Muscle Activity Determined by Cosine Tuning With a Nontrivial Preferred Direction During Isometric Force Exertion by Lower Limb

More Variability in Tibialis Anterior Function during the Adduction of the Foot than Dorsiflexion of the Ankle

INTRODUCTION

The aim of the study was to compare maximal force, force steadiness, and the discharge characteristics of motor units in the tibialis anterior (TA) muscle during submaximal isometric contractions for ankle dorsiflexion and adduction of the foot.

METHODS

Nineteen active young adults performed maximal and submaximal isometric dorsiflexion and adduction contractions at five target forces (5%, 10%, 20%, 40%, and 60% maximal voluntary contraction [MVC]). The activity of motor units in TA was recorded by high-density EMG.

RESULTS

The maximal force was similar between dorsiflexion and adduction, despite EMG amplitude for TA being greater ( P < 0.05) during dorsiflexion than adduction. Τhe coefficient of variation (CV) for force (force steadiness) during dorsiflexion was always less ( P < 0.05) than during adduction, except of 5% MVC force. No differences were observed for mean discharge rate; however, the regression between the changes in discharge rate relative to the change of force was significant for dorsiflexion ( R2 = 0.25, P < 0.05) but not for adduction. Discharge variability, however, was usually less during dorsiflexion. The CV for interspike interval was less ( P < 0.05) at 10%, 20%, and 40% MVC but greater at 60% MVC during dorsiflexion than adduction. Similarly, the SD values of the filtered cumulative spike train of the motor units in TA were less ( P < 0.05) at 5%, 10%, 20%, and 40% MVC during dorsiflexion than adduction.

CONCLUSIONS

Although the mean discharge rate of motor units in TA was similar during foot adduction and ankle dorsiflexion, discharge variability was less during dorsiflexion resulting in less accurate performance of the steady adduction contractions. The neural drive to bifunctional muscles differs during their accessory function, which must be considered for training and rehabilitation interventions.

Alterations in the preferred direction of individual arm muscle activation after stroke

Introduction

Stroke survivors have challenges appropriately coordinating the multiple muscles, resulting in a deficit in motor control. Therefore, comprehending the mechanism underlying abnormal intermuscular coordination becomes crucial in developing effective rehabilitation strategies. Quantitative analyses have been employed at pairwise or multi-dimensional levels to understand the underlying mechanism of abnormal intermuscular coordination and its relationship to motor impairment. However, how alterations in individual muscle activation contribute to abnormal intermuscular coordination, motor impairment, and motor performance remains unclear. Thus, we investigated the alterations in the preferred direction of individual muscles after stroke and their relationship with stroke-induced changes in intermuscular coordination, clinical motor impairment, and qualities of motor performance during isometric force generation in the upper extremity.

Methods

Twenty-four stroke survivors and six age-matched controls were recruited and performed isometric force target matches while recording electromyographic signals from eight upper limb muscles. We determined the preferred activation direction of each muscle, evaluated abnormal intermuscular coordination through a muscle synergy analysis, assessed motor impairment using upper extremity Fugl-Meyer Assessment scores, and examined motor performance characteristics defined by force trajectory features.

Results

The post-stroke alterations in the preferred direction of the brachioradialis, anterior, middle, and posterior deltoid were correlated with the motor impairment level and attributed to the changes in muscle synergy characteristics. Only alterations in the preferred direction of the brachioradialis and posterior deltoid activation in forward-backward and upward-downward axes were associated with the qualities of isometric force generation, respectively.

Discussion

These findings imply that alterations in the preferred direction of individual muscle activation contribute to various aspects of motor deficit following stroke. This insight may serve as a foundation for the development of innovative stroke neurorehabilitation approaches that take into account specific attributes of individual muscle activation, including their preferred activation direction.

Muscle co-activation in the elderly contributes to control of hip and knee joint torque and endpoint force

We investigated the coordinated activity patterns of muscles based on cosine tuning in the elderly during an isometric force exertion task. We also clarified whether these coordinated activity patterns contribute to the control of hip and knee joint torque and endpoint force as co-activation. Preferred direction (PD) of activity for each muscle in 10 young and 8 older males was calculated from the lower limb muscle activity during isometric force exertion task in various directions. The covariance of endpoint force (η) was calculated from the exerted force data using a force sensor. Relationship between PD and η was used to examine the effect of muscle co-activation on the control of endpoint force. Co-activation between rectus femoris and semitendinosus/biceps femoris increased with changes in muscle PD. Additionally, the η values were significantly low, suggesting that co-activation of multiple muscles may contribute to endpoint force exertion. The mechanism for cooperative muscle activity is determined by the cosine tuning of the PD of each muscle, which affects the generation of hip and knee joint torque and endpoint force exertion. Co-activation of each muscle's PD changes with age, causing increased muscle co-activation to control torque and force. We demonstrated that co-activation in the elderly is a stabilizer of unsteady joints and a muscle control strategy for cooperative muscle activity.

