. We induced a horizontal, gravity-independent Kohnstamm movement around the shoulder joint, and applied resistive or assistive torques of 0.5 Nm after 20° angular displacement. A No perturbation control condition was included. Further, participants made velocity-matched voluntary movements, with or without similar perturbations, yielding a 2 × 3 factorial design. Resistive perturbations produced an increase in agonist electromyography (EMG), in both Kohnstamm and voluntary movements, while assistive perturbations produced a decrease. While overall Kohnstamm EMGs were greater than voluntary EMGs, the EMG responses to perturbation, when expressed as a percentage of unperturbed EMG activity, were significantly smaller during Kohnstamm movements than during voluntary movements. The results suggest that the Kohnstamm aftercontraction involves a central drive, coupled with low-gain servo control by a negative feedback loop between afferent input and a central motor command. The combination of strong efferent drive with low reflex gain may characterize involuntary control of postural muscles. Our results question traditional accounts involving purely reflexive mechanisms of postural maintenance. They also question existing high-gain, peripheral accounts of the Kohnstamm phenomenon, as well as accounts involving a central adaptation interacting with muscle receptors via a positive force feedback loop.

Optimizing the Usability of Brain-Computer Interfaces

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Eye-hand coordination during flexible manual interception of an abruptly appearing, moving target

As a vital skill in an evolving world, interception of moving objects relies on accurate prediction of target motion. In natural circumstances, active gaze shifts often accompany hand movements when exploring targets of interest, but how eye and hand movements are coordinated during manual interception and their dependence on visual prediction remain unclear. Here, we trained gaze-unrestrained monkeys to manually intercept targets appearing at random locations and circularly moving with random speeds. We found that well-trained animals were able to intercept the targets with adequate compensation for both sensory transmission and motor delays. Before interception, the animals' gaze followed the targets with adequate compensation for the sensory delay, but not for extra target displacement during the eye movements. Both hand and eye movements were modulated by target kinematics, and their reaction times were correlated. Moreover, retinal errors and reaching errors were correlated across different stages of reach execution. Our results reveal eye-hand coordination during manual interception, yet the eye and hand movements may show different levels of prediction based on the task context.

NEW & NOTEWORTHY Here we studied the eye-hand coordination of monkeys during flexible manual interception of a moving target. Eye movements were untrained and not explicitly associated with reward. We found that the initial saccades toward the moving target adequately compensated for sensory transmission delays, but not for extra target displacement, whereas the reaching arm movements fully compensated for sensorimotor delays, suggesting that the mode of eye-hand coordination strongly depends on behavioral context.

State Estimation for Early Feedback Responses in Reaching: Intramodal or Multimodal?

Humans are highly skilled in controlling their reaching movements, making fast and task-dependent movement corrections to unforeseen perturbations. To guide these corrections, the neural control system requires a continuous, instantaneous estimate of the current state of the arm and body in the world. According to Optimal Feedback Control theory, this estimate is multimodal and constructed based on the integration of forward motor predictions and sensory feedback, such as proprioceptive, visual and vestibular information, modulated by context, and shaped by past experience. But how can a multimodal estimate drive fast movement corrections, given that the involved sensory modalities have different processing delays, different coordinate representations, and different noise levels? We develop the hypothesis that the earliest online movement corrections are based on multiple single modality state estimates rather than one combined multimodal estimate. We review studies that have investigated online multimodal integration for reach control and offer suggestions for experiments to test for the existence of intramodal state estimates. If proven true, the framework of Optimal Feedback Control needs to be extended with a stage of intramodal state estimation, serving to drive short-latency movement corrections.

Cognitive and neuroimaging findings in developmental coordination disorder: new insights from a systematic review of recent research

AIM

To better understand the neural and performance factors that may underlie developmental coordination disorder (DCD), and implications for a multi-component account.

METHOD

A systematic review of the experimental literature published between June 2011 and September 2016 was conducted using a modified PICOS (population, intervention, comparison, outcomes, and study type) framework. A total of 106 studies were included.

RESULTS

Behavioural data from 91 studies showed a broad cluster of deficits in the anticipatory control of movement, basic processes of motor learning, and cognitive control. Importantly, however, performance issues in DCD were often shown to be moderated by task type and difficulty. As well, we saw new evidence of compensatory processes and strategies in several studies. Neuroimaging data (15 studies, including electroencephalography) showed reduced cortical thickness in the right medial orbitofrontal cortex and altered brain activation patterns across functional networks involving prefrontal, parietal, and cerebellar regions in children with DCD than those in comparison groups. Data from diffusion-weighted magnetic resonance imaging suggested reduced white matter organization involving sensorimotor structures and altered structural connectivity across the whole brain network.

INTERPRETATION

Taken together, results support the hypothesis that children with DCD show differences in brain structure and function compared with typically developing children. Behaviourally, these differences may affect anticipatory planning and reduce automatization of movement skill, prompting greater reliance on slower feedback-based control and compensatory strategies. Implications for future research, theory development, and clinical practice are discussed.

MEG Insight into the Spectral Dynamics Underlying Steady Isometric Muscle Contraction

To gain fundamental knowledge on how the brain controls motor actions, we studied in detail the interplay between MEG signals from the primary sensorimotor (SM1) cortex and the contraction force of 17 healthy adult humans (7 females, 10 males). SM1 activity was coherent at ∼20 Hz with surface electromyogram (as already extensively reported) but also with contraction force. In both cases, the effective coupling was dominant in the efferent direction. Across subjects, the level of ∼20 Hz coherence between cortex and periphery positively correlated with the “burstiness” of ∼20 Hz SM1 (Pearson r ≈ 0.65) and peripheral fluctuations (r ≈ 0.9). Thus, ∼20 Hz coherence between cortex and periphery is tightly linked to the presence of ∼20 Hz bursts in SM1 and peripheral activity. However, the very high correlation with peripheral fluctuations suggests that the periphery is the limiting factor. At frequencies <3 Hz, both SM1 signals and ∼20 Hz SM1 envelope were coherent with both force and its absolute change rate. The effective coupling dominated in the efferent direction between (1) force and the ∼20 Hz SM1 envelope and (2) the absolute change rate of the force and SM1 signals. Together, our data favor the view that ∼20 Hz coherence between cortex and periphery during isometric contraction builds on the presence of ∼20 Hz SM1 oscillations and needs not rely on feedback from the periphery. They also suggest that effective cortical proprioceptive processing operates at <3 Hz frequencies, even during steady isometric contractions.

