New perspectives on spinal motor systems

Ethnokinesiology: towards a neuromechanical understanding of cultural differences in movement

Each individual's movements are sculpted by constant interactions between sensorimotor and sociocultural factors. A theoretical framework grounded in motor control mechanisms articulating how sociocultural and biological signals converge to shape movement is currently missing. Here, we propose a framework for the emerging field of ethnokinesiology aiming to provide a conceptual space and vocabulary to help bring together researchers at this intersection. We offer a first-level schema for generating and testing hypotheses about cultural differences in movement to bridge gaps between the rich observations of cross-cultural movement variations and neurophysiological and biomechanical accounts of movement. We explicitly dissociate two interacting feedback loops that determine culturally relevant movement: one governing sensorimotor tasks regulated by neural signals internal to the body, the other governing ecological tasks generated through actions in the environment producing ecological consequences. A key idea is the emergence of individual-specific and culturally influenced motor concepts in the nervous system, low-dimensional functional mappings between sensorimotor and ecological task spaces. Motor accents arise from perceived differences in motor concept topologies across cultural contexts. We apply the framework to three examples: speech, gait and grasp. Finally, we discuss how ethnokinesiological studies may inform personalized motor skill training and rehabilitation, and challenges moving forward.This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.

Differential Impact of Biomechanical Constraints on Control Signal Dimensionality for Gravity Support Versus Propulsion

Neural control of movement has to overcome the problem of redundancy in the multidimensional musculoskeletal system. The problem can be solved by reducing the dimensionality of the control space of motor commands, i.e., through muscle synergies or motor primitives. Evidence for this solution exists, multiple studies have obtained muscle synergies using decomposition methods. These synergies vary across different workspaces and are present in both dominant and non-dominant limbs. Here we explore the dimensionality of control space by examining muscle activity patterns across reaching movements in different directions starting from different postures performed bilaterally by healthy individuals. We further explore the effect of biomechanical constraints on the dimensionality of control space. We are building on top of prior work showing that muscle activity profiles can be explained by applied moments about the limb joints that reflect the biomechanical constraints. These muscle torques derived from motion capture represent the combined actions of muscle contractions that are under the control of the nervous system. Here we test the generalizability of the relationship between muscle torques and muscle activity profiles across different starting positions and between limbs. We also test a hypothesis that the dimensionality of control space is shaped by biomechanical constraints. We used principal component analysis to evaluate the contribution of individual muscles to producing muscle torques across different workspaces and in both dominant and non-dominant limbs. Results generalize and support the hypothesis. We show that the muscle torques that support the limb against gravity are produced by more consistent combinations of muscle co-contraction than those that produce propulsion. This effect was the strongest in the non-dominant arm moving in the lateral workspace on one side of the body.

A comparison of nocturnal upper trapezius muscle activity between chronic neck pain patients with sleep disturbance and healthy participants

BACKGROUND

Sleep disturbances frequently occur in patients with chronic neck pain. In these patients, upper trapezius muscle dysfunction is observed during sleep. This study aimed to evaluate the trapezius muscle activity during sleep among patients with chronic neck pain and sleep disturbances for comparison with healthy subjects.  STUDY DESIGN: Cross-sectional study.

METHODS

Patients with chronic neck pain and healthy subjects participated in the study. Two overnight polysomnography recordings were conducted for each subject. Surface electromyography was utilized to record the nocturnal activity of the right and left upper trapezius muscles throughout the night. The nocturnal upper trapezius activity recording was divided into the following parts: wakefulness, rapid eye movement sleep (REM), and non-rapid eye movement sleep (NREM). The nocturnal activity during NREM sleep was further divided into three parts (stage I NREM sleep, stage II NREM, and stage III NREM. Normalization of EMG signals was performed. The normalized value of nocturnal activity was derived for analysis.

RESULTS

Among 15 patients with chronic neck pain and 15 healthy subjects, statistically significant differences were observed in the nocturnal activity of the upper trapezius. Compared to healthy subjects, the nocturnal activity of the upper trapezius was significantly higher during wakefulness, REM sleep, and NREM II and III sleep in patients with chronic neck pain and sleep disturbances.

CONCLUSION

There was higher nocturnal upper trapezius activity in patients with chronic neck pain compared to healthy controls. The findings suggest a possible pathophysiological mechanism that may relate to chronic neck pain.

TRIAL REGISTRATION

CTRI/2019/09/021028.

A neural circuit for vocal production responds to viscerosensory input in the songbird

Motor performance is monitored continuously by specialized brain circuits and used adaptively to modify behavior on a moment-to-moment basis and over longer time periods. During vocal behaviors, such as singing in songbirds, internal evaluation of motor performance relies on sensory input from the auditory and vocal-respiratory systems. Sensory input from the auditory system to the motor system, often referred to as auditory feedback, has been well studied in singing zebra finches (Taeniopygia guttata), but little is known about how and where nonauditory sensory feedback is evaluated. Here we show that brief perturbations in air sac pressure cause short-latency neural responses in the higher-order song control nucleus HVC (used as proper name), an area necessary for song learning and song production. Air sacs were briefly pressurized through a cannula in anesthetized or sedated adult male zebra finches, and neural responses were recorded in both nucleus parambigualis (PAm), a brainstem inspiratory center, and HVC, a cortical premotor nucleus. These findings show that song control nuclei in the avian song system are sensitive to perturbations directly targeted to vocal-respiratory, or viscerosensory, afferents and support a role for multimodal sensory feedback integration in modifying and controlling vocal control circuits.NEW & NOTEWORTHY This study presents the first evidence of sensory input from the vocal-respiratory periphery directly activating neurons in a motor circuit for vocal production in songbirds. It was previously thought that this circuit relies exclusively on sensory input from the auditory system, but we provide groundbreaking evidence for nonauditory sensory input reaching the higher-order premotor nucleus HVC, expanding our understanding of what sensory feedback may be available for vocal control.

