Motor unit recruitment for dynamic tasks: current understanding and future directions

Skeletal muscle contains many muscle fibres that are functionally grouped into motor units. For any motor task there are many possible combinations of motor units that could be recruited and it has been proposed that a simple rule, the ‘size principle’, governs the selection of motor units recruited for different contractions. Motor units can be characterised by their different contractile, energetic and fatigue properties and it is important that the selection of motor units recruited for given movements allows units with the appropriate properties to be activated. Here we review what is currently understood about motor unit recruitment patterns, and assess how different recruitment patterns are more or less appropriate for different movement tasks. During natural movements the motor unit recruitment patterns vary (not always holding to the size principle) and it is proposed that motor unit recruitment is likely related to the mechanical function of the muscles. Many factors such as mechanics, sensory feedback, and central control influence recruitment patterns and consequently an integrative approach (rather than reductionist) is required to understand how recruitment is controlled during different movement tasks. Currently, the best way to achieve this is through in vivo studies that relate recruitment to mechanics and behaviour. Various methods for determining motor unit recruitment patterns are discussed, in particular the recent wavelet-analysis approaches that have allowed motor unit recruitment to be assessed during natural movements. Directions for future studies into motor recruitment within and between functional task groups and muscle compartments are suggested.     

The fundamental problem of myoskeletal inverse dynamics and its implications.

The validity of current inverse dynamics models utilized for motion analysis is investigated. It is shown that observables generated by the real biosystem, such as ground reaction forces, are incompatible with comparable responses of skeletodynamical inverse models currently in use. This implies that results obtained with such models are erroneous to varying degrees while a quantification of these errors is difficult or impossible. This phenomenon is termed the fundamental myoskeletal inverse dynamics problem. A model fidelity indicator is proposed which, for a specific inverse dynamics model applied to a particular motion, provides a dimensionless numerical measure for the replicative validity of that model and the fidelity of its input data. A practical example demonstrates the usefulness of this indicator. It is suggested that the development of structurally sufficiently complex and biologically more realistic skeletomechanical models as well as substantial error reductions in data measuring and processing procedures will be necessary to improve the accuracy of inverse dynamics model computations.     

Inferior olive mirrors joint dynamics to implement an inverse controller

To produce smooth and coordinated motion, our nervous systems need to generate precisely timed muscle activation patterns that, due to axonal conduction delay, must be generated in a predictive and feedforward manner. Kawato proposed that the cerebellum accomplishes this by acting as an inverse controller that modulates descending motor commands to predictively drive the spinal cord such that the musculoskeletal dynamics are canceled out. This and other cerebellar theories do not, however, account for the rich biophysical properties expressed by the olivocerebellar complex’s various cell types, making these theories difficult to verify experimentally. Here we propose that a multizonal microcomplex’s (MZMC) inferior olivary neurons use their subthreshold oscillations to mirror a musculoskeletal joint’s underdamped dynamics, thereby achieving inverse control. We used control theory to map a joint’s inverse model onto an MZMC’s biophysics, and we used biophysical modeling to confirm that inferior olivary neurons can express the dynamics required to mirror biomechanical joints. We then combined both techniques to predict how experimentally injecting current into the inferior olive would affect overall motor output performance. We found that this experimental manipulation unmasked a joint’s natural dynamics, as observed by motor output ringing at the joint’s natural frequency, with amplitude proportional to the amount of current. These results support the proposal that the cerebellum—in particular an MZMC—is an inverse controller; the results also provide a biophysical implementation for this controller and allow one to make an experimentally testable prediction.     

