Primary motor cortical neurons encode functional muscle synergies

From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis

Objective. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.Approach. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.

Effects of wrist posture and stabilization on precision grip force production and muscle activation patterns

Most of the power for generating forces in the fingers arises from muscles located in the forearm. This configuration maximizes finger joint range of motion while minimizing finger mass and inertia. The resulting multiarticular arrangement of the tendons, however, complicates independent control of the wrist and the digits. Actuating the wrist impacts sensorimotor control of the fingers and vice versa. The goal of this study was to systematically investigate interactions between isometric wrist and digit control. Specifically, we examined how the need to maintain a specified wrist posture influences precision grip. Fifteen healthy adults produced maximum precision grip force at 11 different wrist flexion/extension angles, with the arm supported, under two conditions: 1) the participant maintained the desired wrist angle while performing the precision grip and 2) a robot maintained the specified wrist angle. Wrist flexion/extension posture significantly impacted maximum precision grip force (P < 0.001), with the greatest grip force achieved when the wrist was extended 30° from neutral. External wrist stabilization by the robot led to a 20% increase in precision grip force across wrist postures. Increased force was accompanied by increased muscle activation but with an activation pattern similar to the one used when the participant had to stabilize their wrist. Thus, simultaneous wrist and finger requirements impacted performance of an isometric finger task. External wrist stabilization can promote increased precision grip force resulting from increased muscle activation. These findings have potential clinical significance for individuals with neurologically driven finger weakness, such as stroke survivors.NEW & NOTEWORTHY We explored the interdependence between wrist and fingers by assessing the influence of wrist posture and external stabilization on precision grip force generation. We found that maximum precision grip force occurred at an extended wrist posture and was 20% greater when the wrist was Externally Stabilized. The latter resulted from amplification of muscle activation patterns from the Self-Stabilized condition rather than adoption of new patterns exploiting external wrist stabilization.

Invariant neural dynamics drive commands to control different movements

It has been proposed that the nervous system has the capacity to generate a wide variety of movements because it reuses some invariant code. Previous work has identified that dynamics of neural population activity are similar during different movements, where dynamics refer to how the instantaneous spatial pattern of population activity changes in time. Here, we test whether invariant dynamics of neural populations are actually used to issue the commands that direct movement. Using a brain-machine interface (BMI) that transforms rhesus macaques' motor-cortex activity into commands for a neuroprosthetic cursor, we discovered that the same command is issued with different neural-activity patterns in different movements. However, these different patterns were predictable, as we found that the transitions between activity patterns are governed by the same dynamics across movements. These invariant dynamics are low dimensional, and critically, they align with the BMI, so that they predict the specific component of neural activity that actually issues the next command. We introduce a model of optimal feedback control (OFC) that shows that invariant dynamics can help transform movement feedback into commands, reducing the input that the neural population needs to control movement. Altogether our results demonstrate that invariant dynamics drive commands to control a variety of movements and show how feedback can be integrated with invariant dynamics to issue generalizable commands.

Interplay between external inputs and recurrent dynamics during movement preparation and execution in a network model of motor cortex

The primary motor cortex has been shown to coordinate movement preparation and execution through computations in approximately orthogonal subspaces. The underlying network mechanisms, and the roles played by external and recurrent connectivity, are central open questions that need to be answered to understand the neural substrates of motor control. We develop a recurrent neural network model that recapitulates the temporal evolution of neuronal activity recorded from the primary motor cortex of a macaque monkey during an instructed delayed-reach task. In particular, it reproduces the observed dynamic patterns of covariation between neural activity and the direction of motion. We explore the hypothesis that the observed dynamics emerges from a synaptic connectivity structure that depends on the preferred directions of neurons in both preparatory and movement-related epochs, and we constrain the strength of both synaptic connectivity and external input parameters from data. While the model can reproduce neural activity for multiple combinations of the feedforward and recurrent connections, the solution that requires minimum external inputs is one where the observed patterns of covariance are shaped by external inputs during movement preparation, while they are dominated by strong direction-specific recurrent connectivity during movement execution. Our model also demonstrates that the way in which single-neuron tuning properties change over time can explain the level of orthogonality of preparatory and movement-related subspaces.

Cortico-cortical drive in a coupled premotor-primary motor cortex dynamical system

In the conventional view of sensorimotor control, the premotor cortex (PM) plans actions that are executed by the primary motor cortex (M1). This notion arises in part from many experiments that have imposed a preparatory "planning" period, during which PM becomes active without M1. But during many natural movements, PM and M1 are co-activated, making it difficult to distinguish their functional roles. We leverage coupled dynamical systems models (cDSMs) to uncover interactions between PM and M1 during movements performed with no preparatory period. We build cDSMs using neural and behavioral data recorded from two non-human primates as they performed a reach-grasp-manipulate task. PM and M1 interact dynamically throughout these movements. Whereas PM drives the M1 in some situations, in other situations, M1 drives PM activity, contrary to the conventional assumption. Our DSM framework provides additional predictions differentiating the roles of PM and M1 in controlling movement.

Integrated neural dynamics of sensorimotor decisions and actions

Recent theoretical models suggest that deciding about actions and executing them are not implemented by completely distinct neural mechanisms but are instead two modes of an integrated dynamical system. Here, we investigate this proposal by examining how neural activity unfolds during a dynamic decision-making task within the high-dimensional space defined by the activity of cells in monkey dorsal premotor (PMd), primary motor (M1), and dorsolateral prefrontal cortex (dlPFC) as well as the external and internal segments of the globus pallidus (GPe, GPi). Dimensionality reduction shows that the four strongest components of neural activity are functionally interpretable, reflecting a state transition between deliberation and commitment, the transformation of sensory evidence into a choice, and the baseline and slope of the rising urgency to decide. Analysis of the contribution of each population to these components shows meaningful differences between regions but no distinct clusters within each region, consistent with an integrated dynamical system. During deliberation, cortical activity unfolds on a two-dimensional "decision manifold" defined by sensory evidence and urgency and falls off this manifold at the moment of commitment into a choice-dependent trajectory leading to movement initiation. The structure of the manifold varies between regions: In PMd, it is curved; in M1, it is nearly perfectly flat; and in dlPFC, it is almost entirely confined to the sensory evidence dimension. In contrast, pallidal activity during deliberation is primarily defined by urgency. We suggest that these findings reveal the distinct functional contributions of different brain regions to an integrated dynamical system governing action selection and execution.