Gaze control during reaching is flexibly modulated to optimize task outcome

When reaching for an object with the hand, the gaze is usually directed at the target. In a laboratory setting, fixation is strongly maintained at the reach target until the reaching is completed, a phenomenon known as "gaze anchoring." While conventional accounts of such tight eye-hand coordination have often emphasized the internal synergetic linkage between both motor systems, more recent optimal control theories regard motor coordination as the adaptive solution to task requirements. We here investigated to what degree gaze control during reaching is modulated by task demands. We adopted a gaze-anchoring paradigm in which participants had to reach for a target location. During the reach, they additionally had to make a saccadic eye movement to a salient visual cue presented at locations other than the target. We manipulated the task demands by independently changing reward contingencies for saccade reaction time (RT) and reaching accuracy. On average, both saccade RTs and reach error varied systematically with reward condition, with reach accuracy improving when the saccade was delayed. The distribution of the saccade RTs showed two types of eye movements: fast saccades with short RTs, and voluntary saccade with longer RTs. Increased reward for high reach accuracy reduced the probability of fast saccades but left their latency unchanged. The results suggest that gaze anchoring acts through a suppression of fast saccades, a mechanism that can be adaptively adjusted to the current task demands.NEW & NOTEWORTHY During visually guided reaching, our eyes usually fixate the target and saccades elsewhere are delayed ("gaze anchoring"). We here show that the degree of gaze anchoring is flexibly modulated by the reward contingencies of saccade latency and reach accuracy. Reach error became larger when saccades occurred earlier. These results suggest that early saccades are costly for reaching and the brain modulates inhibitory online coordination from the hand to the eye system depending on task requirements.

Interjoint coupling of position sense reflects sensory contributions of biarticular muscles

Perception of limb position and motion combines sensory information from spindles in muscles that span one joint (monoarticulars) and two joints (biarticulars). This anatomical organization should create interactions in estimating limb position. We developed two models, one with only monoarticulars and one with both monoarticulars and biarticulars, to explore how biarticulars influence estimates of arm position in hand (x, y) and joint (shoulder, elbow) coordinates. In hand coordinates, both models predicted larger medial-lateral than proximal-distal errors, although the model with both muscle groups predicted that biarticulars would reduce this bias. In contrast, the two models made significantly different predictions in joint coordinates. The model with only monoarticulars predicted that errors would be uniformly distributed because estimates of angles at each joint would be independent. In contrast, the model that included biarticulars predicted that errors would be coupled between the two joints, resulting in smaller errors for combinations of flexion or extension at both joints and larger errors for combinations of flexion at one joint and extension at the other joint. We also carried out two experiments to examine errors made by human subjects during an arm position matching task in which a robot passively moved one arm to different positions and the subjects moved their other arm to mirror-match each position. Errors in hand coordinates were similar to those predicted by both models. Critically, however, errors in joint coordinates were only similar to those predicted by the model with monoarticulars and biarticulars. These results highlight how biarticulars influence perceptual estimates of limb position by helping to minimize medial-lateral errors.NEW & NOTEWORTHY It is unclear how sensory information from muscle spindles located within muscles spanning multiple joints influences perception of body position and motion. We address this issue by comparing errors in estimating limb position made by human subjects with predicted errors made by two musculoskeletal models, one with only monoarticulars and one with both monoarticulars and biarticulars. We provide evidence that biarticulars produce coupling of errors between joints, which help to reduce errors.

Cosine tuning determines plantarflexors' activities during human upright standing and is affected by incomplete spinal cord injury

Plantarflexors such as the soleus (SOL) and medial gastrocnemius (MG) play key roles in controlling bipedal stance; however, how the central nervous system controls the activation levels of these plantarflexors is not well understood. Here we investigated how the central nervous system controls the plantarflexors' activation level during quiet standing in a cosine tuning manner where the maximal activation is achieved in a preferred direction (PD). Furthermore, we investigated how spinal cord injury affects these plantarflexors' activations. Thirteen healthy adults (AB) and thirteen individuals with chronic, incomplete spinal cord injury (iSCI) performed quiet standing trials. Their body kinematics and kinetics as well as electromyography signals from the MG and SOL were recorded. In the AB group, we found that the plantarflexors followed the cosine tuning manner during quiet standing. That is, MG was most active when the ratio of plantarflexion torque to knee extension torque was ~2:-3, whereas SOL was most active when the ratio was ~2:1. This suggests that the SOL muscle, despite being a monoarticular muscle, is sensitive to both ankle plantarflexion and knee extension during quiet standing. The difference in the PDs accounts for the phasic activity of MG and for the tonic activity of SOL. Unlike the AB group, the MG's activity was similar to the SOL's activity in the iSCI group, and the SOL PDs were similar to those in the AB group. This result suggests that chronic iSCI affects the control strategy, i.e., cosine tuning, for MG, which may affect standing balance in individuals with iSCI.NEW & NOTEWORTHY Soleus muscle shows a tonic activity whereas medial gastrocnemius muscle shows a phasic activity during quiet standing. Cosine tuning and their preferred direction account for the different muscle activation patterns between these two muscles. In individuals with chronic incomplete spinal cord injury, the preferred direction of gastrocnemius medial head is affected, which may result in their deteriorated standing balance.