Force variability is mostly not motor noise: Theoretical implications for motor control

Variability in muscle force is a hallmark of healthy and pathological human behavior. Predominant theories of sensorimotor control assume ‘motor noise’ leads to force variability and its ‘signal dependence’ (variability in muscle force whose amplitude increases with intensity of neural drive). Here, we demonstrate that the two proposed mechanisms for motor noise (i.e. the stochastic nature of motor unit discharge and unfused tetanic contraction) cannot account for the majority of force variability nor for its signal dependence. We do so by considering three previously underappreciated but physiologically important features of a population of motor units: 1) fusion of motor unit twitches, 2) coupling among motoneuron discharge rate, cross-bridge dynamics, and muscle mechanics, and 3) a series-elastic element to account for the aponeurosis and tendon. These results argue strongly against the idea that force variability and the resulting kinematic variability are generated primarily by ‘motor noise.’ Rather, they underscore the importance of variability arising from properties of control strategies embodied through distributed sensorimotor systems. As such, our study provides a critical path toward developing theories and models of sensorimotor control that provide a physiologically valid and clinically useful understanding of healthy and pathologic force variability.     

Response linearity determined by recruitment strategy in detailed model of nictitating membrane control

Many models of eyeblink conditioning assume that there is a simple linear relationship between the firing patterns of neurons in the interpositus nucleus and the time course of the conditioned response (CR). However, the complexities of muscle behaviour and plant dynamics call this assumption into question. We investigated the issue by implementing the most detailed model available of the rabbit nictitating membrane response (Bartha and Thompson in Biol Cybern 68:135-143, 1992a and in Biol Cybern 68:145-154, 1992b), in which each motor unit of the retractor bulbi muscle is represented by a Hill-type model, driven by a non-linear activation mechanism designed to reproduce the isometric force measurements of Lennerstrand (J Physiol 236:43-55, 1974). Globe retraction and NM extension are modelled as linked second order systems. We derived versions of the model that used a consistent set of SI units, were based on a physically realisable version of calcium kinetics, and used simulated muscle cross-bridges to produce force. All versions showed similar non-linear responses to two basic control strategies. (1) Rate-coding with no recruitment gave a sigmoidal relation between control signal and amplitude of CR, reflecting the measured relation between isometric muscle force and stimulation frequency. (2) Recruitment of similar strength motor units with no rate coding gave a sublinear relation between control signal and amplitude of CR, reflecting the increase in muscle stiffness produced by recruitment. However, the system response could be linearised by either a suitable combination of rate-coding and recruitment, or by simple recruitment of motor units in order of (exponentially) increasing strength. These plausible control strategies, either alone or in combination, would in effect present the cerebellum with the simplified virtual plant that is assumed in many models of eyeblink conditioning. Future work is therefore needed to determine the extent to which motor neuron firing is in fact linearly related to the nictitating membrane response.     

Energy-optimal controls in the mammalian neuromuscular system

The hypothesis is advanced that the specific patterns of motor unit recruitment and stimulation frequencies observed in mammalian skeletal muscle under static isometric contractions are determined by a minimum-energy principle. By performing a constrained energy optimization based on a control model of skeletal muscle comprising three different fibre types, and appropriate expressions for the energy rates, it is indeed possible to obtain detailed predictions of recruitment and stimulation frequency patterns which agree well with the experimentally observed functions, thereby providing strong support for the minimum-energy hypothesis. Since the orderly recruitment sequence determined by the size principle is also, independently, predicted by the minimum-energy principle, it is concluded that there exists a relationship between motor unit size and the myoenergetic properties of the recruited unit. It is suggested that this relationship, together with the possibility of adjusting the relative proportions of the fibre types present in a muscle, constitutes an optimal adaptation of the neuromuscular system for practically all types of muscular performances normally encountered. For various types of muscles, the energy rates as functions of the force output are also discussed.     