Computational prediction of muscle synergy using a finite element framework for a musculoskeletal model on lower limb

Previous studies have demonstrated that the central nervous system activates muscles in module patterns to reduce the complexity needed to control each muscle while producing a movement, which is referred to as muscle synergy. In previous musculoskeletal modeling-based muscle synergy analysis studies, as a result of simplification of the joints, a conventional rigid-body link musculoskeletal model failed to represent the physiological interactions of muscle activation and joint kinematics. However, the interaction between the muscle level and joint level that exists in vivo is an important relationship that influences the biomechanics and neurophysiology of the musculoskeletal system. In the present, a lower limb musculoskeletal model coupling a detailed representation of a joint including complex contact behavior and material representations was used for muscle synergy analysis using a decomposition method of non-negative matrix factorization (NMF). The complexity of the representation of a joint in a musculoskeletal system allows for the investigation of the physiological interactions in vivo on the musculoskeletal system, thereby facilitating the decomposition of the muscle synergy. Results indicated that, the activities of the 20 muscles on the lower limb during the stance phase of gait could be controlled by three muscle synergies, and total variance accounted for by synergies was 86.42%. The characterization of muscle synergy and musculoskeletal biomechanics is consistent with the results, thus explaining the formational mechanism of lower limb motions during gait through the reduction of the dimensions of control issues by muscle synergy and the central nervous system.

F = ma. Is the macaque brain Newtonian?

Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.

Motor invariants in action execution and perception

The nervous system is sensitive to statistical regularities of the external world and forms internal models of these regularities to predict environmental dynamics. Given the inherently social nature of human behavior, being capable of building reliable predictive models of others' actions may be essential for successful interaction. While social prediction might seem to be a daunting task, the study of human motor control has accumulated ample evidence that our movements follow a series of kinematic invariants, which can be used by observers to reduce their uncertainty during social exchanges. Here, we provide an overview of the most salient regularities that shape biological motion, examine the role of these invariants in recognizing others' actions, and speculate that anchoring socially-relevant perceptual decisions to such kinematic invariants provides a key computational advantage for inferring conspecifics' goals and intentions.

Modular organization of locomotor networks in people with severe spinal cord injury

Introduction

Previous studies support modular organization of locomotor circuitry contributing to the activation of muscles in a spatially and temporally organized manner during locomotion. Human spinal circuitry may reorganize after spinal cord injury; however, it is unclear if reorganization of spinal circuitry post-injury affects the modular organization. Here we characterize the modular synergy organization of locomotor muscle activity expressed during assisted stepping in subjects with complete and incomplete spinal cord injury (SCI) of varying chronicity, before any explicit training regimen. We also investigated whether the synergy characteristics changed in two subjects who achieved independent walking after training with spinal cord epidural stimulation.

Methods

To capture synergy structures during stepping, individuals with SCI were stepped on a body-weight supported treadmill with manual facilitation, while electromyography (EMGs) were recorded from bilateral leg muscles. EMGs were analyzed using non-negative matrix factorization (NMF) and independent component analysis (ICA) to identify synergy patterns. Synergy patterns from the SCI subjects were compared across different clinical characteristics and to non-disabled subjects (NDs).

Results

Results for both NMF and ICA indicated that the subjects with SCI were similar among themselves, but expressed a greater variability in the number of synergies for criterion variance capture compared to NDs, and weaker correlation to NDs. ICA yielded a greater number of muscle synergies than NMF. Further, the clinical characteristics of SCI subjects and chronicity did not predict any significant differences in the spatial synergy structures despite any neuroplastic changes. Further, post-training synergies did not become closer to ND synergies in two individuals.

Discussion

These findings suggest fundamental differences between motor modules expressed in SCIs and NDs, as well as a striking level of spatial and temporal synergy stability in motor modules in the SCI population, absent the application of specific interventions.

Agonist-antagonist muscle strain in the residual limb preserves motor control and perception after amputation

Background

Elucidating underlying mechanisms in subject-specific motor control and perception after amputation could guide development of advanced surgical and neuroprosthetic technologies. In this study, relationships between preserved agonist-antagonist muscle strain within the residual limb and preserved motor control and perception capacity are investigated.

Methods

Fourteen persons with unilateral transtibial amputations spanning a range of ages, etiologies, and surgical procedures underwent evaluations involving free-space mirrored motions of their lower limbs. Research has shown that varied motor control in biologically intact limbs is executed by the activation of muscle synergies. Here, we assess the naturalness of phantom joint motor control postamputation based on extracted muscle synergies and their activation profiles. Muscle synergy extraction, degree of agonist-antagonist muscle strain, and perception capacity are estimated from electromyography, ultrasonography, and goniometry, respectively.

Results

Here, we show significant positive correlations (P < 0.005–0.05) between sensorimotor responses and residual limb agonist-antagonist muscle strain. Identified trends indicate that preserving even 20–26% of agonist-antagonist muscle strain within the residuum compared to a biologically intact limb is effective in preserving natural motor control postamputation, though preserving limb perception capacity requires more (61%) agonist-antagonist muscle strain preservation.

Conclusions

The results suggest that agonist-antagonist muscle strain is a characteristic, readily ascertainable residual limb structural feature that can help explain variability in amputation outcome, and agonist-antagonist muscle strain preserving surgical amputation strategies are one way to enable more effective and biomimetic sensorimotor control postamputation.

People who undergo limb amputation can have issues with controlling movement and perception of residual limbs. This, in turn, can impact the success of neuroprosthetic strategies, which use signals from the body to control a prosthetic limb. Here, we wanted to understand how sensory signals within the muscle help to preserve movement and limb perception following amputation. We used ultrasound imaging and other methods to measure muscle activity and limb perception in fourteen people who have undergone lower limb amputations. We show that the level at which the relationship between pairs of related muscles is preserved is associated with more natural control of limb movement after amputation. Developing surgical techniques that preserve this relationship may help people living with amputations to naturally perceive and control their residual limbs, and ultimately may improve controllability of assistive prosthetic devices.

Song et al. study the relationship between agonist-antagonist muscle strain (AMS) and motor control and perception in lower limb amputees, with some receiving a myoneural interface intervention. The authors report that the degree of AMS within the residual limb is associated with preserved motor control and perception.

