Vincent C. K. Cheung

Central and Sensory Contributions to the Activation and Organization of Muscle Synergies during Natural Motor Behaviors

Previous studies have suggested that the motor system may simplify control by combining a small number of muscle synergies represented as activation profiles across a set of muscles. The role of sensory feedback in the activation and organization of synergies has remained an open question. Here, we assess to what extent the motor system relies on centrally organized synergies activated by spinal and/or supraspinal commands to generate motor outputs by analyzing electromyographic (EMG) signals collected from 13 hindlimb muscles of the bullfrog during swimming and jumping, before and after deafferentation. We first established that, for both behaviors, the intact and deafferented data sets possess low and similar dimensionalities. Subsequently, we used a novel reformulation of the non-negative matrix factorization algorithm to simultaneously search for synergies shared by, and synergies specific to, the intact and deafferented data sets. Most muscle synergies were identified as shared synergies, suggesting that EMGs of locomotor behaviors are generated primarily by centrally organized synergies. Both the amplitude and temporal patterns of the activation coefficients of most shared synergies, however, were altered by deafferentation, suggesting that sensory inflow modulates activation of those centrally organized synergies. For most synergies, effects of deafferentation on the activation coefficients were not consistent across frogs, indicating substantial interanimal variability of feedback actions. We speculate that sensory feedback might adapt recruitment of muscle synergies to behavioral constraints, and the few synergies specific to the intact or deafferented states might represent afferent-specific modules or feedback reorganization of spinal neuronal networks.

Stability of muscle synergies for voluntary actions after cortical stroke in humans

Production of voluntary movements relies critically on the functional integration of several motor cortical areas, such as the primary motor cortex, and the spinal circuitries. Surprisingly, after almost 40 years of research, how the motor cortices specify descending neural signals destined for the downstream interneurons and motoneurons has remained elusive. In light of the many recent experimental demonstrations that the motor system may coordinate muscle activations through a linear combination of muscle synergies, we hypothesize that the motor cortices may function to select and activate fixed muscle synergies specified by the spinal or brainstem networks. To test this hypothesis, we recorded electromyograms (EMGs) from 12–16 upper arm and shoulder muscles from both the unaffected and the stroke-affected arms of stroke patients having moderate-to-severe unilateral ischemic lesions in the frontal motor cortical areas. Analyses of EMGs using a nonnegative matrix factorization algorithm revealed that in seven of eight patients the muscular compositions of the synergies for both the unaffected and the affected arms were strikingly similar to each other despite differences in motor performance between the arms, and differences in cerebral lesion sizes and locations between patients. This robustness of muscle synergies that we observed supports the notion that descending cortical signals represent neuronal drives that select, activate, and flexibly combine muscle synergies specified by networks in the spinal cord and/or brainstem. Our conclusion also suggests an approach to stroke rehabilitation by focusing on those synergies with altered activations after stroke.

Muscle synergy patterns as physiological markers of motor cortical damage.

The experimental findings herein reported are aimed at gaining a perspective on the complex neural events that follow lesions of the motor cortical areas. Cortical damage, whether by trauma or stroke, interferes with the flow of descending signals to the modular interneuronal structures of the spinal cord. These spinal modules subserve normal motor behaviors by activating groups of muscles as individual units (muscle synergies). Damage to the motor cortical areas disrupts the orchestration of the modules, resulting in abnormal movements. To gain insights into this complex process, we recorded myoelectric signals from multiple upper-limb muscles in subjects with cortical lesions. We used a factorization algorithm to identify the muscle synergies. Our factorization analysis revealed, in a quantitative way, three distinct patterns of muscle coordination—including preservation, merging, and fractionation of muscle synergies—that reflect the multiple neural responses that occur after cortical damage. These patterns varied as a function of both the severity of functional impairment and the temporal distance from stroke onset. We think these muscle-synergy patterns can be used as physiological markers of the status of any patient with stroke or trauma, thereby guiding the development of different rehabilitation approaches, as well as future physiological experiments for a further understanding of postinjury mechanisms of motor control and recovery.