Cerebellar learning of accurate predictive control for fast-reaching movements

Long conduction delays in the nervous system prevent the accurate control of movements by feedback control alone. We present a new, biologically plausible cerebellar model to study how fast arm movements can be executed in spite of these delays. To provide a realistic test-bed of the cerebellar neural model, we embed the cerebellar network in a simulated biological motor system comprising a spinal cord model and a six-muscle two-dimensional arm model. We argue that if the trajectory errors are detected at the spinal cord level, memory traces in the cerebellum can solve the temporal mismatch problem between efferent motor commands and delayed error signals. Moreover, learning is made stable by the inclusion of the cerebello-nucleo-olivary loop in the model. It is shown that the cerebellar network implements a nonlinear predictive regulator by learning part of the inverse dynamics of the plant and spinal circuit. After learning, fast accurate reaching movements can be generated. Received: 8 February 1999 /Accepted in revised form: 7 August 1999     

Motor learning through the combination of primitives

In this paper we discuss a new perspective on how the central nervous system (CNS) represents and solves some of the most fundamental computational problems of motor control. In particular, we consider the task of transforming a planned limb movement into an adequate set of motor commands. To carry out this task the CNS must solve a complex inverse dynamic problem. This problem involves the transformation from a desired motion to the forces that are needed to drive the limb. The inverse dynamic problem is a hard computational challenge because of the need to coordinate multiple limb segments and because of the continuous changes in the mechanical properties of the limbs and of the environment with which they come in contact. A number of studies of motor learning have provided support for the idea that the CNS creates, updates and exploits internal representations of limb dynamics in order to deal with the complexity of inverse dynamics. Here we discuss how such internal representations are likely to be built by combining the modular primitives in the spinal cord as well as other building blocks found in higher brain structures. Experimental studies on spinalized frogs and rats have led to the conclusion that the premotor circuits within the spinal cord are organized into a set of discrete modules. Each module, when activated, induces a specific force field and the simultaneous activation of multiple modules leads to the vectorial combination of the corresponding fields. We regard these force fields as computational primitives that are used by the CNS for generating a rich grammar of motor behaviours.     

Deep cerebellar neurons mirror the spinal cord’s gain to implement an inverse controller

Smooth and coordinated motion requires precisely timed muscle activation patterns, which due to biophysical limitations, must be predictive and executed in a feed-forward manner. In a previous study, we tested Kawato’s original proposition, that the cerebellum implements an inverse controller, by mapping a multizonal microcomplex’s (MZMC) biophysics to a joint’s inverse transfer function and showing that inferior olivary neuron may use their intrinsic oscillations to mirror a joint’s oscillatory dynamics. Here, to continue to validate our mapping, we propose that climbing fiber input into the deep cerebellar nucleus (DCN) triggers rebounds, primed by Purkinje cell inhibition, implementing gain on IO’s signal to mirror the spinal cord reflex’s gain thereby achieving inverse control. We used biophysical modeling to show that Purkinje cell inhibition and climbing fiber excitation interact in a multiplicative fashion to set DCN’s rebound strength; where the former primes the cell for rebound by deinactivating its T-type Ca2⁺ channels and the latter triggers the channels by rapidly depolarizing the cell. We combined this result with our control theory mapping to predict how experimentally injecting current into DCN will affect overall motor output performance, and found that injecting current will proportionally scale the output and unmask the joint’s natural response as observed by motor output ringing at the joint’s natural frequency. Experimental verification of this prediction will lend support to a MZMC as a joint’s inverse controller and the role we assigned underlying biophysical principles that enable it.     

A complete set of control equations for the human musculo-skeletal system

A mathematical model of the total human musculo-skeletal system is presented. The model comprises a link-mechanical and a musculo-mechanical set of ordinary first-order differential equations which describe the dynamics of the segment model and muscle model respectively. The interdependence of the two sets of equations is demonstrated. The set of musculo-mechanical equations contains the two neuromuscular control parameters motor unit recruitment and stimulation rate, and the significance of such a representation for a control-theoretical treatment of musculo-skeletal systems is discussed. Finally, after a short discussion of the successful application of the present model in the prediction of an optimal human motion, further possibilities are indicated of the use of the model for investigations into the control behaviour of musculo-skeletal systems.     