Differences in postural sway among healthy adults are associated with the ability to perform steady contractions with leg muscles

Upright standing involves small displacements of the center of mass about the base of support. These displacements are often quantified by measuring various kinematic features of the center-of-pressure trajectory. The plantar flexors have often been identified as the key muscles for the control of these displacements; however, studies have suggested that the hip abductor and adductors may also be important. The purpose of our study was to determine the association between the force capabilities of selected leg muscles and sway-area rate across four balance conditions in young (25 ± 4 years; 12/19 women) and older adults (71 ± 5 years; 5/19 women). Due to the marked overlap in sway-area rate between the two age groups, the data were collapsed, and individuals were assigned to groups of low- and high-sway area rates based on a k-medoid cluster analysis. The number of participants assigned to each group varied across balance conditions and a subset of older adults was always included in the low-sway group for each balance condition. The most consistent explanatory variable for the variance in sway-area rate was force control of the hip abductors and ankle dorsiflexors as indicated by the magnitude of the normalized force fluctuations (force steadiness) during a submaximal isometric contraction. The explanatory power of the regression models varied across conditions, thereby identifying specific balance conditions that should be examined further in future studies of postural control.

Independent representations of ipsilateral and contralateral limbs in primary motor cortex

Several lines of research demonstrate that primary motor cortex (M1) is principally involved in controlling the contralateral side of the body. However, M1 activity has been correlated with both contralateral and ipsilateral limb movements. Why does ipsilaterally-related activity not cause contralateral motor output? To address this question, we trained monkeys to counter mechanical loads applied to their right and left limbs. We found >50% of M1 neurons had load-related activity for both limbs. Contralateral loads evoked changes in activity ~10ms sooner than ipsilateral loads. We also found corresponding population activities were distinct, with contralateral activity residing in a subspace that was orthogonal to the ipsilateral activity. Thus, neural responses for the contralateral limb can be extracted without interference from the activity for the ipsilateral limb, and vice versa. Our results show that M1 activity unrelated to downstream motor targets can be segregated from activity related to the downstream motor output.

Neuroanatomical Basis of Individuality in Muscle Tuning Function: Neural Correlates of Muscle Tuning

In a conventional view of motor control, the human brain might employ an optimization principle that leads a stereotypical motor behavior which we observe as an averaged behavioral data over subjects. In this scenario, the inter-individual motor variability is considered as an observation noise. Here, we challenged this view. We considered a motor control task where the human participants manipulated arm force by coordinating shoulder and elbow torques and investigated the muscle-tuning function that represents how the brain distributed the ideal joint torques to multiple muscles. In the experimental data, we observed large inter-individual variability in the profile of a muscle-tuning function. This contradicts with a well-established optimization theory that is based on minimization of muscle energy consumption and minimization of motor variability. We then hypothesized the inter-subject differences in the structure of the motor cortical areas might be the source of the across-subjects variability of the motor behavior. This was supported by a voxel-based morphometry analysis of magnetic resonance imaging; The inter-individual variability of the muscle tuning profile was correlated with that of the gray matter volume in the premotor cortex which is ipsilateral to the used arm (i.e., right hemisphere for the right arm). This study suggests that motor individuality may originate from inter-individual variation in the cortical structure.

Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.

Long-latency reflexes for inter-effector coordination reflect a continuous state feedback controller

Successful performance in many everyday tasks requires compensating unexpected mechanical disturbance to our limbs and body. The long-latency reflex plays an important role in this process because it is the fastest response to integrate sensory information across several effectors, like when linking the elbow and shoulder or the arm and body. Despite the dozens of studies on inter-effector long-latency reflexes, there has not been a comprehensive treatment of how these reveal the basic control organization that sets constraints on any candidate model of neural feedback control such as optimal feedback control. We considered three contrasting ways that controllers can be organized: multiple independent controllers vs. a multiple-input multiple-output (MIMO) controller, a continuous feedback controller vs. an intermittent feedback controller, and a direct MIMO controller vs. a state feedback controller. Following a primer on control theory and review of the relevant evidence, we conclude that continuous state feedback control best describes the organization of inter-effector coordination by the long-latency reflex.