SIGNIFICANCE STATEMENT Accurate motor actions are made possible by continuous communication between the cortex and spinal motoneurons, but the neurophysiological basis of this communication is poorly understood. Using MEG recordings in humans maintaining steady isometric muscle contractions, we found evidence that the cortex sends population-level motor commands that tend to structure according to the ∼20 Hz sensorimotor rhythm, and that it dynamically adapts these commands based on the <3 Hz fluctuations of proprioceptive feedback. To our knowledge, this is the first report to give a comprehensive account of how the human brain dynamically handles the flow of proprioceptive information and converts it into appropriate motor command to keep the contraction force steady.

Consensus Paper: Towards a Systems-Level View of Cerebellar Function: the Interplay Between Cerebellum, Basal Ganglia, and Cortex

Despite increasing evidence suggesting the cerebellum works in concert with the cortex and basal ganglia, the nature of the reciprocal interactions between these three brain regions remains unclear. This consensus paper gathers diverse recent views on a variety of important roles played by the cerebellum within the cerebello-basal ganglia-thalamo-cortical system across a range of motor and cognitive functions. The paper includes theoretical and empirical contributions, which cover the following topics: recent evidence supporting the dynamical interplay between cerebellum, basal ganglia, and cortical areas in humans and other animals; theoretical neuroscience perspectives and empirical evidence on the reciprocal influences between cerebellum, basal ganglia, and cortex in learning and control processes; and data suggesting possible roles of the cerebellum in basal ganglia movement disorders. Although starting from different backgrounds and dealing with different topics, all the contributors agree that viewing the cerebellum, basal ganglia, and cortex as an integrated system enables us to understand the function of these areas in radically different ways. In addition, there is unanimous consensus between the authors that future experimental and computational work is needed to understand the function of cerebellar-basal ganglia circuitry in both motor and non-motor functions. The paper reports the most advanced perspectives on the role of the cerebellum within the cerebello-basal ganglia-thalamo-cortical system and illustrates other elements of consensus as well as disagreements and open questions in the field.

Load Accommodation Strategies and Movement Variability in Single-Leg Landing

Our purpose was to examine changes in participant-specific single-leg landing strategies and intra-individual movement variability following alterations in mechanical task demands via external load and landing height. Nineteen healthy volunteers (15M, 4 F, age: 24.3 ± 4.9 y, mass: 78.5 ± 14.7 kg, height: 1.73 ± 0.08 m) were analyzed among 9 single-leg drop landing trials in each of 6 experimental conditions (3 load and 2 landing height) computed as percentages of participant bodyweight (BW, BW + 12.5%, BW + 25%) and height (H12.5% & H25%). Lower-extremity sagittal joint angles and moments (hip, knee, and ankle), vertical ground reaction forces (GRFz), and electrical muscle activities (gluteus maximus, biceps femoris, vastus medialis, medial gastrocnemius, and tibialis anterior muscles) were analyzed. Individual single-leg drop landing strategies were identified using landing impulse predictions and the Load Accommodation Strategies Model (James et al., 2014). Intra-individual movement variability was assessed from neuromechanical synergies extracted using single-case principal component analyses (PCA). Fewer contrasting single-leg landing strategies were identified among participants under greater mechanical task demands (p < .001) alongside lesser intra-individual movement variability (p < .001). These results reveal changes in movement control under greater mechanical task demands, which may have implications for understanding overuse injury mechanisms in landing.

Cortical control of object-specific grasp relies on adjustments of both activity and effective connectivity: a common marmoset study

Key points

The cortical mechanisms of grasping have been extensively studied in macaques and humans; here, we investigated whether common marmosets could rely on similar mechanisms despite strong differences in hand morphology and grip diversity.

We recorded electrocorticographic activity over the sensorimotor cortex of two common marmosets during the execution of different grip types, which allowed us to study cortical activity (power spectrum) and physiologically inferred connectivity (phase‐slope index).

Analyses were performed in beta (16–35 Hz) and gamma (75–100 Hz) frequency bands and our results showed that beta power varied depending on grip type, whereas gamma power displayed clear epoch‐related modulation.

Strength and direction of inter‐area connectivity varied depending on grip type and epoch.

These findings suggest that fundamental control mechanisms are conserved across primates and, in future research, marmosets could represent an adequate model to investigate primate brain mechanisms.

Abstract

The cortical mechanisms of grasping have been extensively studied in macaques and humans. Here, we investigated whether common marmosets could rely on similar mechanisms despite striking differences in manual dexterity. Two common marmosets were trained to grasp‐and‐pull three objects eliciting different hand configurations: whole‐hand, finger and scissor grips. The animals were then chronically implanted with 64‐channel electrocorticogram arrays positioned over the left premotor, primary motor and somatosensory cortex. Power spectra, reflecting predominantly cortical activity, and phase‐slope index, reflecting the direction of information flux, were studied in beta (16–35 Hz) and gamma (75–100 Hz) bands. Differences related to grip type, epoch (reach, grasp) and cortical area were statistically assessed. Results showed that whole‐hand and scissor grips triggered stronger beta desynchronization than finger grip. Task epochs clearly modulated gamma power, especially for finger and scissor grips. Considering effective connectivity, finger and scissor grips evoked stronger outflow from primary motor to premotor cortex, whereas whole‐hand grip displayed the opposite pattern. These findings suggest that fundamental control mechanisms, relying on adjustments of cortical activity and connectivity, are conserved across primates. Consistently, marmosets could represent a good model to investigate primate brain mechanisms.