Functional architecture of M1 cells encoding movement direction

In this paper we propose a neurogeometrical model of the behaviour of cells of the arm area of the primary motor cortex (M1). We will mathematically express as a fiber bundle the hypercolumnar organization of this cortical area, first modelled by Georgopoulos (Georgopoulos et al., 1982; Georgopoulos, 2015). On this structure, we will consider the selective tuning of M1 neurons of kinematic variables of positions and directions of movement. We will then extend this model to encode the notion of fragments introduced by Hatsopoulos et al. (2007) which describes the selectivity of neurons to movement direction varying in time. This leads to consider a higher dimensional geometrical structure where fragments are represented as integral curves. A comparison with the curves obtained through numerical simulations and experimental data will be presented. Moreover, neural activity shows coherent behaviours represented in terms of movement trajectories pointing to a specific pattern of movement decomposition Kadmon Harpaz et al. (2019). Here, we will recover this pattern through a spectral clustering algorithm in the subriemannian structure we introduced, and compare our results with the neurophysiological one of Kadmon Harpaz et al. (2019).

Reconstructing Synergy-Based Hand Grasp Kinematics from Electroencephalographic Signals

Brain-machine interfaces (BMIs) have become increasingly popular in restoring the lost motor function in individuals with disabilities. Several research studies suggest that the CNS may employ synergies or movement primitives to reduce the complexity of control rather than controlling each DoF independently, and the synergies can be used as an optimal control mechanism by the CNS in simplifying and achieving complex movements. Our group has previously demonstrated neural decoding of synergy-based hand movements and used synergies effectively in driving hand exoskeletons. In this study, ten healthy right-handed participants were asked to perform six types of hand grasps representative of the activities of daily living while their neural activities were recorded using electroencephalography (EEG). From half of the participants, hand kinematic synergies were derived, and a neural decoder was developed, based on the correlation between hand synergies and corresponding cortical activity, using multivariate linear regression. Using the synergies and the neural decoder derived from the first half of the participants and only cortical activities from the remaining half of the participants, their hand kinematics were reconstructed with an average accuracy above 70%. Potential applications of synergy-based BMIs for controlling assistive devices in individuals with upper limb motor deficits, implications of the results in individuals with stroke and the limitations of the study were discussed.

Linking Pain and Motor Control: Conceptualization of Movement Deficits in Patients With Painful Conditions

When people experience or expect pain, they move differently. Pain-altered movement strategies, collectively described here as pain-related movement dysfunction (PRMD), may persist well after pain resolves and, ultimately, may result in altered kinematics and kinetics, future reinjury, and disability. Although PRMD may manifest as abnormal movements that are often evident in clinical assessment, the underlying mechanisms are complex, engaging sensory-perceptual, cognitive, psychological, and motor processes. Motor control theories provide a conceptual framework to determine, assess, and target processes that contribute to normal and abnormal movement and thus are important for physical therapy and rehabilitation practice. Contemporary understanding of motor control has evolved from reflex-based understanding to a more complex task-dependent interaction between cognitive and motor systems, each with distinct neuroanatomic substrates. Though experts have recognized the importance of motor control in the management of painful conditions, there is no comprehensive framework that explicates the processes engaged in the control of goal-directed actions, particularly in the presence of pain. This Perspective outlines sensory-perceptual, cognitive, psychological, and motor processes in the contemporary model of motor control, describing the neural substrates underlying each process and highlighting how pain and anticipation of pain influence motor control processes and consequently contribute to PRMD. Finally, potential lines of future inquiry-grounded in the contemporary model of motor control-are outlined to advance understanding and improve the assessment and treatment of PRMD.

IMPACT

This Perspective proposes that approaching PRMD from a contemporary motor control perspective will uncover key mechanisms, identify treatment targets, inform assessments, and innovate treatments across sensory-perceptual, cognitive, and motor domains, all of which have the potential to improve movement and functional outcomes in patients with painful conditions.

Shoulder position and handedness differentially affect excitability and intracortical inhibition of hand muscles

Individuals with stroke show distinct differences in hand function impairment when the shoulder is in adduction, within the workspace compared to when the shoulder is abducted, away from the body. To better understand how shoulder position affects hand control, we tested the corticomotor excitability and intracortical control of intrinsic and extrinsic hand muscles important for grasp in twelve healthy individuals. Motor evoked potentials (MEP) using single and paired-pulse transcranial magnetic stimulation were elicited in extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), first dorsal interosseous (FDI), and abductor pollicis brevis (APB). The shoulder was fully supported in horizontal adduction (ADD) or abduction (ABD). Separate mixed-effect models were fit to the MEP parameters using shoulder position (or upper-extremity [UE] side) as fixed and participants as random effects. In the non-dominant UE, EDC showed significantly greater MEPs in shoulder ABD than ADD. In contrast, the dominant side EDC showed significantly greater MEPs in ADD compared to ABD; %facilitation of EDC on dominant side showed significant stimulus intensity x position interaction, EDC excitability was significantly greater in ADD at 150% of the resting threshold. Intrinsic hand muscles of the dominant UE received significantly more intracortical inhibition (SICI) when the shoulder was in ADD compared to ABD; there was no position-dependent modulation of SICI on the non-dominant side. Our findings suggest that these resting-state changes in hand muscle excitabilities reflect the natural statistics of UE movements, which in turn may arise from as well as shape the nature of shoulder-hand coupling underlying UE behaviors.

