scholarly journals Task-level feedback can explain temporal recruitment of spatially fixed muscle synergies throughout postural perturbations

2012 ◽  
Vol 107 (1) ◽  
pp. 159-177 ◽  
Author(s):  
Seyed A. Safavynia ◽  
Lena H. Ting
Keyword(s):  
Muscle Activity ◽  
Neural Control ◽  
Temporal Patterns ◽  
Emg Activity ◽  
Muscle Synergies ◽  
Muscle Coordination ◽  
Postural Responses ◽  
Temporal Features ◽  
Low Dimensional ◽  
Task Level

Recent evidence suggests that complex spatiotemporal patterns of muscle activity can be explained with a low-dimensional set of muscle synergies or M-modes. While it is clear that both spatial and temporal aspects of muscle coordination may be low dimensional, constraints on spatial versus temporal features of muscle coordination likely involve different neural control mechanisms. We hypothesized that the low-dimensional spatial and temporal features of muscle coordination are independent of each other. We further hypothesized that in reactive feedback tasks, spatially fixed muscle coordination patterns—or muscle synergies—are hierarchically recruited via time-varying neural commands based on delayed task-level feedback. We explicitly compared the ability of spatially fixed (SF) versus temporally fixed (TF) muscle synergies to reconstruct the entire time course of muscle activity during postural responses to anterior-posterior support-surface translations. While both SF and TF muscle synergies could account for EMG variability in a postural task, SF muscle synergies produced more consistent and physiologically interpretable results than TF muscle synergies during postural responses to perturbations. Moreover, a majority of SF muscle synergies were consistent in structure when extracted from epochs throughout postural responses. Temporal patterns of SF muscle synergy recruitment were well-reconstructed by delayed feedback of center of mass (CoM) kinematics and reproduced EMG activity of multiple muscles. Consistent with the idea that independent and hierarchical low-dimensional neural control structures define spatial and temporal patterns of muscle activity, our results suggest that CoM kinematics are a task variable used to recruit SF muscle synergies for feedback control of balance.

scholarly journals Spatiotemporal distribution of location and object effects in the electromyographic activity of upper extremity muscles during reach-to-grasp

2016 ◽  
Vol 115 (6) ◽  
pp. 3238-3248 ◽  
Author(s):  
Adam G. Rouse ◽  
Marc H. Schieber
Keyword(s):  
Upper Extremity ◽  
Muscle Activity ◽  
Neural Control ◽  
Emg Activity ◽  
Electromyographic Activity ◽  
Proximal Muscle ◽  
Reach To Grasp ◽  
Distal Muscle ◽  
The Subject ◽  
Later Phase

In reaching to grasp an object, proximal muscles that act on the shoulder and elbow classically have been viewed as transporting the hand to the intended location, while distal muscles that act on the fingers simultaneously shape the hand to grasp the object. Prior studies of electromyographic (EMG) activity in upper extremity muscles therefore have focused, by and large, either on proximal muscle activity during reaching to different locations or on distal muscle activity as the subject grasps various objects. Here, we examined the EMG activity of muscles from the shoulder to the hand, as monkeys reached and grasped in a task that dissociated location and object. We quantified the extent to which variation in the EMG activity of each muscle depended on location, on object, and on their interaction—all as a function of time. Although EMG variation depended on both location and object beginning early in the movement, an early phase of substantial location effects in muscles from proximal to distal was followed by a later phase in which object effects predominated throughout the extremity. Interaction effects remained relatively small. Our findings indicate that neural control of reach-to-grasp may occur largely in two sequential phases: the first, serving to project the entire upper extremity toward the intended location, and the second, acting predominantly to shape the entire extremity for grasping the object.

scholarly journals Sensorimotor feedback based on task-relevant error robustly predicts temporal recruitment and multidirectional tuning of muscle synergies

2013 ◽  
Vol 109 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Seyed A. Safavynia ◽  
Lena H. Ting
Keyword(s):  
Sagittal Plane ◽  
Balance Control ◽  
Center Of Mass ◽  
The Body ◽  
Muscle Synergy ◽  
Muscle Synergies ◽  
Muscle Coordination ◽  
Initial State ◽  
Sensorimotor Feedback ◽  
Task Level