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.

An error-tuned model for sensorimotor learning

Current models of sensorimotor control posit that motor commands are generated by combining multiple modules which may consist of internal models, motor primitives or motor synergies. The mechanisms which select modules based on task requirements and modify their output during learning are therefore critical to our understanding of sensorimotor control. Here we develop a novel modular architecture for multi-dimensional tasks in which a set of fixed primitives are each able to compensate for errors in a single direction in the task space. The contribution of the primitives to the motor output is determined by both top-down contextual information and bottom-up error information. We implement this model for a task in which subjects learn to manipulate a dynamic object whose orientation can vary. In the model, visual information regarding the context (the orientation of the object) allows the appropriate primitives to be engaged. This top-down module selection is implemented by a Gaussian function tuned for the visual orientation of the object. Second, each module's contribution adapts across trials in proportion to its ability to decrease the current kinematic error. Specifically, adaptation is implemented by cosine tuning of primitives to the current direction of the error, which we show to be theoretically optimal for reducing error. This error-tuned model makes two novel predictions. First, interference should occur between alternating dynamics only when the kinematic errors associated with each oppose one another. In contrast, dynamics which lead to orthogonal errors should not interfere. Second, kinematic errors alone should be sufficient to engage the appropriate modules, even in the absence of contextual information normally provided by vision. We confirm both these predictions experimentally and show that the model can also account for data from previous experiments. Our results suggest that two interacting processes account for module selection during sensorimotor control and learning.

Research in motor learning has focused on how we acquire new motor memories for novel situations. However, in many real world motor tasks, the challenge is to select appropriate memories for a given context. In such tasks, we are guided by two key types of information. First, contextual information from vision (for example) is available before we perform the task. Second, movement errors are available as we begin to perform the task. Here we present a model that provides a mechanism by which these two processes operate in parallel to enable us to tune and adapt our motor commands. We show that a model consisting of multiple simple modules, each of which can correct errors in a single direction only, can account for learning in multidimensional tasks. The model makes predictions about which tasks should interfere and how experience of errors alone without any contextual information can drive learning. We confirm these predictions in a series of experiments. The model provides a new framework for understanding the interaction between task context and error feedback during sensorimotor control and learning.

Activation of plantar flexor muscles is constrained by multiple muscle synergies rather than joint torques

Behavioral evidence has suggested that a small number of muscle synergies may be responsible for activating a variety of muscles. Nevertheless, such dimensionality reduction may also be explained using the perspective of alternative hypotheses, such as predictions based on linear combinations of joint torques multiplied by corresponding coefficients. To compare the explanatory capacity of these hypotheses for describing muscle activation, we enrolled 12 male volunteers who performed isometric plantar flexor contractions at 10–100% of maximum effort. During each plantar flexor contraction, the knee extensor muscles were isometrically contracted at 0%, 50%, or 100% of maximum effort. Electromyographic activity was recorded from the vastus lateralis, medial gastrocnemius (MG), lateral gastrocnemius (LG), and soleus muscles and quantified using the average rectified value (ARV). At lower plantar flexion torque, regression analysis identified a clear linear relationship between the MG and soleus ARVs and between the MG and LG ARVs, suggesting the presence of muscle synergy (r² > 0.65). The contraction of the knee extensor muscles induced a significant change in the slope of this relationship for both pairs of muscles (MG × soleus, P = 0.002; MG × LG, P = 0.006). Similarly, the slope of the linear relationship between the plantar flexion torque and the ARV of the MG or soleus changed significantly with knee extensor contraction (P = 0.031 and P = 0.041, respectively). These results suggest that muscle synergies characterized by non-mechanical constraints are selectively recruited according to whether contraction of the knee extensor muscles is performed simultaneously, which is relatively consistent with the muscle synergy hypothesis.

Optimal feedback control to describe multiple representations of primary motor cortex neurons

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predicted via feedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.

How does the brain solve muscle redundancy? Filling the gap between optimization and muscle synergy hypotheses

The question of how the central nervous system coordinates redundant muscles has been a long-standing problem in motor neuroscience. The optimization hypothesis posits that the brain can select the muscle activation pattern that minimizes the motor effort cost from among many solutions that satisfy the requirements of the task. On the other hand, the muscle-synergy hypothesis proposes that neurally established functional groupings of muscles alleviate the computational burden associated with motor control and learning. Although the two hypotheses are not mutually exclusive, the relationship between them has not been well analyzed. This is probably because both hypotheses are formulated mathematically without a clear concept of their neural implementation. Here, we introduce a biologically plausible hypothesis ("the forgetting hypothesis") for how optimization is realized by a population of neurons. We further demonstrate that low-dimensional structure can be detected in an optimal network even if no muscle-synergies are explicitly assumed. Finally, we briefly discuss an inherent difficulty in testing the muscle-synergy hypothesis, which arises when population level optimization is assumed.