The cortical mechanisms of grasping have been extensively studied in macaques and humans; here, we investigated whether common marmosets could rely on similar mechanisms despite strong differences in hand morphology and grip diversity.

We recorded electrocorticographic activity over the sensorimotor cortex of two common marmosets during the execution of different grip types, which allowed us to study cortical activity (power spectrum) and physiologically inferred connectivity (phase‐slope index).

Analyses were performed in beta (16–35 Hz) and gamma (75–100 Hz) frequency bands and our results showed that beta power varied depending on grip type, whereas gamma power displayed clear epoch‐related modulation.

Strength and direction of inter‐area connectivity varied depending on grip type and epoch.

These findings suggest that fundamental control mechanisms are conserved across primates and, in future research, marmosets could represent an adequate model to investigate primate brain mechanisms.

Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning

Although primary motor cortex (M1) is intimately involved in the dynamics of limb movement, its inputs may be more closely related to higher-order aspects of movement and multi-modal sensory feedback. Motor learning is thought to result from the adaption of internal models that compute transformations between these representations. While the psychophysics of motor learning has been studied in many experiments, the particular role of M1 in the process remains the subject of debate. Studies of learning-related changes in the spatial tuning of M1 neurons have yielded conflicting results. To resolve the discrepancies, we recorded from M1 during curl field adaptation in a reaching task. Our results suggest that aside from the addition of the load itself, the relation of M1 to movement dynamics remains unchanged as monkeys adapt behaviorally. Accordingly, we implemented a musculoskeletal model to generate synthetic neural activity having a fixed dynamical relation to movement and showed that these simulated neurons reproduced the observed behavior of the recorded M1 neurons. The stable representation of movement dynamics in M1 suggests that behavioral changes are mediated through progressively altered recruitment of M1 neurons, while the output effect of those neurons remained largely unchanged.

Perspectives on classical controversies about the motor cortex

Primary motor cortex has been studied for more than a century, yet a consensus on its functional contribution to movement control is still out of reach. In particular, there remains controversy as to the level of control produced by motor cortex ("low-level" movement dynamics vs. "high-level" movement kinematics) and the role of sensory feedback. In this review, we present different perspectives on the two following questions: What does activity in motor cortex reflect? and How do planned motor commands interact with incoming sensory feedback during movement? The four authors each present their independent views on how they think the primary motor cortex (M1) controls movement. At the end, we present a dialogue in which the authors synthesize their views and suggest possibilities for moving the field forward. While there is not yet a consensus on the role of M1 or sensory feedback in the control of upper limb movements, such dialogues are essential to take us closer to one.

Rapid visuomotor feedback gains are tuned to the task dynamics

Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.

Adaptation to novel dynamics requires learning a motor memory, or a new pattern of predictive feedforward motor commands. Recently, we demonstrated the upregulation of rapid visuomotor feedback gains early in curl force field learning, which decrease once a predictive motor memory is learned. However, even after learning is complete, these feedback gains are higher than those observed in the null field trials. Interestingly, these upregulated feedback gains in the curl field were not observed in a constant force field. Therefore, we suggest that adaptation also involves selectively tuning the feedback sensitivity of the sensorimotor control system to the environment. Here, we test this hypothesis by measuring the rapid visuomotor feedback gains after subjects adapt to a variety of novel dynamics generated by a robotic manipulandum in three experiments. To probe the feedback gains, we measured the magnitude of the motor response to rapid shifts in the visual location of the hand during reaching. While the feedback gain magnitude remained similar over a larger than a fourfold increase in constant background load, the feedback gains scaled with increasing lateral resistance and increasing instability. The third experiment demonstrated that the feedback gains could also be independently tuned to perturbations to the left and right, depending on the lateral resistance, demonstrating the fractionation of feedback gains to environmental dynamics. Our results show that the sensorimotor control system regulates the gain of the feedback system as part of the adaptation process to novel dynamics, appropriately tuning them to the environment.

NEW & NOTEWORTHY Here, we test whether rapid visuomotor feedback responses are selectively tuned to the task dynamics. The responses do not exhibit gain scaling, but they do vary with the level and stability of task dynamics. Moreover, these feedback gains are independently tuned to perturbations to the left and right, depending on these dynamics. Our results demonstrate that the sensorimotor control system regulates the feedback gain as part of the adaptation process, tuning them appropriately to the environment.

Dynamic Multisensory Integration: Somatosensory Speed Trumps Visual Accuracy during Feedback Control

Recent advances in movement neuroscience have consistently highlighted that the nervous system performs sophisticated feedback control over very short time scales (<100 ms for upper limb). These observations raise the important question of how the nervous system processes multiple sources of sensory feedback in such short time intervals, given that temporal delays across sensory systems such as vision and proprioception differ by tens of milliseconds. Here we show that during feedback control, healthy humans use dynamic estimates of hand motion that rely almost exclusively on limb afferent feedback even when visual information about limb motion is available. We demonstrate that such reliance on the fastest sensory signal during movement is compatible with dynamic Bayesian estimation. These results suggest that the nervous system considers not only sensory variances but also temporal delays to perform optimal multisensory integration and feedback control in real-time.

SIGNIFICANCE STATEMENT

Numerous studies have demonstrated that the nervous system combines redundant sensory signals according to their reliability. Although very powerful, this model does not consider how temporal delays may impact sensory reliability, which is an important issue for feedback control because different sensory systems are affected by different temporal delays. Here we show that the brain considers not only sensory variability but also temporal delays when integrating vision and proprioception following mechanical perturbations applied to the upper limb. Compatible with dynamic Bayesian estimation, our results unravel the importance of proprioception for feedback control as a consequence of the shorter temporal delays associated with this sensory modality.

