2.1.

Participants

We studied 22 premature infants with a gestational age of $<32\text{weeks}$ with an indwelling arterial catheter, which had been inserted for clinical reasons at birth. The study was carried out at the Neonatal Care Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen. The study was conducted in parallel with an ongoing study on the link between fetal inflammation and cerebral autoregulation, and hence, only children born at Rigshospitalet were eligible. Infants with major malformations or diagnosis of brain injury at recruitment were excluded. We aimed to examine infants within the first $24\mathrm{h}$ after birth. Median age at examination was 17.4 $(4.0–43.5)\mathrm{h}$ . Clinical data were obtained from the infants’ medical records. The latest blood gas value taken before the examination was also recorded [median time interval between blood sample and examination: 2.0 $(0–6.4)\mathrm{h}$ ]: Demographic and clinical data of the infants are summarized in Table 1 . The Danish Local Ethical Committee approved the study (Journal No. H-A-2007-0109), and written informed parental consent was obtained in all cases.

Table 1

Demographic data.

2.2.

Continuous NIRS and Arterial Blood Pressure Recording

NIRS data were recorded using a NIRO-300 spectrophotometer (Hamamatsu Photonics, Hamamatsu City, Japan). The probes were fixed in a nontransparent, soft probe holder and secured to the frontotemporal or frontoparietal region of the head with a flexible bandage. The interoptode distance was $4\mathrm{cm}$ . Because the skin of premature infants is very thin and vulnerable, no adhesive tape was used. Changes in the relative concentrations of oxygenated and deoxygenated haemoglobin were recorded and used to calculate the oxygenation index (OI). OI is the difference between oxygenated and deoxygenated haemoglobin divided by a factor of 2. Changes in OI have been validated to represent changes in cerebral blood flow in several animal studies.^{10, 11, 12}

ABP waveforms were measured with a pressure transducer connected to the indwelling arterial catheter. Before the mean ABP (MAP) was calculated, the ABP data were filtered by a first-order low-pass filter with a cutoff frequency of $0.3\mathrm{Hz}$ .

NIRS and MAP data were sampled simultaneously and synchronized with arterial saturation (assessed by pulse oximetry) at $2\mathrm{Hz}$ by an analog-to-digital converter, and stored on a laptop for further analysis. Using in-house written software (Labview) recording was automatically stopped if changes in arterial saturation exceeded 5%. The infants were clinically stable during measurements. One investigator (G.H.H.) observed the infants and the NIRS instrument continuously during the measurements and interrupted the recording if movement artefacts caused a baseline shift (e.g., radiographs and manipulation of the infant due to suctioning, etc.) or if there was loss of the ABP signal due to blood-gas sampling. To eliminate episodes of artifacts, the data were divided into $10\text{-}\min$ measurements of uninterrupted recordings.

2.3.

Frequency Analysis

Transfer function analysis of coherence between continuous measurements of OI and MAP were performed in two frequency ranges. The very low-frequency (VLF) range ( $0.003–0.04\mathrm{Hz}$ , corresponding to a cycle length of $25–300\mathrm{s}$ ) and the low-frequency (LF) range ( $0.04–0.1\mathrm{Hz}$ , corresponding to a cycle length of $10–25\mathrm{s}$ ). Each $10\text{-}\min$ measurement was subdivided into three segments of $5\min$ with 50% overlap and a Hanning window was applied to minimize spectral leakage. Both time signals were then detrended and transformed into PSDs by Fourier transform (Matlab, MathWorks, Natick, Massachusetts).

2.4.

Adjustment for Variability in ABP

We adjusted for variability in ABP in two steps: (i) within each measurement (intrameasurement adjustment) and (ii) between repeated measurements (intermeasurement adjustment) (Fig. 2 ). For intrameasurement adjustment, we calculated coherence in three different ways, as follows:

Fig. 2

Example of ten $10\text{-}\min$ measurements in one of the infants (infant number 20) in the VLF range. (b) Illustrates the effect of adjusting for ABP variability among repeated measurements (intermeasurement adjustment). In this example, the measurements with high ABP variability (i.e., mean power of MAP, illustrated with bars) have the highest ${\text{Coherence}}_{\mathrm{ST}}$ (cross), and hence, the adjusted overall estimate of ${\text{Coherence}}_{\mathrm{ST}}$ is higher than the unadjusted. In this case, the estimate even changes from being statistically insignificant to statistically significant $({\text{Coherence}}_{\mathrm{ST}}⩾0)$ . (a) Illustrates the effect of adjusting for ABP variability within measurement number 4 (intrameasurement adjustment). Mean coherence $\left({\mathrm{Coh}}_{\text{mean}}\right)$ [solid line], intrameasurement ABP weighted mean coherence $\left({\mathrm{Coh}}_{\mathrm{MAPwmean}}\right)$ [dashed-doted line], and coherence at the frequency with maximum ${\mathrm{PSD}}_{\mathrm{MAP}}$ $\left({\mathrm{Coh}}_{\mathrm{MAPmax}}\right)$ [dashed line] are shown.

