Stroke is a leading cause of long-term disability worldwide and often impairs walking ability. To improve recovery of walking function post-stroke, researchers have investigated the use of treatments such as fast functional electrical stimulation (FastFES). During FastFES treatments, individuals post-stroke walk on a treadmill at their fastest comfortable speed while electrical stimulation is delivered to two muscles of the paretic ankle, ideally to improve paretic leg propulsion and toe clearance. However, muscle selection and stimulation timing are currently standardized based on clinical intuition and a one-size-fits-all approach, which may explain in part why some patients respond to FastFES training while others do not. This study explores how personalized neuromusculoskeletal models could potentially be used to enable individual-specific selection of target muscles and stimulation timing to address unique functional limitations of individual patients post-stroke. Treadmill gait data, including EMG, surface marker positions, and ground reactions, were collected from an individual post-stroke who was a non-responder to FastFES treatment. The patient’s gait data were used to personalize key aspects of a full-body neuromusculoskeletal walking model, including lower-body joint functional axes, lower-body muscle force generating properties, deformable foot-ground contact properties, and paretic and non-paretic leg neural control properties. The personalized model was utilized within a direct collocation optimal control framework to reproduce the patient’s unstimulated treadmill gait data (verification problem) and to generate three stimulated walking predictions that sought to minimize inter-limb propulsive force asymmetry (prediction problems). The three predictions used: (1) Standard muscle selection (gastrocnemius and tibialis anterior) with standard stimulation timing, (2) Standard muscle selection with optimized stimulation timing, and (3) Optimized muscle selection (soleus and semimembranosus) with optimized stimulation timing. Relative to unstimulated walking, the optimal control problems predicted a 41% reduction in propulsive force asymmetry for scenario (1), a 45% reduction for scenario (2), and a 64% reduction for scenario (3), suggesting that non-standard muscle selection may be superior for this patient. Despite these predicted improvements, kinematic symmetry was not noticeably improved for any of the walking predictions. These results suggest that personalized neuromusculoskeletal models may be able to predict personalized FastFES training prescriptions that could improve propulsive force symmetry, though inclusion of kinematic requirements would be necessary to improve kinematic symmetry as well.

The modular control of muscles in groups, often referred to as muscle synergies, has been proposed to provide a motor repertoire of actions for the robust control of movement. However it is not clear whether muscle synergies identified in one task are also recruited by different neural pathways subserving other motor behaviors. We tested the hypothesis that voluntary and reactive modifications to walking in humans result from the recruitment of locomotor muscle synergies. We recorded the activity of 16 muscles in the right leg as subjects walked a 7.5 m path at two different speeds. To elicit a second motor behavior, midway through the path we imposed ramp and hold translation perturbations of the support surface in each of four cardinal directions. Variations in the temporal recruitment of locomotor muscle synergies could account for cycle-by-cycle variations in muscle activity across strides. Locomotor muscle synergies were also recruited in atypical phases of gait, accounting for both anticipatory gait modifications prior to perturbations and reactive feedback responses to perturbations. Our findings are consistent with the idea that a common pool of spatially-fixed locomotor muscle synergies can be recruited by different neural pathways, including the central pattern generator for walking, brainstem pathways for balance control, and cortical pathways mediating voluntary gait modifications. Together with electrophysiological studies, our work suggests that muscle synergies may provide a library of motor subtasks that can be flexibly recruited by parallel descending pathways to generate a variety of complex natural movements in the upper and lower limbs.

Muscle coordination may be difficult or impossible to predict accurately based on biomechanical considerations alone due to redundancy in the musculoskeletal system. Because many solutions exist for any given movement, the role of the nervous system in further constraining muscle coordination patterns for movement must be considered in both healthy and impaired motor control. Based on computational neuromechanical analyses of experimental data combined with modeling techniques, we have demonstrated several such neural constraints on the temporal and spatial patterns of muscle activity during both locomotion and postural responses to balance perturbations. We hypothesize that subject-specific as well as trial-by-trial differences in muscle activation can be parameterized and understood by a hierarchical and low-dimensional framework that reflects the neural control of task-level goals. In postural control, we demonstrate that temporal patterns of muscle activity may be governed by feedback control of task-level variables that represent the overall goal-directed motion of the body. These temporal patterns then recruit spatially-fixed patterns of muscle activity called muscle synergies that produce the desired task-level biomechanical functions that require multi-joint coordination. Moreover, these principles apply more generally to movement, and in particular to locomotor tasks in both healthy and impaired individuals. Overall, understanding the goals and organization of the neural control of movement may provide useful reduced dimension parameter sets to address the degrees-of-freedom problem in musculoskeletal movement control. More importantly, however, neuromechanical analyses may lend insight and provide a framework for understanding subject-specific and trial-by-trial differences in movement across both healthy and motor-impaired populations.