Motor Cortical Visuomotor Feedback Activity Is Initially Isolated from Downstream Targets in Output-Null Neural State Space Dimensions

Neural circuits must transform new inputs into outputs without prematurely affecting downstream circuits while still maintaining other ongoing communication with these targets. We investigated how this isolation is achieved in the motor cortex when macaques received visual feedback signaling a movement perturbation. To overcome limitations in estimating the mapping from cortex to arm movements, we also conducted brain-machine interface (BMI) experiments where we could definitively identify neural firing patterns as output-null or output-potent. This revealed that perturbation-evoked responses were initially restricted to output-null patterns that cancelled out at the neural population code readout and only later entered output-potent neural dimensions. This mechanism was facilitated by the circuit's large null space and its ability to strongly modulate output-potent dimensions when generating corrective movements. These results show that the nervous system can temporarily isolate portions of a circuit's activity from its downstream targets by restricting this activity to the circuit's output-null neural dimensions.

Control of force during rapid visuomotor force-matching tasks can be described by discrete time PID control algorithms

Force trajectories during isometric force-matching tasks involving isometric contractions vary substantially across individuals. In this study, we investigated if this variability can be explained by discrete time proportional, integral, derivative (PID) control algorithms with varying model parameters. To this end, we analyzed the pinch force trajectories of 24 subjects performing two rapid force-matching tasks with visual feedback. Both tasks involved isometric contractions to a target force of 10% maximal voluntary contraction. One task involved a single action (pinch) and the other required a double action (concurrent pinch and wrist extension). 50,000 force trajectories were simulated with a computational neuromuscular model whose input was determined by a PID controller with different PID gains and frequencies at which the controller adjusted muscle commands. The goal was to find the best match between each experimental force trajectory and all simulated trajectories. It was possible to identify one realization of the PID controller that matched the experimental force produced during each task for most subjects (average index of similarity: 0.87 ± 0.12; 1 = perfect similarity). The similarities for both tasks were significantly greater than that would be expected by chance (single action: p = 0.01; double action: p = 0.04). Furthermore, the identified control frequencies in the simulated PID controller with the greatest similarities decreased as task difficulty increased (single action: 4.0 ± 1.8 Hz; double action: 3.1 ± 1.3 Hz). Overall, the results indicate that discrete time PID controllers are realistic models for the neural control of force in rapid force-matching tasks involving isometric contractions.

Multisensory components of rapid motor responses to fingertip loading

Tactile and muscle afferents provide critical sensory information for grasp control, yet the contribution of each sensory system during online control has not been clearly identified. More precisely, it is unknown how these two sensory systems participate in online control of digit forces following perturbations to held objects. To address this issue, we investigated motor responses in the context of fingertip loading, which parallels the impact of perturbations to held objects on finger motion and fingerpad deformation, and characterized surface recordings of intrinsic (first dorsal interosseous, FDI) and extrinsic (flexor digitorum superficialis, FDS) hand muscles based on statistical modeling. We designed a series of experiments probing the effects of peripheral stimulation with or without anesthesia of the finger, and of task instructions. Loading of the fingertip generated a motor response in FDI at ~60 ms following the perturbation onset, which was only driven by muscle stretch, as the ring-block anesthesia reduced the gain of the response occurring later than 90 ms, leaving responses occurring before this time unaffected. In contrast, the motor response in FDS was independent of the lateral motion of the finger. This response started at ~90 ms on average and was immediately adjusted to task demands. Altogether these results highlight how a rapid integration of partially distinct sensorimotor circuits supports rapid motor responses to fingertip loading.NEW & NOTEWORTHY To grasp and manipulate objects, the brain uses touch signals related to skin deformation as well as sensory information about motion of the fingers encoded in muscle spindles. Here we investigated how these two sensory systems contribute to feedback responses to perturbation applied to the fingertip. We found distinct response components, suggesting that each sensory system engages separate sensorimotor circuits with distinct functions and latencies.

Hand Grasping Synergies As Biometrics

Recently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies-postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.

Quantification of task-dependent cortical activation evoked by robotic continuous wrist joint manipulation in chronic hemiparetic stroke

Background

Cortical damage after stroke can drastically impair sensory and motor function of the upper limb, affecting the execution of activities of daily living and quality of life. Motor impairment after stroke has been thoroughly studied, however sensory impairment and its relation to movement control has received less attention. Integrity of the somatosensory system is essential for feedback control of human movement, and compromised integrity due to stroke has been linked to sensory impairment.

Methods

The goal of this study is to assess the integrity of the somatosensory system in individuals with chronic hemiparetic stroke with different levels of sensory impairment, through a combination of robotic joint manipulation and high-density electroencephalogram (EEG). A robotic wrist manipulator applied continuous periodic disturbances to the affected limb, providing somatosensory (proprioceptive and tactile) stimulation while challenging task execution. The integrity of the somatosensory system was evaluated during passive and active tasks, defined as ‘relaxed wrist’ and ‘maintaining 20% maximum wrist flexion’, respectively. The evoked cortical responses in the EEG were quantified using the power in the averaged responses and their signal-to-noise ratio.

Results

Thirty individuals with chronic hemiparetic stroke and ten unimpaired individuals without stroke participated in this study. Participants with stroke were classified as having severe, mild, or no sensory impairment, based on the Erasmus modification of the Nottingham Sensory Assessment. Under passive conditions, wrist manipulation resulted in contralateral cortical responses in unimpaired and chronic stroke participants with mild and no sensory impairment. In participants with severe sensory impairment the cortical responses were strongly reduced in amplitude, which related to anatomical damage. Under active conditions, participants with mild sensory impairment showed reduced responses compared to the passive condition, whereas unimpaired and chronic stroke participants without sensory impairment did not show this reduction.

Conclusions

Robotic continuous joint manipulation allows studying somatosensory cortical evoked responses during the execution of meaningful upper limb control tasks. Using such an approach it is possible to quantitatively assess the integrity of sensory pathways; in the context of movement control this provides additional information required to develop more effective neurorehabilitation therapies.

