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Postural responses to similar perturbations of standing balance vary widely within and across subjects. Here, we identified two sources of variability and their interactions by combining experimental observations with computational modeling: differences in posture at perturbation onset across trials and differences in task-level goals across subjects. We first collected postural responses to unpredictable backward support-surface translations during standing in 10 young adults. We found that maximal trunk lean in postural responses to backward translations were highly variable both within subjects (mean of ranges = 28.3°) and across subjects (range of means = 39.9°). Initial center of mass (COM) position was correlated with maximal trunk lean during the response, but this relation was subject specific (R(2) = 0.29–0.82). We then used predictive simulations to assess causal relations and interactions with task-level goal. Our simulations showed that initial posture explains the experimentally observed intrasubject variability with a more anterior initial COM position increasing the use of the hip strategy. Differences in task-level goal explain observed intersubject variability with prioritizing effort minimization leading to ankle strategies and prioritizing stability leading to hip strategies. Interactions between initial posture and task-level goal explain observed differences in intrasubject variability across subjects. Our findings suggest that variability in initial posture due to increased sway as observed in older adults might increase the occurrence of less stable postural responses to perturbations. Insight in factors causing movement variability will advance our ability to study the origin of differences between groups and conditions. NEW & NOTEWORTHY Responses to perturbations of standing balance vary both within and between individuals. By combining experimental observations with computational modeling, we identified causes of observed kinematic variability in healthy young adults. First, we found that trial-by-trial differences in posture at perturbation onset explain most of the kinematic variability observed within subjects. Second, we found that differences in prioritizing effort versus stability explained differences in the postural response as well as differences in trial-by-trial variability across subjects.

Task-level goals such as maintaining standing balance are achieved through coordinated muscle activity. Consistent and individualized groupings of synchronously activated muscles can be estimated from muscle recordings in terms of motor modules or muscle synergies, independent of their temporal activation. The structure of motor modules can change with motor training, neurological disorders, and rehabilitation, but the central and peripheral mechanisms underlying motor module structure remain unclear. To assess the role of peripheral somatosensory input on motor module structure, we evaluated changes in the structure of motor modules for reactive balance recovery following pyridoxine-induced large-fiber peripheral somatosensory neuropathy in previously collected data in four adult cats. Somatosensory fiber loss, quantified by postmortem histology, varied from mild to severe across cats. Reactive balance recovery was assessed using multidirectional translational support-surface perturbations over days to weeks throughout initial impairment and subsequent recovery of balance ability. Motor modules within each cat were quantified by non-negative matrix factorization and compared in structure over time. All cats exhibited changes in the structure of motor modules for reactive balance recovery after somatosensory loss, providing evidence that somatosensory inputs influence motor module structure. The impact of the somatosensory disturbance on the structure of motor modules in well-trained adult cats indicates that somatosensory mechanisms contribute to motor module structure, and therefore may contribute to some of the pathological changes in motor module structure in neurological disorders. These results further suggest that somatosensory nerves could be targeted during rehabilitation to influence pathological motor modules for rehabilitation. NEW & NOTEWORTHY Stable motor modules for reactive balance recovery in well-trained adult cats were disrupted following pyridoxine-induced peripheral somatosensory neuropathy, suggesting somatosensory inputs contribute to motor module structure. Furthermore, the motor module structure continued to change as the animals regained the ability to maintain standing balance, but the modules generally did not recover pre-pyridoxine patterns. These results suggest changes in somatosensory input and subsequent learning may contribute to changes in motor module structure in pathological conditions.

Postural muscle activity precedes voluntary movements of the upper limbs. The traditional view of this activity is that it anticipates perturbations to balance caused by the movement of a limb. However, findings from reach-based paradigms have shown that postural adjustments can initiate center of mass displacement for mobility rather than minimize its displacement for stability. Within this context, altering reaching distance beyond the base of support would place increasing constraints on equilibrium during stance. If the underlying composition of anticipatory postural activity is linked to stability, coordination between muscles (i.e., motor modules) may evolve differently as equilibrium constraints increase. We analyzed the composition of motor modules in functional trunk muscles as participants performed multidirectional reaching movements to targets within and beyond the arm’s length. Bilateral trunk and reaching arm muscle activity were recorded. Despite different trunk requirements necessary for successful movement, and the changing biomechanical (i.e., postural) constraints that accompany alterations in reach distance, nonnegative matrix factorization identified functional motor modules derived from preparatory trunk muscle activity that shared common features. Relative similarity in modular weightings (i.e., composition) and spatial activation profiles that reflect movement goals across tasks necessitating differing levels of trunk involvement provides evidence that preparatory postural adjustments are linked to the same task priorities (i.e., movement generation rather than stability). NEW & NOTEWORTHY Reaching within and beyond arm’s length places different task constraints upon the required trunk motion necessary for successful movement execution. The identification of constant modular features, including functional muscle weightings and spatial tuning, lend support to the notion that preparatory postural adjustments of the trunk are tied to the same task priorities driving mobility, regardless of the future postural constraints.