Measured muscle activation patterns often vary significantly from musculoskeletal model predictions that use optimization to resolve redundancy. Although experimental muscle activity exhibits both inter- and intra-subject variability we lack adequate tools to quantify the biomechanical latitude that the nervous system has when selecting muscle activation patterns. Here, we identified feasible ranges of individual muscle activity during force production in a musculoskeletal model to quantify the degree to which biomechanical redundancy allows for variability in muscle activation patterns. In a detailed cat hindlimb model matched to the posture of three cats, we identified the lower and upper bounds on muscle activity in each of 31 muscles during static endpoint force production across different force directions and magnitudes. Feasible ranges of muscle activation were relatively unconstrained across force magnitudes such that only a few (0∼13%) muscles were found to be truly “necessary” (e.g. exhibited non-zero lower bounds) at physiological force ranges. Most muscles were “optional” having zero lower bounds, and frequently had “maximal” upper bounds as well. Moreover, “optional” muscles were never selected by optimization methods that either minimized muscle stress, or that scaled the pattern required for maximum force generation. Therefore, biomechanical constraints were generally insufficient to restrict or specify muscle activation levels for producing a force in a given direction, and many muscle patterns exist that could deviate substantially from one another but still achieve the task. Our approach could be extended to identify the feasible limits of variability in muscle activation patterns in dynamic tasks such as walking.

During postural responses to perturbations, horizontal plane forces generated by the cat hindlimb are stereotypically directed either towards or away from the animal’s center of mass, independent of perturbation direction. We used a static, three-dimensional musculoskeletal model of the hindlimb to investigate possible biomechanical determinants of this “force constraint strategy” (Macpherson, 1988). We hypothesized that directions in which the hindlimb can produce large forces are preferentially used in postural control. We computed feasible force sets (FFS) based on hindlimb configurations of three cats during postural equilibrium tasks (Jacobs and Macpherson, 1996) and compared them to horizontal plane postural force directions. The grand mean FFS was bimodal, with maxima near the posterior-anterior axis (−86±8° and 71±4°), and minima near the medial-lateral axis (177±8° and 8±8°). Postural force directions clustered near both maxima; there were no medial postural forces near the absolute minimum. However, the medians of the anterior and posterior postural force direction histograms in the right hindlimb were rotated counter-clockwise from the FFS maxima (p<0.05; Wilcoxon signed-rank test). Because the posterior-anterior alignment of the FFS is consistent with a hindlimb structure optimized for locomotion, we conclude that the biomechanical capabilities of the hindlimb strongly influence, but do not uniquely determine the force directions observed in the force constraint strategy. Forces used in postural control may reflect a balance between a neural preference for using forces in the directions of large feasible forces and other criteria, such as the stabilization of the center of mass, and muscular coordination strategies.

Although Parkinson disease (PD) causes profound balance impairments, we know very little about how PD impacts the sensorimotor networks we rely on for automatically maintaining balance control. In young healthy people and animals, muscles are activated in a precise temporal and spatial organization when the center of body mass (CoM) is unexpectedly moved that is largely automatic and determined by feedback of CoM motion. Here, we show that PD alters the sensitivity of the sensorimotor feedback transformation. Importantly, sensorimotor feedback transformations for balance in PD remain temporally precise, but become spatially diffuse by recruiting additional muscle activity in antagonist muscles during balance responses. The abnormal antagonist muscle activity remains precisely time-locked to sensorimotor feedback signals encoding undesirable motion of the body in space. Further, among people with PD, the sensitivity of abnormal antagonist muscle activity to CoM motion varies directly with the number of recent falls. Our work shows that in people with PD, sensorimotor feedback transformations for balance are intact but disinhibited in antagonist muscles, likely contributing to balance deficits and falls.