A biomechanical study of load carriage by two paired subjects in response to increased load mass

The biomechanics of load carriage has been studied extensively with regards to single individuals, yet not so much with regards to collective transport. We investigated the biomechanics of walking in 10 paired individuals carrying a load that represented 20%, 30%, or 40% of the aggregated body-masses. We computed the energy recovery rate at the center of mass of the system consisting of the two individuals plus the carried load in order to test to what extent the pendulum-like behavior and the economy of the gait were affected. Joint torque was also computed to investigate the intra- and inter-subject strategies occurring in response to this. The ability of the subjects to move the whole system like a pendulum appeared rendered obvious through shortened step length and lowered vertical displacements at the center of mass of the system, while energy recovery rate and total mechanical energy remained constant. In parallel, an asymmetry of joint moment vertical amplitude and coupling among individuals in all pairs suggested the emergence of a leader/follower schema. Beyond the 30% threshold of increased load mass, the constraints at the joint level were balanced among individuals leading to a degraded pendulum-like behavior.

How to improve the muscle synergy analysis methodology?

Muscle synergy analysis is increasingly used in domains such as neurosciences, robotics, rehabilitation or sport sciences to analyze and better understand motor coordination. The analysis uses dimensionality reduction techniques to identify regularities in spatial, temporal or spatio-temporal patterns of multiple muscle activation. Recent studies have pointed out variability in outcomes associated with the different methodological options available and there was a need to clarify several aspects of the analysis methodology. While synergy analysis appears to be a robust technique, it remain a statistical tool and is, therefore, sensitive to the amount and quality of input data (EMGs). In particular, attention should be paid to EMG amplitude normalization, baseline noise removal or EMG filtering which may diminish or increase the signal-to-noise ratio of the EMG signal and could have major effects on synergy estimates. In order to robustly identify synergies, experiments should be performed so that the groups of muscles that would potentially form a synergy are activated with a sufficient level of activity, ensuring that the synergy subspace is fully explored. The concurrent use of various synergy formulations-spatial, temporal and spatio-temporal synergies- should be encouraged. The number of synergies represents either the dimension of the spatial structure or the number of independent temporal patterns, and we observed that these two aspects are often mixed in the analysis. To select a number, criteria based on noise estimates, reliability of analysis results, or functional outcomes of the synergies provide interesting substitutes to criteria solely based on variance thresholds.

Muscle Synergies in Parkinson's Disease

Over the last two decades, experimental studies in humans and other vertebrates have increasingly used muscle synergy analysis as a computational tool to examine the physiological basis of motor control. The theoretical background of muscle synergies is based on the potential ability of the motor system to coordinate muscles groups as a single unit, thus reducing high-dimensional data to low-dimensional elements. Muscle synergy analysis may represent a new framework to examine the pathophysiological basis of specific motor symptoms in Parkinson's disease (PD), including balance and gait disorders that are often unresponsive to treatment. The precise mechanisms contributing to these motor symptoms in PD remain largely unknown. A better understanding of the pathophysiology of balance and gait disorders in PD is necessary to develop new therapeutic strategies. This narrative review discusses muscle synergies in the evaluation of motor symptoms in PD. We first discuss the theoretical background and computational methods for muscle synergy extraction from physiological data. We then critically examine studies assessing muscle synergies in PD during different motor tasks including balance, gait and upper limb movements. Finally, we speculate about the prospects and challenges of muscle synergy analysis in order to promote future research protocols in PD.

On Primitives in Motor Control

The concept of primitives has been used in motor control both as a theoretical construct and as a means of describing the results of experimental studies involving multiple moving elements. This concept is close to Bernstein’s notion of engrams and level of synergies. Performance primitives have been explored in spaces of peripheral variables but interpreted in terms of neural control primitives. Performance primitives reflect a variety of mechanisms ranging from body mechanics to spinal mechanisms and to supraspinal circuitry. This review suggests that primitives originate at the task level as preferred time functions of spatial referent coordinates or at mappings from higher level referent coordinates to lower level, frequently abundant, referent coordinate sets. Different patterns of performance primitives can emerge depending, in particular, on the external force field.

Does the cerebellum shape the spatiotemporal organization of muscle patterns? Insights from subjects with cerebellar ataxias

The role of the cerebellum in motor control has been investigated extensively, but its contribution to the muscle pattern organization underlying goal-directed movements is still not fully understood. Muscle synergies may be used to characterize multimuscle pattern organization irrespective of time (spatial synergies), in time irrespective of the muscles (temporal synergies), and both across muscles and in time (spatiotemporal synergies). The decomposition of muscle patterns as combinations of different types of muscle synergies offers the possibility to identify specific changes due to neurological lesions. In this study, we recorded electromyographic activity from 13 shoulder and arm muscles in subjects with cerebellar ataxias (CA) and in age-matched healthy subjects (HS) while they performed reaching movements in multiple directions. We assessed whether cerebellar damage affects the organization of muscle patterns by extracting different types of muscle synergies from the muscle patterns of each HS and using these synergies to reconstruct the muscle patterns of all other participants. We found that CA muscle patterns could be accurately captured only by spatial muscle synergies of HS. In contrast, there were significant differences in the reconstruction R² values for both spatiotemporal and temporal synergies, with an interaction between the two synergy types indicating a larger difference for spatiotemporal synergies. Moreover, the reconstruction quality using spatiotemporal synergies correlated with the severity of impairment. These results indicate that cerebellar damage affects the temporal and spatiotemporal organization, but not the spatial organization, of the muscle patterns, suggesting that the cerebellum plays a key role in shaping their spatiotemporal organization.NEW & NOTEWORTHY In recent studies, the decomposition of muscle activity patterns has revealed a modular organization of the motor commands. We show, for the first time, that muscle patterns of subjects with cerebellar damage share with healthy controls spatial, but not temporal and spatiotemporal, modules. Moreover, changes in spatiotemporal organization characterize the severity of the subject's impairment. These results suggest that the cerebellum has a specific role in shaping the spatiotemporal organization of the muscle patterns.

