Jessica L. Allen

Long-term training modifies the modular structure and organization of walking balance control

How does long-term training affect the neural control of movements? Here we tested the hypothesis that long-term training leading to skilled motor performance alters muscle coordination during challenging, as well as nominal everyday motor behaviors. Using motor module (a.k.a., muscle synergy) analyses, we identified differences in muscle coordination patterns between professionally trained ballet dancers (experts) and untrained novices that accompanied differences in walking balance proficiency assessed using a challenging beam-walking test. During beam walking, we found that experts recruited more motor modules than novices, suggesting an increase in motor repertoire size. Motor modules in experts had less muscle coactivity and were more consistent than in novices, reflecting greater efficiency in muscle output. Moreover, the pool of motor modules shared between beam and overground walking was larger in experts compared with novices, suggesting greater generalization of motor module function across multiple behaviors. These differences in motor output between experts and novices could not be explained by differences in kinematics, suggesting that they likely reflect differences in the neural control of movement following years of training rather than biomechanical constraints imposed by the activity or musculoskeletal structure and function. Our results suggest that to learn challenging new behaviors, we may take advantage of existing motor modules used for related behaviors and sculpt them to meet the demands of a new behavior.

Feasible muscle activation ranges based on inverse dynamics analyses of human walking

Although it is possible to produce the same movement using an infinite number of different muscle activation patterns owing to musculoskeletal redundancy, the degree to which observed variations in muscle activity can deviate from optimal solutions computed from biomechanical models is not known. Here, we examined the range of biomechanically permitted activation levels in individual muscles during human walking using a detailed musculoskeletal model and experimentally-measured kinetics and kinematics. Feasible muscle activation ranges define the minimum and maximum possible level of each muscles activation that satisfy inverse dynamics joint torques assuming that all other muscles can vary their activation as needed. During walking, 73% of the muscles had feasible muscle activation ranges that were greater than 95% of the total muscle activation range over more than 95% of the gait cycle, indicating that, individually, most muscles could be fully active or fully inactive while still satisfying inverse dynamics joint torques. Moreover, the shapes of the feasible muscle activation ranges did not resemble previously-reported muscle activation patterns nor optimal solutions, i.e. static optimization and computed muscle control, that are based on the same biomechanical constraints. Our results demonstrate that joint torque requirements from standard inverse dynamics calculations are insufficient to define the activation of individual muscles during walking in healthy individuals. Identifying feasible muscle activation ranges may be an effective way to evaluate the impact of additional biomechanical and/or neural constraints on possible versus actual muscle activity in both normal and impaired movements.

Increased neuromuscular consistency in gait and balance after partnered, dance-based rehabilitation in Parkinson’s disease

Here we examined changes in muscle coordination associated with improved motor performance after partnered, dance-based rehabilitation in individuals with mild to moderate idiopathic Parkinsons disease. Using motor module (a.k.a. muscle synergy) analysis, we identified changes in the modular control of overground walking and standing reactive balance that accompanied clinically meaningful improvements in behavioral measures of balance, gait, and disease symptoms after 3 wk of daily Adapted Tango classes. In contrast to previous studies that revealed a positive association between motor module number and motor performance, none of the six participants in this pilot study increased motor module number despite improvements in behavioral measures of balance and gait performance. Instead, motor modules were more consistently recruited and distinctly organized immediately after rehabilitation, suggesting more reliable motor output. Furthermore, the pool of motor modules shared between walking and reactive balance increased after rehabilitation, suggesting greater generalizability of motor module function across tasks. Our work is the first to show that motor module distinctness, consistency, and generalizability are more sensitive to improvements in gait and balance function after short-term rehabilitation than motor module number. Moreover, as similar differences in motor module distinctness, consistency, and generalizability have been demonstrated previously in healthy young adults with and without long-term motor training, our work suggests commonalities in the structure of muscle coordination associated with differences in motor performance across the spectrum from motor impairment to expertise.NEW & NOTEWORTHY We demonstrate changes in neuromuscular control of gait and balance in individuals with Parkinsons disease after short-term, dance-based rehabilitation. Our work is the first to show that motor module distinctness, consistency, and generalizability across gait and balance are more sensitive than motor module number to improvements in motor performance following short-term rehabilitation. Our results indicate commonalities in muscle coordination improvements associated with motor skill reacquisition due to rehabilitation and motor skill acquisition in healthy individuals.