Muscle synergy analysis yields an efficient and physiologically relevant method of assessing stroke

The Fugl-Meyer Assessment is widely used to test motor function in stroke survivors. In the Fugl-Meyer Assessment, stroke survivors perform several movement tasks and clinicians subjectively rate the performance of each task item. The individual task items in the Fugl-Meyer Assessment are selected on the basis of clinical experience, and their physiological relevance has not yet been evaluated. In the present study, we aimed to objectively rate the performance of task items by measuring the muscle activity of 41 muscles from the upper body while stroke survivors and healthy participants performed 37 Fugl-Meyer Assessment upper extremity task items. We used muscle synergy analysis to compare muscle activity between subjects and found that 13 muscle synergies in the healthy participants (which we defined as standard synergies) were able to reconstruct all of the muscle activity in the Fugl-Meyer Assessment. Among the standard synergies, synergies involving the upper arms, forearms and fingers were activated to varying degrees during different task items. In contrast, synergies involving posterior trunk muscles were activated during all tasks, which suggests the importance of posterior trunk muscle synergies throughout all sequences. Furthermore, we noted the inactivation of posterior trunk muscle synergies in stroke survivors with severe but not mild impairments, suggesting that lower trunk stability and the underlying activity of posterior trunk muscle synergies may have a strong influence on stroke severity and recovery. By comparing the synergies of stroke survivors with standard synergies, we also revealed that some synergies in stroke survivors corresponded to merged standard synergies; the merging rate increased with the impairment of stroke survivors. Moreover, the degrees of severity-dependent changes in the merging rate (the merging rate–severity relationship) were different among different task items. This relationship was significant for 26 task items only and not for the other 11 task items. Because muscle synergy analysis evaluates coordinated muscle activities, this different dependency suggests that these 26 task items are appropriate for evaluating muscle coordination and the extent of its impairment in stroke survivors. Overall, we conclude that the Fugl-Meyer Assessment reflects physiological function and muscle coordination impairment and suggest that it could be performed using a subset of the 37 task items.

Learning plastic matching of robot dynamics in closed-loop central pattern generators

Animals achieve agile locomotion performance with reduced control effort and energy efficiency by leveraging compliance in their muscles and tendons. However, it is not known how biological locomotion controllers learn to leverage the intelligence embodied in their leg mechanics. Here we present a framework to match control patterns and mechanics based on the concept of short-term elasticity and long-term plasticity. Inspired by animals, we design a robot, Morti, with passive elastic legs. The quadruped robot Morti is controlled by a bioinspired closed-loop central pattern generator that is designed to elastically mitigate short-term perturbations using sparse contact feedback. By minimizing the amount of corrective feedback on the long term, Morti learns to match the controller to its mechanics and learns to walk within 1 h. By leveraging the advantages of its mechanics, Morti improves its energy efficiency by 42% without explicit minimization in the cost function.

Sleep architecture and the absence of trapezius muscle atonia in women with chronic whiplash-associated disorder: a pilot study

Sleep disturbances frequently occur in people with whiplash-associated disorder (WAD) and have been evaluated using questionnaires or actigraphy. It is not clear whether sleep architecture, as assessed by polysomnography (PSG), is altered in individuals with WAD. Additionally, in people with WAD, muscle dysfunction is observed during tasks performed during wakefulness. Thus, this study aimed to analyze the sleep architecture of patients with chronic WAD as well as to evaluate trapezius muscle activity during sleep. Nine women with chronic WAD and nine healthy age-matched women participated in the study. Two PSG recordings were conducted for each participant. Surface electromyography signal samples of the right and left trapezius, and mentonian muscles were obtained from N2, N3, and REM sleep stages for analysis. Significant differences were found in the percentages of total sleep time in the N1 and N2 stages between the two groups. While the muscle tone of the three muscles analyzed decreased progressively across the sleep stages in the healthy group, in the chronic WAD group, this decrement was observed only in the mentonian muscle, and the trapezius muscle continued to exhibit the same muscle tone throughout the sleep stages without atonia during REM sleep. The absence of trapezius muscle atonia during REM sleep in the WAD patients may be related to dysfunction of the mechanisms that regulate motor activity.

Brain-spinal cord interaction in long-term motor sequence learning in human: An fMRI study

The spinal cord is important for sensory guidance and execution of skilled movements. Yet its role in human motor learning is not well understood. Despite evidence revealing an active involvement of spinal circuits in the early phase of motor learning, whether long-term learning engages similar changes in spinal cord activation and functional connectivity remains unknown. Here, we investigated spinal-cerebral functional plasticity associated with learning of a specific sequence of visually-guided joystick movements (sequence task) over six days of training. On the first and last training days, we acquired high-resolution functional images of the brain and cervical cord simultaneously, while participants practiced the sequence or a random task while electromyography was recorded from wrist muscles. After six days of training, the subjects' motor performance improved in the sequence compared to the control condition. These behavioral changes were associated with decreased co-contractions and increased reciprocal activations between antagonist wrist muscles. Importantly, early learning was characterized by activation in the C8 level, whereas a more rostral activation in the C6-C7 was found during the later learning phase. Motor sequence learning was also supported by increased spinal cord functional connectivity with distinct brain networks, including the motor cortex, superior parietal lobule, and the cerebellum at the early stage, and the angular gyrus and cerebellum at a later stage of learning. Our results suggest that the early vs. late shift in spinal activation from caudal to rostral cervical segments synchronized with distinct brain networks, including parietal and cerebellar regions, is related to progressive changes reflecting the increasing fine control of wrist muscles during motor sequence learning.

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

Synergistic models have been employed to investigate motor coordination separately in the muscular and kinematic domains. However, the relationship between muscle synergies, constrained to be non-negative, and kinematic synergies, whose elements can be positive and negative, has received limited attention. Existing algorithms for extracting synergies from combined kinematic and muscular data either do not enforce non-negativity constraints or separate non-negative variables into positive and negative components. We propose a mixed matrix factorization (MMF) algorithm based on a gradient descent update rule which overcomes these limitations. It allows to directly assess the relationship between kinematic and muscle activity variables, by enforcing the non-negativity constrain on a subset of variables. We validated the algorithm on simulated kinematic-muscular data generated from known spatial synergies and temporal coefficients, by evaluating the similarity between extracted and ground truth synergies and temporal coefficients when the data are corrupted by different noise levels. We also compared the performance of MMF to that of non-negative matrix factorization applied to separate positive and negative components (NMFpn). Finally, we factorized kinematic and EMG data collected during upper-limb movements to demonstrate the potential of the algorithm. MMF achieved almost perfect reconstruction on noiseless simulated data. It performed better than NMFpn in recovering the correct spatial synergies and temporal coefficients with noisy simulated data. It also allowed to correctly select the original number of ground truth synergies. We showed meaningful applicability to real data; MMF can also be applied to any multivariate data that contains both non-negative and unconstrained variables.

The role of dorsal premotor cortex in joint action stopping

Human sensorimotor interaction requires mutual behavioral adaptation as well as shared cognitive task representations (Joint Action, JA). Yet, an under-investigated aspect of JA is the neurobehavioral mechanisms employed to stop actions if the context calls for it. Sparse evidence points to the possible contribution of the left dorsal premotor cortex (lPMd) in sculpting movements according to the socio-interactive context. To clarify this issue, we ran two experiments integrating a classical stop signal paradigm with an ecological JA task. The first behavioral study shows longer Stop performance in the JA condition. In the second, we use transcranial magnetic stimulation to inhibit the lPMd or a control site (vertex). Results show that lPMd modulates the JA stopping performance. Action stopping is an important component of JA coordination, and here we provide evidence that lPMd is a key node of a brain network recruited for online mutual co-adaptation in social contexts.