Modules in the brain stem and spinal cord underlying motor behaviors

Previous studies using intact and spinalized animals have suggested that coordinated movements can be generated by appropriate combinations of muscle synergies controlled by the central nervous system (CNS). However, which CNS regions are responsible for expressing muscle synergies remains an open question. We address whether the brain stem and spinal cord are involved in expressing muscle synergies used for executing a range of natural movements. We analyzed the electromyographic (EMG) data recorded from frog leg muscles before and after transection at different levels of the neuraxis-rostral midbrain (brain stem preparations), rostral medulla (medullary preparations), and the spinal-medullary junction (spinal preparations). Brain stem frogs could jump, swim, kick, and step, while medullary frogs could perform only a partial repertoire of movements. In spinal frogs, cutaneous reflexes could be elicited. Systematic EMG analysis found two different synergy types: 1) synergies shared between pre- and posttransection states and 2) synergies specific to individual states. Almost all synergies found in natural movements persisted after transection at rostral midbrain or medulla but not at the spinal-medullary junction for swim and step. Some pretransection- and posttransection-specific synergies for a certain behavior appeared as shared synergies for other motor behaviors of the same animal. These results suggest that the medulla and spinal cord are sufficient for the expression of most muscle synergies in frog behaviors. Overall, this study provides further evidence supporting the idea that motor behaviors may be constructed by muscle synergies organized within the brain stem and spinal cord and activated by descending commands from supraspinal areas.

Adjustments of Motor Pattern for Load Compensation Via Modulated Activations of Muscle Synergies During Natural Behaviors

It has been suggested that the motor system may circumvent the difficulty of controlling many degrees of freedom in the musculoskeletal apparatus by generating motor outputs through a combination of discrete muscle synergies. How a discretely organized motor system compensates for diverse perturbations has remained elusive. Here, we investigate whether motor responses observed after an inertial-load perturbation can be generated by altering the recruitment of synergies normally used for constructing unperturbed movements. Electromyographic (EMG, 13 muscles) data were collected from the bullfrog hindlimb during natural behaviors before, during, and after the same limb was loaded by a weight attached to the calf. Kinematic analysis reveals the absence of aftereffect on load removal, suggesting that load-related EMG changes were results of immediate motor pattern adjustments. We then extracted synergies from EMGs using the nonnegative matrix factorization algorithm and developed a procedure for assessing the extent of synergy sharing across different loading conditions. Most synergies extracted were found to be activated in all loaded and unloaded conditions. However, for certain synergies, the amplitude, duration, and/or onset time of their activation bursts were up- or down-modulated during loading. Behavioral parameterizations reveal that load-related modulation of synergy activations depended on the behavioral variety (e.g., kick direction and amplitude) and the movement phase performed. Our results suggest that muscle synergies are robust across different dynamic conditions and immediate motor adjustments can be accomplished by modulating synergy activations. An appendix describes the novel procedure we developed, useful for discovering shared and specific features from multiple data sets.

The effect of arm weight support on upper limb muscle synergies during reaching movements

BackgroundCompensating for the effect of gravity by providing arm-weight support (WS) is a technique often utilized in the rehabilitation of patients with neurological conditions such as stroke to facilitate the performance of arm movements during therapy. Although it has been shown that, in healthy subjects as well as in stroke survivors, the use of arm WS during the performance of reaching movements leads to a general reduction, as expected, in the level of activation of upper limb muscles, the effects of different levels of WS on the characteristics of the kinematics of motion and of the activity of upper limb muscles have not been thoroughly investigated before.MethodsIn this study, we systematically assessed the characteristics of the kinematics of motion and of the activity of 14 upper limb muscles in a group of 9 healthy subjects who performed 3-D arm reaching movements while provided with different levels of arm WS. We studied the hand trajectory and the trunk, shoulder, and elbow joint angular displacement trajectories for different levels of arm WS. Besides, we analyzed the amplitude of the surface electromyographic (EMG) data collected from upper limb muscles and investigated patterns of coordination via the analysis of muscle synergies.ResultsThe characteristics of the kinematics of motion varied across WS conditions but did not show distinct trends with the level of arm WS. The level of activation of upper limb muscles generally decreased, as expected, with the increase in arm WS. The same eight muscle synergies were identified in all WS conditions. Their level of activation depended on the provided level of arm WS.ConclusionsThe analysis of muscle synergies allowed us to identify a modular organization underlying the generation of arm reaching movements that appears to be invariant to the level of arm WS. The results of this study provide a normative dataset for the assessment of the effects of the level of arm WS on muscle synergies in stroke survivors and other patients who could benefit from upper limb rehabilitation with arm WS.