We hypothesized that motor outputs are hierarchically organized such that descending temporal commands based on desired task-level goals flexibly recruit muscle synergies that specify the spatial patterns of muscle coordination that allow the task to be achieved. According to this hypothesis, it should be possible to predict the patterns of muscle synergy recruitment based on task-level goals. We demonstrated that the temporal recruitment of muscle synergies during standing balance control was robustly predicted across multiple perturbation directions based on delayed sensorimotor feedback of center of mass (CoM) kinematics (displacement, velocity, and acceleration). The modulation of a muscle synergy's recruitment amplitude across perturbation directions was predicted by the projection of CoM kinematic variables along the preferred tuning direction(s), generating cosine tuning functions. Moreover, these findings were robust in biphasic perturbations that initially imposed a perturbation in the sagittal plane and then, before sagittal balance was recovered, perturbed the body in multiple directions. Therefore, biphasic perturbations caused the initial state of the CoM to differ from the desired state, and muscle synergy recruitment was predicted based on the error between the actual and desired upright state of the CoM. These results demonstrate that that temporal motor commands to muscle synergies reflect task-relevant error as opposed to sensory inflow. The proposed hierarchical framework may represent a common principle of motor control across motor tasks and levels of the nervous system, allowing motor intentions to be transformed into motor actions.

scholarly journals Neural basis for hand muscle synergies in the primate spinal cord

2017 ◽  
Vol 114 (32) ◽  
pp. 8643-8648 ◽  
Author(s):  
Tomohiko Takei ◽  
Joachim Confais ◽  
Saeka Tomatsu ◽  
Tomomichi Oya ◽  
Kazuhiko Seki
Keyword(s):  
Spinal Cord ◽  
Neural Control ◽  
Cervical Spinal Cord ◽  
Building Blocks ◽  
Muscle Synergies ◽  
Muscle Coordination ◽  
Neural Basis ◽  
Population Activity ◽  
Hand Functions ◽  
Hand Muscle

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.

scholarly journals Estimating muscle activation from EMG using deep learning-based dynamical systems models

2021 ◽  
Author(s):  
Lahiru N. Wimalasena ◽  
Jonas F. Braun ◽  
Mohammad Reza Keshtkaran ◽  
David Hofmann ◽  
Juan Álvaro Gallego ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dynamical Systems ◽  
Muscle Activity ◽  
Neural Control ◽  
Large Scale ◽  
Muscle Activation ◽  
Bayesian Filtering ◽  
Muscle Coordination ◽  
Low Pass

AbstractObjectiveTo study the neural control of movement, it is often necessary to estimate how muscles are activated across a variety of behavioral conditions. However, estimating the latent command signal that underlies muscle activation is challenging due to its complex relation with recorded electromyographic (EMG) signals. Common approaches estimate muscle activation independently for each channel or require manual tuning of model hyperparameters to optimally preserve behaviorally-relevant features.ApproachHere, we adapted AutoLFADS, a large-scale, unsupervised deep learning approach originally designed to de-noise cortical spiking data, to estimate muscle activation from multi-muscle EMG signals. AutoLFADS uses recurrent neural networks (RNNs) to model the spatial and temporal regularities that underlie multi-muscle activation.Main ResultsWe first tested AutoLFADS on muscle activity from the rat hindlimb during locomotion, and found that it dynamically adjusts its frequency response characteristics across different phases of behavior. The model produced single-trial estimates of muscle activation that improved prediction of joint kinematics as compared to low-pass or Bayesian filtering. We also tested the generality of the approach by applying AutoLFADS to monkey forearm muscle activity from an isometric task. AutoLFADS uncovered previously uncharacterized high-frequency oscillations in the EMG that enhanced the correlation with measured force compared to low-pass or Bayesian filtering. The AutoLFADS-inferred estimates of muscle activation were also more closely correlated with simultaneously-recorded motor cortical activity than other tested approaches.SignificanceUltimately, this method leverages both dynamical systems modeling and artificial neural networks to provide estimates of muscle activation for multiple muscles that can be used for further studies of multi-muscle coordination and its control by upstream brain areas.

A Feedback Model Reproduces Muscle Activity During Human Postural Responses to Support-Surface Translations

2008 ◽  
Vol 99 (2) ◽  
pp. 1032-1038 ◽  
Author(s):  
Torrence D. J. Welch ◽  
Lena H. Ting
Keyword(s):  
Postural Control ◽  
Muscle Activity ◽  
Muscle Activation ◽  
Control Process ◽  
Temporal Patterns ◽  
Human Dimensions ◽  
Support Surface ◽  
Level Control ◽  
Muscle Activation Patterns ◽  
Postural Responses

