Partial reconstruction of muscle activity from a pruned network of diverse motor cortex neurons

Evolution, biomechanics, and neurobiology converge to explain selective finger motor control

Humans use their fingers to perform a variety of tasks, from simple grasping to manipulating objects, to typing and playing musical instruments, a variety wider than any other species. The more sophisticated the task, the more it involves individuated finger movements, those in which one or more selected fingers perform an intended action while the motion of other digits is constrained. Here we review the neurobiology of such individuated finger movements. We consider their evolutionary origins, the extent to which finger movements are in fact individuated, and the evolved features of neuromuscular control that both enable and limit individuation. We go on to discuss other features of motor control that combine with individuation to create dexterity, the impairment of individuation by disease, and the broad extent of capabilities that individuation confers on humans. We comment on the challenges facing the development of a truly dexterous bionic hand. We conclude by identifying topics for future investigation that will advance our understanding of how neural networks interact across multiple regions of the central nervous system to create individuated movements for the skills humans use to express their cognitive activity.

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

μSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling

How does the motor cortex (MC) produce purposeful and generalizable movements from the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate the MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory feedback from biologically accurate musculoskeletal models. Given this real-time simulated feedback implemented in advanced physics simulation engines, we use deep reinforcement learning to train the RNN to achieve desired movements under specified neural and musculoskeletal constraints. Activity of the trained model can accurately decode experimentally recorded neural population dynamics and single-unit MC activity, while generalizing well to testing conditions significantly different from training. Simultaneous goal- and data- driven modeling in which we use the recorded neural activity as observed states of the MC further enhances direct and generalizable single-unit decoding. Finally, we show that this framework elucidates computational principles of how neural dynamics enable flexible control of movement and make this framework easy-to-use for future experiments.

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.

Spinal cord bioelectronic interfaces: opportunities in neural recording and clinical challenges

Bioelectronic stimulation of the spinal cord has demonstrated significant progress in restoration of motor function in spinal cord injury (SCI). The proximal, uninjured spinal cord presents a viable target for the recording and generation of control signals to drive targeted stimulation. Signals have been directly recorded from the spinal cord in behaving animals and correlated with limb kinematics. Advances in flexible materials, electrode impedance and signal analysis will allow SCR to be used in next-generation neuroprosthetics. In this review, we summarize the technological advances enabling progress in SCR and describe systematically the clinical challenges facing spinal cord bioelectronic interfaces and potential solutions, from device manufacture, surgical implantation to chronic effects of foreign body reaction and stress-strain mismatches between electrodes and neural tissue. Finally, we establish our vision of bi-directional closed-loop spinal cord bioelectronic bypass interfaces that enable the communication of disrupted sensory signals and restoration of motor function in SCI.

The neural mechanisms of manual dexterity

The hand endows us with unparalleled precision and versatility in our interactions with objects, from mundane activities such as grasping to extraordinary ones such as virtuoso pianism. The complex anatomy of the human hand combined with expansive and specialized neuronal control circuits allows a wide range of precise manual behaviours. To support these behaviours, an exquisite sensory apparatus, spanning the modalities of touch and proprioception, conveys detailed and timely information about our interactions with objects and about the objects themselves. The study of manual dexterity provides a unique lens into the sensorimotor mechanisms that endow the nervous system with the ability to flexibly generate complex behaviour.

Emerging of new bioartificial corticospinal motor synergies using a robotic additional thumb

It is likely that when using an artificially augmented hand with six fingers, the natural five plus a robotic one, corticospinal motor synergies controlling grasping actions might be different. However, no direct neurophysiological evidence for this reasonable assumption is available yet. We used transcranial magnetic stimulation of the primary motor cortex to directly address this issue during motor imagery of objects’ grasping actions performed with or without the Soft Sixth Finger (SSF). The SSF is a wearable robotic additional thumb patented for helping patients with hand paresis and inherent loss of thumb opposition abilities. To this aim, we capitalized from the solid notion that neural circuits and mechanisms underlying motor imagery overlap those of physiological voluntary actions. After a few minutes of training, healthy humans wearing the SSF rapidly reshaped the pattern of corticospinal outputs towards forearm and hand muscles governing imagined grasping actions of different objects, suggesting the possibility that the extra finger might rapidly be encoded into the user’s body schema, which is integral part of the frontal-parietal grasping network. Such neural signatures might explain how the motor system of human beings is open to very quickly welcoming emerging augmentative bioartificial corticospinal grasping strategies. Such an ability might represent the functional substrate of a final common pathway the brain might count on towards new interactions with the surrounding objects within the peripersonal space. Findings provide a neurophysiological framework for implementing augmentative robotic tools in humans and for the exploitation of the SSF in conceptually new rehabilitation settings.

