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1Department of Physiology and Biophysics and 2Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, New York
Submitted 25 June 2004; accepted in final form 16 October 2004
Inspiratory motor discharges, in addition to long-time-scale rhythmic oscillatory bursting, exhibit short-time-scale rhythmic oscillations that have been identified, and subsequently characterized, using power spectral analyses [predominantly fast-Fourier transforms (FFT)]. These analyses assume that the signal being analyzed is stationary; however, this is not the case for most biological signals, which exhibit varying degrees of nonstationarity. To overcome this limitation, time-frequency methods, which provide not only the frequency content but also information regarding the timing of these fast rhythmic oscillations (i.e., dynamics of spectral activity), should be used. Thus this study was performed to investigate the dynamic or time-varying features of spectral activity in inspiratory motor output. Both conventional time-invariant and time-frequency (time-varying) spectral analysis methods were performed on recordings of diaphragm EMG, phrenic nerve, and hypoglossal nerve discharges obtained from spontaneously breathing urethan-anesthetized adult C57BL/6 mice. Conventional time-invariant spectral analysis using a FFT algorithm revealed three dominant peaks in the power spectrum, which were located at 1) 20–46, 2) 83–149, and 3) 177–227 Hz. Time-frequency spectral analysis using a generalized time-frequency representation (TFR) with the smoothed pseudo-Wigner-Ville distribution (SPWD) kernel confirmed the general location of these spectral peaks, identified additional spectral peaks within the frequency ranges described above, and revealed a time-dependent expression of spectral activity within the inspiratory burst for each of the frequency ranges. Furthermore, this method revealed that 1) little or no spectral activity occurs during the initial portion of the inspiratory burst in any of the frequency ranges identified, 2) transient oscillations in the magnitude of spectral power exist where spectral activity occurs, and 3) total spectral power exhibits an augmenting pattern over the course of the inspiratory burst. These data, which provide the first description of spectral content in inspiratory motor discharges in adult mice, show that both time-invariant and time-varying spectral analysis methods are capable of identifying short-time-scale rhythmic oscillations in inspiratory motor discharge (as expected); however, the dynamic (i.e., timing) features of this oscillatory activity can only be obtained using the time-frequency method. We suggest that time-frequency methods, such as the SPWD, should be used in future studies examining short-time-scale (fast) rhythmic oscillations in inspiratory motor discharges, because additional insight into the neural control mechanisms that participate in inspiratory-phase neuronal and motoneuronal synchronization may be obtained.
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