The flexible recruitment of muscle synergies depends on the required force-generating capability

To simplify redundant motor control, the central nervous system (CNS) may modularly organize and recruit groups of muscles as “muscle synergies.” However, smooth and efficient movements are expected to require not only low-dimensional organization, but also flexibility in the recruitment or combination of synergies, depending on force-generating capability of individual muscles. In this study, we examined how the CNS controls activations of muscle synergies as changing joint angles. Subjects performed multidirectional isometric force generations around right ankle and extracted the muscle synergies using nonnegative matrix factorization across various knee and hip joint angles. As a result, muscle synergies were selectively recruited with merging or decomposition as changing the joint angles. Moreover, the activation profiles, including activation levels and the direction indicating the peak, of muscle synergies across force directions depended on the joint angles. Therefore, we suggested that the CNS selects appropriate muscle synergies and controls their activation patterns based on the force-generating capability of muscles with merging or decomposing descending neural inputs.

The mechanical actions of muscles predict the direction of muscle activation during postural perturbations in the cat hindlimb

Humans and cats respond to balance challenges, delivered via horizontal support surface perturbations, with directionally selective muscle recruitment and constrained ground reaction forces. It has been suggested that this postural strategy arises from an interaction of limb biomechanics and proprioceptive networks in the spinal cord. A critical experimental validation of this hypothesis is to test the prediction that the principal directions of muscular activation oppose the directions responding muscles exert their forces on the environment. Therefore, our objective was to quantify the endpoint forces of a diverse set of cat hindlimb muscles and compare them with the directionally sensitive muscle activation patterns generated in the intact and decerebrate cat. We hypothesized that muscles are activated based on their mechanical advantage. Our primary expectation was that the principal direction of muscle activation during postural perturbations will be directed oppositely (180°) from the muscle endpoint ground reaction force. We found that muscle activation during postural perturbations was indeed directed oppositely to the endpoint reaction forces of that muscle. These observations indicate that muscle recruitment during balance challenges is driven, at least in part, by limb architecture. This suggests that sensory sources that provide feedback about the mechanical environment of the limb are likely important to appropriate and effective responses during balance challenges. Finally, we extended the analysis to three dimensions and different stance widths, laying the groundwork for a more comprehensive study of postural regulation than was possible with measurements confined to the horizontal plane and a single stance configuration.

Cerebellar damage diminishes long-latency responses to multijoint perturbations

Damage to the cerebellum can cause significant problems in the coordination of voluntary arm movements. One prominent idea is that incoordination stems from an inability to predictively account for the complex mechanical interactions between the arm's several joints. Motivated by growing evidence that corrective feedback control shares important capabilities and neural substrates with feedforward control, we asked whether cerebellar damage impacts feedback stabilization of the multijoint arm appropriate for the arm's intersegmental dynamics. Specifically, we tested whether cerebellar dysfunction impacts the ability of posterior deltoid to incorporate elbow motion in its long-latency response (R2 = 45-75 ms and R3 = 75-100 ms after perturbation) to an unexpected torque perturbation. Healthy and cerebellar-damaged subjects were exposed to a selected pattern of shoulder-elbow displacements to probe the response pattern from this shoulder extensor muscle. The healthy elderly subjects expressed a long-latency response linked to both shoulder and elbow motion, including an increase/decrease in shoulder extensor activity with elbow flexion/extension. Critically, cerebellar-damaged subjects displayed the normal pattern of activity in the R3 period indicating an intact ability to rapidly integrate multijoint motion appropriate to the arm's intersegmental dynamics. However, cerebellar-damaged subjects had a lower magnitude of activity that was specific to the long-latency period (both R2 and R3) and a slightly delayed onset of multijoint sensitivity. Taken together, our results suggest that the basic motor pattern of the long-latency response is housed outside the cerebellum and is scaled by processes within the cerebellum.

Relationships between neck muscle electromyography and three-dimensional head kinematics during centrally induced torsional head perturbations