The cortical N1 response to balance perturbation is observed in electroencephalography recordings simultaneous to automatic balance-correcting muscle activity. We recently observed larger cortical N1s in individuals who had greater difficulty resisting compensatory steps, suggesting the N1 may be influenced by stepping or changes in response strategy. Here, we test whether the cortical N1 response is influenced by stepping (planned steps versus feet-in-place) or prior planning (planned vs. unplanned steps). We hypothesized that prior planning of a step would reduce the amplitude of the cortical N1 response to balance perturbations. In 19 healthy young adults (ages 19–38; 8 men and 11 women), we measured the cortical N1 amplitude evoked by 48 backward translational support-surface perturbations of unpredictable timing and amplitude in a single experimental session. Participants were asked to plan a stepping reaction on half of perturbations, but to resist stepping otherwise. Perturbations included an easy (8 cm, 16 cm/s) perturbation that was identical across participants and did not naturally elicit compensatory steps, and a height-adjusted difficult (18–22 cm, 38–42 cm/s) perturbation that frequently elicited compensatory steps despite instructions to resist stepping. In contrast to our hypothesis, cortical N1 response amplitudes did not differ between planned and unplanned stepping reactions, but cortical responses were 11% larger with the execution of planned compensatory steps compared with nonstepping responses to difficult perturbations. These results suggest a possible role for the cortical N1 in the execution of compensatory steps for balance recovery, and this role is not influenced by whether the compensatory step was planned before the perturbation. NEW & NOTEWORTHY The cortical N1 response to balance perturbation is larger when executing compensatory steps, suggesting a relationship between the cortical N1 and subsequent motor behavior. Additionally, the cortical N1 response is not impacted by prior planning of the stepping reaction, suggesting that predictability of the motor outcome does not impact the N1 in the same way as predictability of the perturbation stimulus.

Here we examined changes in muscle coordination associated with improved motor performance after partnered, dance-based rehabilitation in individuals with mild to moderate idiopathic Parkinson’s 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 Parkinson’s 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.

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.

In both the upper and lower limbs, evidence suggests that short-latency electromyographic (EMG) responses to mechanical perturbations are modulated based on muscle stretch or joint motion, whereas long-latency responses are modulated based on attainment of task-level goals, e.g., desired direction of limb movement. We hypothesized that long-latency responses are modulated continuously by task-level error feedback. Previously, we identified an error-based sensorimotor feedback transformation that describes the time course of EMG responses to ramp-and-hold perturbations during standing balance ( Safavynia and Ting 2013 ; Welch and Ting 2008 , 2009 ). Here, our goals were 1) to test the robustness of the sensorimotor transformation over a richer set of perturbation conditions and postural states; and 2) to explicitly test whether the sensorimotor transformation is based on task-level vs. joint-level error. We developed novel perturbation trains of acceleration pulses such that perturbations were applied when the body deviated from the desired, upright state while recovering from preceding perturbations. The entire time course of EMG responses (∼4 s) in an antagonistic muscle pair was reconstructed using a weighted sum of center of mass (CoM) kinematics preceding EMGs at long-latency delays (∼100 ms). Furthermore, CoM and joint kinematic trajectories became decorrelated during perturbation trains, allowing us to explicitly compare task-level vs. joint feedback in the same experimental condition. Reconstruction of EMGs was poorer using joint kinematics compared with CoM kinematics and required unphysiologically short (∼10 ms) delays. Thus continuous, long-latency feedback of task-level variables may be a common mechanism regulating long-latency responses in the upper and lower limbs.

We investigated muscle activity, ground reaction forces, and center of mass (CoM) acceleration in two different postural behaviors for standing balance control in humans to determine whether common neural mechanisms are used in different postural tasks. We compared nonstepping responses, where the base of support is stationary and balance is recovered by returning CoM back to its initial position, with stepping responses, where the base of support is enlarged and balance is recovered by pushing the CoM away from the initial position. In response to perturbations of the same direction, these two postural behaviors resulted in different muscle activity and ground reaction forces. We hypothesized that a common pool of muscle synergies producing consistent task-level biomechanical functions is used to generate different postural behaviors. Two sets of support-surface translations in 12 horizontal-plane directions were presented, first to evoke stepping responses and then to evoke nonstepping responses. Electromyographs in 16 lower back and leg muscles of the stance leg were measured. Initially (∼100-ms latency), electromyographs, CoM acceleration, and forces were similar in nonstepping and stepping responses, but these diverged in later time periods (∼200 ms), when stepping occurred. We identified muscle synergies using non-negative matrix factorization and functional muscle synergies that quantified correlations between muscle synergy recruitment levels and biomechanical outputs. Functional muscle synergies that produce forces to restore CoM position in nonstepping responses were also used to displace the CoM during stepping responses. These results suggest that muscle synergies represent common neural mechanisms for CoM movement control under different dynamic conditions: stepping and nonstepping postural responses.

