On the translation of directional motor cortical commands to activation of muscles via spinal interneuronal systems

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.

Motor directional tuning across brain areas: directional resonance and the role of inhibition for directional accuracy

Motor directional tuning (Georgopoulos et al., 1982) has been found in every brain area in which it has been sought for during the past 30-odd years. It is typically broad, with widely distributed preferred directions and a population signal that predicts accurately the direction of an upcoming reaching movement or isometric force pulse (Georgopoulos et al., 1992). What is the basis for such ubiquitous directional tuning? How does the tuning come about? What are the implications of directional tuning for understanding the brain mechanisms of movement in space? This review addresses these questions in the light of accumulated knowledge in various sub-fields of neuroscience and motor behavior. It is argued (a) that direction in space encompasses many aspects, from vision to muscles, (b) that there is a directional congruence among the central representations of these distributed “directions” arising from rough but orderly topographic connectivities among brain areas, (c) that broad directional tuning is the result of broad excitation limited by recurrent and non-recurrent (i.e., direct) inhibition within the preferred direction loci in brain areas, and (d) that the width of the directional tuning curve, modulated by local inhibitory mechanisms, is a parameter that determines the accuracy of the directional command.

Computing reaching dynamics in motor cortex with Cartesian spatial coordinates

How neurons in the primary motor cortex control arm movements is not yet understood. Here we show that the equations of motion governing reaching simplify when expressed in spatial coordinates. In this fixed reference frame, joint torques are the sums of vector cross products between the spatial positions of limb segments and their spatial accelerations and velocities. The consequences that follow from this model explain many properties of neurons in the motor cortex, including directional broad, cosinelike tuning, nonuniformly distributed preferred directions dependent on the workspace, and the rotation of the population vector during arm movements. Remarkably, the torques can be directly computed as a linearly weighted sum of responses from cortical motoneurons, and the muscle tensions can be obtained as rectified linear sums of the joint torques. This allows the required muscle tensions to be computed rapidly from a trajectory in space with a feedforward network model.

Modeling discrete and rhythmic movements through motor primitives: a review

Rhythmic and discrete movements are frequently considered separately in motor control, probably because different techniques are commonly used to study and model them. Yet the increasing interest in finding a comprehensive model for movement generation requires bridging the different perspectives arising from the study of those two types of movements. In this article, we consider discrete and rhythmic movements within the framework of motor primitives, i.e., of modular generation of movements. In this way we hope to gain an insight into the functional relationships between discrete and rhythmic movements and thus into a suitable representation for both of them. Within this framework we can define four possible categories of modeling for discrete and rhythmic movements depending on the required command signals and on the spinal processes involved in the generation of the movements. These categories are first discussed in terms of biological concepts such as force fields and central pattern generators and then illustrated by several mathematical models based on dynamical system theory. A discussion on the plausibility of theses models concludes the work.

Stroke rehabilitation reaches a threshold

Motor training with the upper limb affected by stroke partially reverses the loss of cortical representation after lesion and has been proposed to increase spontaneous arm use. Moreover, repeated attempts to use the affected hand in daily activities create a form of practice that can potentially lead to further improvement in motor performance. We thus hypothesized that if motor retraining after stroke increases spontaneous arm use sufficiently, then the patient will enter a virtuous circle in which spontaneous arm use and motor performance reinforce each other. In contrast, if the dose of therapy is not sufficient to bring spontaneous use above threshold, then performance will not increase and the patient will further develop compensatory strategies with the less affected hand. To refine this hypothesis, we developed a computational model of bilateral hand use in arm reaching to study the interactions between adaptive decision making and motor relearning after motor cortex lesion. The model contains a left and a right motor cortex, each controlling the opposite arm, and a single action choice module. The action choice module learns, via reinforcement learning, the value of using each arm for reaching in specific directions. Each motor cortex uses a neural population code to specify the initial direction along which the contralateral hand moves towards a target. The motor cortex learns to minimize directional errors and to maximize neuronal activity for each movement. The derived learning rule accounts for the reversal of the loss of cortical representation after rehabilitation and the increase of this loss after stroke with insufficient rehabilitation. Further, our model exhibits nonlinear and bistable behavior: if natural recovery, motor training, or both, brings performance above a certain threshold, then training can be stopped, as the repeated spontaneous arm use provides a form of motor learning that further bootstraps performance and spontaneous use. Below this threshold, motor training is “in vain”: there is little spontaneous arm use after training, the model exhibits learned nonuse, and compensatory movements with the less affected hand are reinforced. By exploring the nonlinear dynamics of stroke recovery using a biologically plausible neural model that accounts for reversal of the loss of motor cortex representation following rehabilitation or the lack thereof, respectively, we can explain previously hard to reconcile data on spontaneous arm use in stroke recovery. Further, our threshold prediction could be tested with an adaptive train–wait–train paradigm: if spontaneous arm use has increased in the “wait” period, then the threshold has been reached, and rehabilitation can be stopped. If spontaneous arm use is still low or has decreased, then another bout of rehabilitation is to be provided.

