The independence of impairments in proprioception and visuomotor adaptation after stroke.

BACKGROUND: Proprioceptive impairments are common after stroke and are associated with worse motor recovery and poor rehabilitation outcomes. Motor learning may also be an important factor in motor recovery, and some evidence in healthy adults suggests that reduced proprioceptive function is associated with reductions in motor learning. It is unclear how impairments in proprioception and motor learning relate after stroke. Here we used robotics and a traditional clinical assessment to examine the link between impairments in proprioception after stroke and a type of motor learning known as visuomotor adaptation. METHODS: We recruited participants with first-time unilateral stroke and controls matched for overall age and sex. Proprioceptive impairments in the more affected arm were assessed using robotic arm position- (APM) and movement-matching (AMM) tasks. We also assessed proprioceptive impairments using a clinical scale (Thumb Localization Test; TLT). Visuomotor adaptation was assessed using a task that systematically rotated hand cursor feedback during reaching movements (VMR). We quantified how much participants adapted to the disturbance and how many trials they took to adapt to the same levels as controls. Spearman's rho was used to examine the relationship between proprioception, assessed using robotics and the TLT, and visuomotor adaptation. Data from healthy adults were used to identify participants with stroke who were impaired in proprioception and visuomotor adaptation. The independence of impairments in proprioception and adaptation were examined using Fisher's exact tests. RESULTS: Impairments in proprioception (58.3%) and adaptation (52.1%) were common in participants with stroke (n = 48; 2.10% acute, 70.8% subacute, 27.1% chronic stroke). Performance on the APM task, AMM task, and TLT scores correlated weakly with measures of visuomotor adaptation. Fisher's exact tests demonstrated that impairments in proprioception, assessed using robotics and the TLT, were independent from impairments in visuomotor adaptation in our sample. CONCLUSION: Our results suggest impairments in proprioception may be independent from impairments in visuomotor adaptation after stroke. Further studies are needed to understand factors that influence the relationship between motor learning, proprioception and other rehabilitation outcomes throughout stroke recovery.

The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke.

BACKGROUND: Robots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impairment in individuals using kinematic data, however, can be challenging. Machine learning techniques offer a potential solution to this problem. In the present manuscript we examine proprioception in stroke survivors using a robotic arm position matching task. Proprioception is impaired in 50-60% of stroke survivors and has been associated with poorer motor recovery and longer lengths of hospital stay. We present a simple cut-off score technique for individual kinematic parameters and an overall task score to determine impairment. We then compare the ability of different machine learning (ML) techniques and the above-mentioned task score to correctly classify individuals with or without stroke based on kinematic data. METHODS: Participants performed an Arm Position Matching (APM) task in an exoskeleton robot. The task produced 12 kinematic parameters that quantify multiple attributes of position sense. We first quantified impairment in individual parameters and an overall task score by determining if participants with stroke fell outside of the 95% cut-off score of control (normative) values. Then, we applied five machine learning algorithms (i.e., Logistic Regression, Decision Tree, Random Forest, Random Forest with Hyperparameters Tuning, and Support Vector Machine), and a deep learning algorithm (i.e., Deep Neural Network) to classify individual participants as to whether or not they had a stroke based only on kinematic parameters using a tenfold cross-validation approach. RESULTS: We recruited 429 participants with neuroimaging-confirmed stroke (< 35 days post-stroke) and 465 healthy controls. Depending on the APM parameter, we observed that 10.9-48.4% of stroke participants were impaired, while 44% were impaired based on their overall task score. The mean performance metrics of machine learning and deep learning models were: accuracy 82.4%, precision 85.6%, recall 76.5%, and F1 score 80.6%. All machine learning and deep learning models displayed similar classification accuracy; however, the Random Forest model had the highest numerical accuracy (83%). Our models showed higher sensitivity and specificity (AUC = 0.89) in classifying individual participants than the overall task score (AUC = 0.85) based on their performance in the APM task. We also found that variability was the most important feature in classifying performance in the APM task. CONCLUSION: Our ML models displayed similar classification performance. ML models were able to integrate more kinematic information and relationships between variables into decision making and displayed better classification performance than the overall task score. ML may help to provide insight into individual kinematic features that have previously been overlooked with respect to clinical importance.

Similar stretch reflexes and behavioral patterns are expressed by the dominant and nondominant arms during postural control.