The relationship between neck muscle electromyography (EMG) and torsional head rotation (about the nasooccipital axis) is difficult to assess during normal gaze behaviors with the head upright. Here, we induced acute head tilts similar to cervical dystonia (torticollis) in two monkeys by electrically stimulating 20 interstitial nucleus of Cajal (INC) sites or inactivating 19 INC sites by injection of muscimol. Animals engaged in a simple gaze fixation task while we recorded three-dimensional head kinematics and intramuscular EMG from six bilateral neck muscle pairs. We used a cross-validation-based stepwise regression to quantitatively examine the relationships between neck EMG and torsional head kinematics under three conditions: 1) unilateral INC stimulation (where the head rotated torsionally toward the side of stimulation); 2) corrective poststimulation movements (where the head returned toward upright); and 3) unilateral INC inactivation (where the head tilted toward the opposite side of inactivation). Our cross-validated results of corrective movements were slightly better than those obtained during unperturbed gaze movements and showed many more torsional terms, mostly related to velocity, although some orientation and acceleration terms were retained. In addition, several simplifying principles were identified. First, bilateral muscle pairs showed similar, but opposite EMG-torsional coupling terms, i.e., a change in torsional kinematics was associated with increased muscle activity on one side and decreased activity on the other side. s, whenever torsional terms were retained in a given muscle, they were independent of the inputs we tested, i.e., INC stimulation vs. corrective motion vs. INC inactivation, and left vs. right INC data. These findings suggest that, despite the complexity of the head-neck system, the brain can use a single, bilaterally coupled inverse model for torsional head control that is valid across different behaviors and movement directions. Combined with our previous data, these new data provide the terms for a more complete three-dimensional model of EMG: head rotation coupling for the muscles and gaze behaviors that we recorded.

Learning with slight forgetting optimizes sensorimotor transformation in redundant motor systems

Recent theoretical studies have proposed that the redundant motor system in humans achieves well-organized stereotypical movements by minimizing motor effort cost and motor error. However, it is unclear how this optimization process is implemented in the brain, presumably because conventional schemes have assumed a priori that the brain somehow constructs the optimal motor command, and largely ignored the underlying trial-by-trial learning process. In contrast, recent studies focusing on the trial-by-trial modification of motor commands based on error information suggested that forgetting (i.e., memory decay), which is usually considered as an inconvenient factor in motor learning, plays an important role in minimizing the motor effort cost. Here, we examine whether trial-by-trial error-feedback learning with slight forgetting could minimize the motor effort and error in a highly redundant neural network for sensorimotor transformation and whether it could predict the stereotypical activation patterns observed in primary motor cortex (M1) neurons. First, using a simple linear neural network model, we theoretically demonstrated that: 1) this algorithm consistently leads the neural network to converge at a unique optimal state; 2) the biomechanical properties of the musculoskeletal system necessarily determine the distribution of the preferred directions (PD; the direction in which the neuron is maximally active) of M1 neurons; and 3) the bias of the PDs is steadily formed during the minimization of the motor effort. Furthermore, using a non-linear network model with realistic musculoskeletal data, we demonstrated numerically that this algorithm could consistently reproduce the PD distribution observed in various motor tasks, including two-dimensional isometric torque production, two-dimensional reaching, and even three-dimensional reaching tasks. These results may suggest that slight forgetting in the sensorimotor transformation network is responsible for solving the redundancy problem in motor control.

It is thought that the brain can optimize motor commands to produce efficient movements; however, it is unknown how this optimization process is implemented in the brain. Here we examine a biologically plausible hypothesis in which slight forgetting in the motor learning process plays an important role in the optimization process. Using a neural network model for motor learning, we initially theoretically demonstrated that motor learning with a slight forgetting factor consistently led the network to converge at an optimal state. In addition, by applying the forgetting scheme to a more sophisticated neural network model with realistic musculoskeletal data, we showed that the model could account for the reported stereotypical activity patterns of muscles and motor cortex neurons in various motor tasks. Our results support the hypothesis that slight forgetting, which is conventionally considered to diminish motor learning performance, plays a crucial role in the optimization process of the redundant motor system.

A fingerprint method for variability and robustness analysis of stochastically controlled cellular actuator arrays

In this paper we present a ‘fingerprint method’ for modeling and subsequently characterizing stochastically controlled actuator arrays. The actuator arrays are built from small actuator cells with structural elasticity. These cells are controlled using a bistable stochastic process wherein all cells are given a common input probability (control) value which they use to determine whether to actuate or relax. Arranging the cells in different networks gives different actuator array properties, which must be found before the actuator arrays can be applied to manipulators. The fingerprint method is used to describe and automatically generate every possible stochastic actuator array topology for a given number of cells, and to calculate actuator array properties such as: travel, required actuator strength/displacement, force range, force variance, and robustness for any array topology. The properties of several illustrative examples are shown and a discussion covers the importance of the properties, and trends between actuator array layouts and their properties. Finally, results from a validation experiment using a stochastically controlled solenoid array are presented.