Stroke often leaves patients with predominantly unilateral functional limitations of the arm and hand. Although recovery of function after stroke is often achieved by compensatory use of the less affected limb, improving use of the more affected limb has been associated with increased quality of life. Here, we developed a biologically plausible model of bilateral reaching movements to investigate the mechanisms and conditions leading to effective rehabilitation. Our motor cortex model accounts for the experimental observation that motor training can reverse the loss of cortical representation due to lesion. Further, our model predicts that if spontaneous arm use is above a certain threshold, then training can be stopped, as the repeated spontaneous use provides a form of motor learning that further improves performance and spontaneous use. Below this threshold, training is “in vain,” and compensatory movements with the less affected hand are reinforced. Our model is a first step in the development of adaptive and cost-effective rehabilitation methods tailored to individuals poststroke.

Connected corticospinal sites show enhanced tuning similarity at the onset of voluntary action

Corticospinal (CS) pathways provide the structural foundation for executing voluntary movements. Although the anatomy of these pathways is well explored, little is known about spinal decoding of parametric information transmitted via this route during voluntary movements. We addressed this question by simultaneously recording cortical and spinal activity in primates performing an isometric wrist task with multiple targets while measuring CS interactions. Single-pulse cortical stimulation effectively produced a short-latency (presumably monosynaptic) spinal response and thus revealed functionally connected CS sites. Spinal and cortical neurons recorded from connected CS sites showed alignment of directional-torque tuning that peaked at torque onset, consistent with the enhanced cortical drive active during this period. This increased tuning similarity was accompanied by an increased trial-to-trial covariability of firing. Whereas functional CS interactions were dynamic, the efficacy of cortical stimulation was unaffected by the motor state. These results suggest that around the onset of motor action there is a period of facilitated information transfer during which cortical command has greater efficacy in recruiting spinal neurons with matching tuning properties. Dynamic alignment of response properties may form the basis for a spinal readout mechanism of descending motor commands in which directional-torque is a parameter that is preserved across interacting CS sites.

Coding of movement- and force-related information in primate primary motor cortex: a computational approach

Coordinated movements result from descending commands transmitted by central motor systems to the muscles. Although the resulting effect of the commands has the dimension of a muscular force, it is unclear whether the information transmitted by the commands concerns movement kinematics (e.g. position, velocity) or movement dynamics (e.g. force, torque). To address this issue, we used an optimal control model of movement production that calculates inputs to motoneurons that are appropriate to drive an articulated limb toward a goal. The model quantitatively accounted for kinematic, kinetic and muscular properties of planar, shoulder/elbow arm-reaching movements of monkeys, and reproduced detailed features of neuronal correlates of these movements in primate motor cortex. The model also reproduced qualitative spatio-temporal characteristics of movement- and force-related single neuron discharges in non-planar reaching and isometric force production tasks. The results suggest that the nervous system of the primate controls movements through a muscle-based controller that could be located in the motor cortex.