Gene Expression Changes in the Motor Cortex Mediating Motor Skill Learning

The primary motor cortex (M1) supports motor skill learning, yet little is known about the genes that contribute to motor cortical plasticity. Such knowledge could identify candidate molecules whose targeting might enable a new understanding of motor cortical functions, and provide new drug targets for the treatment of diseases which impair motor function, such as ischemic stroke. Here, we assess changes in the motor-cortical transcriptome across different stages of motor skill acquisition. Adult rats were trained on a gradually acquired appetitive reach and grasp task that required different strategies for successful pellet retrieval, or a sham version of the task in which the rats received pellet reward without needing to develop the reach and grasp skill. Tissue was harvested from the forelimb motor-cortical area either before training commenced, prior to the initial rise in task performance, or at peak performance. Differential classes of gene expression were observed at the time point immediately preceding motor task improvement. Functional clustering revealed that gene expression changes were related to the synapse, development, intracellular signaling, and the fibroblast growth factor (FGF) family, with many modulated genes known to regulate synaptic plasticity, synaptogenesis, and cytoskeletal dynamics. The modulated expression of synaptic genes likely reflects ongoing network reorganization from commencement of training till the point of task improvement, suggesting that motor performance improves only after sufficient modifications in the cortical circuitry have accumulated. The regulated FGF-related genes may together contribute to M1 remodeling through their roles in synaptic growth and maturation.

On nonnegative matrix factorization algorithms for signal-dependent noise with application to electromyography data

Nonnegative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two nonnegative matrices, W and H, where . It has been successfully applied in the analysis and interpretation of large-scale data arising in neuroscience, computational biology, and natural language processing, among other areas. A distinctive feature of NMF is its nonnegativity constraints that allow only additive linear combinations of the data, thus enabling it to learn parts that have distinct physical representations in reality. In this letter, we describe an information-theoretic approach to NMF for signal-dependent noise based on the generalized inverse gaussian model. Specifically, we propose three novel algorithms in this setting, each based on multiplicative updates, and prove monotonicity of updates using the EM algorithm. In addition, we develop algorithm-specific measures to evaluate their goodness of fit on data. Our methods are demonstrated using experimental data from electromyography studies, as well as simulated data in the extraction of muscle synergies, and compared with existing algorithms for signal-dependent noise.

An Optogenetic Demonstration of Motor Modularity in the Mammalian Spinal Cord

Motor modules are neural entities hypothesized to be building blocks of movement construction. How motor modules are underpinned by neural circuits has remained obscured. As a first step towards dissecting these circuits, we optogenetically evoked motor outputs from the lumbosacral spinal cord of two strains of transgenic mice – the Chat, with channelrhodopsin (ChR2) expressed in motoneurons, and the Thy1, expressed in putatively excitatory neurons. Motor output was represented as a spatial field of isometric ankle force. We found that Thy1 force fields were more complex and diverse in structure than Chat fields: the Thy1 fields comprised mostly non-parallel vectors while the Chat fields, mostly parallel vectors. In both, most fields elicited by co-stimulation of two laser beams were well explained by linear combination of the separately-evoked fields. We interpreted the Thy1 force fields as representations of spinal motor modules. Our comparison of the Chat and Thy1 fields allowed us to conclude, with reasonable certainty, that the structure of neuromotor modules originates from excitatory spinal interneurons. Our results not only demonstrate, for the first time using optogenetics, how the spinal modules follow linearity in their combinations, but also provide a reference against which future optogenetic studies of modularity can be compared.

Non-negative matrix factorization algorithms modeling noise distributions within the exponential family

We developed non-negative factorization algorithms based on statistical distributions which are members of the exponential family, and using multiplicative update rules. We compared in detail the performance of algorithms derived using two particular exponential family distributions, assuming either constant variance noise (Gaussian) or signal dependent noise (gamma). These algorithms were compared on both simulated data sets and on muscle activation patterns collected from behaving animals. We found that on muscle activation patterns, which are expected to be corrupted by signal dependent noise, the factorizations identified by the algorithm assuming gamma distributed data were more robust than those identified by the algorithm assuming Gaussian distributed data