Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

Investigation of adaptive muscle synergy modulated motor responses to grasping perturbations

This study investigated how muscle synergies adapt in response to unexpected changes in object weight during lifting tasks. The aim was to discover which motor control strategies individuals use to maintain their grasping performance. Muscle synergies were extracted from the muscle activity of fifteen healthy participants who lifted objects of identical appearance but varying weights in a randomized order, which introduced artificial perturbations. Reaching and manipulation phases of object lifting were analyzed using constrained non-negative matrix factorization and k-means clustering. Participants exhibited a perturbation-independent and thus consistent recruitment of spatial synergy components, while significant adaptations in muscle synergy activation occurred in response to unexpected perturbations. Perturbations caused by unexpectedly heavy objects led to delayed and gradual increases in muscle synergy activation until the force required to lift the object was reached. In contrast, perturbations caused by lighter objects led to reductions in excess muscle synergy activation occurring later. Sensorimotor control maintains the modularity of muscle synergies. Even when external mechanical perturbations occur, the grasping performance is preserved, and control is adapted solely through muscle synergy activation. These results suggest that using pure spatial synergy components as control signals for myoelectric arm prostheses may prevent them from malfunctioning due to external perturbations.

A Study of the Effects of Motor Experience on Neuromuscular Control Strategies During Sprint Starts

Much of the current research on sprint start has attempted to analyze the biomechanical characteristics of elite athletes to provide guidance on the training of sprint technique, with less attention paid to the effects of motor experience gained from long-term training on neuromuscular control characteristics. The present study attempted to investigate the effect of motor experience on the modular organization of the neuromuscular system during starting, based on he clarification of the characteristics of muscle synergies during starting. It was found that exercise experience did not promote an increase in the number of synergies but rather a more focused timing of the activation of each synergy, allowing athletes to quickly complete the postural transition from crouching to running during the starting.

Synergy quality assessment of muscle modules for determining learning performance using a realistic musculoskeletal model

How our central nervous system efficiently controls our complex musculoskeletal system is still debated. The muscle synergy hypothesis is proposed to simplify this complex system by assuming the existence of functional neural modules that coordinate several muscles. Modularity based on muscle synergies can facilitate motor learning without compromising task performance. However, the effectiveness of modularity in motor control remains debated. This ambiguity can, in part, stem from overlooking that the performance of modularity depends on the mechanical aspects of modules of interest, such as the torque the modules exert. To address this issue, this study introduces two criteria to evaluate the quality of module sets based on commonly used performance metrics in motor learning studies: the accuracy of torque production and learning speed. One evaluates the regularity in the direction of mechanical torque the modules exert, while the other evaluates the evenness of its magnitude. For verification of our criteria, we simulated motor learning of torque production tasks in a realistic musculoskeletal system of the upper arm using feed-forward neural networks while changing the control conditions. We found that the proposed criteria successfully explain the tendency of learning performance in various control conditions. These result suggest that regularity in the direction of and evenness in magnitude of mechanical torque of utilized modules are significant factor for determining learning performance. Although the criteria were originally conceived for an error-based learning scheme, the approach to pursue which set of modules is better for motor control can have significant implications in other studies of modularity in general.

Spatiotemporal patterns of throwing muscle synergies in yips-affected baseball players