Parallel Specification of Visuomotor Feedback Gains during Bimanual Reaching to Independent Goals

During goal-directed reaching, rapid visuomotor feedback processes enable the human motor system to quickly correct for errors in the trajectory of the hand that arise from motor noise and, in some cases, external perturbations. To date, these visuomotor responses, the gain of which is sensitive to features of the task and environment, have primarily been examined in the context of unimanual reaching movements toward a single target. However, many natural tasks involve moving both hands together, often to separate targets, such that errors can occur in parallel and at different spatial locations. Here, we examined the resource capacity of automatic visuomotor corrective mechanisms by comparing feedback gains during bimanual reaches, toward two targets, to feedback gains during unimanual reaches toward single targets. To investigate the sensitivity of the feedback gains and their relation to visual-spatial processing, we manipulated the widths of the targets and participants’ gaze location. We found that the gain of corrective responses to cursor displacements, while strongly modulated by target width and gaze position, were only slightly reduced during bimanual control. Our results show that automatic visuomotor corrective mechanisms can efficiently operate in parallel across multiple spatial locations.

Kinesthetic deficits after perinatal stroke: robotic measurement in hemiparetic children

Background

While sensory dysfunction is common in children with hemiparetic cerebral palsy (CP) secondary to perinatal stroke, it is an understudied contributor to disability with limited objective measurement tools. Robotic technology offers the potential to objectively measure complex sensorimotor function but has been understudied in perinatal stroke. The present study aimed to quantify kinesthetic deficits in hemiparetic children with perinatal stroke and determine their association with clinical function.

Methods

Case–control study. Participants were 6–19 years of age. Stroke participants had MRI confirmed unilateral perinatal arterial ischemic stroke or periventricular venous infarction, and symptomatic hemiparetic cerebral palsy. Participants completed a robotic assessment of upper extremity kinesthesia using a robotic exoskeleton (KINARM). Four kinesthetic parameters (response latency, initial direction error, peak speed ratio, and path length ratio) and their variabilities were measured with and without vision. Robotic outcomes were compared across stroke groups and controls and to clinical measures of sensorimotor function.

Results

Forty-three stroke participants (23 arterial, 20 venous, median age 12 years, 42% female) were compared to 106 healthy controls. Stroke cases displayed significantly impaired kinesthesia that remained when vision was restored. Kinesthesia was more impaired in arterial versus venous lesions and correlated with clinical measures.

Conclusions

Robotic assessment of kinesthesia is feasible in children with perinatal stroke. Kinesthetic impairment is common and associated with stroke type. Failure to correct with vision suggests sensory network dysfunction.

Sensory tractography and robot-quantified proprioception in hemiparetic children with perinatal stroke

Perinatal stroke causes most hemiparetic cerebral palsy, resulting in lifelong disability. We have demonstrated the ability of robots to quantify sensory dysfunction in hemiparetic children but the relationship between such deficits and sensory tract structural connectivity has not been explored. It was aimed to characterize the relationship between the dorsal column medial lemniscus (DCML) pathway connectivity and proprioceptive dysfunction in children with perinatal stroke. Twenty-nine participants (6-19 years old) with MRI-classified, unilateral perinatal ischemic stroke (14 arterial, 15 venous), and upper extremity deficits were recruited from a population-based cohort and compared with 21 healthy controls. Diffusion tensor imaging (DTI) defined DCML tracts and five diffusion properties were quantified: fractional anisotropy (FA), mean, radial, and axial diffusivities (MD, RD, AD), and fiber count. A robotic exoskeleton (KINARM) tested upper limb proprioception in an augmented reality environment. Correlations between robotic measures and sensory tract diffusion parameters were evaluated. Lesioned hemisphere sensory tracts demonstrated lower FA and higher MD, RD, and AD compared with the non-dominant hemisphere of controls. Dominant (contralesional) hemisphere tracts were not different from controls. Both arterial and venous stroke groups demonstrated impairments in proprioception that correlated with lesioned hemisphere DCML tract diffusion properties. Sensory tract connectivity is altered in the lesioned hemisphere of hemiparetic children with perinatal stroke. A correlation between lesioned DCML tract diffusion properties and robotic proprioceptive measures suggests clinical relevance and a possible target for therapeutic intervention. Hum Brain Mapp 38:2424-2440, 2017. © 2017 Wiley Periodicals, Inc.

Nerve-Specific Input Modulation to Spinal Neurons during a Motor Task in the Monkey

If not properly regulated, the large amount of reafferent sensory signals generated by our own movement could destabilize the CNS. We investigated how input from peripheral nerves to spinal cord is modulated during behavior. We chronically stimulated the deep radial nerve (DR; proprioceptive, wrist extensors), the median nerve (M; mixed, wrist flexors and palmar skin) and the superficial radial nerve (SR; cutaneous, hand dorsum) while four monkeys performed a delayed wrist flexion-extension task. Spinal neurons putatively receiving direct sensory input were defined based on their evoked response latency following nerve stimulation. We compared the influence of behavior on the evoked response (responsiveness to a specific peripheral input) and firing rate of 128 neuron-nerve pairs based on their source nerve. Firing rate increased during movement regardless of source nerve, whereas evoked response modulation was strikingly nerve-dependent. In SR (n = 47) and M (n = 27) neurons (cutaneous or mixed input), the evoked response was suppressed during wrist flexion and extension. In contrast, in DR neurons (n = 54, pure proprioceptive input), the evoked response was facilitated exclusively during movements corresponding to the contraction of DR spindle-bearing muscles (i.e., wrist extension). Furthermore, modulations of firing rate and evoked response were uncorrelated in SR and M neurons, whereas they tended to be positively comodulated in DR neurons. Our results suggest that proprioceptive and cutaneous inputs to the spinal cord are modulated differently during voluntary movements, suggesting a refined gating mechanism of sensory signals according to behavior.SIGNIFICANCE STATEMENT Voluntary movements produce copious sensory signals, which may overwhelm the CNS if not properly regulated. This regulation is called "gating" and occurs at several levels of the CNS. To evaluate the specificity of sensory gating, we investigated how different sources of somatosensory inputs to the spinal cord were modulated while monkeys performed wrist movements. We recorded activity from spinal neurons that putatively received direct connections from peripheral nerves while stimulating their source nerves, and measured the evoked responses. Whereas cutaneous inputs were suppressed regardless of the type of movement, muscular inputs were specifically facilitated during relevant movements. We conclude that, even at the spinal level, sensory gating is a refined and input-specific process.