Limb dominance is evident in many daily activities, leading to the prominent idea that each hemisphere of the brain specializes in controlling different aspects of movement. Past studies suggest that the dominant arm is primarily controlled via an internal model of limb dynamics that enables the nervous system to produce efficient movements. In contrast, the nondominant arm may be primarily controlled via impedance mechanisms that rely on the strong modulation of sensory feedback from individual joints to control limb posture. We tested whether such differences are evident in behavioral responses and stretch reflexes following sudden displacement of the arm during posture control. Experiment 1 applied specific combinations of elbow-shoulder torque perturbations (the same for all participants). Peak joint displacements, return times, end point accuracy, and the directional tuning and amplitude of stretch reflexes in nearly all muscles were not statistically different between the two arms. Experiment 2 induced specific combinations of joint motion (the same for all participants). Again, peak joint displacements, return times, end point accuracy, and the directional tuning and amplitude of stretch reflexes in nearly all muscles did not differ statistically when countering the imposed loads with each arm. Moderate to strong correlations were found between stretch reflexes and behavioral responses to the perturbations with the two arms across both experiments. Collectively, the results do not support the idea that the dominant arm specializes in exploiting internal models and the nondominant arm in impedance control by increasing reflex gains to counter sudden loads imposed on the arms during posture control.NEW & NOTEWORTHY A prominent hypothesis is that the nervous system controls the dominant arm through predictive internal models and the nondominant arm through impedance mechanisms. We tested whether stretch reflexes of muscles in the two arms also display such specialization during posture control. Nearly all behavioral responses and stretch reflexes did not differ statistically but were strongly correlated between the arms. The results indicate individual signatures of feedback control that are common for the two arms.

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.

Visual Feedback Processing of the Limb Involves Two Distinct Phases.

Muscle responses to mechanical disturbances exhibit two distinct phases: a response starting at ~20 ms that is fairly stereotyped, and a response starting at ~60 ms modulated by many behavioral contexts including goal-redundancy and environmental obstacles. Muscle responses to disturbances of visual feedback of the hand arise within ~90 ms. However, little is known whether these muscle responses are sensitive to behavioral contexts. We had 49 human participants (27 male) execute goal-directed reaches with visual feedback of their hand presented as a cursor. On random trials, the cursor jumped laterally to the reach direction, and thus, required a correction to attain the goal. The first experiment demonstrated that the response amplitude starting at 90 ms scaled with jump magnitude, but only for jumps <2 cm. For larger jumps, the duration of the muscle response scaled with the jump size starting after 120 ms. The second experiment demonstrated that the early response was sensitive to goal redundancy as wider targets evoked a smaller corrective response. The third experiment demonstrated that the early response did not consider the presence of obstacles, as this response routinely drove participants directly to the goal even though this path was blocked by an obstacle. Instead, the appropriate muscle response to navigate around the obstacle started after 120 ms. Our findings highlight that visual feedback of the limb involves two distinct phases: a response starting at 90 ms with limited sensitivity to jump magnitude and sensitive to goal-redundancy, and a response starting at 120 ms with increased sensitivity to jump magnitude and environmental factors.SIGNIFICANCE STATEMENT The motor system can integrate proprioceptive feedback to guide an ongoing action in ~60 ms and is flexible to a broad range of behavioral contexts. In contrast, the present study identified that the motor response to a visual disturbance exhibits two distinct phases: an early response starting at 90 ms with limited scaling with disturbance size and sensitivity to goal-redundancy, and a slower response starting after 120 ms with increased sensitivity to disturbance size and sensitive to environmental obstacles. These data suggest visual feedback of the hand is processed through two distinct feedback processes.

Feedforward and Feedback Control Share an Internal Model of the Arm's Dynamics.