Biomechanics of Vertical Posture and Control with Referent Joint Configurations

Our study compared the results of two methods of analysis of postural sway during human quiet standing, the rambling-trembling (Rm-Tr) decomposition and the analysis of the point of intersection of the ground reaction forces (zIP analysis). Young, healthy subjects were required to stand naturally and with an increased level of leg/trunk muscle co-activation under visual feedback on the magnitude of a combined index of muscle activation (muscle mode). The main findings included the shift of zIP toward higher frequencies and strong correlations between Tr and zIP when the subjects stood with increased muscle co-activation. We interpret the results within the idea of whole-body control with a set of primitives associated with referent coordinates in the joint configuration space.

Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces

In the 1960s, Evarts first recorded the activity of single neurons in motor cortex of behaving monkeys (Evarts, 1968). In the 50 years since, great effort has been devoted to understanding how single neuron activity relates to movement. Yet these single neurons exist within a vast network, the nature of which has been largely inaccessible. With advances in recording technologies, algorithms, and computational power, the ability to study these networks is increasing exponentially. Recent experimental results suggest that the dynamical properties of these networks are critical to movement planning and execution. Here we discuss this dynamical systems perspective and how it is reshaping our understanding of the motor cortices. Following an overview of key studies in motor cortex, we discuss techniques to uncover the "latent factors" underlying observed neural population activity. Finally, we discuss efforts to use these factors to improve the performance of brain-machine interfaces, promising to make these findings broadly relevant to neuroengineering as well as systems neuroscience.

An experimental and computational framework for modeling multi-muscle responses to transcranial magnetic stimulation of the human motor cortex

Current knowledge of coordinated motor control of multiple muscles is derived primarily from invasive stimulation-recording techniques in animal models. Similar studies are not generally feasible in humans, so a modeling framework is needed to facilitate knowledge transfer from animal studies. We describe such a framework that uses a deep neural network model to map finite element simulation of transcranial magnetic stimulation induced electric fields (E-fields) in motor cortex to recordings of multi-muscle activation. Critically, we show that model generalization is improved when we incorporate empirically derived physiological models for E-field to neuron firing rate and low-dimensional control via muscle synergies.

Positive verbal suggestion optimizes postural control

Balance is a very important function that allows maintaining a stable stance needed for many daily life activities and for preventing falls. We investigated whether balance control could be improved by a placebo procedure consisting of verbal suggestion. Thirty healthy volunteers were randomized in two groups (placebo and control) and asked to perform a single-leg stance task in which they had to stand as steadily as possible on the dominant leg. The task was repeated in three sessions (T0, T1, T2). At T1 and T2 an inert treatment was applied on the leg, by informing the placebo group that it was effective in improving balance. The control group was overtly told that treatment was inert. An accelerometer applied on participants’ leg allowed to measure body sways in different directions. Subjective parameters, like perception of stability, were also collected. Results showed that the placebo group had less body sways than the control group at T2, both in the three-dimensional space and in the anterior-posterior direction. Furthermore, the placebo group perceived to be more stable than the control group. This study represents the first evidence that placebo effect optimizes posture, with a potential translational impact in patients with postural and gait disturbances.

Indirect, referent control of motor actions underlies directional tuning of neurons

Many neurons of the primary motor cortex (M1) are maximally sensitive to "preferred" hand movement directions and generate progressively less activity with movements away from these directions. M1 activity also correlates with other biomechanical variables. These findings are predominantly interpreted in a framework in which the brain preprograms and directly specifies the desired motor outcome. This approach is inconsistent with the empirically derived equilibrium-point hypothesis, in which the brain can control motor actions only indirectly, by changing neurophysiological parameters that may influence, but remain independent of, biomechanical variables. The controversy is resolved on the basis of experimental findings and theoretical analysis of how sensory and central influences are integrated in the presence of the fundamental nonlinearity of neurons: electrical thresholds. In the presence of sensory inputs, electrical thresholds are converted into spatial thresholds that predetermine the position of the body segments at which muscles begin to be activated. Such thresholds may be considered as referent points of respective spatial frames of reference (FRs) in which neurons, including motoneurons, are centrally predetermined to work. By shifting the referent points of respective FRs, the brain elicits intentional actions. Pure involuntary reactions to perturbations are accomplished in motionless FRs. Neurons are primarily sensitive to shifts in referent directions, i.e., shifts in spatial FRs, whereas emergent neural activity may or may not correlate with different biomechanical variables depending on the motor task and external conditions. Indirect, referent control of posture and movement symbolizes a departure from conventional views based on direct preprogramming of the motor outcome.

Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements.

