Synaptic and functional linkages between spinal premotor interneurons and hand-muscle activity during precision grip

Muscle synergies for multidirectional isometric force generation during maintenance of upright standing posture

Muscle synergies are defined as coordinated recruitment of groups of muscles with specific activation balances and time profiles aimed at generating task-specific motor commands. While muscle synergies in postural control have been investigated primarily in reactive balance conditions, the neuromechanical contribution of muscle synergies during voluntary control of upright standing is still unclear. In this study, muscle synergies were investigated during the generation of isometric force at the trunk during the maintenance of standing posture. Participants were asked to maintain the steady-state upright standing posture while pulling forces of different magnitudes were applied at the level at the waist in eight horizontal directions. Muscle synergies were extracted by nonnegative matrix factorization from sixteen lower limb and trunk muscles. An average of 5-6 muscle synergies were sufficient to account for a wide variety of EMG waveforms associated with changes in the magnitude and direction of pulling forces. A cluster analysis partitioned the muscle synergies of the participants into a large group of clusters according to their similarity, indicating the use of a subjective combination of muscles to generate a multidirectional force vector in standing. Furthermore, we found a participant-specific distribution in the values of cosine directional tuning parameters of synergy amplitude coefficients, suggesting the existence of individual neuromechanical strategies to stabilize the whole-body posture. Our findings provide a starting point for the development of novel diagnostic tools to assess muscle coordination in postural control and lay the foundation for potential applications of muscle synergies in rehabilitation.

Rectified Latent Variable Model-Based EMG Factorization of Inhibitory Muscle Synergy Components Related to Aging, Expertise and Force-Tempo Variations

Muscle synergy has been widely acknowledged as a possible strategy of neuromotor control, but current research has ignored the potential inhibitory components in muscle synergies. Our study aims to identify and characterize the inhibitory components within motor modules derived from electromyography (EMG), investigate the impact of aging and motor expertise on these components, and better understand the nervous system's adaptions to varying task demands. We utilized a rectified latent variable model (RLVM) to factorize motor modules with inhibitory components from EMG signals recorded from ten expert pianists when they played scales and pieces at different tempo-force combinations. We found that older participants showed a higher proportion of inhibitory components compared with the younger group. Senior experts had a higher proportion of inhibitory components on the left hand, and most inhibitory components became less negative with increased tempo or decreased force. Our results demonstrated that the inhibitory components in muscle synergies could be shaped by aging and expertise, and also took part in motor control for adapting to different conditions in complex tasks.

Neural Network Models for Spinal Implementation of Muscle Synergies

Muscle synergies have been proposed as functional modules to simplify the complexity of body motor control; however, their neural implementation is still unclear. Converging evidence suggests that output projections of the spinal premotor interneurons (PreM-INs) underlie the formation of muscle synergies, but they exhibit a substantial variation across neurons and exclude standard models assuming a small number of unitary “modules” in the spinal cord. Here we compared neural network models for muscle synergies to seek a biologically plausible model that reconciles previous clinical and electrophysiological findings. We examined three neural network models: one with random connections (non-synergy model), one with a small number of spinal synergies (simple synergy model), and one with a large number of spinal neurons representing muscle synergies with a certain variation (population synergy model). We found that the simple and population synergy models emulate the robustness of muscle synergies against cortical stroke observed in human stroke patients. Furthermore, the size of the spinal variation of the population synergy matched well with the variation in spinal PreM-INs recorded in monkeys. These results suggest that a spinal population with moderate variation is a biologically plausible model for the neural implementation of muscle synergies.

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.

Synergistic Organization of Neural Inputs from Spinal Motor Neurons to Extrinsic and Intrinsic Hand Muscles

Our current understanding of synergistic muscle control is based on the analysis of muscle activities. Modules (synergies) in muscle coordination are extracted from electromyographic (EMG) signal envelopes. Each envelope indirectly reflects the neural drive received by a muscle; therefore, it carries information on the overall activity of the innervating motor neurons. However, it is not known whether the output of spinal motor neurons, whose number is orders of magnitude greater than the muscles they innervate, is organized in a low-dimensional fashion when performing complex tasks. Here, we hypothesized that motor neuron activities exhibit a synergistic organization in complex tasks and therefore that the common input to motor neurons results in a large dimensionality reduction in motor neuron outputs. To test this hypothesis, we factorized the output spike trains of motor neurons innervating 14 intrinsic and extrinsic hand muscles and analyzed the dimensionality of control when healthy individuals exerted isometric forces using seven grip types. We identified four motor neuron synergies, accounting for >70% of the variance of the activity of 54.1 ± 12.9 motor neurons, and we identified four functionally similar muscle synergies. However, motor neuron synergies better discriminated individual finger forces than muscle synergies and were more consistent with the expected role of muscles actuating each finger. Moreover, in a few cases, motor neurons innervating the same muscle were active in separate synergies. Our findings suggest a highly divergent net neural inputs to spinal motor neurons from spinal and supraspinal structures, contributing to the dimensionality reduction captured by muscle synergies.SIGNIFICANCE STATEMENT We addressed whether the output of spinal motor neurons innervating multiple hand muscles could be accounted for by a modular organization, i.e., synergies, previously described to account for the coordination of multiple muscles. We found that motor neuron synergies presented similar dimensionality (implying a >10-fold reduction in dimensionality) and structure as muscle synergies. Nonetheless, the synergistic behavior of subsets of motor neurons within a muscle was also observed. These results advance our understanding of how neuromuscular control arises from mapping descending inputs to muscle activation signals. We provide, for the first time, insights into the organization of neural inputs to spinal motor neurons which, to date, has been inferred through analysis of muscle synergies.