Although feedback models have been used to simulate body motions in human postural control, it is not known whether muscle activation patterns generated by the nervous system during postural responses can also be explained by a feedback control process. We investigated whether a simple feedback law could explain temporal patterns of muscle activation in response to support-surface translations in human subjects. Previously, we used a single-link inverted-pendulum model with a delayed feedback controller to reproduce temporal patterns of muscle activity during postural responses in cats. We scaled this model to human dimensions and determined whether it could reproduce human muscle activity during forward and backward support-surface perturbations. Through optimization, we found three feedback gains (on pendulum acceleration, velocity, and displacement) and a common time delay that allowed the model to best match measured electromyographic (EMG) signals. For each muscle and each subject, the entire time courses of EMG signals during postural responses were well reconstructed in muscles throughout the lower body and resembled the solution derived from an optimal control model. In ankle muscles, >75% of the EMG variability was accounted for by model reconstructions. Surprisingly, >67% of the EMG variability was also accounted for in knee, hip, and pelvis muscles, even though motion at these joints was minimal. Although not explicitly required by our optimization, pendulum kinematics were well matched to subject center-of-mass (CoM) kinematics. Together, these results suggest that a common set of feedback signals related to task-level control of CoM motion is used in the temporal formation of muscle activity during postural control.

scholarly journals Consequences of biomechanically constrained tasks in the design and interpretation of synergy analyses

2015 ◽  
Vol 113 (7) ◽  
pp. 2102-2113 ◽  
Author(s):  
Katherine M. Steele ◽  
Matthew C. Tresch ◽  
Eric J. Perreault
Keyword(s):  
Matrix Factorization ◽  
Muscle Activity ◽  
Neural Control ◽  
Muscle Activation ◽  
Neuromuscular Control ◽  
Task Constraints ◽  
Factorization Algorithms ◽  
Low Dimensional ◽  
Or Task ◽  
And Task

Matrix factorization algorithms are commonly used to analyze muscle activity and provide insight into neuromuscular control. These algorithms identify low-dimensional subspaces, commonly referred to as synergies, which can describe variation in muscle activity during a task. Synergies are often interpreted as reflecting underlying neural control; however, it is unclear how these analyses are influenced by biomechanical and task constraints, which can also lead to low-dimensional patterns of muscle activation. The aim of this study was to evaluate whether commonly used algorithms and experimental methods can accurately identify synergy-based control strategies. This was accomplished by evaluating synergies from five common matrix factorization algorithms using muscle activations calculated from 1) a biomechanically constrained task using a musculoskeletal model and 2) without task constraints using random synergy activations. Algorithm performance was assessed by calculating the similarity between estimated synergies and those imposed during the simulations; similarities ranged from 0 (random chance) to 1 (perfect similarity). Although some of the algorithms could accurately estimate specified synergies without biomechanical or task constraints (similarity >0.7), with these constraints the similarity of estimated synergies decreased significantly (0.3–0.4). The ability of these algorithms to accurately identify synergies was negatively impacted by correlation of synergy activations, which are increased when substantial biomechanical or task constraints are present. Increased variability in synergy activations, which can be captured using robust experimental paradigms that include natural variability in motor activation patterns, improved identification accuracy but did not completely overcome effects of biomechanical and task constraints. These results demonstrate that a biomechanically constrained task can reduce the accuracy of estimated synergies and highlight the importance of using experimental protocols with physiological variability to improve synergy analyses.

Task-Specific Organization of Activity in Human Jaw Muscles

1988 ◽  
Vol 31 (4) ◽  
pp. 670-680 ◽  
Author(s):  
Christopher A. Moore ◽  
Anne Smith ◽  
Robert L. Ringel
Keyword(s):  
Speech Production ◽  
Muscle Activity ◽  
Pattern Generator ◽  
Task Demands ◽  
Emg Activity ◽  
Muscle Synergies ◽  
Mandibular Movement ◽  
Jaw Muscles ◽  
Activation Patterns ◽  
Jaw Muscle

Coordination of jaw muscle activity for speech production sometimes has been modeled using nonspeech behaviors. This orientation has been especially true in representations of mandibular movement in which the synergy of jaw muscles for speech production has been suggested to be derived from the central pattern generator (CPG) for chewing. The present investigation compared the coordination of EMG activity in mandibular muscles over a range of speech and nonspeech tasks. Results of a cross-correlational analysis between EMG signals demonstrated that the muscle synergies of the mandibular system depend on task demands. Contrary to some of the models discussed, continuous speech production yielded activation patterns that were clearly not related to coordinative patterns generated by the chewing CPG.

scholarly journals Functional connectivity in neuromuscular system underlying bimanual muscle synergies