The role of primary motor cortex in goal-directed movements: insights from neurophysiological studies on non-human primates

Neurophysiological studies on non-human primates have provided a large body of information on the response patterns of neurons in primary motor cortex during volitional motor tasks. Rather than finding a single simple pattern of activity in primary motor cortex neurons, these studies illustrate that neural activity in this area reflects many different types of information, including spatial goals, hand motion, joint motion, force output and electromyographic activity. This richness in the response characteristics of neurons makes estimates of any single variable on motor performance from population signals imprecise and prone to errors. It initially seems puzzling that so many different types of information are represented in primary motor cortex. However, such richness in neural responses reflects its important role in converting high-level behavioral goals generated in other cortical regions into complex spatiotemporal patterns to control not only alpha-motoneuron activity but also other features of spinal processing.     

Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions.

We investigated the firing rate of motor units in the vastus lateralis muscle in five healthy young men (mean = 21.4 yr, SD = 0.9) during a sequence of isometric constant-torque contractions repeated to exhaustion. The contractions were sustained at 20% of the maximal voluntary level, measured at the beginning of the test sequence. Electromyographic (EMG) signals were recorded via quadrifilar fine-wire electrodes and subsequently decomposed into their constituent motor unit action potentials to obtain the motor unit firing times. In addition, we measured the whole muscle mechanical properties during the fatigue task using electrical stimulation. The firing rate of motor units first decreased within the first 10-20% of the endurance time of the contractions and then increased. The firing rate increase was accompanied by recruitment of additional motor units as the force output remained constant. The elicited twitch and tetanic torque responses first increased and then decreased. The two processes modulated in a complementary fashion at the same time. Our data suggest that, when the vastus lateralis muscle is activated to maintain a constant torque output, its motoneuron pool receives a net excitatory drive that first decreases to compensate for the short-lived potentiation of the muscle force twitch and then increases to compensate for the diminution of the force twitch. The underlying inverse relationship between the firing rate and the recruitment threshold that has been reported for nonfatigued contractions is maintained. We, therefore, conclude that the central nervous system control of vastus lateralis motor units remains invariant during fatigue in submaximal isometric isotonic contractions.     

A general myocybernetic control model of skeletal muscle

A general myocybernetic control model of skeletal muscle is presented which constitutes an extension, to general control modes, of a previously published control model. The restriction, in the previous model, to a constant number of stimulated motor units has been removed and the new model allows for both a varying number of stimulated motor units and a varying average stimulation rate. The general model is tested by comparing its predictions with experimental records of the force output of the quadriceps femoris muscle. It is found that the model correctly predicts the initial excitation-contraction delay, the dips in the force record, and several other contraction phenomena.     

The extraction of neural strategies from the surface EMG.

This brief review examines some of the methods used to infer central control strategies from surface electromyogram (EMG) recordings. Among the many uses of the surface EMG in studying the neural control of movement, the review critically evaluates only some of the applications. The focus is on the relations between global features of the surface EMG and the underlying physiological processes. Because direct measurements of motor unit activation are not available and many factors can influence the signal, these relations are frequently misinterpreted. These errors are compounded by the counterintuitive effects that some system parameters can have on the EMG signal. The phenomenon of crosstalk is used as an example of these problems. The review describes the limitations of techniques used to infer the level of muscle activation, the type of motor unit recruited, the upper limit of motor unit recruitment, the average discharge rate, and the degree of synchronization between motor units. Although the global surface EMG is a useful measure of muscle activation and assessment, there are limits to the information that can be extracted from this signal.     