Contribution of muscle short-range stiffness to initial changes in joint kinetics and kinematics during perturbations to standing balance: A simulation study

Simulating realistic musculoskeletal dynamics is critical to understanding neural control of muscle activity evoked in sensorimotor feedback responses that have inherent neural transmission delays. Thus, the initial mechanical response of muscles to perturbations in the absence of any change in muscle activity determines which corrective neural responses are required to stabilize body posture. Muscle short-range stiffness, a history-dependent property of muscle that causes a rapid and transient rise in muscle force upon stretch, likely affects musculoskeletal dynamics in the initial mechanical response to perturbations. Here we identified the contributions of short-range stiffness to joint torques and angles in the initial mechanical response to support surface translations using dynamic simulation. We developed a dynamic model of muscle short-range stiffness to augment a Hill-type muscle model. Our simulations show that short-range stiffness can provide stability against external perturbations during the neuromechanical response delay. Assuming constant muscle activation during the initial mechanical response, including muscle short-range stiffness was necessary to account for the rapid rise in experimental sagittal plane knee and hip joint torques that occurs simultaneously with very small changes in joint angles and reduced root mean square errors between simulated and experimental torques by 56% and 47%, respectively. Moreover, forward simulations lacking short-range stiffness produced unreasonably large joint angle changes during the initial response. Using muscle models accounting for short-range stiffness along with other aspects of history-dependent muscle dynamics may be important to advance our ability to simulate inherently unstable human movements based on principles of neural control and biomechanics.

Why Is Neuromechanical Modeling of Balance and Locomotion So Hard

A goal and challenge in neuromechanical modeling is to develop validated simulations to predict the effects of neuromotor deficits and therapies on movements. This has been particularly challenging in balance and locomotion because they are inherently unstable, making it difficult to explore model parameters in a way that still coordinates the body in a functional way. Integrating realistic and validated musculoskeletal models with neural control mechanisms is critical to our ability to predict how human robustly move in the environment. Here we briefly review both human locomotion models, which generally focus on modeling the physical dynamics of movement with simplified models of neural control, as well as balance models, which model sensorimotor dynamics and processing with simplified biomechanical models. Combining complex neural and musculoskeletal models increases the redundancy in a model and allows us to study how motor variability and robustness are exploited to produce movements in both healthy and impaired individuals. To advance, the integration of neuromechanical modeling and experimental approaches will be critical in testing specific hypotheses concerning how and why neuromechanical flexibility is both exploited and constrained under various movement contexts. We give a few examples of how the close interplay between models and experiments can reveal neuromechanical principles of movement.

Hip and ankle responses for reactive balance emerge from varying priorities to reduce effort and kinematic excursion: a simulation study

Although standing balance is important in many daily activities, there has been little effort in developing detailed musculoskeletal models and simulations of balance control compared to other whole-body motor activities. Our objective was to develop a musculoskeletal model of human balance that can be used to predict movement patterns in reactive balance control. Similar to prior studies using torque-driven models, we investigated how movement patterns during a reactive balance response are affected by high-level task goals (e.g., reducing center-of-mass movement, maintaining vertical trunk orientation, and minimizing effort). We generated 23 forward dynamics simulations where optimal muscle excitations were found using cost functions with different weights on minimizing these high-level goals. Variations in hip and ankle angles observed experimentally (peak hip flexion=7.9-53.1°, peak dorsiflexion=0.5-4.7°) could be predicted by varying the priority of these high-level goals. More specifically, minimizing center-of-mass motion produced a hip strategy (peak hip flexion and ankle dorsiflexion angles of 45.5° and 2.3°, respectively) and the response shifted towards an ankle strategy as the priority to keep the trunk vertical was increased (peak hip and ankle angles of 13.7° and 8.5°, respectively). We also found that increasing the priority to minimize muscle stress always favors a hip strategy. These results are similar to those from sagittal-plane torque-driven models. Our muscle-actuated model facilitates the investigation of neuromechanical interactions governing reactive balance control to predict muscle activity and movement patterns based on interactions between neuromechanical elements such as spinal reflexes, muscle short-range stiffness, and task-level sensorimotor feedback.