Patients with autonomic failure exhibited statistically significant differences in the wavelet scale of maximum cross-correlation upon posture change compared to age-matched controls.
Observational
Does wavelet cross-correlation analysis detect differences in cerebral autoregulation dynamics between patients with autonomic failure and controls?
Wavelet cross-correlation provides a useful method for investigating cerebral autoregulation dynamics without assuming stationarity of the time series.
Wavelet cross-correlation (WCC) is used to analyse the relationship between low-frequency oscillations in near-infrared spectroscopy (NIRS) measured cerebral oxyhaemoglobin (O(2)Hb) and mean arterial blood pressure (MAP) in patients suffering from autonomic failure and age-matched controls. Statistically significant differences are found in the wavelet scale of maximum cross-correlation upon posture change in patients, but not in controls. We propose that WCC analysis of the relationship between O(2)Hb and MAP provides a useful method of investigating the dynamics of cerebral autoregulation using the spontaneous low-frequency oscillations that are typically observed in both variables without having to make the assumption of stationarity of the time series. It is suggested that for a short-duration clinical test previous transfer-function-based approaches to analyse this relationship may suffer due to the inherent nonstationarity of low-frequency oscillations that are observed in the resting brain.
Rowley et al. (Fri,) conducted a observational in Autonomic failure. Autonomic failure vs. Age-matched controls was evaluated on Wavelet scale of maximum cross-correlation upon posture change. Patients with autonomic failure exhibited statistically significant differences in the wavelet scale of maximum cross-correlation upon posture change compared to age-matched controls.
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