Corticospinal gating during action preparation and movement in the primate motor cortex

During everyday actions there is a need to be able to withhold movements until the most appropriate time. This motor inhibition is likely to rely on multiple cortical and subcortical areas, but the primary motor cortex (M1) is a critical component of this process. However, the mechanisms behind this inhibition are unclear, particularly the role of the corticospinal system, which is most often associated with driving muscles and movement. To address this, recordings were made from identified corticospinal (PTN, n = 94) and corticomotoneuronal (CM, n = 16) cells from M1 during an instructed delay reach-to-grasp task. The task involved the animals withholding action for ~2 s until a GO cue, after which they were allowed to reach and perform the task for a food reward. Analysis of the firing of cells in M1 during the delay period revealed that, as a population, non-CM PTNs showed significant suppression in their activity during the cue and instructed delay periods, while CM cells instead showed a facilitation during the preparatory delay. Analysis of cell activity during movement also revealed that a substantial minority of PTNs (27%) showed suppressed activity during movement, a response pattern more suited to cells involved in withholding rather than driving movement. These results demonstrate the potential contributions of the M1 corticospinal system to withholding of actions and highlight that suppression of activity in M1 during movement preparation is not evenly distributed across different neural populations.

NEW & NOTEWORTHY Recordings were made from identified corticospinal (PTN) and corticomotoneuronal (CM) cells during an instructed delay task. Activity of PTNs as a population was suppressed during the delay, in contrast to CM cells, which were facilitated. A minority of PTNs showed a rate profile that might be expected from inhibitory cells and could suggest that they play an active role in action suppression, most likely through downstream inhibitory circuits.

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.

Neuroprosthetic-enabled control of graded arm muscle contraction in a paralyzed human

Neuroprosthetics that combine a brain computer interface (BCI) with functional electrical stimulation (FES) can restore voluntary control of a patients’ own paralyzed limbs. To date, human studies have demonstrated an “all-or-none” type of control for a fixed number of pre-determined states, like hand-open and hand-closed. To be practical for everyday use, a BCI-FES system should enable smooth control of limb movements through a continuum of states and generate situationally appropriate, graded muscle contractions. Crucially, this functionality will allow users of BCI-FES neuroprosthetics to manipulate objects of different sizes and weights without dropping or crushing them. In this study, we present the first evidence that using a BCI-FES system, a human with tetraplegia can regain volitional, graded control of muscle contraction in his paralyzed limb. In addition, we show the critical ability of the system to generalize beyond training states and accurately generate wrist flexion states that are intermediate to training levels. These innovations provide the groundwork for enabling enhanced and more natural fine motor control of paralyzed limbs by BCI-FES neuroprosthetics.

From the motor cortex to the movement and back again

The motor cortex controls motor behaviors by generating movement-specific signals and transmitting them through spinal cord circuits and motoneurons to the muscles. Precise and well-coordinated muscle activation patterns are necessary for accurate movement execution. Therefore, the activity of cortical neurons should correlate with movement parameters. To investigate the specifics of such correlations among activities of the motor cortex, spinal cord network and muscles, we developed a model for neural control of goal-directed reaching movements that simulates the entire pathway from the motor cortex through spinal cord circuits to the muscles controlling arm movements. In this model, the arm consists of two joints (shoulder and elbow), whose movements are actuated by six muscles (4 single-joint and 2 double-joint flexors and extensors). The muscles provide afferent feedback to the spinal cord circuits. Cortical neurons are defined as cortical "controllers" that solve an inverse problem based on a proposed straight-line trajectory to a target position and a predefined bell-shaped velocity profile. Thus, the controller generates a motor program that produces a task-specific activation of low-level spinal circuits that in turn induce the muscle activation realizing the intended reaching movement. Using the model, we describe the mechanisms of correlation between cortical and motoneuronal activities and movement direction and other movement parameters. We show that the directional modulation of neuronal activity in the motor cortex and the spinal cord may result from direction-specific dynamics of muscle lengths. Our model suggests that directional modulation first emerges at the level of muscle forces, augments at the motoneuron level, and further increases at the level of the motor cortex due to the dependence of frictional forces in the joints, contractility of the muscles and afferent feedback on muscle lengths and/or velocities.

Neuronal Correlates of Functional Coupling between Reach- and Grasp-Related Components of Muscle Activity

Coordinated reach-to-grasp movements require precise spatiotemporal synchrony between proximal forelimb muscles (shoulder, elbow) that transport the hand toward a target during reach, and distal muscles (wrist, digit) that simultaneously preshape and orient the hand for grasp. The precise mechanisms through which the redundant neuromuscular circuitry coordinates reach with grasp, however, remain unclear. Recently, Geed and Van Kan (2016) demonstrated, using exploratory factor analysis (EFA), that limited numbers of global, template-like transport/preshape- and grasp-related muscle components underlie the complexity and variability of intramuscular electromyograms (EMGs) of up to 21 distal and proximal muscles recorded while monkeys performed reach-to-grasp tasks. Importantly, transport/preshape- and grasp-related muscle components showed invariant spatiotemporal coupling, which provides a potential mechanism for coordinating forelimb muscles during reach-to-grasp movements. In the present study, we tested whether ensemble discharges of forelimb neurons in the cerebellar nucleus interpositus (NI) and its target, the magnocellular red nucleus (RNm), a source of rubrospinal fibers, function as neuronal correlates of the transport/preshape- and grasp-related muscle components we identified. EFA applied to single-unit discharges of populations of NI and RNm neurons recorded while the same monkeys that were used previously performed the same reach-to-grasp tasks, revealed neuronal components in the ensemble discharges of both NI and RNm neuronal populations with characteristics broadly similar to muscle components. Subsets of NI and RNm neuronal components were strongly and significantly crosscorrelated with subsets of muscle components, suggesting that similar functional units of reach-to-grasp behavior are expressed by NI and RNm neuronal populations and forelimb muscles. Importantly, like transport/preshape- and grasp-related muscle components, their NI and RNm neuronal correlates showed invariant spatiotemporal coupling. Clinical and lesion studies have reported disruption of coupling between reach and grasp following cerebellar damage; the present results expand on those studies by identifying a neuronal mechanism that may underlie cerebellar contributions to spatiotemporal coordination of distal and proximal limb muscles during reaching to grasp. We conclude that finding similar functional units of behavior expressed at multiple levels of information processing along interposito-rubrospinal pathways and forelimb muscles supports the hypothesis that functionally related populations of NI and RNm neurons act synergistically in the control of complex coordinated motor behaviors.