2016 ◽  
Author(s):  
Ingmar E. J. de Vries ◽  
Andreas Daffertshofer ◽  
Dick F. Stegeman ◽  
Tjeerd W. Boonstra
Keyword(s):  
Functional Connectivity ◽  
Muscle Activity ◽  
Uncontrolled Manifold ◽  
Emg Activity ◽  
Muscle Synergies ◽  
Neural Pathways ◽  
Beta Band ◽  
Corticomuscular Coherence ◽  
Intermuscular Coherence ◽  
High Coordination

AbstractNeural synchrony has been suggested as mechanism for integrating distributed sensorimotor systems involved in coordinated movement. To test the role of corticomuscular and intermuscular coherence in the formation of bimanual muscle synergies, we experimentally manipulated the degree of coordination between hand muscles by varying the sensitivity of the visual feedback to differences in bilateral force. In 16 healthy participants, cortical activity was measured using 64-channel electroencephalography (EEG) and muscle activity of the flexor pollicis brevis muscle of both hands using 8×8-channel high-density electromyography (HDsEMG). Using the uncontrolled manifold framework, coordination between bilateral forces was quantified by the synergy index RV in the time and frequency domain. Functional connectivity was assed using corticomuscular coherence between muscle activity and cortical source activity and intermuscular coherence between bilateral EMG activity. As expected, bimanual synergies were stronger in the high coordination condition. RV was higher in the high coordination condition in frequencies between 0 and 0.5 Hz, and above 2 Hz. For the 0.5-2 Hz frequency band this pattern was inverted. Corticomuscular coherence in the beta band (16-30 Hz) was maximal in the contralateral motor cortex and was reduced in the high coordination condition. In contrast, intermuscular coherence was observed at 5-12 Hz and increased with bimanual coordination. Within-subject comparisons revealed a negative correlation between RV and corticomuscular coherence and a positive correlation between RV and intermuscular coherence. Our findings suggest two distinct neural pathways: (1) Corticomuscular coherence reflects direct corticospinal projections involved in controlling individual muscles; (2) intermuscular coherence reflects diverging pathways involved in the coordination of multiple muscles.

A Limited Set of Muscle Synergies for Force Control During a Postural Task

2005 ◽  
Vol 93 (1) ◽  
pp. 609-613 ◽  
Author(s):  
Lena H. Ting ◽  
Jane M. Macpherson
Keyword(s):  
Degrees Of Freedom ◽  
Neural Control ◽  
Muscle Activation ◽  
Balance Control ◽  
Computational Techniques ◽  
Muscle Synergies ◽  
Support Surface ◽  
Muscle Activation Patterns ◽  
Automatic Postural Response ◽  
Postural Responses

Recently developed computational techniques have been used to reduce muscle activation patterns of high complexity to a simple synergy organization and to bring new insights to the long-standing degrees of freedom problem in motor control. We used a nonnegative factorization approach to identify muscle synergies during postural responses in the cat and to examine the functional significance of such synergies for natural behaviors. We hypothesized that the simplification of neural control afforded by muscle synergies must be matched by a similar reduction in degrees of freedom at the biomechanical level. Electromyographic data were recorded from 8–15 hindlimb muscles of cats exposed to 16 directions of support surface translation. Results showed that as few as four synergies could account for >95% of the automatic postural response across all muscles and all directions. Each synergy was activated for a specific set of perturbation directions, and moreover, each was correlated with a unique vector of endpoint force under the limb. We suggest that, within the context of active balance control, postural synergies reflect a neural command signal that specifies endpoint force of a limb.

scholarly journals Bite-to-bite variation of muscular activity in cats

1990 ◽  
Vol 151 (1) ◽  
pp. 1-19
Author(s):  
C. Gans ◽  
G. C. Gorniak ◽  
W. K. Morgan
Keyword(s):  
Muscle Activity ◽  
Neural Control ◽  
Muscular Activity ◽  
Relative Magnitude ◽  
Emg Activity ◽  
Jaw Movements ◽  
Food Texture

Changes in electromyographic (EMG) activity between successive bites in the jaw-closing muscles of cats are described for the reduction of food with different textures. Plots of these data suggest, and statistics confirm, that one cannot predict the magnitude of EMG activity from one bite to the next, but that predicting the direction of change in EMG activity (i.e. increases or decreases) may be possible, given the relative magnitude of a bite. The patterns observed also show that the degree of variability between masticatory cycles differs among the jaw-closing muscles and with food texture. These patterns of muscle activity are related to the neural control of jaw movements.

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