Motor unit firing rates during constant isometric contraction: establishing and comparing an age-related pattern among muscles

Motor unit (MU) firing rates (FRs) are lower in aged adults, compared with young, at relative voluntary contraction intensities. However, from a variety of independent studies of disparate muscles, the age-related degree of difference in FR among muscles is unclear. Using a standardized statistical approach with data derived from primary studies, we quantified differences in FRs across several muscles between younger and older adults. The data set included 12 different muscles in young (18-35 yr) and older adults (62-93 yr) from 18 published and one unpublished study. Experiments recorded single MU activity from intramuscular electromyography during constant isometric contraction at different (step-like) voluntary intensities. For each muscle, FR ranges and FR variance explained by voluntary contraction intensity were determined using bootstrapping. Dissimilarity of FR variance among muscles was calculated by Euclidean distances. There were threefold differences in the absolute frequency of FR ranges across muscles in the young (soleus 8-16 and superior trapezius 20-49 Hz), but in the old, FR ranges were more similar and lower for nine out of 12 muscles. In contrast, the explained FR variance from voluntary contraction intensity in the older group had 1.6-fold greater dissimilarity among muscles than the young (P < 0.001), with FR variance differences being muscle dependent. Therefore, differences between muscle FR ranges were not explained by how FRs scale to changes in voluntary contraction intensity within each muscle. Instead, FRs were muscle dependent but were more dissimilar among muscles in the older group in their responsiveness to voluntary contraction intensity.NEW & NOTEWORTHY The mean frequency of motor unit firing rates were compared systematically among several muscles and between young and older adults from new and published data sets. Firing rates among muscles were lower and more similar during voluntary isometric contraction in older than younger adults. Firing rate responses from voluntary contraction intensity were muscle dependent and more dissimilar among muscles in the older than young adults.

Towards Cell and Subtype Resolved Functional Organization: Mouse as a Model for the Cortical Control of Movement

Despite a long history of interrogation, the functional organization of motor cortex remains obscure. A major barrier has been the inability to measure and perturb activity with sufficient resolution to reveal clear functional elements within motor cortex and its associated circuits. Increasingly, the mouse has been employed as a model to facilitate application of contemporary approaches with the potential to surmount this barrier. In this brief essay, we consider these approaches and their use in the context of studies aimed at resolving the logic of motor cortical operation.

Structure of Population Activity in Primary Motor Cortex for Single Finger Flexion and Extension

How is the primary motor cortex (M1) organized to control fine finger movements? We investigated the population activity in M1 for single finger flexion and extension, using 7T functional magnetic resonance imaging (fMRI) in female and male human participants and compared these results to the neural spiking patterns recorded in two male monkeys performing the identical task. fMRI activity patterns were distinct for movements of different fingers, but were quite similar for flexion and extension of the same finger. In contrast, spiking patterns in monkeys were quite distinct for both fingers and directions, which is similar to what was found for muscular activity patterns. The discrepancy between fMRI and electrophysiological measurements can be explained by two (non-mutually exclusive) characteristics of the organization of finger flexion and extension movements. Given that fMRI reflects predominantly input and recurrent activity, the results can be explained by an architecture in which neural populations that control flexion or extension of the same finger produce distinct outputs, but interact tightly with each other and receive similar inputs. Additionally, neurons tuned to different movement directions for the same finger (or combination of fingers) may cluster closely together, while neurons that control different finger combinations may be more spatially separated. When measuring this organization with fMRI at a coarse spatial scale, the activity patterns for flexion and extension of the same finger would appear very similar. Overall, we suggest that the discrepancy between fMRI and electrophysiological measurements provides new insights into the general organization of fine finger movements in M1.SIGNIFICANCE STATEMENT The primary motor cortex (M1) is important for producing individuated finger movements. Recent evidence shows that movements that commonly co-occur are associated with more similar activity patterns in M1. Flexion and extension of the same finger, which never co-occur, should therefore be associated with distinct representations. However, using carefully controlled experiments and multivariate analyses, we demonstrate that human fMRI activity patterns for flexion or extension of the same finger are highly similar. In contrast, spiking patterns measured in monkey M1 are clearly distinct. This suggests that populations controlling opposite movements of the same finger, while producing distinct outputs, may cluster together and share inputs and local processing. These results provide testable hypotheses about the organization of hand control in M1.

Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective

For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.

A muscle-activity-dependent gain between motor cortex and EMG

Whether one is delicately placing a contact lens on the surface of the eye or lifting a heavy weight from the floor, the motor system must produce a wide range of forces under different dynamical loads. How does the motor cortex, with neurons that have a limited activity range, function effectively under these widely varying conditions? In this study, we explored the interaction of activity in primary motor cortex (M1) and muscles (electromyograms, EMGs) of two male rhesus monkeys for wrist movements made during three tasks requiring different dynamical loads and forces. Despite traditionally providing adequate predictions in single tasks, in our experiments, a single linear model failed to account for the relation between M1 activity and EMG across conditions. However, a model with a gain parameter that increased with the target force remained accurate across forces and dynamical loads. Surprisingly, this model showed that a greater proportion of EMG changes were explained by the nonlinear gain than the linear mapping from M1. In addition to its theoretical implications, the strength of this nonlinearity has important implications for brain-computer interfaces (BCIs). If BCI decoders are to be used to control movement dynamics (including interaction forces) directly, they will need to be nonlinear and include training data from broad data sets to function effectively across tasks. Our study reinforces the need to investigate neural control of movement across a wide range of conditions to understand its basic characteristics as well as translational implications. NEW & NOTEWORTHY We explored the motor cortex-to-electromyogram (EMG) mapping across a wide range of forces and loading conditions, which we found to be highly nonlinear. A greater proportion of EMG was explained by a nonlinear gain than a linear mapping. This nonlinearity allows motor cortex to control the wide range of forces encountered in the real world. These results unify earlier observations and inform the next-generation brain-computer interfaces that will control movement dynamics and interaction forces.

Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response

Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid "tangling": moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling.

Perspectives on classical controversies about the motor cortex

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

Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex

Blocking motor cortical output with lesions or pharmacological inactivation has identified movements that require motor cortex. Yet, when and how motor cortex influences muscle activity during movement execution remains unresolved. We addressed this ambiguity using measurement and perturbation of motor cortical activity together with electromyography in mice during two forelimb movements that differ in their requirement for cortical involvement. Rapid optogenetic silencing and electrical stimulation indicated that short-latency pathways linking motor cortex with spinal motor neurons are selectively activated during one behavior. Analysis of motor cortical activity revealed a dramatic change between behaviors in the coordination of firing patterns across neurons that could account for this differential influence. Thus, our results suggest that changes in motor cortical output patterns enable a behaviorally selective engagement of short-latency effector pathways. The model of motor cortical influence implied by our findings helps reconcile previous observations on the function of motor cortex.

Tensor Analysis Reveals Distinct Population Structure that Parallels the Different Computational Roles of Areas M1 and V1

Cortical firing rates frequently display elaborate and heterogeneous temporal structure. One often wishes to compute quantitative summaries of such structure—a basic example is the frequency spectrum—and compare with model-based predictions. The advent of large-scale population recordings affords the opportunity to do so in new ways, with the hope of distinguishing between potential explanations for why responses vary with time. We introduce a method that assesses a basic but previously unexplored form of population-level structure: when data contain responses across multiple neurons, conditions, and times, they are naturally expressed as a third-order tensor. We examined tensor structure for multiple datasets from primary visual cortex (V1) and primary motor cortex (M1). All V1 datasets were ‘simplest’ (there were relatively few degrees of freedom) along the neuron mode, while all M1 datasets were simplest along the condition mode. These differences could not be inferred from surface-level response features. Formal considerations suggest why tensor structure might differ across modes. For idealized linear models, structure is simplest across the neuron mode when responses reflect external variables, and simplest across the condition mode when responses reflect population dynamics. This same pattern was present for existing models that seek to explain motor cortex responses. Critically, only dynamical models displayed tensor structure that agreed with the empirical M1 data. These results illustrate that tensor structure is a basic feature of the data. For M1 the tensor structure was compatible with only a subset of existing models.