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Interaction requires mutual adaptation and a shared cognitive task representation

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Sensorimotor representations must be negotiated between partners to achieve the goal

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Motor suppression mechanisms might be essential in Joint Action coordination

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Dorsal premotor cortex (PMd) plays a key role in guiding Joint Action coordination

A markerless platform for ambulatory systems neuroscience

Motor systems neuroscience seeks to understand how the brain controls movement. To minimize confounding variables, large-animal studies typically constrain body movement from areas not under observation, ensuring consistent, repeatable behaviors. Such studies have fueled decades of research, but they may be artificially limiting the richness of neural data observed, preventing generalization to more natural movements and settings. Neuroscience studies of unconstrained movement would capture a greater range of behavior and a more complete view of neuronal activity, but instrumenting an experimental rig suitable for large animals presents substantial engineering challenges. Here, we present a markerless, full-body motion tracking and synchronized wireless neural electrophysiology platform for large, ambulatory animals. Composed of four depth (RGB-D) cameras that provide a 360° view of a 4.5-square-meters enclosed area, this system is designed to record a diverse range of neuroethologically relevant behaviors. This platform also allows for the simultaneous acquisition of hundreds of wireless neural recording channels in multiple brain regions. As behavioral and neuronal data are generated at rates below 200 megabytes per second, a single desktop can facilitate hours of continuous recording. This setup is designed for systems neuroscience and neuroengineering research, where synchronized kinematic behavior and neural data are the foundation for investigation. By enabling the study of previously unexplored movement tasks, this system can generate insights into the functioning of the mammalian motor system and provide a platform to develop brain-machine interfaces for unconstrained applications.

Serotonergic Facilitation of Forelimb Functional Recovery in Rats with Cervical Spinal Cord Injury

Serotonergic agents can improve the recovery of motor ability after a spinal cord injury. Herein, we compare the effects of buspirone, a 5-HT1A receptor partial agonist, to fluoxetine, a selective serotonin reuptake inhibitor, on forelimb motor function recovery after a C4 bilateral dorsal funiculi crush in adult female rats. After injury, single pellet reaching performance and forelimb muscle activity decreased in all rats. From 1 to 6 weeks after injury, rats were tested on these tasks with and without buspirone (1-2 mg/kg) or fluoxetine (1-5 mg/kg). Reaching and grasping success rates of buspirone-treated rats improved rapidly within 2 weeks after injury and plateaued over the next 4 weeks of testing. Electromyography (EMG) from selected muscles in the dominant forelimb showed that buspirone-treated animals used new reaching strategies to achieve success after the injury. However, forelimb performance dramatically decreased within 2 weeks of buspirone withdrawal. In contrast, fluoxetine treatment resulted in a more progressive rate of improvement in forelimb performance over 8 weeks after injury. Neither buspirone nor fluoxetine significantly improved quadrupedal locomotion on the horizontal ladder test. The improved accuracy of reaching and grasping, patterns of muscle activity, and increased excitability of spinal motor-evoked potentials after buspirone administration reflect extensive reorganization of connectivity within and between supraspinal and spinal sensory-motor netxcopy works. Thus, both serotonergic drugs, buspirone and fluoxetine, neuromodulated these networks to physiological states that enabled markedly improved forelimb function after cervical spinal cord injury.

Embodied skillful performance: where the action is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are smoothly performed without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist: that is, they cast skillful performance as a knowledge-driven process. Optimal motor control theory (OMCT), as representative par excellence of such approaches, casts skillful performance as an instruction, instantiated in the brain, that needs to be executed-a motor command. This paper aims to show the limitations of such instructionist approaches to skillful performance. We specifically address the question of whether the assumption of control-theoretic models is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists of the execution of theoretical instructions harnessed in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from OMCT. The final sections of this paper examine predictive coding and active inference-behavioral modeling frameworks that descend, but are distinct, from OMCT-and argue that the instructionist, control-theoretic assumptions are ill-motivated in light of new developments in active inference.

Muscle modes of the equestrian rider at walk, rising trot and canter

Equestrian sports have been a source of numerous studies throughout the past two decades, however, few scientists have focused on the biomechanical effects, including muscle activation, that the horse has on the rider. Because equitation is a sport of two (the horse-human dyad), we believe there is a need to fill in the knowledge gap in human biomechanics during riding. To investigate the differences between novice and advanced riders at a neuromuscular level we characterized the motor output of a set of riders’ key muscles during horse riding. Six recreational riders (24 ± 7 years) and nine professional riders (31 ± 5 years) from the Spanish Classical School of Riding (Lipica) volunteered to take part in this study. Riders’ upper body, core and lower limb muscles were monitored and synchronized with inertial data from the left horse’s leg at walk, rising trot and canter. We used principal component analysis to extract muscle modes. Three modes were identified in the advanced group whereas five modes were identified in the novice group. From the novice group, one mode united dorsal and ventral muscles of the body (reciprocal mode). Advanced riders showed higher core muscles engagement and better intermuscular coordination. We concluded that advanced horse riding is characterized by an ability to activate muscles contralaterally but not reciprocally (dorsal-ventral contraction). In addition, activating each muscle independently with different levels of activation, and the ability to quickly decrease overall muscle activity is distinctive of advanced riders.

Hierarchical motor control in mammals and machines

Advances in artificial intelligence are stimulating interest in neuroscience. However, most attention is given to discrete tasks with simple action spaces, such as board games and classic video games. Less discussed in neuroscience are parallel advances in "synthetic motor control". While motor neuroscience has recently focused on optimization of single, simple movements, AI has progressed to the generation of rich, diverse motor behaviors across multiple tasks, at humanoid scale. It is becoming clear that specific, well-motivated hierarchical design elements repeatedly arise when engineering these flexible control systems. We review these core principles of hierarchical control, relate them to hierarchy in the nervous system, and highlight research themes that we anticipate will be critical in solving challenges at this disciplinary intersection.