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

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

Distinct sensorimotor feedback loops for dynamic and static control of primate precision grip

Volitional limb motor control involves dynamic and static muscle actions. It remains elusive how such distinct actions are controlled through separated or shared neural circuits. Here we explored the potential separation for dynamic and static controls in primate hand actions, by investigating the neuronal coherence between local field potentials (LFPs) of the spinal cord and the forelimb electromyographic activity (EMGs), and LFPs of the motor cortex and the EMGs during the performance of a precision grip in macaque monkeys. We observed the emergence of beta-range coherence with EMGs at spinal cord and motor cortex in the separated phases; spinal coherence during the grip phase and cortical coherence during the hold phase. Further, both of the coherences were influenced by bidirectional interactions with reasonable latencies as beta oscillatory cycles. These results indicate that dedicated feedback circuits comprising spinal and cortical structures underlie dynamic and static controls of dexterous hand actions.

Reorganization of finger covariation patterns represented in the corticospinal system by learning of a novel movement irrelevant to common daily movements

How dexterous finger movements are acquired by the nervous system is a fundamental question in the neuroscience field. Previous studies have demonstrated that finger movements can be decomposed into finger covariation patterns, and these patterns are represented in the corticospinal system. However, it remains unclear how such covariation patterns represented in the corticospinal system develop during the acquisition of novel finger movements. In this study, each subject learned to perform a novel finger movement, which was mapped to a region outside the movement subspace spanned by common finger movements seen in daily life, through a custom task. After subjects practiced the task, we detected changes in the finger covariation patterns derived from artificially (transcranial magnetic stimulation) evoked finger joint movements. The artificially evoked movement-derived patterns seen after the training period were associated with both the novel and common finger movements. Regarding the patterns extracted from the artificially evoked movements, the number required to explain most of the variance in the data was unchanged after the training period. Our results indicate that novel finger movements are acquired through the reorganization of preexisting finger covariation patterns represented in the corticospinal system rather than the development of new patterns. These findings might have implications for the basic mechanism responsible for the development of movement repertories in the nervous system.NEW & NOTEWORTHY Various types of finger movements involve common finger covariation patterns, and these patterns are represented in the corticospinal system. Here we examined how a novel finger covariation pattern is acquired in that system through training of a novel finger movement that is irrelevant to common finger movements. Using transcranial magnetic stimulation, we found that the preexisting patterns that contribute to finer control of finger movements are rapidly reorganized to encode the novel pattern through the training.

Muscle Synergy for Hand Motions Based on Electromyography Analysis

This study aimed to investigate the muscle synergy for hand motions of both the left and right hands based on surface electromyography (sEMG) signals. Six hand motions, including hand grasp (HG), four fingers flexion (FFF), index finger extension (IFE), thumb internal rotation (TIR), thumb opposition (TO), key pinch (KP), were examined in the experiment. The sEMG signals were recorded from brachioradialis (BRA), flexor carpi ulnaris (FCU), flexor carpi radialis (FCR), extensor digitorum communis (EDC), flexor digitorum superficialis (FDS), abductor pollicis brevis (APB), first dorsal interosseous (FDI) and abductor digiti minimi (ADM) of both the left and right hands. A co-contraction index (CI) followed by a correlation coefficients (CC) was applied to quantify the muscle synergy during the hand motions and to compare the synergistic patterns across hands. Results shown that the left and right hands had similar muscle synergy (CC (CC>0.9) for any of the motions. For both hands, there were strong correlations between the synergistic matrix of different motions (CC (CC>0.85), including the correlations between HG and FFF, between FFF and IFE, and between TO and KP. By contrast, weak correlations between the synergistic matrix for IFE and that for TIR (CC (CC<0.1), suggesting a low similarity of the muscle synergy for the two motions. This study shed light on how the muscles of the left and right hands are involved and co-contracted for the dexterous hand motions.

