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.

Balance, Body Motion, and Muscle Activity After High-Volume Short-Term Dance-Based Rehabilitation in Persons With Parkinson Disease: A Pilot Study

“…Surface EMG (Konigsberg Instruments, Pasadena, CA) was collected from leg and trunk muscles, high-pass filtered (35 Hz, third-order zero-lag Butterworth filter), demeaned, rectified, and low-pass filtered (40 Hz). 48,52,53 EMG was analyzed from ankle muscles tibialis anterior (TA) and medial gastrocnemius (MG), recorded bilaterally. 22,54 During backward sway, agonist TA is lengthened and antagonist MG is shortened.…”

Assessment of Multi-Joint Coordination and Adaptation in Standing Balance: A Novel Device and System Identification Technique

“…These parameters are described by two prior studies [17], [24]. Time delays are based on literature describing long-latency responses of the upper and lower body [31][32][33][34]. All model simulations were performed in Matlab Simulink, with a sample frequency of 100 Hz.…”

Review and perspective: neuromechanical considerations for predicting muscle activation patterns for movement

“…Further evidence that task-level and not joint-level feedback control governs temporal patterns is the robutstness of the CoM feedback model predictions in explaining the timecourse of lower limb and back muscles when differing joint-level postural strategies are employed (i.e. “ankle” and “hip” strategies) [39] and across long perturbations in which joint and CoM dynamics become uncorrelated [40]. Similarly, torque-level feedback models have revealed that each joint torque for balance control must be derived from sensory feedback arising from all other joints [41], such that local feedback mechanisms are insufficient to explain neural strategies for standing balance.…”