Recent work has shown that, when countering external forces, the nervous system adjusts not only predictive (i.e., feedforward) control of reaching but also reflex (i.e., feedback) responses to mechanical perturbations. Here we show that altering the physical properties of the arm (i.e., intersegmental dynamics) causes the nervous system to adjust feedforward control and that this learning transfers to feedback responses even though the latter were never directly trained. Forty-five human participants (30 females) performed a single-joint elbow reaching task and countered mechanical perturbations that created pure elbow motion. In our first experiment, we altered intersegmental dynamics by asking participants to generate pure elbow movements when the shoulder joint was either free to rotate or locked by the robotic manipulandum. With the shoulder unlocked, we found robust activation of shoulder flexor muscles for pure elbow flexion trials, as required to counter the interaction torques that arise at the shoulder because of forearm rotation. After locking the shoulder joint, which cancels these interaction torques, we found a substantial reduction in shoulder muscle activity over many trials. In our second experiment, we tested whether such learning transfers to feedback control. Mechanical perturbations applied to the arm with the shoulder unlocked revealed that feedback responses also account for intersegmental dynamics. After locking the shoulder joint, we found a substantial reduction in shoulder feedback responses, as appropriate for the altered intersegmental dynamics. Our work suggests that feedforward and feedback control share an internal model of the arm's dynamics.SIGNIFICANCE STATEMENT Here we show that altering the physical properties of the arm causes people to learn new motor commands and that this learning transfers to their reflex responses to unexpected mechanical perturbations, even though the reflex responses were never directly trained. Our results suggest that feedforward motor commands and reflex responses share an internal model of the arm's dynamics.

Effort matching between arms depends on relative limb geometry and personal control.

Proprioception encompasses our sense of position and movement of our limbs, as well as the effort with which we engage in voluntary actions. Historically, sense of effort has been linked to centrally generated signals that elicit voluntary movements. We were interested in determining the effect of differences in limb geometry and personal control on sense of effort. In experiment 1, subjects exerted either extension or flexion torques to resist a torque applied by a robot exoskeleton to their reference elbow. They attempted to match this torque by exerting an equal effort torque (in a congruent direction with the reference arm) with their opposite (matching) arm in different limb positions (±15°). Subjects produced greater matching torque when their matching arm exerted effort toward the mirrored position of the reference (e.g., reference/matching arms at 90°/105° elbow flexion) vs. away (e.g., 90°/75° flexion). In experiment 2, a larger angular difference between arms (30°) resulted in a larger discrepancy in matched torques. Furthermore, in both experiments 1 and 2, subjects tended to overestimate the reference arm torque. This motivated a third experiment to determine whether providing more personal control might influence perceived effort and reduce the overestimation of the reference torques that we observed ( experiments 3a and 3b). Overestimation of the matched torques decreased significantly when subjects self-selected the reference torque that they were matching. Collectively, our data suggest that perceived effort between arms can be influenced by signals relating to the relative geometry of the limbs and the personal control of motor output during action. NEW & NOTEWORTHY This work highlights how limb geometry influences our sense of effort during voluntary motor actions. It also suggests that loss of personal control during motor actions leads to an increase in perceived effort.

Compensating for intersegmental dynamics across the shoulder, elbow, and wrist joints during feedforward and feedback control.

Moving the arm is complicated by mechanical interactions that arise between limb segments. Such intersegmental dynamics cause torques applied at one joint to produce movement at multiple joints, and in turn, the only way to create single joint movement is by applying torques at multiple joints. We investigated whether the nervous system accounts for intersegmental limb dynamics across the shoulder, elbow, and wrist joints during self-initiated planar reaching and when countering external mechanical perturbations. Our first experiment tested whether the timing and amplitude of shoulder muscle activity account for interaction torques produced during single-joint elbow movements from different elbow initial orientations and over a range of movement speeds. We found that shoulder muscle activity reliably preceded movement onset and elbow agonist activity, and was scaled to compensate for the magnitude of interaction torques arising because of forearm rotation. Our second experiment tested whether elbow muscles compensate for interaction torques introduced by single-joint wrist movements. We found that elbow muscle activity preceded movement onset and wrist agonist muscle activity, and thus the nervous system predicted interaction torques arising because of hand rotation. Our third and fourth experiments tested whether shoulder muscles compensate for interaction torques introduced by different hand orientations during self-initiated elbow movements and to counter mechanical perturbations that caused pure elbow motion. We found that the nervous system predicted the amplitude and direction of interaction torques, appropriately scaling the amplitude of shoulder muscle activity during self-initiated elbow movements and rapid feedback control. Taken together, our results demonstrate that the nervous system robustly accounts for intersegmental dynamics and that the process is similar across the proximal to distal musculature of the arm as well as between feedforward (i.e., self-initiated) and feedback (i.e., reflexive) control.NEW & NOTEWORTHY Intersegmental dynamics complicate the mapping between applied joint torques and the resulting joint motions. We provide evidence that the nervous system robustly predicts these intersegmental limb dynamics across the shoulder, elbow, and wrist joints during reaching and when countering external perturbations.