Large-Scale Organization of Preferred Directions in the Motor Cortex. II. Analysis of Local Distributions

The spatial arrangement of preferred directions (PDs) in the primary motor cortex has revealed evidence for columnar organization and short-range order. We investigated the large-scale properties of this arrangement. We recorded neural activity at sites on a grid covering a large region of the arm area of the motor cortex while monkeys performed a 3D reaching task. Sites were projected to the cortical surface along anatomically defined cortical columns and a PD was extracted from each site with directionally tuned activity. We analyzed the resulting 2D surface map of PDs. Consistent with previous studies, we found that any particular reaching direction was rerepresented at many points across the recorded area. In particular, we determined that the median radius of a cortical region required to represent the full complement of reaching directions is at most 1 mm. We also found that for the majority of regions of this size, the distribution of PDs within them exhibits an enrichment for the representation of forward and backward reaching directions (see companion paper). Finally, we found that the error of a population vector estimate of reaching direction constructed from neural activity within these regions is small on average, but varies significantly across different sections of the motor cortex, with the highest levels of error sustained near the fundus of the central sulcus and lowest levels achieved near the crown. We interpret these findings in the context of two well-known features of motor cortex, that is, its highly distributed anatomical organization and its behaviorally dependent plasticity.

Dynamic updating of distributed neural representations using forward models

In this paper, we present a continuous attractor network model that we hypothesize will give some suggestion of the mechanisms underlying several neural processes such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor transformations, motor control, motor imagery, and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies (Decety and Sommerville in Trends Cogn Sci 7:527-533, 2003; Oztop et al. in Neural Netw 19(3):254-271, 2006; Wolpert et al. in Philos Trans R Soc 358:593-602, 2003). With respect to these studies, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue are relevant for a better understanding of the neural processes of the brain.

Innate versus learned movements--a false dichotomy?

It is argued that the nervous systems of vertebrates are equipped with a "motor infrastructure," which enables them to perform the full extent of the motor repertoire characteristic of their particular species. In the human, it extends from the networks/circuits underlying locomotion and feeding to sound production in speech and arm-hand-finger coordination. Contrary to current opinion, these diverse motor patterns should be labeled as voluntary, because they can be recruited at will. Moreover, most, if not all, of the motor patterns available at birth are subject to maturation and are modified substantially through learning. We thus argue that the all-too-common distinction between learned and innate movements is based on a fundamental misconception about the neural control of the vertebrate motor system.

Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability

The directional control of reaching after stroke was simulated by including cell death and firing-rate noise in a population vector model of movement control. In this model, cortical activity was assumed to cause the hand to move in the direction of a population vector, defined by a summation of responses from neurons with cosine directional tuning. Two types of directional error were analyzed: the between-target variability, defined as the standard deviation of the directional error across a wide range of target directions, and the within-target variability, defined as the standard deviation of the directional error for many reaches to a single target.

Both between and within-target variability increased with increasing cell death. The increase in between-target variability arose because cell death caused a nonuniform distribution of preferred directions. The increase in within-target variability arose because the magnitude of the population vector decreased more quickly than its standard deviation for increasing cell death, provided appropriate levels of firing-rate noise were present. Comparisons to reaching data from 29 stroke subjects revealed similar increases in between and within-target variability as clinical impairment severity increased. Relationships between simulated cell death and impairment severity were derived using the between and within-target variability results. For both relationships, impairment severity increased similarly with decreasing percentage of surviving cells, consistent with results from previous imaging studies. These results demonstrate that a population vector model of movement control that incorporates cosine tuning, linear summation of unitary responses, firing-rate noise, and random cell death can account for some features of impaired arm movement after stroke.

Population computation of vectorial transformations

Many neurons of the central nervous system are broadly tuned to some sensory or motor variables. This property allows one to assign to each neuron a preferred attribute (PA). The width of tuning curves and the distribution of PAs in a population of neurons tuned to a given variable define the collective behavior of the population. In this article, we study the relationship of the nature of the tuning curves, the distribution of PAs, and computational properties of linear neuronal populations. We show that noise-resistant distributed linear algebraic processing and learning can be implemented by a population of cosine tuned neurons assuming a nonuniform but regular distribution of PAs. We extend these results analytically to the noncosine tuning and uniform distribution case and show with a numerical simulation that the results remain valid for a nonuniform regular distribution of PAs for broad noncosine tuning curves. These observations provide a theoretical basis for modeling general nonlinear sensorimotor transformations as sets of local linearized representations.