Avoiding Accidents at the Champagne Reception

Using a lifting and balancing task, we contrasted two alternative views of planning joint actions: one postulating that joint action involves distinct predictions for self and other, the other postulating that joint action involves coordinated plans between the coactors and reuse of bimanual models. We compared compensatory movements required to keep a tray balanced when 2 participants lifted glasses from each other’s trays at the same time (simultaneous joint action) and when they took turns lifting (sequential joint action). Compared with sequential joint action, simultaneous joint action made it easier to keep the tray balanced. Thus, in keeping with the view that bimanual models are reused for joint action, predicting the timing of their own lifting action helped participants compensate for another person’s lifting action. These results raise the possibility that simultaneous joint actions do not necessarily require distinguishing between one’s own and the coactor’s contributions to the action plan and may afford an agent-neutral stance.

Shared Mechanisms in the Estimation of Self-Generated Actions and the Prediction of Other's Actions by Humans

The question of how humans predict outcomes of observed motor actions by others is a fundamental problem in cognitive and social neuroscience. Previous theoretical studies have suggested that the brain uses parts of the forward model (used to estimate sensory outcomes of self-generated actions) to predict outcomes of observed actions. However, this hypothesis has remained controversial due to the lack of direct experimental evidence. To address this issue, we analyzed the behavior of darts experts in an understanding learning paradigm and utilized computational modeling to examine how outcome prediction of observed actions affected the participants’ ability to estimate their own actions. We recruited darts experts because sports experts are known to have an accurate outcome estimation of their own actions as well as prediction of actions observed in others. We first show that learning to predict the outcomes of observed dart throws deteriorates an expert’s abilities to both produce his own darts actions and estimate the outcome of his own throws (or self-estimation). Next, we introduce a state-space model to explain the trial-by-trial changes in the darts performance and self-estimation through our experiment. The model-based analysis reveals that the change in an expert’s self-estimation is explained only by considering a change in the individual’s forward model, showing that an improvement in an expert’s ability to predict outcomes of observed actions affects the individual’s forward model. These results suggest that parts of the same forward model are utilized in humans to both estimate outcomes of self-generated actions and predict outcomes of observed actions.

Trial-by-Trial Motor Cortical Correlates of a Rapidly Adapting Visuomotor Internal Model

Accurate motor control is mediated by internal models of how neural activity generates movement. We examined neural correlates of an adapting internal model of visuomotor gain in motor cortex while two macaques performed a reaching task in which the gain scaling between the hand and a presented cursor was varied. Previous studies of cortical changes during visuomotor adaptation focused on preparatory and perimovement epochs and analyzed trial-averaged neural data. Here, we recorded simultaneous neural population activity using multielectrode arrays and focused our analysis on neural differences in the period before the target appeared. We found that we could estimate the monkey's internal model of the gain using the neural population state during this pretarget epoch. This neural correlate depended on the gain experienced during recent trials and it predicted the speed of the subsequent reach. To explore the utility of this internal model estimate for brain-machine interfaces, we performed an offline analysis showing that it can be used to compensate for upcoming reach extent errors. Together, these results demonstrate that pretarget neural activity in motor cortex reflects the monkey's internal model of visuomotor gain on single trials and can potentially be used to improve neural prostheses.SIGNIFICANCE STATEMENT When generating movement commands, the brain is believed to use internal models of the relationship between neural activity and the body's movement. Visuomotor adaptation tasks have revealed neural correlates of these computations in multiple brain areas during movement preparation and execution. Here, we describe motor cortical changes in a visuomotor gain change task even before a specific movement is cued. We were able to estimate the gain internal model from these pretarget neural correlates and relate it to single-trial behavior. This is an important step toward understanding the sensorimotor system's algorithms for updating its internal models after specific movements and errors. Furthermore, the ability to estimate the internal model before movement could improve motor neural prostheses being developed for people with paralysis.

Decoding hand gestures from primary somatosensory cortex using high-density ECoG

Electrocorticography (ECoG) based Brain-Computer Interfaces (BCIs) have been proposed as a way to restore and replace motor function or communication in severely paralyzed people. To date, most motor-based BCIs have either focused on the sensorimotor cortex as a whole or on the primary motor cortex (M1) as a source of signals for this purpose. Still, target areas for BCI are not confined to M1, and more brain regions may provide suitable BCI control signals. A logical candidate is the primary somatosensory cortex (S1), which not only shares similar somatotopic organization to M1, but also has been suggested to have a role beyond sensory feedback during movement execution. Here, we investigated whether four complex hand gestures, taken from the American sign language alphabet, can be decoded exclusively from S1 using both spatial and temporal information. For decoding, we used the signal recorded from a small patch of cortex with subdural high-density (HD) grids in five patients with intractable epilepsy. Notably, we introduce a new method of trial alignment based on the increase of the electrophysiological response, which virtually eliminates the confounding effects of systematic and non-systematic temporal differences within and between gestures execution. Results show that S1 classification scores are high (76%), similar to those obtained from M1 (74%) and sensorimotor cortex as a whole (85%), and significantly above chance level (25%). We conclude that S1 offers characteristic spatiotemporal neuronal activation patterns that are discriminative between gestures, and that it is possible to decode gestures with high accuracy from a very small patch of cortex using subdurally implanted HD grids. The feasibility of decoding hand gestures using HD-ECoG grids encourages further investigation of implantable BCI systems for direct interaction between the brain and external devices with multiple degrees of freedom.