Multifactorial motor behavior assessment for real-time evaluation of emerging therapeutics to treat neurologic impairments

Integrating multiple assessment parameters of motor behavior is critical for understanding neural activity dynamics during motor control in both intact and dysfunctional nervous systems. Here, we described a novel approach (termed Multifactorial Behavioral Assessment (MfBA)) to integrate, in real-time, electrophysiological and biomechanical properties of rodent spinal sensorimotor network activity with behavioral aspects of motor task performance. Specifically, the MfBA simultaneously records limb kinematics, multi-directional forces and electrophysiological metrics, such as high-fidelity chronic intramuscular electromyography synchronized in time to spinal stimulation in order to characterize spinal cord functional motor evoked potentials (fMEPs). Additionally, we designed the MfBA to incorporate a body weight support system to allow bipedal and quadrupedal stepping on a treadmill and in an open field environment to assess function in rodent models of neurologic disorders that impact motor activity. This novel approach was validated using, a neurologically intact cohort, a cohort with unilateral Parkinsonian motor deficits due to midbrain lesioning, and a cohort with complete hind limb paralysis due to T8 spinal cord transection. In the SCI cohort, lumbosacral epidural electrical stimulation (EES) was applied, with and without administration of the serotonergic agonist Quipazine, to enable hind limb motor functions following paralysis. The results presented herein demonstrate the MfBA is capable of integrating multiple metrics of motor activity in order to characterize relationships between EES inputs that modulate mono- and polysynaptic outputs from spinal circuitry which in turn, can be used to elucidate underlying electrophysiologic mechanisms of motor behavior. These results also demonstrate that proposed MfBA is an effective tool to integrate biomechanical and electrophysiology metrics, synchronized to therapeutic inputs such as EES or pharmacology, during body weight supported treadmill or open field motor activities, to target a high range of variations in motor behavior as a result of neurological deficit at the different levels of CNS.

Multiphase and Multivariable Linear Controllers That Account for the Joint Torques in Normal Human Walking

OBJECTIVE

The objective of this paper is to investigate whether a small number of sequentially composed multivariable linear controllers can be used to recover a defining relation between the joint torques, angles, and velocities hidden in the walking data of multiple human subjects.

METHODS

We solve a mixed integer programming problem that defines the optimal multivariable and multiphase relation between the torques, angles, and velocities for the hip, knee, and ankle joints.

RESULTS

Using the data of seven healthy subjects, we show that the aforementioned relation can be remarkably well represented by four sequentially composed and independently activated multivariable linear controllers; the controllers account for [Formula: see text] (mean ± sem) of the variance in the joint torques across subjects, and [Formula: see text] of the variance for a new subject. We further show that each controller is associated with one of the four phases of the gait cycle, separated by toe-off and heel-strike.

CONCLUSION

The proposed controller generalizes previously developed multiphase single variable, and single phase multivariable controllers, to a multiphase multivariable controller that better explains the walking data of multiple subjects, and better generalizes to new subjects.

SIGNIFICANCE

Our result provides strong support to extend previously developed decoupled single joint controllers to coupled multijoint multivariable controllers for the control of human assistive and augmentation devices.

Motor Control After Human SCI Through Activation of Muscle Synergies Under Spinal Cord Stimulation

Spinal cord stimulation (SCS) has enabled motor recovery in paraplegics with motor complete spinal cord injury (SCI). However, the physiological mechanisms underlying this recovery are unknown. This paper analyzes muscle synergies in two motor complete SCI patients under SCS during standing and compares them with muscle synergies in healthy subjects, in order to help elucidate the mechanisms that enable motor control through SCS. One challenge is that standard muscle synergy extraction algorithms, such as non-negative matrix factorization (NMF), fail when applied to SCI patients under SCS. We develop a new algorithm-rShiftNMF-to extract muscle synergies in these cases. We find muscle synergies extracted by rShiftNMF are significantly better at interpreting electromyography (EMG) activity, and resulting synergy features are more physiologically meaningful. By analyzing muscle synergies from SCI patients and healthy subjects, we find that: 1) SCI patients rely significantly on muscle synergy activation to generate motor activity; 2) interleaving SCS can selectively activate an additional muscle synergy that is critical to SCI standing; and 3) muscle synergies extracted from SCI patients under SCS differ substantially from those extracted from healthy subjects. We provide evidence that after spinal cord injury, SCS influences motor function through muscle synergy activation.

The Effects of Selective Muscle Weakness on Muscle Coordination in the Human Arm

Despite the fundamental importance of muscle coordination in daily life, it is currently unclear how muscle coordination adapts when the musculoskeletal system is perturbed. In this study, we quantified the impact of selective muscle weakness on several metrics of muscle coordination. Seven healthy subjects performed 2D and 3D isometric force target matches, while electromyographic (EMG) signals were recorded from 13 elbow and shoulder muscles. Subsequently, muscle weakness was induced by a motor point block of brachialis muscle. Postblock subjects repeated the force generation tasks. We quantified muscle coordination pre- and postblock using three metrics: tuning curve preferred direction, tuning curve area, and motor modules analysis via nonnegative matrix factorization. For most muscles, the tuning direction for the 2D protocol was not substantially altered postblock, while tuning areas changed more drastically. Typically, five motor modules were identified from the 3D task, and four motor modules were identified in the 2D task; this result held across both pre- and postblock conditions. The composition of one or two motor modules, ones that involved mainly the activation of shoulder muscles, was altered postblock. Our results demonstrate that selective muscle weakness can induce nonintuitive alternations in muscle coordination in the mechanically redundant human arm.

EEG topographies provide subject-specific correlates of motor control

Electroencephalography (EEG) of brain activity can be represented in terms of dynamically changing topographies (microstates). Notably, spontaneous brain activity recorded at rest can be characterized by four distinctive topographies. Despite their well-established role during resting state, their implication in the generation of motor behavior is debated. Evidence of such a functional role of spontaneous brain activity would provide support for the design of novel and sensitive biomarkers in neurological disorders. Here we examined whether and to what extent intrinsic brain activity contributes and plays a functional role during natural motor behaviors. For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers. We show that, in every subject, well-known resting-state microstates persist during movement execution with similar topographies and temporal characteristics, but are supplemented by novel task-related microstates. We then show that the subject-specific microstates' dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy. These findings provide first evidence that spontaneous brain activity encodes detailed information about motor control, offering as such the prospect of a novel tool for the definition of subject-specific biomarkers of brain plasticity and recovery in neuro-motor disorders.

Detailed spatial characterization of superficial hip muscle activation during walking: A multi-electrode surface EMG investigation of the gluteal region in healthy older adults

PURPOSE

A multi-electrode array was used to generate spatially resolved Surface electromyography (SEMG) data of the hip muscles in healthy older adults. The cohort was meant to serve as an age-matched, normal control population for future surgical and rehabilitative studies in patients undergoing total hip arthroplasty, in view of the large, continuously increasing number of hip joint replacements.