Neuroscientists commonly measure the time-varying activity of neurons in the brain. Early studies explored how such activity directly encodes sensory stimuli. Since then neural responses have also been found to encode abstract parameters such as expected reward. Yet not all aspects of neural activity directly encode identifiable parameters: patterns of activity sometimes reflect the evolution of underlying internal computations, and may be only obliquely related to specific parameters. For example, it remains debated whether cortical activity during movement relates to parameters such as reach velocity, to parameters such as muscle activity, or to underlying computations that culminate in the production of muscle activity. To address this question we exploited an unexpected fact. When activity directly encodes a parameter it tends to be mathematically simple in a very particular way. When activity reflects the evolution of a computation being performed by the network, it tends to be mathematically simple in a different way. We found that responses in a visual area were simple in the first way, consistent with encoding of parameters. We found that responses in a motor area were simple in the second way, consistent with participation in the underlying computations that culminate in movement.

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.

Timing of Cortico-Muscle Transmission During Active Movement

Numerous studies have reported large disparities between short cortico-muscle conduction latencies and long recorded delays between cortical firing and evoked muscle activity. Using methods such as spike- and stimulus-triggered averaging of electromyographic (EMG) activity, previous studies have shown that the time delay between corticomotoneuronal (CM) cell firing and onset of facilitation of forelimb muscle activity ranges from 6.7 to 9.8 ms, depending on the muscle group tested. In contrast, numerous studies have reported delays of 60-122 ms between cortical cell firing onset and either EMG or movement onset during motor tasks. To further investigate this disparity, we simulated rapid active movement by applying frequency-modulated stimulus trains to M1 cortical sites in a rhesus macaque performing a movement task. This yielded corresponding EMG modulations, the latency of which could be measured relative to the stimulus modulations. The overall mean delay from stimulus frequency modulation to EMG modulation was 11.5 ± 5.6 ms, matching closely the conduction time through the cortico-muscle pathway (12.6 ± 2.0 ms) derived from poststimulus facilitation peaks computed at the same sites. We conclude that, during active movement, the delay between modulated M1 cortical output and its impact on muscle activity approaches the physical cortico-muscle conduction time.

A neural network that finds a naturalistic solution for the production of muscle activity

It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we trained recurrent neural networks to reproduce the muscle activity of reaching monkeys. Models had to infer dynamics that could transform simple inputs into temporally and spatially complex patterns of muscle activity. Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands. This solution closely resembled, at both the single-neuron and population levels, what was observed in neural recordings from the same monkeys. Notably, data and simulations agreed only when models were optimized to find simple solutions. An appealing interpretation is that the empirically observed dynamics of motor cortex may reflect a simple solution to the problem of generating temporally patterned descending commands.

Conditional genetic deletion of PTEN after a spinal cord injury enhances regenerative growth of CST axons and motor function recovery in mice

Previous studies indicate that conditional genetic deletion of phosphatase and tensin homolog (PTEN) in neonatal mice enhances the ability of axons to regenerate following spinal cord injury (SCI) in adults. Here, we assessed whether deleting PTEN in adult neurons post-SCI is also effective, and whether enhanced regenerative growth is accompanied by enhanced recovery of voluntary motor function. PTEN(loxP/loxP) mice received moderate contusion injuries at cervical level 5 (C5). One group received unilateral injections of adeno-associated virus expressing CRE (AAV-CRE) into the sensorimotor cortex; controls received a vector expressing green fluorescent protein (AAV-GFP) or injuries only (no vector injections). Forelimb function was tested for 14weeks post-SCI using a grip strength meter (GSM) and a hanging task. The corticospinal tract (CST) was traced by injecting mini-ruby BDA into the sensorimotor cortex. Forelimb gripping ability was severely impaired immediately post-SCI but recovered slowly over time. The extent of recovery was significantly greater in PTEN-deleted mice in comparison to either the AAV-GFP group or the injury only group. BDA tract tracing revealed significantly higher numbers of BDA-labeled axons in caudal segments in the PTEN-deleted group compared to control groups. In addition, in the PTEN-deleted group, there were exuberant collaterals extending from the main tract rostral to the lesion and into and around the scar tissue at the injury site. These results indicate that PTEN deletion in adult mice shortly post-SCI can enhance regenerative growth of CST axons and forelimb motor function recovery.

Motor cortex single-neuron and population contributions to compensation for multiple dynamic force fields

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields.