Individual muscle control using an exoskeleton robot for muscle function testing

Healthy individuals modulate muscle activation patterns according to their intended movement and external environment. Persons with neurological disorders (e.g., stroke and spinal cord injury), however, have problems in movement control due primarily to their inability to modulate their muscle activation pattern in an appropriate manner. A functionality test at the level of individual muscles that investigates the activity of a muscle of interest on various motor tasks may enable muscle-level force grading. To date there is no extant work that focuses on the application of exoskeleton robots to induce specific muscle activation in a systematic manner. This paper proposes a new method, named "individual muscle-force control" using a wearable robot (an exoskeleton robot, or a power-assisting device) to obtain a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by hand. A computational algorithm systematically computes control commands to a wearable robot so that a desired muscle activation pattern for target muscle forces is induced. It also computes an adequate amount and direction of a force that a subject needs to exert against a handle by his/her hand. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscular function tests on target muscles by arbitrarily inducing muscle activation patterns. This paper presents a basic concept, mathematical formulation, and solution of the individual muscle-force control and its implementation to a muscle control system with an exoskeleton-type robot for upper extremity. Simulation and experimental results in healthy individuals justify the use of an exoskeleton robot for future muscle function testing in terms of the variety of muscle activity data.

The coordination of movement: optimal feedback control and beyond

Optimal control theory and its more recent extension, optimal feedback control theory, provide valuable insights into the flexible and task-dependent control of movements. Here, we focus on the problem of coordination, defined as movements that involve multiple effectors (muscles, joints or limbs). Optimal control theory makes quantitative predictions concerning the distribution of work across multiple effectors. Optimal feedback control theory further predicts variation in feedback control with changes in task demands and the correlation structure between different effectors. We highlight two crucial areas of research, hierarchical control and the problem of movement initiation, that need to be developed for an optimal feedback control theory framework to characterise movement coordination more fully and to serve as a basis for studying the neural mechanisms involved in voluntary motor control.

Long-Latency Responses During Reaching Account for the Mechanical Interaction Between the Shoulder and Elbow Joints

Although considerable research indicates that reaching movements rely on knowledge of the arm's mechanical properties and environment to anticipate and counter predictable loads, far less research has examined whether this degree of sophistication is present for on-line corrections during reaching. Here we examine the R2/3 response to mechanical perturbations (45–100 ms, also called the long-latency reflex), which is highly flexible and includes the fastest possible contribution from primary motor cortex, a key neural substrate for self-initiated action. Torque perturbations were occasionally and unexpectedly applied to the subject's shoulder and/or elbow in the course of performing reaching movements. Critically, these perturbations would evoke different patterns of feedback corrections from a shoulder extensor muscle if it countered only the local shoulder displacement relative to unperturbed motion or accounted for the mechanical interactions between the shoulder and elbow joints and countered the underlying shoulder torque. Our results show that the earliest response (R1: 20–45 ms) reflected local shoulder displacement, whereas the R2/3 response (45–100 ms) reflected knowledge of multijoint dynamics. Moreover, the same pattern of feedback occurred whether the shoulder muscle helped initiate the movement (during its agonist phase) or helped terminate the movement (during its antagonist phase). These results contribute to the accumulating evidence that highly sophisticated feedback control underlies motor behavior and are consistent with a shared neural substrate, such as primary motor cortex, for feedforward and feedback control.

Changes in muscle and joint coordination in learning to direct forces

While it has been suggested that bi-articular muscles have a specialized role in directing external reaction forces, it is unclear how humans learn to coordinate mono- and bi-articular muscles to perform force-directing tasks. Participants were asked to direct pedal forces in a specified target direction during one-legged cycling. We expected that with practice, performance improvement would be associated with specific changes in joint torque patterns and mono- and bi-articular muscular coordination. Nine male participants practiced pedaling an ergometer with only their left leg, and were instructed to always direct their applied pedal force perpendicular to the crank arm (target direction) and to maintain a constant pedaling speed. After a single practice session, the mean error between the applied and target pedal force directions decreased significantly. This improved performance was accompanied by a significant decrease in the amount of ankle angular motion and a smaller increase in knee and hip angular motion. This coincided with a re-organization of lower extremity joint torques, with a decrease in ankle plantarflexor torque and an increase in knee and hip flexor torques. Changes were seen in both mono- and bi-articular muscle activity patterns. The mono-articular muscles exhibited greater alterations, and appeared to contribute to both mechanical work and force-directing. With practice, a loosening of the coupling between bi-articular thigh muscle activation and joint torque co-regulation was observed. The results demonstrated that participants were able to learn a complex and dynamic force-directing task by changing the direction of their applied pedal forces through re-organization of joint torque patterns and mono- and bi-articular muscle coordination.

Decoding two-dimensional movement trajectories using electrocorticographic signals in humans

Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.