METHODS

Bilateral hip muscle SEMG activity, including tensor fasciae latae (TFL), gluteus medius (Gmed), and gluteus maximus (Gmax), was measured during locomotion on a walkway at self-selected slow, normal, and fast walking speeds (age-matched cohort of 29 females and 25 males). Eight equally-spaced, vertically oriented bipolar channels were applied on a horizontal line at mid-distance between iliac crest and greater trochanter (length 17.5 cm; named P1 to P8). Time-independent parameters (e.g., mean amplitude) were derived from the amplitude curves expressed as root mean square.

RESULTS

The acquired SEMG data were not significantly influenced by gender (p = 0.202) or side (p = 0.313) and were therefore pooled. The most ventral to central electrode positions P1 to P5, representing TFL and ventral to central Gmed, showed the highest mean amplitude levels (averaged over the whole stride; 0.001 < p < 0.027 against P6 to P8; Bonferroni-adjusted paired t-test) at all walking speeds. Also, the respective curves showed two distinct amplitude peaks (representing load acceptance and hip stabilization during mid-stance), with a continuous increase of the first peak from P1 to P4 (most pronounced at fast speed) and the second peak from P1 to P3. Independently of the underlying individual muscles, both peaks displayed a continuous time shift from the most dorsal P8 to the most ventral P1 position, with the peaks for the ventral positions occurring at later time points during the normalized stride.

CONCLUSIONS

The continuously changing activation patterns of the superficial muscles in the gluteal region during walking may reflect function-driven, finely tuned coordination patterns of neighboring muscles and muscle segments, rather than independent activation of anatomically defined muscles. This may be important for the definition of specific target parameters for the improvement and/or normalization of muscle function during training and post-injury rehabilitation.

Physiological Markers of Motor Inhibition during Human Behavior

Transcranial magnetic stimulation (TMS) studies in humans have shown that many behaviors engage processes that suppress excitability within the corticospinal tract. Inhibition of the motor output pathway has been extensively studied in the context of action stopping, where a planned movement needs to be abruptly aborted. Recent TMS work has also revealed markers of motor inhibition during the preparation of movement. Here, we review the evidence for motor inhibition during action stopping and action preparation, focusing on studies that have used TMS to monitor changes in the excitability of the corticospinal pathway. We discuss how these physiological results have motivated theoretical models of how the brain selects actions, regulates movement initiation and execution, and switches from one state to another.

Ejaculatory training lengthens the ejaculation latency and facilitates the functioning of the spinal generator for ejaculation of rats with rapid ejaculation

A spinal pattern generator controls the ejaculatory response. Central pattern generators (CPGs) may be entrained to improve the motor patterns under their control. In the present study we tested the hypothesis that training of the spinal generator for ejaculation (SGE) by daily copulation until ejaculation, could promote substantive changes in its functioning permitting a better SGE control of the genital motor pattern of ejaculation (GMPE) and, as a consequence, a normalization of the ejaculation latency of rats with rapid ejaculation. To that aim, we evaluated in sexually experienced male rats with rapid ejaculation (1) the effects of daily copulation to ejaculation, following different entrainment schedules, on their ejaculation latencies, (2) the impact of these different ejaculatory entrainment schedules upon the parameters of the GMPE and (3) the possible emergence of persistent changes in the functioning of the SGE associated to the daily ejaculation entrainment schedules. The data obtained show that intense ejaculatory training of rats with rapid ejaculation lengthens the ejaculation latency during copulation and augments the ejaculatory capacity of the SGE in this population when spinalized. Thus, present data reveal that like other CPGs, the SGE can be trained and put forward that training of the SGE by daily copulation to ejaculation might be a promising alternative that should be taken into consideration for the treatment of premature ejaculation.

Immature Spinal Locomotor Output in Children with Cerebral Palsy

Detailed descriptions of gait impairments have been reported in cerebral palsy (CP), but it is still unclear how maturation of the spinal motoneuron output is affected. Spatiotemporal alpha-motoneuron activation during walking can be assessed by mapping the electromyographic activity profiles from several, simultaneously recorded muscles onto the anatomical rostrocaudal location of the motoneuron pools in the spinal cord, and by means of factor analysis of the muscle activity profiles. Here, we analyzed gait kinematics and EMG activity of 11 pairs of bilateral muscles with lumbosacral innervation in 35 children with CP (19 diplegic, 16 hemiplegic, 2-12 years) and 33 typically developing (TD) children (1-12 years). TD children showed a progressive reduction of EMG burst durations and a gradual reorganization of the spatiotemporal motoneuron output with increasing age. By contrast, children with CP showed very limited age-related changes of EMG durations and motoneuron output, as well as of limb intersegmental coordination and foot trajectory control (on both sides for diplegic children and the affected side for hemiplegic children). Factorization of the EMG signals revealed a comparable structure of the motor output in children with CP and TD children, but significantly wider temporal activation patterns in children with CP, resembling the patterns of much younger TD infants. A similar picture emerged when considering the spatiotemporal maps of alpha-motoneuron activation. Overall, the results are consistent with the idea that early injuries to developing motor regions of the brain substantially affect the maturation of the spinal locomotor output and consequently the future locomotor behavior.

Tuning of robotic therapy controllers for stroke gait: Using isometrically constrained EMG modular structures

The development of EMG based controllers for automated gait orthosis is a promising intervention for post-stroke gait rehabilitation and assistive device applications. However, the integration of stroke specific modular neuromuscular patterns for the design of active gait assistance controller is largely unexplored. To this end, this work presents preliminary evidence of the associated variability between lower limb muscle activation patterns underlying discrete isometric control objectives and the analogous dynamic subtask during treadmill gait using matrix factorization algorithms. We find that common muscle coordination patterns underlying both isometric and dynamic gait submovements can be used to reconstruct relevant spatio-temporal patterns for healthy locomotion. We propose that synergies extracted isometrically may be sufficient to tune adaptive controller assistance for disruptive linkages in the EMG control signal to impact gait dysfunction.