Effect of interstimulus interval on cortical proprioceptive responses to passive finger movements

Shortening of the interstimulus interval (ISI) generally leads to attenuation of cortical sensory responses. For proprioception, however, this ISI effect is still poorly known. Our aim was to characterize the ISI dependence of movement-evoked proprioceptive cortical responses and to find the optimum ISI for proprioceptive stimulation. We measured, from 15 healthy adults, magnetoencephalographic responses to passive flexion and extension movements of the right index finger. The movements were generated by a movement actuator at fixed ISIs of 0.5, 1, 2, 4, 8, and 16 s, in separate blocks. The responses peaked at ~ 70 ms (extension) and ~ 90 ms (flexion) in the contralateral primary somatosensory cortex. The strength of the cortical source increased with the ISI, plateauing at the 8-s ISI. Modeling the ISI dependence with an exponential saturation function revealed response lifetimes of 1.3 s (extension) and 2.2 s (flexion), implying that the maximum signal-to-noise ratio (SNR) in a given measurement time is achieved with ISIs of 1.7 s and 2.8 s respectively. We conclude that ISIs of 1.5-3 s should be used to maximize SNR in recordings of proprioceptive cortical responses to passive finger movements. Our findings can benefit the assessment of proprioceptive afference in both clinical and research settings.

Neuronal correlates of continuous manual tracking under varying visual movement feedback in a virtual reality environment

To accurately guide one's actions online, the brain predicts sensory action feedback ahead of time based on internal models, which can be updated by sensory prediction errors. The underlying operations can be experimentally investigated in sensorimotor adaptation tasks, in which moving under perturbed sensory action feedback requires internal model updates. Here we altered healthy participants' visual hand movement feedback in a virtual reality setup, while assessing brain activity with functional magnetic resonance imaging (fMRI). Participants tracked a continually moving virtual target object with a photorealistic, three-dimensional (3D) virtual hand controlled online via a data glove. During the continuous tracking task, the virtual hand's movements (i.e., visual movement feedback) were repeatedly periodically delayed, which participants had to compensate for to maintain accurate tracking. This realistic task design allowed us to simultaneously investigate processes likely operating at several levels of the brain's motor control hierarchy. FMRI revealed that the length of visual feedback delay was parametrically reflected by activity in the inferior parietal cortex and posterior temporal cortex. Unpredicted changes in visuomotor mapping (at transitions from synchronous to delayed visual feedback periods or vice versa) activated biological motion-sensitive regions in the lateral occipitotemporal cortex (LOTC). Activity in the posterior parietal cortex (PPC), focused on the contralateral anterior intraparietal sulcus (aIPS), correlated with tracking error, whereby this correlation was stronger in participants with higher tracking performance. Our results are in line with recent proposals of a wide-spread cortical motor control hierarchy, where temporoparietal regions seem to evaluate visuomotor congruence and thus possibly ground a self-attribution of movements, the LOTC likely processes early visual prediction errors, and the aIPS computes action goal errors and possibly corresponding motor corrections.

Direction-dependent arm kinematics reveal optimal integration of gravity cues

The brain has evolved an internal model of gravity to cope with life in the Earth's gravitational environment. How this internal model benefits the implementation of skilled movement has remained unsolved. One prevailing theory has assumed that this internal model is used to compensate for gravity's mechanical effects on the body, such as to maintain invariant motor trajectories. Alternatively, gravity force could be used purposely and efficiently for the planning and execution of voluntary movements, thereby resulting in direction-depending kinematics. Here we experimentally interrogate these two hypotheses by measuring arm kinematics while varying movement direction in normal and zero-G gravity conditions. By comparing experimental results with model predictions, we show that the brain uses the internal model to implement control policies that take advantage of gravity to minimize movement effort.

Nonlinear Connectivity in the Human Stretch Reflex Assessed by Cross-Frequency Phase Coupling

Communication between neuronal populations is facilitated by synchronization of their oscillatory activity. Although nonlinearity has been observed in the sensorimotor system, its nonlinear connectivity has not been widely investigated yet. This study investigates nonlinear connectivity during the human stretch reflex based on neuronal synchronization. Healthy participants generated isotonic wrist flexion while receiving a periodic mechanical perturbation to the wrist. Using a novel cross-frequency phase coupling metric, we estimate directional nonlinear connectivity, including time delay, from the perturbation to brain and to muscle, as well as from brain to muscle. Nonlinear phase coupling is significantly stronger from the perturbation to the muscle than to the brain, with a shorter time delay. The time delay from the perturbation to the muscle is 33 ms, similar to the reported latency of the spinal stretch reflex at the wrist. Source localization of nonlinear phase coupling from the brain to the muscle suggests activity originating from the motor cortex, although its effect on the stretch reflex is weak. As such nonlinear phase coupling between the perturbation and muscle activity is dominated by the spinal reflex loop. This study provides new evidence of nonlinear neuronal synchronization in the stretch reflex at the wrist joint with respect to spinal and transcortical loops.

Post-Movement Beta Activity in Sensorimotor Cortex Indexes Confidence in the Estimations from Internal Models

Beta oscillations are a dominant feature of the sensorimotor system. A transient and prominent increase in beta oscillations is consistently observed across the sensorimotor cortical-basal ganglia network after cessation of voluntary movement: the post-movement beta synchronization (PMBS). Current theories about the function of the PMBS have been focused on either the closure of motor response or the processing of sensory afferance. Computational models of sensorimotor control have emphasized the importance of the integration between feedforward estimation and sensory feedback, and therefore the putative motor and sensory functions of beta oscillations may reciprocally interact with each other and in fact be indissociable. Here we show that the amplitude of sensorimotor PMBS is modulated by the history of visual feedback of task-relevant errors, and negatively correlated with the trial-to-trial exploratory adjustment in a sensorimotor adaptation task in young healthy human subjects. The PMBS also negatively correlated with the uncertainty associated with the feedforward estimation, which was recursively updated in light of new sensory feedback, as identified by a Bayesian learning model. These results reconcile the two opposing motor and sensory views of the function of PMBS, and suggest a unifying theory in which PMBS indexes the confidence in internal feedforward estimation in Bayesian sensorimotor integration. Its amplitude simultaneously reflects cortical sensory processing and signals the need for maintenance or adaptation of the motor output, and if necessary, exploration to identify an altered sensorimotor transformation.