"Yips" are involuntary movements that interfere with the automatic execution of sports movements. However, how the coordination among the various muscles necessary for sports movements is impaired in athletes with yips remains to be fully understood. This study aimed to assess whether muscle synergy analysis through non-negative matrix factorization (NMF) could identify impaired spatiotemporal muscle coordination in baseball players with throwing yips. Twenty-two college baseball players, including 12 with and 10 without yips symptoms participated in the study. Electromyographic activity was recorded from 13 ipsilateral upper extremity muscles during full-effort throwing. Muscle synergies were extracted through NMF. Cluster analysis was conducted to identify any common spatiotemporal patterns of muscle synergies in players with yips. Whether individual players with yips showed deviations in spatiotemporal patterns of muscle synergies compared with control players was also investigated. Four muscle synergies were extracted for each player, but none were specific to the yips group. However, a more detailed analysis of individual players revealed that two of the three players who presented dystonic symptoms during the experiment exhibited specific patterns that differed from those in control players. By contrast, each player whose symptoms were not reproduced during the experiment presented spatiotemporal patterns of muscle synergies similar to those of the control group. The results of this study indicate no common spatiotemporal pattern of muscle synergies specific to the yips group. Furthermore, these results suggest that the spatiotemporal pattern of muscle synergies in baseball throwing motion is not impaired in situations where symptoms are not reproduced even if the players have yips symptoms. However, muscle synergy analysis can identify the characteristics of muscle coordination of players who exhibit dystonic movements. These findings can be useful in developing personalized therapeutic strategies based on individual characteristics of yips symptoms.

Theoretical limits on the speed of learning inverse models explain the rate of adaptation in arm reaching tasks

An essential aspect of human motor learning is the formation of inverse models, which map desired actions to motor commands. Inverse models can be learned by adjusting parameters in neural circuits to minimize errors in the performance of motor tasks through gradient descent. However, the theory of gradient descent establishes limits on the learning speed. Specifically, the eigenvalues of the Hessian of the error surface around a minimum determine the maximum speed of learning in a task. Here, we use this theoretical framework to analyze the speed of learning in different inverse model learning architectures in a set of isometric arm-reaching tasks. We show theoretically that, in these tasks, the error surface and, thus the speed of learning, are determined by the shapes of the force manipulability ellipsoid of the arm and the distribution of targets in the task. In particular, rounder manipulability ellipsoids generate a rounder error surface, allowing for faster learning of the inverse model. Rounder target distributions have a similar effect. We tested these predictions experimentally in a quasi-isometric reaching task with a visuomotor transformation. The experimental results were consistent with our theoretical predictions. Furthermore, our analysis accounts for the speed of learning in previous experiments with incompatible and compatible virtual surgery tasks, and with visuomotor rotation tasks with different numbers of targets. By identifying aspects of a task that influence the speed of learning, our results provide theoretical principles for the design of motor tasks that allow for faster learning.

Muscle coordination patterns in regulation of medial gastrocnemius activation during walking

The ankle plantar flexion in the late stance phase is referred to as the ankle push-off. When the ankle push-off force is enhanced, compensatory adjustments occur in the adjacent phases. The muscle control that achieves these compensatory movements remains unknown, although they are expected to be coordinately regulated across multiple muscles and phases. Muscle synergy is used as a quantification technique for muscle coordination, and this analysis enables the comparison of synchronized activity between multiple muscles. Therefore, this study aimed to elucidate the tuning of muscle synergies in muscle activation adjustment of push-off. It is hypothesized that muscle activation adjustment of push-off is performed in the muscle synergy related to ankle push-off and in the muscle synergy that activates during the adjacent push-off phase. Eleven healthy men participated, and participants manipulated the activity of the medial gastrocnemius during walking through visual feedback. Two conditions were compared as experimental conditions: increasing the muscle activity to 1.6 times that during normal walking (High) and matching it with that during normal walking (Normal). Twelve muscle activities in the trunk and lower limb and kinematic data were recorded. Muscle synergies were extracted by the non-negative matrix factorization. No significant difference was observed in the number of synergies (High: 3.5 ± 0.8, Normal: 3.7 ± 0.9, p = 0.21) and muscle synergy activation timing and duration between the High and Normal conditions (p > 0.27). However, significant differences were observed in the peak muscle activity during the late stance phase of the rectus femoris (RF), biceps femoris (BF) between conditions (RF at High: 0.32 ± 0.21, RF at Normal: 0.45 ± 0.17, p = 0.02; BF at High: 0.16 ± 0.01, BF at Normal: 0.08 ± 0.06 p = 0.02). Although the quantification of force exertion has not been conducted, the modulation of RF and BF activation could have occurred due to the attempts to help knee flexion. Muscle synergies during normal walking are therefore maintained, and slight adjustments in the amplitude of muscle activity occurred for each muscle.