For intermeasurement adjustment, we adjusted each infant’s overall estimate of coherence for the varying degree of variability in ABP between repeated measurements. This was performed by a weighted analysis. We calculated a weighting factor for each measurement as the proportion of mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ in the measurement concerned compared to the overall sum of mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ in each infant. This factor, reaching the sum of 1 in each infant, was used to weight each repeated measurement. The weighted model is referred to as “intermeasurement ABP weighting.”

2.5.

Threshold of Significant Coherence

We estimated the 95% confidence limit for each coherence measurement mentioned above using Monte Carlo simulations. This confidence limit was then used as a threshold $\left({\text{Threshold}}_{\mathrm{COH}}\right)$ to determine whether a measurement is coherent or not (i.e., coherent if the coherence measurement is above ${\text{Threshold}}_{\mathrm{COH}}$ ). In the Monte Carlo simulations, OI and MAP signals were both reshuffled randomly to obtain uncorrelated signals with signal variances that were the same as the original signal. The same procedure was repeated 10,000 times, and 10,000 values resulted for each coherence measurement. These 10,000 values were then sorted in ascending order, and the 95% percentile was taken as the 95% confidence limit of ${\text{Threshold}}_{\mathrm{COH}}$ . We standardized each measurement of coherence by subtracting ${\text{Threshold}}_{\mathrm{COH}}$ from it yielding standardized coherence $\left({\text{Coherence}}_{\mathrm{ST}}\right)$ . ${\text{Coherence}}_{\mathrm{ST}}⩾0$ indicates imperfect cerebral autoregulation.

2.6.

Measure of Precision

We applied different analytical approaches to the same data and compared their precision in terms of repeatability between repeated measurements. We hypothesized that cerebral autoregulation stayed stable between two successive $10\text{-}\min$ measurements, and hence, that a high repeatability reflects a high signal-to-noise ratio (i.e., a low amount of measurement error). Statistically, we looked for the analytical approach with the best discrimination among infants based on their level of coherence. This means that we looked for the analytical approach with the highest variability in level of coherence among infants (i.e., a large range of estimates among infants) that at the same time had a small range of variation between repeated measurements in the same infant [i.e., a small standard error (SE)]. These conditions are combined in the $P$ value. Hence, we chose to favor the analytical approach with the most significant difference in level of coherence among our infants.

2.7.

Statistical Methods

Averages of coherence, ${\text{Coherence}}_{\mathrm{ST}}$ , ThresholdCOH, and variability in MAP were calculated for each infant, and from these averages, we calculated a grand mean. Variability in MAP in each measurement was calculated as the square root of mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ in each frequency range. To estimate the variability in mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ between measurements, we calculated each infant’s coefficient of variation.

One-way analysis of variance for repeated measurements with spatial power as covariance structure was used to describe each infants overall estimate of ${\text{Coherence}}_{\mathrm{ST}}$ (“proc mixed” in SAS 9.1, SAS Institute). This covariance structure takes the nonequidistant nature of the measurements due to artifacts, blood-gas sampling, or changes in arterial saturation exceeding 5% into account. We used two models. Model 1 used infant identification as the only covariate, allowing each infant to have its own level of ${\text{Coherence}}_{\mathrm{ST}}$ with no effect of time. Model 2 added time and interaction between time and infant identification, thereby allowing each infant to have an individual level and trend of ${\text{Coherence}}_{\mathrm{ST}}$ . We used model 2 to identify a possible drifting nature of cerebral autoregulation. We used model 1 to calculate each infant’s overall estimate of coherence and to divide the infants into the two following groups: effective autoregulation (i.e., ${\text{Coherence}}_{\mathrm{ST}}<0$ ) and imperfect autoregulation (i.e., ${\text{Coherence}}_{\mathrm{ST}}⩾0$ ). An independent-samples T-test was conducted to compare gestational age (GA) and postnatal age for infants in these two groups. A nonparametric Fisher’s exact test was used to test if clinical state (use of inotropics or mechanical ventilation), incidence of intraventricular haemorrhage (IVH) grade 3–4, or death in the first month of life differed between these groups (SPSS statistics 17.0). $P<0.05$ was considered statistically significant.