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

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

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

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

An eye-to-hand magnet effect reveals distinct spatial interference in motor planning and execution.

An important question in oculomanual control is whether motor planning and execution modulate interference between motion of the eyes and hands. Here we investigated oculomanual interference using a novel paradigm that required saccadic eye movements and unimanual finger tapping. We examined finger trajectories for spatial interference caused by concurrent saccades. The first experiment used synchronous cues so that saccades and taps shared a common timekeeping goal. We found that finger trajectories showed bilateral interference where either finger was attracted in the direction of the accompanying saccade. The second experiment avoided interference due to shared planning resources by examining interference caused by reactive saccades. Here, we observed a lesser degree of execution-dependent coupling where the finger trajectory deviated only when reactive saccades were directed toward the hemifield of the responding hand. Our results show that distinct forms of eye-to-hand coupling emerge according to the demands of the task.

The nervous system tunes sensorimotor gains when reaching in variable mechanical environments.

Humans often move in the presence of mechanical disturbances that can vary in direction and amplitude throughout movement. These disturbances can jeopardize the outcomes of our actions, such as when drinking from a glass of water on a turbulent flight or carrying a cup of coffee while walking on a busy sidewalk. Here, we examine control strategies that allow the nervous system to maintain performance when reaching in the presence of mechanical disturbances that vary randomly throughout movement. Healthy participants altered their control strategies to make movements more robust against disturbances. The change in control was associated with faster reaching movements and increased responses to proprioceptive and visual feedback that were tuned to the variability of the disturbances. Our findings highlight that the nervous system exploits a continuum of control strategies to increase its responsiveness to sensory feedback when reaching in the presence of increasingly variable physical disturbances.

Different Control Strategies Drive Interlimb Differences in Performance and Adaptation during Reaching Movements in Novel Dynamics.

Humans exhibit lateralization such that most individuals typically show a preference for using one arm over the other for a range of movement tasks. The computational aspects of movement control leading to these differences in skill are not yet understood. It has been hypothesized that the dominant and nondominant arms differ in terms of the use of predictive or impedance control mechanisms. However, previous studies present confounding factors that prevented clear conclusions: either the performances were compared across two different groups, or in a design in which asymmetrical transfer between limbs could take place. To address these concerns, we studied a reach adaptation task during which healthy volunteers performed movements with their right and left arms in random order. We performed two experiments. Experiment 1 (18 participants) focused on adaptation to the presence of a perturbing force field (FF) and experiment 2 (12 participants) focused on rapid adaptations in feedback responses. The randomization of the left and right arm led to simultaneous adaptation, allowing us to study lateralization in single individuals with symmetrical and minimal transfer between limbs. This design revealed that participants could adapt control of both arms, with both arms showing similar performance levels. The nondominant arm initially presented a slightly worst performance but reached similar levels of performance in late trials. We also observed that the nondominant arm showed a different control strategy compatible with robust control when adapting to the force field perturbation. EMG data showed that these differences in control were not caused by differences in co-contraction across the arms. Thus, instead of assuming differences in predictive or reactive control schemes, our data show that in the context of optimal control, both arms can adapt, and that the nondominant arm uses a more robust, model-free strategy likely to compensate for less accurate internal representations of movement dynamics.

Visual feedback-dependent modulation of arousal, postural control, and muscle stretch reflexes assessed in real and virtual environments.

Introduction: The mechanisms regulating neuromuscular control of standing balance can be influenced by visual sensory feedback and arousal. Virtual reality (VR) is a cutting-edge tool for probing the neural control of balance and its dependence on visual feedback, but whether VR induces neuromodulation akin to that seen in real environments (eyes open vs. closed or ground level vs. height platform) remains unclear. Methods: Here we monitored 20 healthy young adults (mean age 23.3 ± 3.2 years; 10 females) during four conditions of quiet standing. Two real world conditions (eyes open and eyes closed; REO and REC) preceded two eyes-open virtual 'low' (ground level; VRL) and 'high' (14 m height platform; VRH) conditions. We measured arousal via electrodermal activity and psychosocial questionnaires rating perceived fear and anxiety. We recorded surface electromyography over the right soleus, medial gastrocnemius, and tibialis anterior, and performed force plate posturography. As a proxy for modulations in neural control, we assessed lower limb reflexive muscle responses evoked by tendon vibration and electrical stimulation. Results: Physiological and perceptual indicators of fear and anxiety increased in the VRH condition. Background soleus muscle activation was not different across conditions; however, significant increases in muscle activity were observed for medial gastrocnemius and tibialis anterior in VRH relative to REO. The mean power frequency of postural sway also increased in the VRH condition relative to REO. Finally, with a fixed stimulus level across conditions, mechanically evoked reflexes remained constant, while H-reflex amplitudes decreased in strength within virtual reality. Discussion: Notably, H-reflexes were lower in the VRL condition than REO, suggesting that these ostensibly similar visual environments produce different states of reflexive balance control. In summary, we provide novel evidence that VR can be used to modulate upright postural control, but caution that standing balance in analogous real and virtual environments may involve different neural control states.