Grasp-Based Functional Coupling Between Reach- and Grasp-Related Components of Forelimb Muscle Activity

How are appropriate combinations of forelimb muscles selected during reach-to-grasp movements in the presence of neuromotor redundancy and important task-related constraints? The authors tested whether grasp type or target location preferentially influence the selection and synergistic coupling between forelimb muscles during reach-to-grasp movements. Factor analysis applied to 14-20 forelimb electromyograms recorded from monkeys performing reach-to-grasp tasks revealed 4-6 muscle components that showed transport/preshape- or grasp-related features. Weighting coefficients of transport/preshape-related components demonstrated strongest similarities for reaches that shared the same grasp type rather than the same target location. Scaling coefficients of transport/preshape- and grasp-related components showed invariant temporal coupling. Thus, grasp type influenced strongly both transport/preshape- and grasp-related muscle components, giving rise to grasp-based functional coupling between forelimb muscles.

Premotor and Motor Cortices Encode Reward

Rewards associated with actions are critical for motivation and learning about the consequences of one's actions on the world. The motor cortices are involved in planning and executing movements, but it is unclear whether they encode reward over and above limb kinematics and dynamics. Here, we report a categorical reward signal in dorsal premotor (PMd) and primary motor (M1) neurons that corresponds to an increase in firing rates when a trial was not rewarded regardless of whether or not a reward was expected. We show that this signal is unrelated to error magnitude, reward prediction error, or other task confounds such as reward consumption, return reach plan, or kinematic differences across rewarded and unrewarded trials. The availability of reward information in motor cortex is crucial for theories of reward-based learning and motivational influences on actions.

A four-dimensional virtual hand brain-machine interface using active dimension selection

OBJECTIVE

Brain-machine interfaces (BMI) traditionally rely on a fixed, linear transformation from neural signals to an output state-space. In this study, the assumption that a BMI must control a fixed, orthogonal basis set was challenged and a novel active dimension selection (ADS) decoder was explored.

APPROACH

ADS utilizes a two stage decoder by using neural signals to both (i) select an active dimension being controlled and (ii) control the velocity along the selected dimension. ADS decoding was tested in a monkey using 16 single units from premotor and primary motor cortex to successfully control a virtual hand avatar to move to eight different postures.

MAIN RESULTS

Following training with the ADS decoder to control 2, 3, and then 4 dimensions, each emulating a grasp shape of the hand, performance reached 93% correct with a bit rate of 2.4 bits s(-1) for eight targets. Selection of eight targets using ADS control was more efficient, as measured by bit rate, than either full four-dimensional control or computer assisted one-dimensional control.

SIGNIFICANCE

ADS decoding allows a user to quickly and efficiently select different hand postures. This novel decoding scheme represents a potential method to reduce the complexity of high-dimension BMI control of the hand.

Similar Motor Cortical Control Mechanisms for Precise Limb Control during Reaching and Locomotion

Throughout the course of evolution there has been a parallel development of the complexity and flexibility of the nervous system and the skeletomuscular system that it controls. This development is particularly evident for the cerebral cortical areas and the transformation of the use of the upper limbs from a purely locomotor function to one including, or restricted to, reaching and grasping. This study addresses the issue of whether the control of reaching has involved the development of new cortical circuits or whether the same neurons are used to control both locomotion and reaching. We recorded the activity of pyramidal tract neurons in the motor cortex of the cat both during voluntary gait modifications and during reaching. All cells showed generally similar patterns of activity in both tasks. More specifically, we showed that, in many cases, cells maintained a constant temporal relationship to the activity of synergistic muscle groups in each task. In addition, in some cells the relationship between the intensity of the cell discharge activity and the magnitude of the EMG activity was equally constant during gait modifications and reaching. As such, the results are compatible with the hypothesis that the corticospinal circuits used to control reaching evolved from those used to precisely modify gait.

Flexible adaptation to an artificial recurrent connection from muscle to peripheral nerve in man

Controlling a neuroprosthesis requires learning a novel input-output transformation; however, how subjects incorporate this into limb control remains obscure. To elucidate the underling mechanisms, we investigated the motor adaptation process to a novel artificial recurrent connection (ARC) from a muscle to a peripheral nerve in healthy humans. In this paradigm, the ulnar nerve was electrically stimulated in proportion to the activation of the flexor carpi ulnaris (FCU), which is ulnar-innervated and monosynaptically innervated from Ia afferents of the FCU, defined as the "homonymous muscle," or the palmaris longus (PL), which is not innervated by the ulnar nerve and produces similar movement to the FCU, defined as the "synergist muscle." The ARC boosted the activity of the homonymous muscle and wrist joint movement during a visually guided reaching task. Participants could control muscle activity to utilize the ARC for the volitional control of wrist joint movement and then readapt to the absence of the ARC to either input muscle. Participants reduced homonymous muscle recruitment with practice, regardless of the input muscle. However, the adaptation process in the synergist muscle was dependent on the input muscle. The activity of the synergist muscle decreased when the input was the homonymous muscle, whereas it increased when it was the synergist muscle. This reorganization of the neuromotor map, which was maintained as an aftereffect of the ARC, was observed only when the input was the synergist muscle. These findings demonstrate that the ARC induced reorganization of neuromotor map in a targeted and sustainable manner.