Motor synergy generalization framework for new targets in multi-planar and multi-directional reaching task

Humans can rapidly adapt to new situations, even though they have redundant degrees of freedom (d.f.). Previous studies in neuroscience revealed that human movements could be accounted for by low-dimensional control signals, known as motor synergies. Many studies have suggested that humans use the same repertories of motor synergies among similar tasks. However, it has not yet been confirmed whether the combinations of motor synergy repertories can be re-used for new targets in a systematic way. Here we show that the combination of motor synergies can be generalized to new targets that each repertory cannot handle. We use the multi-directional reaching task as an example. We first trained multiple policies with limited ranges of targets by reinforcement learning and extracted sets of motor synergies. Finally, we optimized the activation patterns of sets of motor synergies and demonstrated that combined motor synergy repertories were able to reach new targets that were not achieved with either original policies or single repertories of motor synergies. We believe this is the first study that has succeeded in motor synergy generalization for new targets in new planes, using a full 7-d.f. arm model, which is a realistic mechanical environment for general reaching tasks.

Muscle synergies of multi-directional postural control in astronauts on Earth after a long-term stay in space

Movements of the human biological system have adapted to the physical environment under the 1-g gravitational force on Earth. However, the effects of microgravity in space on the underlying functional neuromuscular control behaviors remain poorly understood. Here, we aimed to elucidate the effects of prolonged exposure to a microgravity environment on the functional coordination of multiple muscle activities. The activities of 16 lower limb muscles of 5 astronauts who stayed in space for at least 3 months were recorded while they maintained multidirectional postural control during bipedal standing. The coordinated activation patterns of groups of muscles, i.e., muscle synergies, were estimated from the muscle activation datasets using a factorization algorithm. The experiments were repeated a total of 5 times for each astronaut, once before and 4 times after spaceflight. The compositions of muscle synergies were altered, with a constant number of synergies, after long-term exposure to microgravity, and the extent of the changes was correlated with the severity of the deficits in postural stability. Furthermore, the muscle synergies extracted 3 months after the return were similar in their activation profile but not in their muscle composition compared with those extracted in the preflight condition. These results suggest that the modularity in the neuromuscular system became reorganized to adapt to the microgravity environment and then possibly reoptimized to the new sensorimotor environment after the astronauts were re-exposed to a gravitational force. It is expected that muscle synergies can be used as physiological markers of the status of astronauts with gravity-dependent change.

Task space exploration improves adaptation after incompatible virtual surgeries

Humans have a remarkable capacity to learn new motor skills, a process that requires novel muscle activity patterns. Muscle synergies may simplify the generation of muscle patterns through the selection of a small number of synergy combinations. Learning new motor skills may then be achieved by acquiring novel muscle synergies. In a previous study, we used myoelectric control to construct virtual surgeries that altered the mapping from muscle activity to cursor movements. After compatible virtual surgeries, which could be compensated by recombining subject-specific muscle synergies, participants adapted quickly. In contrast, after incompatible virtual surgeries, which could not be compensated by recombining existing synergies, participants explored new muscle patterns, but failed to adapt. Here, we tested whether task space exploration can promote learning of novel muscle synergies, required to overcome an incompatible surgery. Participants performed the same reaching task as in our previous study, but with more time to complete each trial, thus allowing for exploration. We found an improvement in trial success after incompatible virtual surgeries. Remarkably, improvements in movement direction accuracy after incompatible surgeries occurred faster for corrective movements than for the initial movement, suggesting that learning of new synergies is more effective when used for feedback control. Moreover, reaction time was significantly higher after incompatible than after compatible virtual surgeries, suggesting an increased use of an explicit adaptive strategy to overcome incompatible surgeries. Taken together, these results indicate that exploration is important for skill learning and suggest that human participants, with sufficient time, can learn new muscle synergies.