A neural-network system for control of eye movements: basic mechanisms

This paper presents a neural-network-based system that can generate and control movements of the eyes. It was inspired by a number of experimental observations on the saccadic and gaze systems of monkeys and cats. Because of the generality of the approach undertaken, the system can be regarded as a demonstration of how parallel distributed processing principles, namely learning and attractor dynamics, can be integrated with experimental findings, as well as a biologically inspired controller for a dexterous robotic orientation device. The system is composed of three parts: a dynamic motor map, a push-pull circuitry, and a plant. The dynamics of the motor map is generated by a multi-layer network that was trained to compute a bidimensional temporal-spatial transformation. Simulation results indicate (1) that the system is able to reproduce some of the properties observed in the biological system at the neural and movement levels and (2) that the dynamics of the motor map remains stereotyped even when the motor map is subject to abnormal stimulation patterns. The latter result emphasizes the role of the topographic projection that connects the motor map to the push-pull circuitry in determining the features of the resulting movements.     

Computational nature of human adaptive control during learning of reaching movements in force fields

Learning to make reaching movements in force fields was used as a paradigm to explore the system architecture of the biological adaptive controller. We compared the performance of a number of candidate control systems that acted on a model of the neuromuscular system of the human arm and asked how well the dynamics of the candidate system compared with the movement characteristics of 16 subjects. We found that control via a supra-spinal system that utilized an adaptive inverse model resulted in dynamics that were similar to that observed in our subjects, but lacked essential characteristics. These characteristics pointed to a different architecture where descending commands were influenced by an adaptive forward model. However, we found that control via a forward model alone also resulted in dynamics that did not match the behavior of the human arm. We considered a third control architecture where a forward model was used in conjunction with an inverse model and found that the resulting dynamics were remarkably similar to that observed in the experimental data. The essential property of this control architecture was that it predicted a complex pattern of near-discontinuities in hand trajectory in the novel force field. A nearly identical pattern was observed in our subjects, suggesting that generation of descending motor commands was likely through a control system architecture that included both adaptive forward and inverse models. We found that as subjects learned to make reaching movements, adaptation rates for the forward and inverse models could be independently estimated and the resulting changes in performance of subjects from movement to movement could be accurately accounted for. Results suggested that the adaptation of the forward model played a dominant role in the motor learning of subjects. After a period of consolidation, the rates of adaptation in the internal models were significantly larger than those observed before the memory had consolidated. This suggested that consolidation of motor memory coincided with freeing of certain computational resources for subsequent learning. Received: 01 January 1998 / Accepted in revised form: 26 January 1999     

Selective motor unit recruitment via intrafascicular multielectrode stimulation

Recruitment of force via independent asynchronous firing of large numbers of motor units produces the grace and endurance of physiological motion. We have investigated the possibility of reproducing this physiological recruitment strategy by determining the selectivity of access to large numbers of independent motor units through intrafascicular multielectrode stimulation (IFMS) of the peripheral nerve. A Utah Slanted Electrode Array containing 100, 0.5-1.5 mm-long penetrating electrodes was inserted into the sciatic nerve of a cat, and forces generated by the 3 heads of triceps surea in response to electrical stimulation of the nerve were monitored via force transducers attached to their tendons. We found a mean of 17.4 +/- 4.9 (mean +/- SEM) electrodes selectively excited maximal forces in medial gastrocnemius before exciting another muscle. Among electrodes demonstrating selectivity at threshold, a mean of 7.3 +/- 2.7 electrodes were shown to recruit independent populations of motor units innervating medial gastrocnemius (overlap < 20%). Corresponding numbers of electrodes were reported for lateral gastrocnemius and soleus, as well. We used these stimulation data to emulate physiological recruitment strategies, and found that independent motor unit pool recruitment approximates physiological activation more closely than does intensity-based recruitment or frequency-based recruitment.     