In order to estimate the minimum monitoring time needed to reach a robust estimate of cerebral autoregulation, we examined statistical power of ${\mathrm{Coh}}_{\text{mean}}$ , ${\mathrm{Coh}}_{\mathrm{MAPwmean}}$ , and ${\mathrm{Coh}}_{\mathrm{MAPmax}}$ by means of a simulation study. Using estimated values of ${\text{Coherence}}_{\mathrm{ST}}$ , the within infant variation, the autocorrelation, and the residual variation from model 1 with an autoregressive structure, we generated data sets with 22 infants for each of the three measurements of coherence. The number of measurements varied between 5 and 35. Statistical power according to number of measurements was then estimated by the empirical rejection rate in the 1000 simulated data sets.

3.1.

General Features of Blood Pressure, Coherence, and Threshold of Coherence

We monitored 22 infants continuously with a median duration of 2.1 $(1.2–3.7)\mathrm{h}$ yielding 215 measurements of $10\min$ [median: 10, (4–17)]. Grand mean variability in MAP was $4.8\mathrm{mm}$ Hg (SD: 1.5) in VLF and $1.8\mathrm{mm}$ Hg (SD: 0.7) in LF. Mean coefficient of variation in mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ between measurements was 62% (SD: 21%) and 50% (SD: 21%) for VLF and LF, respectively ( $P$ : 0.06).

${\mathrm{Coh}}_{\mathrm{MAPmax}}$ had the highest ${\text{Threshold}}_{\mathrm{COH}}$ in both frequency bands [0.80 (SD: 0.002) in both VLF and LF] followed by ${\mathrm{Coh}}_{\mathrm{MAPwmean}}$ [VLF: 0.50 (SD: 0.001) and LF: 0.47 (SD: 0.007)] and ${\mathrm{Coh}}_{\text{mean}}$ [VLF: 0.47 (SD: 0.006) and LF: 0.45 (SD: 0.005)].

The variability between infants in single measurements of ${\mathrm{Coh}}_{\text{mean}}$ was 0.06 (Table 2 ), while the variability in ${\mathrm{Coh}}_{\text{mean}}$ within infants was 0.1. This means that the imprecision of a measurement of coherence from a single $10\text{-}\min$ measurement exceeded the variability in grand mean coherence between infants.

Table 2

Grand mean of each infant’s average coherence, threshold of coherence, and standardized coherence (CoherenceST) .

3.2.

Intrameasurement Adjustment for Blood Pressure Variability

Overall, ${\mathrm{Coh}}_{\text{mean}}$ had the lowest SE of estimated ${\text{Coherence}}_{\mathrm{ST}}$ . ${\mathrm{Coh}}_{\mathrm{MAPwmean}}$ had a slightly higher SE of estimated ${\text{Coherence}}_{\mathrm{ST}}$ , while ${\mathrm{Coh}}_{\mathrm{MAPmax}}$ had the highest. The range of estimated ${\text{Coherence}}_{\mathrm{ST}}$ showed the same, however, less pronounced, tendency (Table 3 ). Figure 3 illustrates this tendency on infant level. These observations are combined in the $P$ values of a significant difference in ${\text{Coherence}}_{\mathrm{ST}}$ among infants (Table 3), where ${\mathrm{Coh}}_{\text{mean}}$ turned out with the most significant difference. ${\mathrm{Coh}}_{\mathrm{MAPwmean}}$ was only able to distinguish among infants significantly in the LF-band, whereas no significant differences among infants were seen for ${\mathrm{Coh}}_{\mathrm{MAPmax}}$ .

Fig. 3

Estimated ${\text{Coherence}}_{\mathrm{ST}}$ and number of measurements (in square brackets) in each infant illustrating the effect of intrameasurement adjustment for ABP variability in VLF and LF. Mean coherence $\left({\mathrm{Coh}}_{\text{mean}}\right)$ [△], intrameasurement ABP weighted mean coherence $\left({\mathrm{Coh}}_{\mathrm{MAPwmean}}\right)$ [◻], and coherence at the frequency with maximum ${\mathrm{PSD}}_{\mathrm{MAP}}$ $\left({\mathrm{Coh}}_{\mathrm{MAPmax}}\right)$ [○] are shown. Error bars represent $1.96\times$ SE of the estimate. Infants treated with Dopamine during the measurement are marked with (D). Infants who developed IVH grade 3–4 are marked with (H). Infants who died in the first month of life are marked with ${}^{(†)}$ .

Table 3

Effect of adjusting for ABP variability within each measurement.

3.3.