Representation of Muscle Synergies in the Primate Brain

Evidence suggests that the CNS uses motor primitives to simplify movement control, but whether it actually stores primitives instead of computing solutions on the fly to satisfy task demands is a controversial and still-unanswered possibility. Also in contention is whether these primitives take the form of time-invariant muscle coactivations ("spatial" synergies) or time-varying muscle commands ("spatiotemporal" synergies). Here, we examined forelimb muscle patterns and motor cortical spiking data in rhesus macaques (Macaca mulatta) handling objects of variable shape and size. From these data, we extracted both spatiotemporal and spatial synergies using non-negative decomposition. Each spatiotemporal synergy represents a sequence of muscular or neural activations that appeared to recur frequently during the animals' behavior. Key features of the spatiotemporal synergies (including their dimensionality, timing, and amplitude modulation) were independently observed in the muscular and neural data. In addition, both at the muscular and neural levels, these spatiotemporal synergies could be readily reconstructed as sequential activations of spatial synergies (a subset of those extracted independently from the task data), suggestive of a hierarchical relationship between the two levels of synergies. The possibility that motor cortex may execute even complex skill using spatiotemporal synergies has novel implications for the design of neuroprosthetic devices, which could gain computational efficiency by adopting the discrete and low-dimensional control that these primitives imply.

SIGNIFICANCE STATEMENT

We studied the motor cortical and forearm muscular activity of rhesus macaques (Macaca mulatta) as they reached, grasped, and carried objects of varied shape and size. We applied non-negative matrix factorization separately to the cortical and muscular data to reduce their dimensionality to a smaller set of time-varying "spatiotemporal" synergies. Each synergy represents a sequence of cortical or muscular activity that recurred frequently during the animals' behavior. Salient features of the synergies (including their dimensionality, timing, and amplitude modulation) were observed at both the cortical and muscular levels. The possibility that the brain may execute even complex behaviors using spatiotemporal synergies has implications for neuroprosthetic algorithm design, which could become more computationally efficient by adopting the discrete and low-dimensional control that they afford.

EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks

GOAL

Sensorimotor-based brain-computer interfaces (BCIs) have achieved successful control of real and virtual devices in up to three dimensions; however, the traditional sensor-based paradigm limits the intuitive use of these systems. Many control signals for state-of-the-art BCIs involve imagining the movement of body parts that have little to do with the output command, revealing a cognitive disconnection between the user's intent and the action of the end effector. Therefore, there is a need to develop techniques that can identify with high spatial resolution the self-modulated neural activity reflective of the actions of a helpful output device.

METHODS

We extend previous EEG source imaging (ESI) work to decoding natural hand/wrist manipulations by applying a novel technique to classifying four complex motor imaginations of the right hand: flexion, extension, supination, and pronation.

RESULTS

We report an increase of up to 18.6% for individual task classification and 12.7% for overall classification using the proposed ESI approach over the traditional sensor-based method.

CONCLUSION

ESI is able to enhance BCI performance of decoding complex right-hand motor imagery tasks.

SIGNIFICANCE

This study may lead to the development of BCI systems with naturalistic and intuitive motor imaginations, thus facilitating broad use of noninvasive BCIs.

Brain-controlled muscle stimulation for the restoration of motor function

Loss of the ability to move, as a consequence of spinal cord injury or neuromuscular disorder, has devastating consequences for the paralyzed individual, and great economic consequences for society. Functional electrical stimulation (FES) offers one means to restore some mobility to these individuals, improving not only their autonomy, but potentially their general health and well-being as well. FES uses electrical stimulation to cause the paralyzed muscles to contract. Existing clinical systems require the stimulation to be preprogrammed, with the patient typically using residual voluntary movement of another body part to trigger and control the patterned stimulation. The rapid development of neural interfacing in the past decade offers the promise of dramatically improved control for these patients, potentially allowing continuous control of FES through signals recorded from motor cortex, as the patient attempts to control the paralyzed body part. While application of these 'brain-machine interfaces' (BMIs) has undergone dramatic development for control of computer cursors and even robotic limbs, their use as an interface for FES has been much more limited. In this review, we consider both FES and BMI technologies and discuss the prospect for combining the two to provide important new options for paralyzed individuals.

Representation of continuous hand and arm movements in macaque areas M1, F5, and AIP: a comparative decoding study

OBJECTIVE

In the last decade, multiple brain areas have been investigated with respect to their decoding capability of continuous arm or hand movements. So far, these studies have mainly focused on motor or premotor areas like M1 and F5. However, there is accumulating evidence that anterior intraparietal area (AIP) in the parietal cortex also contains information about continuous movement.

APPROACH

In this study, we decoded 27 degrees of freedom representing complete hand and arm kinematics during a delayed grasping task from simultaneously recorded activity in areas M1, F5, and AIP of two macaque monkeys (Macaca mulatta).

MAIN RESULTS

We found that all three areas provided decoding performances that lay significantly above chance. In particular, M1 yielded highest decoding accuracy followed by F5 and AIP. Furthermore, we provide support for the notion that AIP does not only code categorical visual features of objects to be grasped, but also contains a substantial amount of temporal kinematic information.

SIGNIFICANCE

This fact could be utilized in future developments of neural interfaces restoring hand and arm movements.

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

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

Neuromechanical principles underlying movement modularity and their implications for rehabilitation

Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e., motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson's disease. Recent studies provide insights into the yet-unknown underlying neural mechanisms of motor modules, motor impairment, and motor learning and may lead to better understanding of the causal nature of modularity and its underlying neural substrates.

