Long-latency muscle activity reflects continuous, delayed sensorimotor feedback of task-level and not joint-level error

Abstract

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.

Keywords: balance; electromyography; postural control; reflex; sensorimotor response.

Fig. 1.

Support-surface perturbation characteristics for discrete perturbations vs. perturbation trains. Discrete ramp-and-hold perturbations (two left panels) exhibited one acceleration burst and a single velocity level. Perturbation trains (right panel) featured multiple acceleration bursts of equal magnitude in forward and backward directions, resulting in stepped velocities in forward and backward directions. Note that the first and last acceleration bursts were at one-half the magnitude of the other acceleration bursts to ensure that the platform had a nonzero velocity level at the midpoint of the perturbation.

Fig. 2.

Reconstruction of electromyographic (EMG) activity based on center of mass (CoM) or joint kinematics. A: schematic diagram of EMG illustrating that delayed CoM or joint angle kinematics (ankle, knee, hip) were multiplied by feedback gains, added, and half-wave rectified to reconstruct muscle activity throughout discrete perturbations and perturbation trains. Tibialis anterior (TA) and medial gastrocnemius (MG) were reconstructed using both individual joint kinematic signals, as well as combinations of joint kinematics (ankle/knee, knee/hip, ankle/hip), resulting in six input signals. B: the time course of reconstructed EMG signals were a linear sum of delayed displacement, velocity, and acceleration. θ_n, Angular displacements; d, displacement; v, velocity; a, acceleration; θ, θ̇, and θ̈: joint angular displacement, velocity, and acceleration, respectively; λ, time delay; k_d, k_v, and k_a: feedback gains on CoM displacement, velocity, and acceleration, respectively; k_θ, k_θ̇, and k_θ̈: feedback gains on θ, θ̇, and θ̈, respectively. [Adapted with permission from Welch and Ting (2009)].

Fig. 3.

Postural responses to discrete perturbations and perturbation trains. A: average CoM kinematics in discrete perturbations (two left panels) and perturbation trains (right panel). Discrete perturbations started at rest, such that CoM acceleration and velocity were always in the same direction. In perturbation trains, acceleration bursts occurred during different magnitudes and directions of CoM displacement and velocity (gray shaded boxes). Positive values of CoM kinematics indicate anterior motion, and negative values indicate posterior motion. B: in discrete perturbations, TA was activated in forward perturbations, and MG was activated in backward perturbations. Muscle activity was well-reconstructed using delayed CoM feedback. In perturbation trains, both TA and MG were active, and their activity was also reconstructed by delayed (80–100 ms) feedback on CoM kinematics (blue traces). This demonstrates that the activity of each muscle depended on a combination of CoM acceleration, velocity, and displacement. Muscle traces were considered well-reconstructed when r² ≥ 0.5 or variability accounted for (VAF) ≥ 75%.

Fig. 4.

Comparison of CoM feedback gains in discrete perturbations (Disc) and perturbation trains (Train). A: across subjects, k_v and k_a had significantly higher magnitude in perturbation trains vs. discrete perturbations. For both TA (left column) and MG (right column), each pair of connected dots represents the magnitude of feedback gains that best reconstructed averaged muscle activity for one subject in discrete perturbations and perturbation trains (n = 23). There was larger intersubject variability in average feedback gain values in perturbation trains compared with discrete perturbations. †P < 10⁻⁴ for mean comparisons using Student's t-test. ‡P < 10⁻⁴ for variance comparisons using F-test of equality of variance across all subjects. B: in a representative subject, average gains k_v and k_a (horizontal lines) were also lower in discrete perturbations. Feedback gains were also more variable from trial to trial in perturbation trains compared with discrete perturbations; vertical lines indicate standard deviation of the mean.

Fig. 5.

Reconstructions of muscle activity in discrete perturbations using CoM vs. joint angle kinematics. Average muscle activity and joint angle kinematics for ankle, knee, and hip are shown for a representative subject. A: reconstructions of EMGs based on individual joint kinematics (red traces) were not as high as reconstructions using CoM kinematics (blue traces). Moreover, delays using joint kinematics (red traces) were unphysiologically short. B: EMG reconstructions based on combinations of joint kinematics improved fits, even in TA, which only crosses the ankle. Nevertheless, muscle reconstructions using CoM kinematics (blue traces) better matched EMG than reconstructions using joint angles.

Fig. 6.

Reconstructions of muscle activity in perturbation trains using CoM vs. joint angle kinematics. Muscle reconstructions are shown for the same subject as in Fig. 5. Muscle reconstructions using CoM kinematics (blue traces) had higher goodness-of-fit values than reconstructions using joint angle kinematics (red traces) in both A, individual joint reconstructions, and B, joint combination reconstructions.

Fig. 7.

Goodness-of-fit comparisons for muscle reconstructions using CoM vs. individual and integrated combinations of joint kinematics. A: TA reconstructions. B: MG reconstructions. Goodness-of-fit measures (r² and VAF) were significantly higher using CoM kinematics (dark gray bars) vs. individual or integrated combinations of joint kinematics (knee/hip, ankle/hip: light gray bars). Reconstructions improved when using combinations of joint kinematics. *P < 0.05 for mean comparisons using Dunnett's post hoc comparisons. A, ankle; K, knee; H, hip; AK, ankle/knee; KH, knee/hip; AH, ankle/hip.