Using Perturbations to Probe the Neural Control of Rhythmic Movements

TL;DR

This paper introduces a method using phase-dependent impulse response functions derived from harmonic transfer functions to analyze neural control of rhythmic movements like walking, based on perturbation responses.

Contribution

It extends linear time periodic system theory to characterize neural control of rhythmic behaviors, including phase resetting effects, using IRFs from perturbation data.

Findings

IRFs can infer properties of neural feedback and muscle-to-movement mapping

Method applied successfully to simulated and experimental walking data

Phase-dependent responses reveal control mechanisms in rhythmic movements

Abstract

Small continuous sensory and mechanical perturbations have often been used to identify properties of the closed-loop neural control of posture and other systems that are approximately linear time invariant. Here we extend this approach to study the neural control of rhythmic behaviors such as walking. Our method is based on the theory of linear time periodic systems, with modifications to account for ability of perturbations to reset the phase of a rhythmic behavior. We characterize responses to perturbations in the frequency domain using harmonic transfer functions and then convert to the time domain to obtain phase-dependent impulse response functions (IRFs) that describe the response to a small brief perturbation at any phase of the rhythmic behavior. IRFs describing responses of kinematic variables and muscle activations (measured by EMG) to sensory and mechanical perturbations can be used to infer properties of the plant, the mapping from muscle activation to movement, and of neural feedback, the mapping from movement to muscle activation. We illustrate our method by applying it to simulated data from a model and experimental data of subjects walking on a treadmill perturbed by movement of the visual scene.