Wavelet coherence analysis of dynamic cerebral autoregulation in neonatal hypoxic-ischemic encephalopathy

Abstract

Cerebral autoregulation represents the physiological mechanisms that keep brain perfusion relatively constant in the face of changes in blood pressure and thus plays an essential role in normal brain function. This study assessed cerebral autoregulation in nine newborns with moderate-to-severe hypoxic-ischemic encephalopathy (HIE). These neonates received hypothermic therapy during the first 72 h of life while mean arterial pressure (MAP) and cerebral tissue oxygenation saturation (SctO2) were continuously recorded. Wavelet coherence analysis, which is a time-frequency domain approach, was used to characterize the dynamic relationship between spontaneous oscillations in MAP and SctO2. Wavelet-based metrics of phase, coherence and gain were derived for quantitative evaluation of cerebral autoregulation. We found cerebral autoregulation in neonates with HIE was time-scale-dependent in nature. Specifically, the spontaneous changes in MAP and SctO2 had in-phase coherence at time scales of less than 80 min (< 0.0002 Hz in frequency), whereas they showed anti-phase coherence at time scales of around 2.5 h (~ 0.0001 Hz in frequency). Both the in-phase and anti-phase coherence appeared to be related to worse clinical outcomes. These findings suggest the potential clinical use of wavelet coherence analysis to assess dynamic cerebral autoregulation in neonatal HIE during hypothermia.

Keywords: Cerebral autoregulation; Hypothermia; Hypoxic–ischemic encephalopathy (HIE); Near infrared spectroscopy (NIRS); Wavelet coherence.

Figures

Fig. 1

Quantification of results in wavelet coherence analysis: (a) Squared cross-wavelet coherence, RMAP → SctO22(n, s), from a HIE neonate. The x-axis represents time, the y-axis represents scale (which has been converted to the equivalent Fourier period), and the color scale represents the magnitude of R2. The cone of influence (COI) where edge effects should be considered is shown as a lighter shade. The black line contours designate areas of significant coherence (p < 0.05). The arrows designate the relative phase between MAP and SctO2: a rightward-pointing arrow indicates in-phase coherence between the two signals (Δφ = 0), a leftward-pointing arrow indicates anti-phase coherence (Δφ = π). (b) Regions of significant coherence (p < 0.05) are classified into four phase ranges: Δφ = 0 ± π/4 (cyan), π/2 ± π/4 (green), π ± π/4 (purple), and − π/2 ± π/4 (red). Regions of non-significant coherence and/or with edge effect appear as blue background. (c) Percentage of significant coherence, P(s), is quantified in each of the four phase ranges. P(s) was a function of scale, which is plotted on the y axis (that has been converted to equivalent Fourier period). At each scale, P(s) was calculated as the percentage of time during which the R2 was statistically significant (p < 0.05). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2

Wavelet-based MAP → SctO2 coherence in two neonates with abnormal outcomes: (a) In this squared cross-wavelet coherence, RMAP → SctO22(n, s), from the first neonate who was treated for moderate encephalopathy, the x-axis represents time, the y-axis represents scale (which has been converted to equivalent Fourier period), and the color scale represents the magnitude of R2. The cone of influence (COI) where edge effects should be considered is shown as a lighter shade. The black line contours designate areas of significant coherence (p < 0.05). The arrows designate the relative phase between MAP and SctO2: a rightward-pointing arrow indicates in-phase coherence between the two signals (Δφ = 0), a leftward-pointing arrow indicates anti-phase coherence (Δφ = π). (b) An enlarged segment of the real-time MAP and SctO2 data from the first neonate. The two signals fluctuate synchronously in a clear in-phase relationship. (c) Squared cross-wavelet coherence, RMAP → SctO22(n, s), is shown from the second neonate who was treated for severe encephalopathy. (d) An enlarged segment of the real-time MAP and SctO2 data is shown from the second neonate. The two signals fluctuate in a clear anti-phase relationship with a periodicity of around 2 h. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

Scale-dependent percentage of significant MAP → SctO2 coherence, P(s), quantified in two phase ranges: (a) Δφ = 0 ± π/4, and (b) Δφ = π ± π/4. In each graph, the solid lines denote the group-averaged values and the shaded regions denote the standard errors.

Fig. 4

Wavelet coherence and gain estimation at group level: (a) mean percentage of the significant coherence, Pmean, over the time scales of 7.5 min to 5 h, and (b) mean gain associated with the significant coherence, Gmean. In each graph, the data are plotted as mean ± standard error.

Fig. 5

Estimated Fourier power spectrum density (PSD) of (a) spontaneous MAP oscillations, and (b) spontaneous SctO2 oscillations in HIE neonates during hypothermia. PSD was estimated based on the normalized MAP and SctO2 time series (the original time series divided by their mean values) and, therefore, had an arbitrary unit (a.u.).