Joint cross-correlation analysis reveals complex, time-dependent functional relationship between cortical neurons and arm electromyograms

Correlation between cortical activity and electromyographic (EMG) activity of limb muscles has long been a subject of neurophysiological studies, especially in terms of corticospinal connectivity. Interest in this issue has recently increased due to the development of brain-machine interfaces with output signals that mimic muscle force. For this study, three monkeys were implanted with multielectrode arrays in multiple cortical areas. One monkey performed self-timed touch pad presses, whereas the other two executed arm reaching movements. We analyzed the dynamic relationship between cortical neuronal activity and arm EMGs using a joint cross-correlation (JCC) analysis that evaluated trial-by-trial correlation as a function of time intervals within a trial. JCCs revealed transient correlations between the EMGs of multiple muscles and neural activity in motor, premotor and somatosensory cortical areas. Matching results were obtained using spike-triggered averages corrected by subtracting trial-shuffled data. Compared with spike-triggered averages, JCCs more readily revealed dynamic changes in cortico-EMG correlations. JCCs showed that correlation peaks often sharpened around movement times and broadened during delay intervals. Furthermore, JCC patterns were directionally selective for the arm-reaching task. We propose that such highly dynamic, task-dependent and distributed relationships between cortical activity and EMGs should be taken into consideration for future brain-machine interfaces that generate EMG-like signals.

Primary Motor Cortex Neurons during Individuated Finger and Wrist Movements: Correlation of Spike Firing Rates with the Motion of Individual Digits versus Their Principal Components

The joints of the hand provide 24 mechanical degrees of freedom. Yet 2-7 principal components (PCs) account for 80-95% of the variance in hand joint motion during tasks that vary from grasping to finger spelling. Such findings have led to the hypothesis that the brain may simplify operation of the hand by preferentially controlling PCs. We tested this hypothesis using data recorded from the primary motor cortex (M1) during individuated finger and wrist movements. Principal component analysis (PCA) of the simultaneous position of the five digits and the wrist showed relatively consistent kinematic synergies across recording sessions in two monkeys. The first three PCs typically accounted for 85% of the variance. Cross-correlations then were calculated between the firing rate of single neurons and the simultaneous flexion/extension motion of each of the five digits and the wrist, as well as with each of their six PCs. For each neuron, we then compared the maximal absolute value of the cross-correlations (MAXC) achieved with the motion of any digit or the wrist to the MAXC achieved with motion along any PC axis. The MAXC with a digit and the MAXC with a PC were themselves highly correlated across neurons. A minority of neurons correlated more strongly with a PC than with any digit. But for the populations of neurons sampled from each of two subjects, MAXCs with digits were slightly but significantly higher than those with PCs. We therefore reject the hypothesis that M1 neurons preferentially control PCs of hand motion. We cannot exclude the possibility that M1 neurons might control kinematic synergies identified using linear or non-linear methods other than PCA. We consider it more likely, however, that neurons in other centers of the motor system - such as the pontomedullary reticular formation and the spinal gray matter - drive synergies of movement and/or muscles, which M1 neurons act to fractionate in producing individuated finger and wrist movements.

Muscle synergies evoked by microstimulation are preferentially encoded during behavior

Electrical microstimulation studies provide some of the most direct evidence for the neural representation of muscle synergies. These synergies, i.e., coordinated activations of groups of muscles, have been proposed as building blocks for the construction of motor behaviors by the nervous system. Intraspinal or intracortical microstimulation (ICMS) has been shown to evoke muscle patterns that can be resolved into a small set of synergies similar to those seen in natural behavior. However, questions remain about the validity of microstimulation as a probe of neural function, particularly given the relatively long trains of supratheshold stimuli used in these studies. Here, we examined whether muscle synergies evoked during ICMS in two rhesus macaques were similarly encoded by nearby motor cortical units during a purely voluntary behavior involving object reach, grasp, and carry movements. At each microstimulation site we identified the synergy most strongly evoked among those extracted from muscle patterns evoked over all microstimulation sites. For each cortical unit recorded at the same microstimulation site, we then identified the synergy most strongly encoded among those extracted from muscle patterns recorded during the voluntary behavior. We found that the synergy most strongly evoked at an ICMS site matched the synergy most strongly encoded by proximal units more often than expected by chance. These results suggest a common neural substrate for microstimulation-evoked motor responses and for the generation of muscle patterns during natural behaviors.