Cortical activation related to arm-movement combinations

Recent studies support the long-standing hypothesis that continuous arm movements consist of overlapping, discrete submovements. However, the cortical activation associated with these submovements is unclear. We tested the hypothesis that electroencephalography (EEG) activity would more strongly correspond to the particular combinations of muscle electrical activity, the independent components (ICs) of surface electromyography (EMG), than the surface EMG from individual muscles alone. We examined data recorded from two normal subjects performing sustained submaximal contractions or continual, unpaced repetitive movements of the arm. Independent component analysis (ICA) was used to determine the ICs of the multichannel EMG recordings (EMGICs). ICA was also used to calculate the coupling between the simultaneously recorded EEG and the EMG from a single muscle (Subject 1) or the EMGICs (Subject 2). The EMGICs were either tonic or phasic. The significant couplings between the EEG and the EMGICs were different for each EMGIC. The distribution on the scalp of the coupling between the EEG and tonic EMGICs and those of the single-muscle EMG were similar and followed topographic patterns in sensorimotor regions. Couplings between the EEG and phasic EMGICs were bifrontal, lateral, and bioccipital and were significantly stronger than the coupling between a single muscle's EMG and the EEG (p < 2 x 10(-5)) or another EMG combination derived from principal component analysis. These preliminary results support the notion that electrophysiological cortical activations are more significantly related to the ICs of muscle activations than to the activations of individual muscles alone.

Phasic and tonic coupling between EEG and EMG demonstrated with independent component analysis

The authors describe a method for demonstrating the tonic and phasic couplings between suitably time-aligned surface eletromyographs (sEMGs) and the simultaneously recorded EEGs. The method, based on independent component analysis, was applied to data recorded from two normal subjects performing sustained submaximal contractions or continual repetitive movements of the arm. Augmented datasets, consisting of the EEG and either the sEMG from a single muscle (subject 1) or a combination of sEMGs from several muscles (subject 2), were analyzed with independent component analysis to determine the EEG/sEMG coupling. Each derived coupling consisted of a spatial distribution on the scalp and a waveform representing an EEG channel combination coactivating with the sEMG. The combinations of sEMGs, derived by applying independent component analysis to the simultaneous sEMG recordings from several muscles to create sEMG independent components (ICs), were either tonic or phasic with differing periods of activation. The topographic distributions on the scalp of the couplings between the EEG and sEMG ICs were different for each sEMG IC. The spatial distributions of the couplings between tonic sEMG ICs or single-muscle sEMGs and the EEG followed topographic patterns in sensorimotor regions. Phasic couplings were bifrontal, lateral, and bioccipital. Calculation of coherence between the sEMG ICs and calculated EEG combinations agreed well with the frequency spectra of the independent component analysis-derived coupling waveforms. These preliminary results demonstrate that detection of both the tonic and phasic coupling between the sEMG and the EEG is possible when monitoring unpaced proximal arm movement. This may thus be a practical means of exploring the dynamic cortical/muscle relationships in subjects unable to perform fine finger movements, such as patients recovering from stroke.

Motor systems

The field of motor control has broadened considerably over the past decade. Increasingly detailed information has accrued about the cellular and molecular processes involved in motor pattern generation and motor learning while, at the other extreme, the comparison of studies in humans and monkeys has begun to bridge the gap between cognitive and motor functions. The most striking feature of recent research has been the intense use of electrophysiological procedures in behaving monkeys and non-invasive imaging procedures in humans to elucidate details of sensory-motor transformations and the functional roles of different brain regions in the learning, planning and execution of movements.

