Defining rhythmic locomotor burst patterns using a continuous wavelet transform

Abstract

We review an objective and automated method for analyzing locomotor electrophysiology data with improved speed and accuracy. Manipulating central pattern generator (CPG) organization via mouse genetics has been a critical advance in the study of this circuit. Better quantitative measures of the locomotor data will further enhance our understanding of CPG development and function. Current analysis methods aim to measure locomotor cycle period, rhythmicity, and left–right and flexor–extensor phase; however, these methods have not been optimized to detect or quantify subtle changes in locomotor output. Because multiple experiments suggest that development of the CPG is robust and that the circuit is able to achieve organized behavior by several means, we sought to find a more objective and sensitive method for quantifying locomotor output. Recently, a continuous wavelet transform (CWT) has been applied to spinal cord ventral root recordings with promising results. The CWT provides greater resolution of cycle period, phase, and rhythmicity, and is proving to be a superior technique in assessing subtle changes in locomotion due to genetic perturbations of the underlying circuitry.