SIGNIFICANCE STATEMENT

For optimal sensorimotor control, sensory feedback and feedforward estimation of a movement's sensory consequences should be weighted by the inverse of their corresponding uncertainties, which require recursive updating in a dynamic environment. We show that post-movement beta activity (13-30 Hz) over sensorimotor cortex in young healthy subjects indexes the evaluation of uncertainty in feedforward estimation. Our work contributes to the understanding of the function of beta oscillations in sensorimotor control, and provides further insight into how aberrant beta activity can contribute to the pathophysiology of movement disorders.

The Neural Mechanism Exploration of Adaptive Motor Control: Dynamical Economic Cell Allocation in the Primary Motor Cortex

Adaptive flexibility is of significance for the smooth and efficient movements in goal attainment. However, the underlying work mechanism of the cerebral cortex in adaptive motor control still remains unclear. How does the cerebral cortex organize and coordinate the activity of a large population of cells in the implementation of various motor strategies? To explore this issue, single-unit activities from the M1 region and kinematic data were recorded simultaneously in monkeys performing 3D reach-to-grasp tasks with different perturbations. Varying motor control strategies were employed and achieved in different perturbed tasks, via the dynamic allocation of cells to modulate specific movement parameters. An economic principle was proposed for the first time to describe a basic rule for cell allocation in the primary motor cortex. This principle, defined as the Dynamic Economic Cell Allocation Mechanism (DECAM), guarantees benefit maximization in cell allocation under limited neuronal resources, and avoids committing resources to uneconomic investments for unreliable factors with no or little revenue. That is to say, the cells recruited are always preferentially allocated to those factors with reliable return; otherwise, the cells are dispatched to respond to other factors about task. The findings of this study might partially reveal the working mechanisms underlying the role of the cerebral cortex in adaptive motor control, wherein is also of significance for the design of future intelligent brain-machine interfaces and rehabilitation device.

Quantifying Nonlinear Contributions to Cortical Responses Evoked by Continuous Wrist Manipulation

Cortical responses to continuous stimuli as recorded using either magneto- or electroencephalography (EEG) have shown power at harmonics of the stimulated frequency, indicating nonlinear behavior. Even though the selection of analysis techniques depends on the linearity of the system under study, the importance of nonlinear contributions to cortical responses has not been formally addressed. The goal of this paper is to quantify the nonlinear contributions to the cortical response obtained from continuous sensory stimulation. EEG was used to record the cortical response evoked by continuous movement of the wrist joint of healthy subjects applied with a robotic manipulator. Multisine stimulus signals (i.e., the sum of several sinusoids) elicit a periodic cortical response and allow to assess the nonlinear contributions to the response. Wrist dynamics (relation between joint angle and torque) were successfully linearized, explaining 99% of the response. In contrast, the cortical response revealed a highly nonlinear relation; where most power (  ∼ 80 %) occurred at non-stimulated frequencies. Moreover, only 10% of the response could be explained using a nonparametric linear model. These results indicate that the recorded evoked cortical responses are governed by nonlinearities and that linear methods do not suffice when describing the relation between mechanical stimulus and cortical response.

A cortically-inspired model for inverse kinematics computation of a humanoid finger with mechanically coupled joints

The human hand's versatility allows for robust and flexible grasping. To obtain such efficiency, many robotic hands include human biomechanical features such as fingers having their two last joints mechanically coupled. Although such coupling enables human-like grasping, controlling the inverse kinematics of such mechanical systems is challenging. Here we propose a cortical model for fine motor control of a humanoid finger, having its two last joints coupled, that learns the inverse kinematics of the effector. This neural model functionally mimics the population vector coding as well as sensorimotor prediction processes of the brain's motor/premotor and parietal regions, respectively. After learning, this neural architecture could both overtly (actual execution) and covertly (mental execution or motor imagery) perform accurate, robust and flexible finger movements while reproducing the main human finger kinematic states. This work contributes to developing neuro-mimetic controllers for dexterous humanoid robotic/prosthetic upper-extremities, and has the potential to promote human-robot interactions.

A motor planning stage represents the shape of upcoming movement trajectories

Interactions with our environment require curved movements that depend not only on the final position of the hand but also on the path used to achieve it. Current studies in motor control, however, largely focus on point-to-point movements and do not consider how movements with specific desired trajectories might arise. In this study, we examined intentionally curved reaching movements that navigate paths around obstacles. We found that the preparation of these movements incurred a large reaction-time cost. This cost could not be attributed to nonmotor task requirements (e.g., stimulus perception) and was independent of the execution difficulty (i.e., extent of curvature) of the movement. Additionally, this trajectory representation cost was not observed for point-to-point reaches but could be optionally included if the task encouraged consideration of straight trajectories. Therefore, when the path of a movement is task relevant, the shape of the desired trajectory is overtly represented as a stage of motor planning. This trajectory representation ability may help explain the vast repertoire of human motor behaviors.

Sensorimotor Convergence in Circuitry of the Motor Cortex

Scientists and philosophers have long appreciated that active somatosensation requires the sensory and motor systems to exchange information about body the body's movements as well as touch in order to accurately interpret incoming somatosensory information and plan future movements. However, the circuitry underlying this sensory and motor integration is complicated and is difficult to study without tools to label specific cellular components in the various brain regions involved. Here, I review the general pathways that convey ascending sensory and descending motor information, using the rodent whisker system as a model to take advantage of the cell type specificity possible in this model. I then detail the circuits in motor cortex in which incoming information from somatosensory cortex and thalamus is integrated. I close with a brief description of changes in these circuits during motor learning.