Multijoint error compensation mediates unstable object control.

A key feature of skilled object control is the ability to correct performance errors. This process is not straightforward for unstable objects (e.g., inverted pendulum or "stick" balancing) because the mechanics of the object are sensitive to small control errors, which can lead to rapid performance changes. In this study, we have characterized joint recruitment and coordination processes in an unstable object control task. Our objective was to determine whether skill acquisition involves changes in the recruitment of individual joints or distributed error compensation. To address this problem, we monitored stick-balancing performance across four experimental sessions. We confirmed that subjects learned the task by showing an increase in the stability and length of balancing trials across training sessions. We demonstrated that motor learning led to the development of a multijoint error compensation strategy such that after training, subjects preferentially constrained joint angle variance that jeopardized task performance. The selective constraint of destabilizing joint angle variance was an important metric of motor learning. Finally, we performed a combined uncontrolled manifold-permutation analysis to ensure the variance structure was not confounded by differences in the variance of individual joint angles. We showed that reliance on multijoint error compensation increased, whereas individual joint variation (primarily at the wrist joint) decreased systematically with training. We propose a learning mechanism that is based on the accurate estimation of sensory states.

Assessing Impairments in Visuomotor Adaptation After Stroke.

BACKGROUND: Motor impairment in the arms is common after stroke and many individuals participate in therapy to improve function. It is assumed that individuals with stroke can adapt and improve their movements using feedback that arises from movement or is provided by a therapist. Here we investigated visuomotor adaptation in individuals with sub-acute and chronic stroke. OBJECTIVE: We examined the impact of the stroke-affected arm (dominant or non-dominant), time post-stroke, and relationships with clinical measures of motor impairment and functional independence. METHODS: Participants performed reaching movements with their arm supported in a robotic exoskeleton. We rotated the relationship between the motion of the participant's hand and a feedback cursor displayed in their workspace. Outcome measures included the amount that participants adapted their arm movements and the number of trials they required to adapt. RESULTS: Participants with stroke (n = 36) adapted less and required more trials to adapt than controls (n = 29). Stroke affecting the dominant arm impaired the amount of adaptation more than stroke affecting the non-dominant arm. Overall, 53% of participants with stroke were impaired in one or more measures of visuomotor adaptation. Initial adaptation was weakly correlated with time post-stroke, and the amount of adaptation correlated moderately with clinical measures of motor impairment and functional independence. CONCLUSION: Our findings reveal impairments in visuomotor adaptation that are associated with motor impairment and function after stroke. Longitudinal studies are needed to understand the relationship between adaptation and recovery attained in a therapy setting.

Learning a stick-balancing task involves task-specific coupling between posture and hand displacements.

Theories of motor learning argue that the acquisition of novel motor skills requires a task-specific organization of sensory and motor subsystems. We examined task-specific coupling between motor subsystems as subjects learned a novel stick-balancing task. We focused on learning-induced changes in finger movements and body sway and investigated the effect of practice on their coupling. Eight subjects practiced balancing a cylindrical wooden stick for 30 min a day during a 20 day learning period. Finger movements and center of pressure trajectories were recorded in every fifth practice session (4 in total) using a ten camera VICON motion capture system interfaced with two force platforms. Motor learning was quantified using average balancing trial lengths, which increased with practice and confirmed that subjects learned the task. Nonlinear time series and phase space reconstruction methods were subsequently used to investigate changes in the spatiotemporal properties of finger movements, body sway and their progressive coupling. Systematic increases in subsystem coupling were observed despite reduced autocorrelation and differences in the temporal properties of center of pressure and finger trajectories. The average duration of these coupled trajectories increased systematically across the learning period. In short, the abrupt transition between coupled and decoupled subsystem dynamics suggested that stick balancing is regulated by a hierarchical control mechanism that switches from collective to independent control of the finger and center of pressure. In addition to traditional measures of motor performance, dynamical analyses revealed changes in motor subsystem organization that occurred when subjects learned a novel stick-balancing task.