Cortical activity in the null space: permitting preparation without movement

Neural circuits must perform computations and then selectively output the results to other circuits. Yet synapses do not change radically at millisecond timescales. A key question then is: how is communication between neural circuits controlled? In motor control, brain areas directly involved in driving movement are active well before movement begins. Muscle activity is some readout of neural activity, yet remains largely unchanged during preparation. Here we find that during preparation, while the monkey holds still, changes in motor cortical activity cancel out at the level of these population readouts. Motor cortex can thereby prepare the movement without prematurely causing it. Further, we found evidence that this mechanism also operates in dorsal premotor cortex (PMd), largely accounting for how preparatory activity is attenuated in primary motor cortex (M1). Selective use of “output-null” vs. “output-potent” patterns of activity may thus help control communication to the muscles and between these brain areas.

Edging toward entelechy in motor control

The organization and functional logic of corticospinal motor neurons and their target connections remains unclear, despite their evident influence on movement. Spinal interneurons mediate much of this influence, yet we know little about the way in which corticospinal neurons engage spinal interneurons. This is perhaps not surprising given that the principles of organization of local spinal microcircuits remain elusive--we have glimpses of an underlying order but lack a comprehensive view of their functional architecture. In this brief essay we make a case that a new focus on the intersection of cortical and spinal circuits may provide clarity to the interpretation of corticospinal motor neuron firing patterns and help specify the logic of corticospinal motor neuronal function.

Effects of Parkinson's disease on optimization and structure of variance in multi-finger tasks

We explored the role of the basal ganglia in two components of multi-finger synergies by testing a group of patients with early-stage Parkinson's disease and a group of healthy controls. Synergies were defined as co-varied adjustments of commands to individual fingers that reduced variance of the total force and moment of force. The framework of the uncontrolled manifold hypothesis was used to quantify such co-variation patterns, while average performance across repetitive trials (sharing patterns) was analyzed using the analytical inverse optimization (ANIO) approach. The subjects performed four-finger pressing tasks that involved the accurate production of combinations of the total force and total moment of force and also repetitive trials at two selected combinations of the total force and moment. The ANIO approach revealed significantly larger deviations of the experimental data planes from an optimal plane for the patients compared to the control subjects. The synergy indices computed for total force stabilization were significantly higher in the control subjects compared to the patients; this was not true for synergy indices computed for moment of force stabilization. The differences in the synergy indices were due to the larger amount of variance that affected total force in the patients, while the amount of variance that did not affect total force was comparable between the groups. We conclude that the basal ganglia play an important role in both components of synergies reflecting optimization of the sharing patterns and stability of performance with respect to functionally important variables.

Restoration of upper limb movement via artificial corticospinal and musculospinal connections in a monkey with spinal cord injury

Functional loss of limb control in individuals with spinal cord injury or stroke can be caused by interruption of corticospinal pathways, although the neural circuits located above and below the lesion remain functional. An artificial neural connection that bridges the lost pathway and connects cortical to spinal circuits has potential to ameliorate the functional loss. We investigated the effects of introducing novel artificial neural connections in a paretic monkey that had a unilateral spinal cord lesion at the C2 level. The first application bridged the impaired spinal lesion. This allowed the monkey to drive the spinal stimulation through volitionally controlled power of high-gamma activity in either the premotor or motor cortex, and thereby to acquire a force-matching target. The second application created an artificial recurrent connection from a paretic agonist muscle to a spinal site, allowing muscle-controlled spinal stimulation to boost on-going activity in the muscle. These results suggest that artificial neural connections can compensate for interrupted descending pathways and promote volitional control of upper limb movement after damage of descending pathways such as spinal cord injury or stroke.

Neural bases of hand synergies

The human hand has so many degrees of freedom that it may seem impossible to control. A potential solution to this problem is “synergy control” which combines dimensionality reduction with great flexibility. With applicability to a wide range of tasks, this has become a very popular concept. In this review, we describe the evolution of the modern concept using studies of kinematic and force synergies in human hand control, neurophysiology of cortical and spinal neurons, and electromyographic (EMG) activity of hand muscles. We go beyond the often purely descriptive usage of synergy by reviewing the organization of the underlying neuronal circuitry in order to propose mechanistic explanations for various observed synergy phenomena. Finally, we propose a theoretical framework to reconcile important and still debated concepts such as the definitions of “fixed” vs. “flexible” synergies and mechanisms underlying the combination of synergies for hand control.