Acquisition of skilled finger movements is accompanied by reorganization of the corticospinal system

Dexterous finger movements are often characterized by highly coordinated movements. Such coordination might be derived from reorganization of the corticospinal system. In this study, we investigated 1) the manner in which finger movement covariation patterns are acquired, by examining the effects of the implicit and explicit learning of a serial reaction time task (SRTT), and 2) how such changes in finger coordination are represented in the corticospinal system. The subjects learned a button press sequence in both implicit and explicit learning conditions. In the implicit conditions, they were naive about what they were learning, whereas in the explicit conditions the subjects consciously learned the order of the sequence elements. Principal component analysis decomposed both the voluntary movements produced during the SRTT and the passive movements evoked by transcranial magnetic stimulation (TMS) over the primary motor cortex into a set of five finger joint covariation patterns. The structures of the voluntary and passive TMS-evoked movement patterns were reorganized by implicit learning but not explicit learning. Furthermore, in the implicit learning conditions the finger covariation patterns derived from the TMS-evoked and voluntary movements spanned similar movement subspaces. These results provide the first evidence that skilled sequential finger movements are acquired differently through implicit and explicit learning, i.e., the changes in finger coordination patterns induced by implicit learning are accompanied by functional reorganization of the corticospinal system, whereas explicit learning results in faster recruitment of individual finger movements without causing any changes in finger coordination.

NEW & NOTEWORTHY Skilled sequential multifinger movements are characterized as highly coordinated movement patterns. These finger coordination patterns are represented in the corticospinal system, yet it still remains unclear how these patterns are acquired through implicit and explicit motor sequence learning. A direct comparison of learning-related changes between actively generated finger movements and passively evoked finger movements by TMS provided evidence that finger coordination patterns represented in the corticospinal system are reorganized through implicit, but not explicit, sequence learning.

Number of Synergies Is Dependent on Spasticity and Gait Kinetics in Children With Cerebral Palsy

PURPOSE

Children with cerebral palsy have motor dysfunctions, which are mainly associated with the loss of motor coordination. For the assessment of motor coordination, muscle synergies calculated by nonnegative matrix factorization have been investigated. However, the characteristics of muscle synergies in children with cerebral palsy are not understood.

METHODS

This study compared the number of muscle synergies during gait between children with cerebral palsy and children with typical development and clarified whether certain clinical parameters differed according to the number of synergies.

RESULTS

Children with cerebral palsy had significantly fewer synergies than children developing typically. The extent of spasticity and gait kinetics differed according to the number of synergies.

CONCLUSION

Increases in the number of synergies are limited by severe spasticity. The muscle synergies calculated by nonnegative matrix factorization have the potential to enable the quantification of motor coordination during gait.

Neural basis for hand muscle synergies in the primate spinal cord

Grasping is a highly complex movement that requires the coordination of multiple hand joints and muscles. Muscle synergies have been proposed to be the functional building blocks that coordinate such complex motor behaviors, but little is known about how they are implemented in the central nervous system. Here we demonstrate that premotor interneurons (PreM-INs) in the primate cervical spinal cord underlie the spatiotemporal patterns of hand muscle synergies during a voluntary grasping task. Using spike-triggered averaging of hand muscle activity, we found that the muscle fields of PreM-INs were not uniformly distributed across hand muscles but rather distributed as clusters corresponding to muscle synergies. Moreover, although individual PreM-INs have divergent activation patterns, the population activity of PreM-INs reflects the temporal activation of muscle synergies. These findings demonstrate that spinal PreM-INs underlie the muscle coordination required for voluntary hand movements in primates. Given the evolution of neural control of primate hand functions, we suggest that spinal premotor circuits provide the fundamental coordination of multiple joints and muscles upon which more fractionated control is achieved by superimposed, phylogenetically newer, pathways.

Motor primitives--new data and future questions

Motor primitives allow integration across scales in the motor system and may link movement construction and circuit organization. This review examines support for primitives, and new data relating primitives to concrete circuit elements across species. Both kinematic motor primitives and muscle synergy/kinetic motor primitives are reviewed. Motor primitives allow a modular hierarchy that may be re-used by volitional systems in novel ways. They can provide a developmental bootstrap for ethologically important actions. Collections of primitives somewhat constrain motor acts, but at the same time sets of primitives facilitate the rapid construction of these constrained actions, and can allow use of simpler controls. Novel motor skill likely requires augmentation to transcend the constraints present in initial collections of low level motor primitives. The benefits and limitations of motor primitives and the recognized knowledge gaps and needs for future research are briefly discussed.

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

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