Individual Differences in Sensorimotor Adaptation Are Conserved Over Time and Across Force-Field Tasks.

Sensorimotor adaptation enables the nervous system to modify actions for different conditions and environments. Many studies have investigated factors that influence adaptation at the group level. There is growing recognition that individuals vary in their ability to adapt motor skills and that a better understanding of individual differences in adaptation may inform how motor skills are taught and rehabilitated. Here we examined individual differences in the adaptation of upper-limb reaching movements. We quantified the extent to which participants adapted their movements to a velocity-dependent force field during an initial session, at 24 h, and again 1-week later. Participants (n = 28) displayed savings, which was expressed as greater initial adaptation when re-exposed to the force field. Individual differences in adaptation across various stages of the experiment displayed weak-strong reliability, such that individuals who adapted to a greater extent in the initial session tended to do so when re-exposed to the force field. Our second experiment investigated if individual differences in adaptation are also present when participants adapt to different force fields or a force field and visuomotor rotation. Separate groups of participants adapted to position- and velocity-dependent force fields (Experiment 2a; n = 20) or a velocity-dependent force field and visuomotor rotation in a single session (Experiment 2b; n = 20). Participants who adapted to a greater extent to velocity-dependent forces tended to show a greater extent of adaptation when exposed to position-dependent forces. In contrast, correlations were weak between various stages of adaptation to the force-field and visuomotor rotation. Collectively, our study reveals individual differences in adaptation that are reliable across repeated exposure to the same force field and present when adapting to different force fields.

The energetic basis for smooth human arm movements.

The central nervous system plans human reaching movements with stereotypically smooth kinematic trajectories and fairly consistent durations. Smoothness seems to be explained by accuracy as a primary movement objective, whereas duration seems to economize energy expenditure. But the current understanding of energy expenditure does not explain smoothness, so that two aspects of the same movement are governed by seemingly incompatible objectives. Here, we show that smoothness is actually economical, because humans expend more metabolic energy for jerkier motions. The proposed mechanism is an underappreciated cost proportional to the rate of muscle force production, for calcium transport to activate muscle. We experimentally tested that energy cost in humans (N = 10) performing bimanual reaches cyclically. The empirical cost was then demonstrated to predict smooth, discrete reaches, previously attributed to accuracy alone. A mechanistic, physiologically measurable, energy cost may therefore explain both smoothness and duration in terms of economy, and help resolve motor redundancy in reaching movements.

Altered Vestibular Balance Function in Combat Sport Athletes.

Combat sports pose a risk for accumulative injuries to the nervous system, yet fighters have remained an understudied population. Here, our purpose was to determine whether repetitive blows to the head have an effect on vestibular balance reflexes in combat sports athletes. We compared lower-limb muscle responses evoked with electrical vestibular stimuluation (EVS) between fighters (boxing/muay thai) and non-fighter controls. Each participant received stochastic vestibular stimulation (0-25 Hz, ±3 mA) over their mastoid processes while they stood relaxed with their head to the left or right. Surface electromyography was recorded from the medial gastrocnemius and soleus muscles bilaterally. Short and medium latency response (SLR/MLR) peaks were significantly delayed in the fighter group compared to controls. SLR and MLR peak amplitudes were also significantly lower in fighters. Fighter-estimated cumulative repetitive head impact (RHI) events demonstrated strong positive correlations with the timing of SLR and MLR peaks. Cumulative RHI events also negatively correlated with peak MLR amplitude and response gain at frequencies above 5 Hz. Our results provide evidence of a progressive vestibular impairment in combat sports athletes, potentially resulting from blows to the head accumulated in sparring practice and competitive bouts throughout their careers. Taken together, EVS-based vestibular assessments may provide a valuable clinical diagnostic tool and help better inform "return-to-play" and career-length decisions for not only combat sports athletes, but potentially other populations at risk of RHIs.