Bilateral otolith contribution to spatial coding in the vestibular system

Recent work on the coding of spatial information in central otolith neurons has significantly advanced our knowledge of signal transformation from head-fixed otolith coordinates to space-centered coordinates during motion. In this review, emphasis is placed on the neural mechanisms by which signals generated at the bilateral labyrinths are recognized as gravity-dependent spatial information and in turn as substrate for otolithic reflexes. We first focus on the spatiotemporal neuronal response patterns (i.e. one- and two-dimensional neurons) to pure otolith stimulation, as assessed by single unit recording from the vestibular nucleus in labyrinth-intact animals. These spatiotemporal features are also analyzed in association with other electrophysiological properties to evaluate their role in the central construction of a spatial frame of reference in the otolith system. Data derived from animals with elimination of inputs from one labyrinth then provide evidence that during vestibular stimulation signals arising from a single utricle are operative at the level of both the ipsilateral and contralateral vestibular nuclei. Hemilabyrinthectomy also revealed neural asymmetries in spontaneous activity, response dynamics and spatial coding behavior between neuronal subpopulations on the two sides and as a result suggested a segregation of otolith signals reaching the ipsilateral and contralateral vestibular nuclei. Recent studies have confirmed and extended previous observations that the recovery of resting activity within the vestibular nuclear complex during vestibular compensation is related to changes in both intrinsic membrane properties and capacities to respond to extracellular factors. The bilateral imbalance provides the basis for deranged spatial coding and motor deficits accompanying hemilabyrinthectomy. Taken together, these experimental findings indicate that in the normal state converging inputs from bilateral vestibular labyrinths are essential to spatiotemporal signal transformation at the central otolith neurons during low-frequency head movements.     

Influence of amplitude cancellation on the simulated surface electromyogram.

The purpose of the study was to quantify the influence of selected motor unit properties and patterns of activity on amplitude cancellation in the simulated surface electromyogram (EMG). The study involved computer simulations of a motor unit population with physiologically defined recruitment and rate coding characteristics that activated muscle fibers whose potentials were recorded on the skin over the muscle. Amplitude cancellation was quantified as the percent difference in signal amplitude when motor unit potentials were summed before and after rectification. The simulations involved varying the level of activation for the motor unit population, the recording configuration, the upper limit of motor unit recruitment, peak discharge rates, the amount of motor unit synchronization, muscle fiber length, the thickness of the subcutaneous tissue, and the motor unit properties that change with advancing age. The results confirmed a previous experimental report (Day SJ and Hulliger M, J Neurophysiol 86: 2144-2158, 2001) that amplitude cancellation in the surface EMG can reach 62% at maximal activation. A decrease in the range of amplitudes of the motor unit potentials, as can occur during fatiguing contractions, increased amplitude cancellation up to approximately 85%. Differences in the amount of amplitude cancellation were observed across all simulated conditions, and resulted in substantial changes in the absolute magnitude of the EMG signal. The most profound factors influencing amplitude cancellation were the number of active motor units and the duration of the action potentials. The effects of amplitude cancellation were minimal (<5%) when the EMG amplitude was normalized to maximal values, with the exception of variations in peak discharge rate and recruitment range, which resulted in differences up to 17% in the normalized EMG signal across conditions. These results indicate the amount of amplitude cancellation that can occur in various experimental conditions and its influence on absolute and relative measures of EMG amplitude.     

Neural correlates of internal-model loading

Skilled object manipulation requires knowledge, or internal models, of object dynamics relating applied force to motion , and our ability to handle myriad objects indicates that the brain maintains multiple models . Recent behavioral studies have shown that once learned, an internal model of an object with novel dynamics can be rapidly recruited and derecruited as the object is grasped and released . We used event-related fMRI to investigate neural activity linked to grasping an object with recently learned dynamics in preparation for moving it after a delay. Subjects also performed two control tasks in which they either moved without the object in hand or applied isometric forces to the object. In all trials, subjects received a cue indicating which task to perform in response to a go signal delivered 5-10 s later. We examined BOLD responses during the interval between the cue and go and assessed the conjunction of the two contrasts formed by comparing the primary task to each control. The analysis revealed significant activity in the ipsilateral cerebellum and the contralateral and supplementary motor areas. We propose that these regions are involved in internal-model recruitment in preparation for movement execution.