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This Article Full Text Full Text (PDF) All Versions of this Article: 98/5/2807    most recent 00740.2007v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in ISI Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Mor, Y. Articles by Lev-Tov, A. Search for Related Content PubMed PubMed Citation Articles by Mor, Y. Articles by Lev-Tov, A.

Analysis of Rhythmic Patterns Produced by Spinal Neural Networks

Department of Anatomy and Cell Biology, the Hebrew University Medical School, Jerusalem, Israel

Submitted 3 July 2007; accepted in final form 22 August 2007

A network of spinal neurons known as central pattern generator (CPG) produces the rhythmic motor patterns required for coordinated swimming, walking, and running in mammals. Because the output of this network varies with time, its analysis cannot be performed by statistical methods that assume data stationarity. The present work uses short-time Fourier (STFT) and wavelet-transform (WT) algorithms to analyze the nonstationary rhythmic signals produced in isolated spinal cords of neonatal rats during activation of the CPGs. The STFT algorithm divides the time series into consecutive overlapping or nonoverlapping windows and repeatedly applies the Fourier transform across the signal. The WT algorithm decomposes the signal using a family of wavelets varying in scale, resulting in a set of wavelet coefficients presented onto a continuous frequency range over time. Our studies revealed that a Morlet WT algorithm was the tool of choice for analyzing the CPG output. Cross-WT and wavelet coherence were used to determine interrelations between pairs of time series in time and frequency domain, while determining the critical values for statistical significance of the coherence spectra using Monte Carlo simulations of white-noise series. The ability of the cross-Morlet WT and cross-WT coherence algorithms to efficiently extract the rhythmic parameters of complex nonstationary output of spinal pattern generators over a wide range of frequencies with time is demonstrated in this work under different experimental conditions. This ability can be exploited to create a quantitative dynamic portrait of experimental and clinical data under various physiological and pathological conditions.