Postural stability depends on interactions between the musculoskeletal system and neural control mechanisms. We present a frontal plane model stabilized by delayed feedback to analyze the effects of altered stance width on postural responses to perturbations. We hypothesized that changing stance width alters the mechanical dynamics of the body and limits the range of delayed feedback gains that produce stable postural behaviors. Surprisingly, mechanical stability was found to decrease as stance width increased due to decreased effective inertia. Furthermore, due to sensorimotor delays and increased leverage of hip joint torque on center-of-mass motion, the magnitudes of the stabilizing delayed feedback gains decreased as stance width increased. Moreover, the ranges of the stable feedback gains were nonoverlapping across different stance widths such that using a single neural feedback control strategy at both narrow and wide stances could lead to instability. The set of stable feedback gains was further reduced by constraints on foot lift-off and perturbation magnitude. Simulations were fit to experimentally measured kinematics, and the identified feedback gains corroborated model predictions. In addition, analytical gain margin of the linearized system was found to predict step transitions without the need for simulation. In conclusion, this model offers a method to dissociate the complex interactions between postural configuration, delayed sensorimotor feedback, and nonlinear foot lift-off constraints. The model demonstrates that stability at wide stances can only be achieved if delayed neural feedback gains decrease. This model may be useful in explaining both expected and paradoxical changes in stance width in healthy and neurologically impaired individuals.

The musculoskeletal redundancy of the body provides multiple solutions for performing motor tasks. We have proposed that the nervous system solves this unconstrained problem through the recruitment of motor modules or functional muscle synergies that map motor intention to action. Consistent with this hypothesis, we showed that trial-by-trial variations in muscle activation for multidirectional balance control in humans were constrained by a small set of muscle synergies. However, apparent muscle synergy structures could arise from characteristic patterns of sensory input resulting from perturbations or from low-dimensional optimal motor solutions. Here we studied electromyographic (EMG) responses for balance control across a range of biomechanical contexts, which alter not only the sensory inflow generated by postural perturbations, but also the muscle activation patterns used to restore balance. Support-surface translations in 12 directions were delivered to subjects standing in six different postural configurations: one-leg, narrow, wide, very wide, crouched, and normal stance. Muscle synergies were extracted from each condition using nonnegative matrix factorization. In addition, muscle synergies from the normal stance condition were used to reconstruct muscle activation patterns across all stance conditions. A consistent set of muscle synergies were recruited by each subject across conditions. When balance demands were extremely different from the normal stance (e.g., one-legged or crouched stance), task-specific muscle synergies were recruited in addition to the preexisting ones, rather generating de novo muscle synergies. Taken together, our results suggest that muscle synergies represent consistent motor modules that map intention to action, regardless of the biomechanical context of the task.

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.

Postural control is a natural behavior that requires the spatial and temporal coordination of multiple muscles. Complex muscle activation patterns characterizing postural responses suggest the need for independent muscle control. However, our previous work shows that postural responses in cats can be robustly reproduced by the activation of a few muscle synergies. We now investigate whether a similar neural strategy is used for human postural control. We hypothesized that a few muscle synergies could account for the intertrial variability in automatic postural responses from different perturbation directions, as well as different postural strategies. Postural responses to multidirectional support-surface translations in 16 muscles of the lower back and leg were analyzed in nine healthy subjects. Six or fewer muscle synergies were required to reproduce the postural responses of each subject. The composition and temporal activation of several muscle synergies identified across all subjects were consistent with the previously identified “ankle” and “hip” strategies in human postural responses. Moreover, intertrial variability in muscle activation patterns was successfully reproduced by modulating the activity of the various muscle synergies. This suggests that trial-to-trial variations in the activation of individual muscles are correlated and, moreover, represent variations in the amplitude of descending neural commands that activate individual muscle synergies. Finally, composition and temporal activation of most of the muscle synergies were similar across subjects. These results suggest that muscle synergies represent a general neural strategy underlying muscle coordination in postural tasks.