Neural Network Models for Spinal Implementation of Muscle Synergies

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

Modulation of spatial and temporal modules in lower limb muscle activations during walking with simulated reduced gravity

Gravity plays a crucial role in shaping patterned locomotor output to maintain dynamic stability during locomotion. The present study aimed to clarify the gravity-dependent regulation of modules that organize multiple muscle activities during walking in humans. Participants walked on a treadmill at seven speeds (1-6 km h-1 and a subject- and gravity-specific speed determined by the Froude number (Fr) corresponding to 0.25) while their body weight was partially supported by a lift to simulate walking with five levels of gravity conditions from 0.07 to 1 g. Modules, i.e., muscle-weighting vectors (spatial modules) and phase-dependent activation coefficients (temporal modules), were extracted from 12 lower-limb electromyographic (EMG) activities in each gravity (Fr ~ 0.25) using nonnegative matrix factorization. Additionally, a tensor decomposition model was fit to the EMG data to quantify variables depending on the gravity conditions and walking speed with prescribed spatial and temporal modules. The results demonstrated that muscle activity could be explained by four modules from 1 to 0.16 g and three modules at 0.07 g, and the modules were shared for both spatial and temporal components among the gravity conditions. The task-dependent variables of the modules acting on the supporting phase linearly decreased with decreasing gravity, whereas that of the module contributing to activation prior to foot contact showed nonlinear U-shaped modulation. Moreover, the profiles of the gravity-dependent modulation changed as a function of walking speed. In conclusion, reduced gravity walking was achieved by regulating the contribution of prescribed spatial and temporal coordination in muscle activities.

The effects of motor modularity on performance, learning and generalizability in upper-extremity reaching: a computational analysis

It has been hypothesized that the central nervous system simplifies the production of movement by limiting motor commands to a small set of modules known as muscle synergies. Recently, investigators have questioned whether a low-dimensional controller can produce the rich and flexible behaviours seen in everyday movements. To study this issue, we implemented muscle synergies in a biomechanically realistic model of the human upper extremity and performed computational experiments to determine whether synergies introduced task performance deficits, facilitated the learning of movements, and generalized to different movements. We derived sets of synergies from the muscle excitations our dynamic optimizations computed for a nominal task (reaching in a plane). Then we compared the performance and learning rates of a controller that activated all muscles independently to controllers that activated the synergies derived from the nominal reaching task. We found that a controller based on synergies had errors within 1 cm of a full-dimensional controller and achieved faster learning rates (as estimated from computational time to converge). The synergy-based controllers could also accomplish new tasks-such as reaching to targets on a higher or lower plane, and starting from alternative initial poses-with average errors similar to a full-dimensional controller.

Modularity speeds up motor learning by overcoming mechanical bias in musculoskeletal geometry

We can easily learn and perform a variety of movements that fundamentally require complex neuromuscular control. Many empirical findings have demonstrated that a wide range of complex muscle activation patterns could be well captured by the combination of a few functional modules, the so-called muscle synergies. Modularity represented by muscle synergies would simplify the control of a redundant neuromuscular system. However, how the reduction of neuromuscular redundancy through a modular controller contributes to sensorimotor learning remains unclear. To clarify such roles, we constructed a simple neural network model of the motor control system that included three intermediate layers representing neurons in the primary motor cortex, spinal interneurons organized into modules and motoneurons controlling upper-arm muscles. After a model learning period to generate the desired shoulder and/or elbow joint torques, we compared the adaptation to a novel rotational perturbation between modular and non-modular models. A series of simulations demonstrated that the modules reduced the effect of the bias in the distribution of muscle pulling directions, as well as in the distribution of torques associated with individual cortical neurons, which led to a more rapid adaptation to multi-directional force generation. These results suggest that modularity is crucial not only for reducing musculoskeletal redundancy but also for overcoming mechanical bias due to the musculoskeletal geometry allowing for faster adaptation to certain external environments.