Characterization of torque-related activity in primary motor cortex during a multijoint postural task

The present study examined neural activity in the shoulder/elbow region of primary motor cortex (M1) during a whole-limb postural task. By selectively imposing torques at the shoulder, elbow, or both joints we addressed how neurons represent changes in torque at a single joint, multiple joints, and their interrelation. We observed that similar proportions of neurons reflected changes in torque at the shoulder, elbow, and both joints and these neurons were highly intermingled across the cortical surface. Most torque-related neurons were reciprocally excited and inhibited (relative to their unloaded baseline activity) by opposing flexor and extensor torques at a single joint. Although coexcitation/coinhibition was occasionally observed at a single joint, it was rarely observed at both joints. A second analysis assessed the relationship between single-joint and multijoint activity. In contrast to our previous observations, we found that neither linear nor vector summation of single-joint activities could capture the breadth of neural responses to multijoint torques. Finally, we studied the neurons' directional tuning across all the torque conditions, i.e., in joint-torque space. Our population of M1 neurons exhibited a strong bimodal distribution of preferred-torque directions (PTDs) that was biased toward shoulder-extensor/elbow-flexor (whole-limb flexor) and shoulder-flexor/elbow-extensor (whole-limb extensor) torques. Notably, we recently observed a similar bimodal distribution of PTDs in a sample of proximal arm muscles. This observation illustrates the intimate relationship between M1 and the motor periphery.

Nonuniform Distribution of Reach-Related and Torque-Related Activity in Upper Arm Muscles and Neurons of Primary Motor Cortex

The present study examined the activity of primate shoulder and elbow muscles using a novel reaching task. We enforced similar patterns of center-out movement while the animals countered viscous loads at their shoulder, elbow, both joints, or neither joint. Accordingly, we could examine reach-related activity during the unloaded condition and torque-related activity by comparing activity across load conditions. During unloaded reaching the upper arm muscles exhibited a bimodal distribution of preferred hand direction. Maximal reach-related activity occurred with hand movements mostly toward or away from the body. Arm muscles also exhibited a bimodal distribution of their preferred torque direction. Maximal torque-related activity typically occurred with shoulder-extension/elbow-flexion torque or shoulder-flexion/elbow-extension torque. Similar biases in reach-related and torque-related activity could be reproduced by optimizing a global measure of muscle activity. These biases were also observed in the neural activity of primary motor cortex (M1). The parallels between M1 and muscular activity demonstrate another link between motor cortical processing and the motor periphery and may reflect an optimization process performed by the sensorimotor system.

Primate Upper Limb Muscles Exhibit Activity Patterns That Differ From Their Anatomical Action During a Postural Task

The present study examined muscular activity in the primate proximal forelimb during a posture task. By applying loads selectively to the shoulder, elbow, or both joints, we observed that monoarticular shoulder and elbow muscles varied their activity with loads at the unspanned joint. Shoulder monoarticulars changed activity with elbow torque and elbow monoarticulars changed activity with shoulder torque. Due to this additional modulation, the maximal activation of monoarticular muscles was deviated from their anatomical action toward either shoulder-extension/elbow-flexion or shoulder-flexion/elbow-extension. Biarticular muscles also expressed deviations in their preferred torque direction toward either shoulder-extension/elbow-flexion or shoulder-flexion/elbow-extension. The biased distribution of preferred torque directions in proximal forelimb muscles could be modeled by the minimization of a global measure of muscle activity. Moreover, arm-related neurons of primary motor cortex exhibit a similar bias in preferred torque directions consistent with the intimate relationship between the primary motor cortex and the motor periphery.

Uncertainty of knee joint muscle activity during knee joint torque exertion: the significance of controlling adjacent joint torque

In the single-joint torque exertion task, which has been widely used to control muscle activity, only the relevant joint torque is specified. However, the neglect of the neighboring joint could make the procedure unreliable, considering our previous result that even monoarticular muscle activity level is indefinite without specifying the adjacent joint torque. Here we examined the amount of hip joint torque generated with knee joint torque and its influence on the activity of the knee joint muscles. Twelve healthy subjects were requested to exert various levels of isometric knee joint torque. The knee and hip joint torques were obtained by using a custom-made device. Because no information about hip joint torque was provided to the subjects, the hip joint torque measured here was a secondary one associated with the task. The amount of hip joint torque varied among subjects, indicating that they adopted various strategies to achieve the task. In some subjects, there was a considerable internal variability in the hip joint torque. Such variability was not negligible, because the knee joint muscle activity level with respect to the knee joint torque, as quantified by surface electromyography (EMG), changed significantly when the subjects were requested to change the strategy. This change occurred in a very systematic manner: in the case of the knee extension, as the hip flexion torque was larger, the activity of mono- and biarticular knee extensors decreased and increased, respectively. These results indicate that the conventional single knee joint torque exertion has the drawback that the intersubject and/or intertrial variability is inevitable in the relative contribution among mono- and biarticular muscles because of the uncertainty of the hip joint torque. We discuss that the viewpoint that both joint torques need to be considered will bring insights into various controversial problems such as the shape of the EMG-force relationship, neural factors that help determine the effect of muscle strength training, and so on.