Maintenance of balance between motor cortical excitation and inhibition after long-term training

Motor learning with professional experience leads to cortical reorganization with plasticity. Long-term training facilitates motor cortical excitability. It is not clear how beneficial cortical plasticity is maintained during long-term training. We studied this question in 15 elite badminton athletes and 15 novices. We hypothesize that motor cortical excitation increases after long-term training and this is accompanied by increased motor cortical inhibition. Motor cortical excitation was measured with motor-evoked potential (MEP) input-output curve using transcranial magnetic stimulation (TMS). Motor cortical inhibition was measured with short-interval intracortical inhibition (SICI) and long-interval intracortical inhibition (LICI) by a paired-pulse TMS paradigm. We found MEP was increased at high TMS intensity and the MEP input-output curve was steeper in athletes compared to novices. Both SICI and LICI were also increased in athletes. In addition, both SICI and LICI were correlated with the slope of MEP input-output curve in athletes but not in novices. The slope of MEP input-output curve, SICI and LICI were also correlated with the training time in athletes. We conclude that both cortical excitation and cortical inhibition are increased, and that the balance between cortical excitation and inhibition is maintained during long-term training.

Computational models and motor learning paradigms: Could they provide insights for neuroplasticity after stroke? An overview

Computational approaches for modelling the central nervous system (CNS) aim to develop theories on processes occurring in the brain that allow the transformation of all information needed for the execution of motor acts. Computational models have been proposed in several fields, to interpret not only the CNS functioning, but also its efferent behaviour. Computational model theories can provide insights into neuromuscular and brain function allowing us to reach a deeper understanding of neuroplasticity. Neuroplasticity is the process occurring in the CNS that is able to permanently change both structure and function due to interaction with the external environment. To understand such a complex process several paradigms related to motor learning and computational modeling have been put forward. These paradigms have been explained through several internal model concepts, and supported by neurophysiological and neuroimaging studies. Therefore, it has been possible to make theories about the basis of different learning paradigms according to known computational models. Here we review the computational models and motor learning paradigms used to describe the CNS and neuromuscular functions, as well as their role in the recovery process. These theories have the potential to provide a way to rigorously explain all the potential of CNS learning, providing a basis for future clinical studies.

Neuroethological Concepts and their Transfer to Walking Machines

A systems approach to animal motor behavior reveals concepts that can be useful for the pragmatic design of walking machines. This is because the relation of animal behavior to its underlying nervous control algorithms bears many parallels to the relation of machine function to electronic control. Here, three major neuroethological concepts of motor behavior are described in terms of a conceptual framework based on artificial neural networks (ANN). Central patterns of activity and postural reflexes are both interpreted as a result of feedback loops, with the distinction of loops via an internal model from loops via the physical environment (body, external world). This view allows continuous transitions between predictive (centrally driven) and reactive (reflex driven) motor systems. Motor primitives, behavioral modules that are elicited by distinct commands, are also considered. ANNs capture these three major concepts in terms of a formal description, in which the interactions and mutual interdependences of the various output parameters are comprised by the weight matrix of the net. Based upon behavioral observations of insect walking, we further demonstrate how a decentralized network of separate modules, each one described by an ANN, can account for adaptive behavior. Complex coordination patterns of several manipulators are controlled by imposing simple interaction rules between limbs, and by exploiting the interaction of the body with its physical environment. Finally, we discuss the technical use of leg-like active tactile sensors for obstacle detection, and we show how specific design of such active sensors may increase efficiency of walking on rough terrain. Applied to active sensors, an example of parallel, self-organizing forward models on the basis of extended Kohonen maps is presented to emphasize the potential of adaptive forward models in motor control.

The Primary Motor and Premotor Areas of the Human Cerebral Cortex

Brodmann's cytoarchitectonic map of the human cortex designates area 4 as cortex in the anterior bank of the precentral sulcus and area 6 as cortex encompassing the precentral gyrus and the posterior portion of the superior frontal gyrus on both the lateral and medial surfaces of the brain. More than 70 years ago, Fulton proposed a functional distinction between these two areas, coining the terms primary motor areafor cortex in Brodmann area 4 and premotor areafor cortex in Brodmann area 6. The parcellation of the cortical motor system has subsequently become more complex. Several nonprimary motor areas have been identified in the brain of the macaque monkey, and associations between anatomy and function in the human brain are being tested continuously using brain mapping techniques. In the present review, the authors discuss the unique properties of the primary motor area (M1), the dorsal portion of the premotor cortex (PMd), and the ventral portion of the premotor cortex (PMv). They end this review by discussing how the premotor areas influence M1.

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

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

Spatiotemporal neuromodulation therapies engaging muscle synergies improve motor control after spinal cord injury

Electrical neuromodulation of lumbar segments improves motor control after spinal cord injury in animal models and humans. However, the physiological principles underlying the effect of this intervention remain poorly understood, which has limited this therapeutic approach to continuous stimulation applied to restricted spinal cord locations. Here, we developed novel stimulation protocols that reproduce the natural dynamics of motoneuron activation during locomotion. For this, we computed the spatiotemporal activation pattern of muscle synergies during locomotion in healthy rats. Computer simulations identified optimal electrode locations to target each synergy through the recruitment of proprioceptive feedback circuits. This framework steered the design of spatially selective spinal implants and real–time control software that modulate extensor versus flexor synergies with precise temporal resolution. Spatiotemporal neuromodulation therapies improved gait quality, weight–bearing capacities, endurance and skilled locomotion in multiple rodent models of spinal cord injury. These new concepts are directly translatable to strategies to improve motor control in humans.

Mapping Sub-Second Structure in Mouse Behavior

Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that 3D mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.

Neuromuscular adjustments of gait associated with unstable conditions

A compact description of coordinated muscle activity is provided by the factorization of electromyographic (EMG) signals. With the use of this approach, it has consistently been shown that multimuscle activity during human locomotion can be accounted for by four to five modules, each one comprised of a basic pattern timed at a different phase of gait cycle and the weighting coefficients of synergistic muscle activations. These modules are flexible, in so far as the timing of patterns and the amplitude of weightings can change as a function of gait speed and mode. Here we consider the adjustments of the locomotor modules related to unstable walking conditions. We compared three different conditions, i.e., locomotion of healthy subjects on slippery ground (SL) and on narrow beam (NB) and of cerebellar ataxic (CA) patients on normal ground. Motor modules were computed from the EMG signals of 12 muscles of the right lower limb using non-negative matrix factorization. The unstable gait of SL, NB, and CA showed significant changes compared with controls in the stride length, stride width, range of angular motion, and trunk oscillations. In most subjects of all three unstable conditions, >70% of the overall variation of EMG waveforms was accounted for by four modules that were characterized by a widening of muscle activity patterns. This suggests that the nervous system adopts the strategy of prolonging the duration of basic muscle activity patterns to cope with unstable conditions resulting from either slippery ground, reduced support surface, or pathology.

