Reorganization of Muscle Coordination Underlying Motor Learning in Cycling Tasks.

Diego Torricelli,Daniel Nemati Tobaruela,Jose L Pons,Cristiano De Marchis,Andrea D’Avella,Filipe Oliveira Barroso

doi:10.3389/fbioe.2020.00800

Abstract

The hypothesis of modular control, which stands on the existence of muscle synergies as building blocks of muscle coordination, has been investigated in a great variety of motor tasks and species. Yet, its role during learning processes is still largely unexplored. To what extent is such modular control flexible, in terms of spatial structure and temporal activation, to externally or internally induced adaptations, is a debated issue. To address this question, we designed a biofeedback experiment to induce changes in the timing of muscle activations during leg cycling movements. The protocol consisted in delaying the peak of activation of one target muscle and using its electromyography (EMG) envelope as visual biofeedback. For each of the 10 healthy participants, the protocol was repeated for three different target muscles: Tibialis Anterioris (TA), Gastrocnemius Medialis (GM), and Vastus Lateralis (VL). To explore the effects of the conditioning protocol, we analyzed changes in the activity of eight lower limb muscles by applying different models of modular motor control [i.e., fixed spatial components (FSC) and fixed temporal components (FTC)]. Our results confirm the hypothesis that visual EMG biofeedback is able to induce changes in muscle coordination. Subjects were able to shift the peak of activation of the target muscle, with a delay of (49 ± 27°) across subjects and conditions. This time shift generated a reorganization of all the other muscles in terms of timing and amplitude. By using different models of modular motor control, we demonstrated that neither spatially invariant nor temporally invariant muscle synergies alone were able to account for these changes in muscle coordination after learning, while temporally invariant muscle synergies with adjustments in timing could capture most of muscle activity adaptations observed after the conditioning protocol. These results suggest that short-term learning in rhythmic tasks is built upon synergistic temporal commands that are robust to changes in the task demands.

Highlights

Understanding how the central nervous system (CNS) orchestrates muscle coordination is a fundamental step to deepen our knowledge in the mechanisms underlying movement generation, motor skill acquisition, and motor adaptation to externally induced perturbation
No statistically significant difference in pedaling cadence (PC) was observed between the PRE and POST-1 trials for all the analyzed biofeedback conditions (PCPRE_TA = 66.1 ± 5.9 r/min, PCPOST_TA = 67.1 ± 8.4 r/min, PCPRE_VL = 68.7 ± 5.3 r/min, PCPOST_VL = 66.1 ± 8.6 r/min, PCPRE_GM = 65.9 ± 10.3 r/min, PCPOST_GM = 66.3 ± 10.7 r/min), so that any PRE–POST1 difference in muscle activation and timing is not a cadence-driven effect
We showed that muscle coordination during pedaling can be voluntarily changed through a conditioning procedure based on EMG visual feedback on one single muscle

Summary

Introduction

Understanding how the central nervous system (CNS) orchestrates muscle coordination is a fundamental step to deepen our knowledge in the mechanisms underlying movement generation, motor skill acquisition, and motor adaptation to externally induced perturbation. De Marchis et al (2013b) have explored the shortterm learning mechanisms in a novel pedaling paradigm using visual biofeedback of pedal force They showed that short-term motor learning could be accounted for by the use of baseline synergies plus a few additional ones. Jacobs et al (2018) analyzed the re-organization of muscle coordination during adaptation to walking in a powered ankle exoskeleton They showed that subjects adapted the temporal activation patterns during the adaptation phase, keeping unaltered the pre-existing synergies both in number and spatial composition. Modular motor control models have been explored during visuomotor adaptation tasks, highlighting that a complete adaptation to visuo-motor distortions can be achieved by tuning the recruitment of a set of fixed spatial synergies. A similar experiment by De Marchis et al (2018) explored whether the same adaptation mechanism was present when the visuo-motor perturbation was applied to only a portion of the workspace, highlighting that a different recruitment of the same baseline spatial synergies led to the same full biomechanical adaptation when the order of the perturbations was changed

Methods

Results

Conclusion