Prediction of muscle activities from electrocorticograms in primary motor cortex of primates

Electrocorticography (ECoG) has drawn attention as an effective recording approach for brain-machine interfaces (BMI). Previous studies have succeeded in classifying movement intention and predicting hand trajectories from ECoG. Despite such successes, however, there still remains considerable work for the realization of ECoG-based BMIs as neuroprosthetics. We developed a method to predict multiple muscle activities from ECoG measurements. We also verified that ECoG signals are effective for predicting muscle activities in time varying series when performing sequential movements. ECoG signals were band-pass filtered into separate sensorimotor rhythm bands, z-score normalized, and smoothed with a Gaussian filter. We used sparse linear regression to find the best fit between frequency bands of ECoG and electromyographic activity. The best average correlation coefficient and the normalized root-mean-square error were 0.92±0.06 and 0.06±0.10, respectively, in the flexor digitorum profundus finger muscle. The δ (1.5∼4Hz) and γ2 (50∼90Hz) bands contributed significantly more strongly than other frequency bands (P<0.001). These results demonstrate the feasibility of predicting muscle activity from ECoG signals in an online fashion.

Dissociating motor cortex from the motor

During closed-loop control of a brain-computer interface, neurons in the primary motor cortex can be intensely active even though the subject may be making no detectable movement or muscle contraction. How can neural activity in the primary motor cortex become dissociated from the movements and muscles of the native limb that it normally controls? Here we examine circumstances in which motor cortex activity is known to dissociate from movement--including mental imagery, visuo-motor dissociation and instructed delay. Many such motor cortex neurons may be related to muscle activity only indirectly. Furthermore, the integration of thousands of synaptic inputs by individual α-motoneurons means that under certain circumstances even cortico-motoneuronal cells, which make monosynaptic connections to α-motoneurons, can become dissociated from muscle activity. The natural ability of motor cortex neurons under voluntarily control to become dissociated from bodily movement may underlie the utility of this cortical area for controlling brain-computer interfaces.

Reconstruction of flexor and extensor muscle activities from electroencephalography cortical currents

The ability to reconstruct muscle activity time series from electroencephalography (EEG) may lead to drastic improvements in brain-machine interfaces (BMIs) by providing a means for realistic continuous reproduction of dexterous movements in human beings. However, it is considered difficult to isolate signals related to individual muscle activities from EEG because EEG sensors record a mixture of signals originating from many cortical regions. Here, we challenge this assumption by reconstructing agonist and antagonist muscle activities (i.e. filtered electromyography (EMG) signals) from EEG cortical currents estimated using a hierarchical Bayesian EEG inverse method. Results of 5 volunteer subjects performing isometric right wrist flexion and extension tasks showed that individual muscle activity time series, as well as muscle activities at different force levels, were well reconstructed using EEG cortical currents and with significantly higher accuracy than when directly reconstructing from EEG sensor signals. Moreover, spatial distribution of weight values for reconstruction models revealed that highly contributing cortical sources to flexion and extension tasks were mutually exclusive, even though they were mapped onto the same cortical region. These results suggest that EEG sensor signals were reasonably isolated into cortical currents using the applied method and provide the first evidence that agonist and antagonist muscle activity time series can be reconstructed using EEG cortical currents.

Age-related changes in the control of finger force vectors

We explored changes in finger interaction in the process of healthy aging as a window into neural control strategies of natural movements. In particular, we quantified the amount of force produced by noninstructed fingers in different directions, the amount of force produced by the instructed finger orthogonally to the task direction, and the strength of multifinger synergies stabilizing the total force magnitude and direction during accurate force production. Healthy elderly participants performed accurate isometric force production tasks in five directions by individual fingers and by all four fingers acting together. Their data were compared with a dataset obtained in a similar earlier study of young subjects. Finger force vectors were measured using six-component force/torque sensors. Multifinger synergies were quantified using the framework of the uncontrolled manifold hypothesis. The elderly participants produced lower force magnitudes by noninstructed fingers and higher force magnitudes by instructed fingers in nontask directions. They showed strong synergies stabilizing the magnitude and direction of the total force vector. However, the synergy indexes were significantly lower than those observed in the earlier study of young subjects. The results are consistent with an earlier hypothesis of preferential weakening of intrinsic hand muscles with age. We interpret the findings as a shift in motor control from synergic to element-based, which may be causally linked to the documented progressive neuronal death at different levels of the neural axis.