Neural adaptation in the generation of rhythmic behavior

Motor systems can adapt rapidly to changes in external conditions and to switching of internal goals. They can also adapt slowly in response to training, alterations in the mechanics of the system, and any changes in the system resulting from injury. This article reviews the mechanisms underlying short- and long-term adaptation in rhythmic motor systems. The neuronal networks underlying the generation of rhythmic motor patterns (central pattern generators; CPGs) are extremely flexible. Neuromodulators, central commands, and afferent signals all influence the pattern produced by a CPG by altering the cellular and synaptic properties of individual neurons and the coupling between different populations of neurons. This flexibility allows the generation of a variety of motor patterns appropriate for the mechanical requirements of different forms of a behavior. The matching of motor output to mechanical requirements depends on the capacity of pattern-generating networks to adapt to slow changes in body mechanics and persistent errors in performance. Afferent feedback from body and limb proprioceptors likely plays an important role in driving these long-term adaptive processes.

Cortical control of reaching movements

Recent studies provide further support for the hypothesis that spatial representations of limb position, target locations, and potential motor actions are expressed in the neuronal activity in parietal cortex. In contrast, precentral cortical activity more strongly expresses processes involved in the selection and execution of motor actions. As a general conceptual framework, these processes may be interpreted in terms of such formalisms as sensorimotor transformations and 'internal models'.

Comparison of onset time and magnitude of activity for proximal arm muscles and motor cortical cells before reaching movements

The activity of motor cortical cells and proximal arm muscles during the initiation of planar reaching movements were analyzed to identify whether features of coordinated motor patterns of muscles spanning the elbow and shoulder were evident in the discharge patterns of motor cortical cells. Shoulder and elbow muscles were divided into four groups, flexors and extensors at each joint. Features of the initial agonist activity, onset time and magnitude, at the shoulder and elbow were compared for movements in different spatial directions. As observed for human movements, differences in the onset time and the relative magnitude of electromyographic activity (EMG) of muscles acting about the shoulder and elbow were dependent on the direction of movement. Motor cortical cells were categorized as elbow or shoulder related on the basis of their response to passive movement of the joints. Differences in the onset time and the relative magnitude of activity of cells related to the shoulder and elbow were both dependent on the direction of movement and were similar to those observed for muscles spanning these joints. There was a modest, but significant correlation between the onset time and magnitude of EMG for individual muscles. A similar magnitude-time coupling was observed for individual motor cortical cells. Variations in the discharge pattern of motor cortical cells before movement that mirror those observed for muscles spanning the shoulder and elbow support the potential role of primary motor cortex in the selection, timing, and magnitude of agonist motor patterns at the shoulder and elbow to initiate reaching movements.

Neural Networks and Motor Control

Motor control is accomplished by the cooperative interaction of many brain networks, among which the motor cortex holds a central place. This article reviews some of the structural and functional properties of neurons of the motor cortical network, some principles of connectivity with other motor networks, the handling of spatial information regarding reaching movements, and some ideas on how motor cortical commands could be translated to muscle activations by spinal motor networks. Finally, I review recent neural network modeling studies of motor cortical ensemble operations.

Arm movements in monkeys: behavior and neurophysiology

Reaching to objects of interest is very common in the behavioral repertoire of primates. Monkeys possess keen binocular vision and make graceful and accurate arm movements. This review focuses on behavioral and neurophysiological aspects of eye-hand coordination in behaving monkeys, including neural coding mechanisms at the single cell level and in neuronal populations. The results of these studies have converged to a common behavioral-neurophysiological ground and provided a springboard for studies of brain mechanisms underlying motor cognitive function.

Keeping in mind the mind: mental functions, networks and neurosurgery

The object of the neurosurgeons daily endeavour, the human brain, is less well understood in its overall organization than any other organ. This puts the neurosurgeon in a very difficult position. However, a substantial body of knowledge has been accumulated during recent years, and scientists from a variety of different disciplines have worked out theoretical frameworks to accomodate the available data. Here we present some of the evolving concepts on the organization of the substrate of the mind. Review of the literature shows that application of mathematical neural network models to the nervous system is very successful in explaining function. An implicit aspect of neural network models is that information storage is not localized in certain neurons but that the information is stored as the global pattern of activity in the network. Because networks of the brain involve often millions of neurons, exact identification and comparison with the theoretical models is not possible today.