Interlimb coordination in body-weight supported locomotion: A pilot study

Locomotion involves complex neural networks responsible for automatic and volitional actions. During locomotion, motor strategies can rapidly compensate for any obstruction or perturbation that could interfere with forward progression. In this pilot study, we examined the contribution of interlimb pathways for evoking muscle activation patterns in the contralateral limb when a unilateral perturbation was applied and in the case where body weight was externally supported. In particular, the latency of neuromuscular responses was measured, while the stimulus to afferent feedback was limited. The pilot experiment was conducted with six healthy young subjects. It employed the MIT-Skywalker (beta-prototype), a novel device intended for gait therapy. Subjects were asked to walk on the split-belt treadmill, while a fast unilateral perturbation was applied mid-stance by unexpectedly lowering one side of the split-treadmill walking surfaces. Subject's weight was externally supported via the body-weight support system consisting of an underneath bicycle seat and the torso was stabilized via a loosely fitted chest harness. Both the weight support and the chest harness limited the afferent feedback. The unilateral perturbations evoked changes in the electromyographic activity of the non-perturbed contralateral leg. The latency of all muscle responses exceeded 100ms, which precludes the conjecture that spinal cord alone is responsible for the perturbation response. It suggests the role of supraspinal or midbrain level pathways at the inter-leg coordination during gait.

Efferent Feedback in a Spinal-Like Controller: Reaching With Perturbations

We use simulations of a controller that adopts a spinal-like network topology for goal-oriented reaching and assess its sensitivity to the dynamics of internal elements that allow context-independent performance. Such internal elements are often referred to as inverse or forward models of the periphery dynamics, depending on the proposed controller theory. Here, the "models" are used in a forward implementation, and we evaluate how the controller's performance would be affected by the nature of the model. For each point-to-point reaching motion experiment, we use forms of internal "efference models" (e.g., full mathematical representations of peripheral dynamics, simple spindle feedback, etc.) driven by motor reafference, then compare hand trajectories and hand path speeds in the presence or absence of external perturbations. It is demonstrated that a simple velocity-based model reduced the effects of dynamic perturbations by as much as 66%. In addition, the 2D hand trajectories varied from a biological reference by only 0.05 cm. Thus, the controller facilitated biological like motions while providing response to dynamic events which are omitted in earlier biomimetic controllers. This research suggests that these spinal-like systems are robust and tunable via gain-fields without the need of context dependent pre-planning.

Influence of Delay Period Duration on Inhibitory Processes for Response Preparation

In this study, we examined the dynamics of inhibitory preparatory processes, using a delayed response task in which a cue signaled a left or right index finger (Experiment 1) or hand (Experiment 2) movement in advance of an imperative signal. In Experiment 1, we varied the duration of the delay period (200, 500, and 900 ms). When transcranial magnetic stimulation (TMS) was applied 100 ms before the imperative, motor evoked potentials (MEPs) elicited in the first dorsal interosseous were strongly inhibited. For delays of 500 ms or longer, this inhibition was greater when the targeted muscle was selected compared with when it was not selected. In contrast, the magnitude of inhibition just after the cue was inversely related to the duration of the delay period, and the difference between the selected and nonselected conditions was attenuated. In Experiment 2, TMS and peripheral nerve stimulation procedures were used during a 300-ms delay period. MEPs in the flexor carpi radialis for both selected and nonselected conditions were inhibited, but without any change in the H-reflex. Taken together, these results reveal the dual influence of temporal constraints associated with anticipation and urgency on inhibitory processes recruited during response preparation.

Alpha and beta band event-related desynchronization reflects kinematic regularities

The short-lasting attenuation of brain oscillations is termed event-related desynchronization (ERD). It is frequently found in the alpha and beta bands in humans during generation, observation, and imagery of movement and is considered to reflect cortical motor activity and action-perception coupling. The shared information driving ERD in all these motor-related behaviors is unknown. We investigated whether particular laws governing production and perception of curved movement may account for the attenuation of alpha and beta rhythms. Human movement appears to be governed by relatively few kinematic laws of motion. One dominant law in biological motion kinematics is the 2/3 power law (PL), which imposes a strong dependency of movement speed on curvature and is prominent in action-perception coupling. Here we directly examined whether the 2/3 PL elicits ERD during motion observation by characterizing the spatiotemporal signature of ERD. ERDs were measured while human subjects observed a cloud of dots moving along elliptical trajectories either complying with or violating the 2/3 PL. We found that ERD within both frequency bands was consistently stronger, arose faster, and was more widespread while observing motion obeying the 2/3 PL. An activity pattern showing clear 2/3 PL preference and lying within the alpha band was observed exclusively above central motor areas, whereas 2/3 PL preference in the beta band was observed in additional prefrontal-central cortical sites. Our findings reveal that compliance with the 2/3 PL is sufficient to elicit a selective ERD response in the human brain.

An adaptive spinal-like controller: tunable biomimetic behavior for a robotic limb

Background

Spinal-like regulators have recently been shown to support complex behavioral patterns during volitional goal-oriented reaching paradigms. We use an interpretation of the adaptive spinal-like controller as inspiration for the development of a controller for a robotic limb. It will be demonstrated that a simulated robot arm with linear actuators can achieve biological-like limb movements. In addition, it will be shown that programmability in the regulator enables independent spatial and temporal changes to be defined for movement tasks, downstream of central commands using sensory stimuli. The adaptive spinal-like controller is the first to demonstrate such behavior for complex motor behaviors in multi-joint limb movements.

Methods

The controller is evaluated using a simulated robotic apparatus and three goal-oriented reaching paradigms: 1) shaping of trajectory profiles during reaching; 2) sensitivity of trajectories to sudden perturbations; 3) reaching to a moving target. The experiments were designed to highlight complex motor tasks that are omitted in earlier studies, and important for the development of improved artificial limb control.

Results

In all three cases the controller was able to reach the targets without a priori planning of end-point or segmental motor trajectories. Instead, trajectory spatio-temporal dynamics evolve from properties of the controller architecture using the spatial error (vector distance to goal). Results show that curvature amplitude in hand trajectory paths are reduced by as much as 98% using simple gain scaling techniques, while adaptive network behavior allows the regulator to successfully adapt to perturbations and track a moving target. An important observation for this study is that all motions resemble human-like movements with non-linear muscles and complex joint mechanics.

Conclusions

The controller shows that it can adapt to various behavioral contexts which are not included in previous biomimetic studies. The research supplements an earlier study by examining the tunability of the spinal-like controller for complex reaching tasks. This work is a step toward building more robust controllers for powered artificial limbs.