Descending pathways in motor control

Each of the descending pathways involved in motor control has a number of anatomical, molecular, pharmacological, and neuroinformatic characteristics. They are differentially involved in motor control, a process that results from operations involving the entire motor network rather than from the brain commanding the spinal cord. A given pathway can have many functional roles. This review explores to what extent descending pathways are highly conserved across species and concludes that there are actually rather widespread species differences, for example, in the transmission of information from the corticospinal tract to upper limb motoneurons. The significance of direct, cortico-motoneuronal (CM) connections, which were discovered a little more than 50 years ago, is reassessed. I conclude that although these connections operate in parallel with other less direct linkages to motoneurons, CM influence is significant and may subserve some special functions including adaptive motor behaviors involving the distal extremities.

Coordinate transformation is first completed downstream of primary motor cortex

It was suggested previously that the transformation of action to muscle-based coding is completed in the primary motor cortex (M1). This is consistent with a predominant direct pathway leading from M1 to motoneurons. Accordingly, spinal segmental interneurons that are located downstream to M1 are expected to show muscle-like coding properties. We addressed this hypothesis using simultaneous recording of cortical and spinal activity in primates performing an isometric wrist task with multiple targets and two hand postures. Here we show that while the motor cortex follows an intermediate coordinate frame, spinal interneurons already follow a muscle-like coordinate frame. We thus suggest that the final steps in coordinate transformation of motor commands take place downstream of M1 via corticospinal interactions.

Multi-muscle synergies in an unusual postural task: quick shear force production

We considered a hypothetical two-level hierarchy participating in the control of vertical posture. The framework of the uncontrolled manifold (UCM) hypothesis was used to explore the muscle groupings (M-modes) and multi-M-mode synergies involved in the stabilization of a time profile of the shear force in the anterior-posterior direction. Standing subjects were asked to produce pulses of shear force into a target using visual feedback while trying to minimize the shift of the center of pressure (COP). Principal component analysis applied to integrated muscle activation indices identified three M-modes. The composition of the M-modes was similar across subjects and the two directions of the shear force pulse. It differed from the composition of M-modes described in earlier studies of more natural actions associated with large COP shifts. Further, the trial-to-trial M-mode variance was partitioned into two components: one component that does not affect a particular performance variable (V(UCM)), and its orthogonal component (V(ORT)). We argued that there is a multi-M-mode synergy stabilizing this particular performance variable if V(UCM) is higher than V(ORT). Overall, we found a multi-M-mode synergy stabilizing both shear force and COP coordinate. For the shear force, this synergy was strong for the backward force pulses and nonsignificant for the forward pulses. An opposite result was found for the COP coordinate: the synergy was stronger for the forward force pulses. The study shows that M-mode composition can change in a task-specific way and that two different performance variables can be stabilized using the same set of elemental variables (M-modes). The different dependences of the ΔV indices for the shear force and COP coordinate on the force pulse direction supports applicability of the principle of superposition (separate controllers for different performance variables) to the control of different mechanical variables in postural tasks. The M-mode composition allows a natural mechanical interpretation.

Do corticomotoneuronal cells predict target muscle EMG activity?

Data from two rhesus macaques were used to investigate the pattern of cortical cell activation during reach-to-grasp movements in relation to the corresponding activation pattern of the cell's facilitated target muscles. The presence of postspike facilitation (PSpF) in spike-triggered averages (SpTAs) of electromyographic (EMG) activity was used to identify cortical neurons with excitatory synaptic linkages with motoneurons. EMG activity from 22 to 24 muscles of the forelimb was recorded together with the activity of M1 cortical neurons. The extent of covariation was characterized by 1) identifying the task segment containing the cell and target muscle activity peaks, 2) quantifying the timing and overlap between corticomotoneuronal (CM) cell and EMG peaks, and 3) applying Pearson correlation analysis to plots of CM cell firing rate versus EMG activity of the cell's facilitated muscles. At least one firing rate peak, for nearly all (95%) CM cells tested, matched a corresponding peak in the EMG activity of the cell's target muscles. Although some individual CM cells had very strong correlations with target muscles, overall, substantial disparities were common. We also investigated correlations for ensembles of CM cells sharing the same target muscle. The ensemble population activity of even a small number of CM cells influencing the same target muscle produced a relatively good match (r >/= 0.8) to target muscle EMG activity. Our results provide evidence in support of the notion that corticomotoneuronal output from primary motor cortex encodes movement in a framework of muscle-based parameters, specifically muscle-activation patterns as reflected in EMG activity.