We recently showed that four muscle synergies can reproduce multiple muscle activation patterns in cats during postural responses to support surface translations. We now test the robustness of functional muscle synergies, which specify muscle groupings and the active force vectors produced during postural responses under several biomechanically distinct conditions. We aimed to determine whether such synergies represent a generalized control strategy for postural control or if they are merely specific to each postural task. Postural responses to multidirectional translations at different fore-hind paw distances and to multidirectional rotations at the preferred stance distance were analyzed. Five synergies were required to adequately reconstruct responses to translation at the preferred stance distance—four were similar to our previous analysis of translation, whereas the fifth accounted for the newly added background activity during quiet stance. These five control synergies could account for >80% total variability or r2> 0.6 of the electromyographic and force tuning curves for all other experimental conditions. Forces were successfully reconstructed but only when they were referenced to a coordinate system that rotated with the limb axis as stance distance changed. Finally, most of the functional muscle synergies were similar across all of the six cats in terms of muscle synergy number, synergy activation patterns, and synergy force vectors. The robustness of synergy organization across perturbation types, postures, and animals suggests that muscle synergies controlling task-variables are a general construct used by the CNS for balance control.

This study sought to identify the sensory signals that encode perturbation direction rapidly enough to shape the directional tuning of the automatic postural response. We compared reactions to 16 directions of pitch and roll rotation and 16 directions of linear translation in the horizontal plane in freely standing cats. Rotations and translations that displaced the center of mass in the same direction relative to the feet evoked similar patterns of muscle activity and active ground-reaction force, suggesting the presence of a single, robust postural strategy for stabilizing the center of mass in both rotation and translation. Therefore we postulated there should be a common sensory input that encodes the direction of the perturbation and leads to the directional tuning of the early electromyographic burst in the postural response. We compared the mechanical changes induced by rotations and translations prior to the active, postural response. The only consistent feature common to the full range of rotation and translation directions was the initial change in ground-reaction force angle. Other variables including joint angles, ground-reaction force magnitudes, center of pressure, and center of mass in space showed opposite or nonsignificant changes for rotation and translation. Change in force angle at the paw reflects the ratio of loading force to slip force, analogous to slips during finger grip tasks. We propose that cutaneous sensors in the foot soles detect change in ground-reaction force angle and provide the critical input underlying the directional tuning of the automatic postural response for balance.

Phase reversal of biomechanical functions and muscle activity in backward pedaling. Computer simulations of pedaling have shown that a wide range of pedaling tasks can be performed if each limb has the capability of executing six biomechanical functions, which are arranged into three pairs of alternating antagonistic functions. An Ext/Flex pair accelerates the limb into extension or flexion, a Plant/Dorsi pair accelerates the foot into plantarflexion or dorsiflexion, and an Ant/Post pair accelerates the foot anteriorly or posteriorly relative to the pelvis. Because each biomechanical function (i.e., Ext, Flex, Plant, Dorsi, Ant, or Post) contributes to crank propulsion during a specific region in the cycle, phasing of a muscle is hypothesized to be a consequence of its ability to contribute to one or more of the biomechanical functions. Analysis of electromyogram (EMG) patterns has shown that this biomechanical framework assists in the interpretation of muscle activity in healthy and hemiparetic subjects during forward pedaling. Simulations show that backward pedaling can be produced with a phase shift of 180° in the Ant/Post pair. No phase shifts in the Ext/Flex and Plant/Dorsi pairs are then necessary. To further test whether this simple yet biomechanically viable strategy may be used by the nervous system, EMGs from 7 muscles in 16 subjects were measured during backward as well as forward pedaling. As predicted, phasing in vastus medialis (VM), tibialis anterior (TA), medial gastrocnemius (MG), and soleus (SL) were unaffected by pedaling direction, with VM and SL contributing to Ext, MG to Plant, and TA to Dorsi. In contrast, phasing in biceps femoris (BF) and semimembranosus (SM) were affected by pedaling direction, as predicted, compatible with their contribution to the directionally sensitive Post function. Phasing of rectus femoris (RF) was also affected by pedaling direction; however, its ability to contribute to the directionally sensitive Ant function may only be expressed in forward pedaling. RF also contributed significantly to the directionally insensitive Ext function in both forward and backward pedaling. Other muscles also appear to have contributed to more than one function, which was especially evident in backward pedaling (i.e., BF, SM, MG, and TA to Flex). We conclude that the phasing of only the Ant and Post biomechanical functions are directionally sensitive. Further, we suggest that task-dependent modulation of the expression of the functions in the motor output provides this biomechanics-based neural control scheme with the capability to execute a variety of lower limb tasks, including walking.

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 <