Although the neural basis of balance control remains unknown, recent studies suggest that a feedback law on center-of-mass (CoM) kinematics determines the temporal patterning of muscle activity during human postural responses. We hypothesized that the same feedback law would also explain variations in muscle activity to support-surface translation as perturbation characteristics vary. Subject CoM motion was experimentally modulated using 34 different anterior–posterior support-surface translations of varying peak acceleration and velocity but the same total displacement. Electromyographic (EMG) recordings from several muscles of the lower limbs and trunk were compared to predicted EMG patterns from an inverted pendulum model under delayed feedback control. In both recorded and predicted EMG patterns, the initial burst of muscle activity scaled linearly with peak acceleration, whereas the tonic “plateau” region scaled with peak velocity. The relatively invariant duration of the initial burst was modeled by incorporating a transient, time-limited encoding of CoM acceleration inspired by muscle spindle primary afferent dynamic responses. The entire time course of recorded and predicted muscle activity compared favorably across all conditions, suggesting that the initial burst of muscle activity is not generated by feedforward neural mechanisms. Perturbation conditions were presented randomly and subjects maintained relatively constant feedback gains across all conditions. In contrast, an optimal feedback solution based on a trade-off between CoM stabilization and energy expenditure predicted that feedback gains should change with perturbation characteristics. These results suggest that an invariant feedback law was used to generate the entire time course of muscle activity across a variety of postural disturbances.

We developed wavelet-based functional ANOVA (wfANOVA) as a novel approach for comparing neurophysiological signals that are functions of time. Temporal resolution is often sacrificed by analyzing such data in large time bins, increasing statistical power by reducing the number of comparisons. We performed ANOVA in the wavelet domain because differences between curves tend to be represented by a few temporally localized wavelets, which we transformed back to the time domain for visualization. We compared wfANOVA and ANOVA performed in the time domain (tANOVA) on both experimental electromyographic (EMG) signals from responses to perturbation during standing balance across changes in peak perturbation acceleration (3 levels) and velocity (4 levels) and on simulated data with known contrasts. In experimental EMG data, wfANOVA revealed the continuous shape and magnitude of significant differences over time without a priori selection of time bins. However, tANOVA revealed only the largest differences at discontinuous time points, resulting in features with later onsets and shorter durations than those identified using wfANOVA (P < 0.02). Furthermore, wfANOVA required significantly fewer (∼¼×; P < 0.015) significant F tests than tANOVA, resulting in post hoc tests with increased power. In simulated EMG data, wfANOVA identified known contrast curves with a high level of precision (r2 = 0.94 ± 0.08) and performed better than tANOVA across noise levels (P < <0.01). Therefore, wfANOVA may be useful for revealing differences in the shape and magnitude of neurophysiological signals (e.g., EMG, firing rates) across multiple conditions with both high temporal resolution and high statistical power.

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.

The pendulum test is a sensitive clinical assessment of spasticity where the lower leg is dropped from the horizontal position and features of limb motion are recorded. Three key kinematic features are associated with the degree of severity of spasticity in children with cerebral palsy: decreased initial limb excursion, reduced number of limb oscillations, and a non-vertical resting limb angle. While spasticity is attributed to increased velocity-dependent resistance to motion, prior models simulating increased sensorimotor feedback of muscle velocity fail to explain the key pendulum test kinematic outcomes in spastic individuals. Here we hypothesized that increased muscle tone, causing a transient increase in muscle force, i.e. short-range stiffness, could account for reduced first swing excursion and non-vertical resting limb angle. We further hypothesized that hyperreflexia modeled based on muscle fiber force, and not velocity, feedback would be necessary to reduce the number of oscillations because of its interaction with transiently increased muscle force due to short-range stiffness. We simulated the lower leg as a torque-driven single-link pendulum. Muscle tone was modeled as a constant baseline joint torque, short-range stiffness torque was dependent on the level of muscle tone, and delayed sensory feedback torque to simulate reflex activity was based on either muscle velocity or force. Muscle tone and transient short-range stiffness were necessary to simulate decreased initial swing excursion and non-vertical resting leg angle. Moreover, the reduction in the number of oscillations was best reproduced by simulating stretch reflex activity in terms of force, and not velocity, feedback. Varying only baseline muscle torque and reflex gain, we simulated a range of pendulum test kinematics observed across different levels of spasticity. Our model lends insight into physiological mechanisms of spasticity whose contributions can vary on an individual-specific basis, and potentially across different neurological disorders that manifest spasticity as a symptom.