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

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

Differences between kinematic synergies and muscle synergies during two-digit grasping

The large number of mechanical degrees of freedom of the hand is not fully exploited during actual movements such as grasping. Usually, angular movements in various joints tend to be coupled, and EMG activities in different hand muscles tend to be correlated. The occurrence of covariation in the former was termed kinematic synergies, in the latter muscle synergies. This study addresses two questions: (i) Whether kinematic and muscle synergies can simultaneously accommodate for kinematic and kinetic constraints. (ii) If so, whether there is an interrelation between kinematic and muscle synergies. We used a reach-grasp-and-pull paradigm and recorded the hand kinematics as well as eight surface EMGs. Subjects had to either perform a precision grip or side grip and had to modify their grip force in order to displace an object against a low or high load. The analysis was subdivided into three epochs: reach, grasp-and-pull, and static hold. Principal component analysis (PCA, temporal or static) was performed separately for all three epochs, in the kinematic and in the EMG domain. PCA revealed that (i) Kinematic- and muscle-synergies can simultaneously accommodate kinematic (grip type) and kinetic task constraints (load condition). (ii) Upcoming grip and load conditions of the grasp are represented in kinematic- and muscle-synergies already during reach. Phase plane plots of the principal muscle-synergy against the principal kinematic synergy revealed (iii) that the muscle-synergy is linked (correlated, and in phase advance) to the kinematic synergy during reach and during grasp-and-pull. Furthermore (iv), pair-wise correlations of EMGs during hold suggest that muscle-synergies are (in part) implemented by coactivation of muscles through common input. Together, these results suggest that kinematic synergies have (at least in part) their origin not just in muscular activation, but in synergistic muscle activation. In short: kinematic synergies may result from muscle synergies.

Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles

The primary focus of rehabilitation for individuals with loss of upper limb movement as a result of acquired brain injury is the relearning of specific motor skills and daily tasks. This relearning is essential because the loss of upper limb movement often results in a reduced quality of life. Although rehabilitation strives to take advantage of neuroplastic processes during recovery, results of traditional approaches to upper limb rehabilitation have not entirely met this goal. In contrast, enriched training tasks, simulated with a wide range of low- to high-end virtual reality-based simulations, can be used to provide meaningful, repetitive practice together with salient feedback, thereby maximizing neuroplastic processes via motor learning and motor recovery. Such enriched virtual environments have the potential to optimize motor learning by manipulating practice conditions that explicitly engage motivational, cognitive, motor control, and sensory feedback-based learning mechanisms. The objectives of this article are to review motor control and motor learning principles, to discuss how they can be exploited by virtual reality training environments, and to provide evidence concerning current applications for upper limb motor recovery. The limitations of the current technologies with respect to their effectiveness and transfer of learning to daily life tasks also are discussed.

A neuroanatomical framework for upper limb synergies after stroke

Muscle synergies describe common patterns of co- or reciprocal activation that occur during movement. After stroke, these synergies change, often in stereotypical ways. The mechanism underlying this change reflects damage to key motor pathways as a result of the stroke lesion, and the subsequent reorganization along the neuroaxis, which may be further detrimental or restorative to motor function. The time course of abnormal synergy formation seems to lag spontaneous recovery that occurs in the initial weeks after stroke. In healthy individuals, motor cortical activity, descending via the corticospinal tract (CST) is the predominant driver of voluntary behavior. When the CST is damaged after stroke, other descending pathways may be up-regulated to compensate. The contribution of these pathways may emerge as new synergies take shape at the chronic stage after stroke, as a result of plasticity along the neuroaxis. The location of the stroke lesion and properties of the secondary descending pathways and their regulation are then critical for shaping the synergies in the remaining motor behavior. A consideration of the integrity of remaining descending motor pathways may aid in the design of new rehabilitation therapies.

Do sensorimotor β-oscillations maintain muscle synergy representations in primary motor cortex?

Coherent β-oscillations are a dominant feature of the sensorimotor system yet their function remains enigmatic. We propose that, in addition to cell intrinsic and/or local network interactions, they are supported by activity propagating recurrently around closed neural 'loops' between primary motor cortex (M1), muscles, and back to M1 via somatosensory pathways. Individual loops reciprocally connect individual muscle synergies ('motor primitives') with their representations in M1, and the conduction time around each loop resonates with the periodic spiking of its constituent neurons/muscles. During β-oscillations, this resonance strengthens within-loop connectivity (via long-term potentiation, LTP), whereas non-resonance between different loops weakens connectivity (via long-term depression, LTD) between M1 representations of different muscle synergies. In this way, β-oscillations help maintain accurate and discrete representations of muscle synergies in M1.

Distinct neural patterns enable grasp types decoding in monkey dorsal premotor cortex

OBJECTIVE

Recent studies have shown that dorsal premotor cortex (PMd), a cortical area in the dorsomedial grasp pathway, is involved in grasp movements. However, the neural ensemble firing property of PMd during grasp movements and the extent to which it can be used for grasp decoding are still unclear.

APPROACH

To address these issues, we used multielectrode arrays to record both spike and local field potential (LFP) signals in PMd in macaque monkeys performing reaching and grasping of one of four differently shaped objects.

MAIN RESULTS

Single and population neuronal activity showed distinct patterns during execution of different grip types. Cluster analysis of neural ensemble signals indicated that the grasp related patterns emerged soon (200-300 ms) after the go cue signal, and faded away during the hold period. The timing and duration of the patterns varied depending on the behaviors of individual monkey. Application of support vector machine model to stable activity patterns revealed classification accuracies of 94% and 89% for each of the two monkeys, indicating a robust, decodable grasp pattern encoded in the PMd. Grasp decoding using LFPs, especially the high-frequency bands, also produced high decoding accuracies.

SIGNIFICANCE

This study is the first to specify the neuronal population encoding of grasp during the time course of grasp. We demonstrate high grasp decoding performance in PMd. These findings, combined with previous evidence for reach related modulation studies, suggest that PMd may play an important role in generation and maintenance of grasp action and may be a suitable locus for brain-machine interface applications.