Intermeasurement Adjustment for Blood Pressure Variability

We found a positive but insignificant correlation between each infant’s mean variability in MAP and estimates of ${\text{Coherence}}_{\mathrm{ST}}$ based on ${\mathrm{Coh}}_{\text{mean}}$ (VLF: $r^2=0.07$ , $P=0.2$ ; LF: $r^2=0.001$ , $P=0.9$ ). However, residual plots of estimated ${\text{Coherence}}_{\mathrm{ST}}$ (model 1) against mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ showed a pattern of decreasing residuals with increasing mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ (data not shown). We therefore weighted the estimated ${\mathrm{Coh}}_{\text{mean}}$ with mean ${\mathrm{PSD}}_{\mathrm{MAP}}$ in each measurement (intermeasurement ABP weighting). This weighting resulted in a wider range of estimated ${\text{Coherence}}_{\mathrm{ST}}$ among infants (VLF: 0.28, LF: 0.26) accompanied by almost no change in the SE (VLF: 0.03, LF: 0.04). Consequently, the $P$ values were diminished (i.e., discrimination among infants increased). In VLF the $P$ value changed from 0.0013 to 0.0007 and in LF from 0.0012 to 0.0001).

3.4.

Level of ${\text{Coherence}}_{\mathrm{ST}}$

Because ${\mathrm{Coh}}_{\text{mean}}$ and intermeasurement adjustment for blood pressure variability performed best in terms of precision, we used these conditions in model 1 to estimate each infant’s overall level of ${\text{Coherence}}_{\mathrm{ST}}$ in both frequency bands. Indication of imperfect cerebral autoregulation [i.e., ${\text{Coherence}}_{\mathrm{ST}}⩾0$ , was found in 41% $(n=9)$ and 50% $(n=11)$ of the infants for VLF and LF, respectively]. There was no significant difference in GA, postnatal age, or ${\mathrm{P}}_{\mathrm{a}}\mathrm{C}{\mathrm{O}}_2$ between infants with an imperfect $({\text{Coherence}}_{\mathrm{ST}}⩾0)$ and infants with an effective autoregulation $({\text{Coherence}}_{\mathrm{ST}}<0)$ . Two infants received treatment with Dopamine, two infants had mechanical ventilation, two infants developed IVH grade 3–4, and two infants died in the first month of life (Table 1). None of these parameters differed significantly between the two groups.

3.5.

Statistical Power

We estimated statistical power by the percentage among 1000 simulated data sets, where the null hypothesis (i.e., no significant difference in ${\text{Coherence}}_{\mathrm{ST}}$ among infants) was rejected and plotted power against number of measurements for the three coherence measurements in VLF and LF (Fig. 4 ). The increase of statistical power was highest for ${\mathrm{Coh}}_{\text{mean}}$ with the line approaching a power of 80% by 22 $\left(3.7\mathrm{h}\right)$ and 8 $\left(1.3\mathrm{h}\right)$ measurements, for VLF and LF, respectively. The line for ${\mathrm{Coh}}_{\mathrm{MAPwmean}}$ had a much smaller slope and only reached a power of approximately 35% and 60% when simulating 35 measurements $\left(5.8\mathrm{h}\right)$ , for VLF and LF, respectively. For ${\mathrm{Coh}}_{\mathrm{MAPmax}}$ the simulation study showed no change in power with an increasing number of measurements.

Fig. 4

Analysis of statistical power of the three coherence measurements in the (a) very low-frequency and (b) low-frequency range. Power is calculated as percentage of simulations out of 1000, where 22 infants’ standardized coherence differed significantly. Mean coherence $\left({\mathrm{Coh}}_{\text{mean}}\right)$ (continuous line), intrameasurement ABP weighted mean coherence $\left({\mathrm{Coh}}_{\mathrm{MAPwmean}}\right)$ (dotted line), and coherence at the frequency with maximum ${\mathrm{PSD}}_{\mathrm{MAP}}$ $\left({\mathrm{Coh}}_{\mathrm{MAPmax}}\right)$ (dashed line) are shown.

3.6.

Time Trend

A time trend ( $P$ $\text{value}⩽0.05$ in model 2) was only demonstrated for ${\mathrm{Coh}}_{\text{mean}}$ in the VLF-range (data not shown). No significant drift was found in the LF range, where statistical power was almost three times bigger. Consequently, our results do not provide compelling evidence of a drift in coherence over time.

3.7.

Frequency Dependency

We found no evident concordance between estimated ${\text{Coherence}}_{\mathrm{ST}}$ in VLF and LF (estimates are adjusted for inter-measurement variability in ABP) (Fig. 5 ). Only 12 infants had concordant results when classified according to ${\text{Coherence}}_{\mathrm{ST}}<0$ or ${\text{Coherence}}_{\mathrm{ST}}⩾0$ . Even though the scatter plot is dominated by noise (i.e., wide confidence intervals of estimated ${\text{Coherence}}_{\mathrm{ST}}$ in individual infants), it seems as if noise alone does not explain the lack of concordance between the two frequency ranges. However, the difference between estimated ${\text{Coherence}}_{\mathrm{ST}}$ in VLF and LF was not significant ( $P=0.3$ in paired sampled T-test).

Fig. 5

Scatter plot of estimated ${\text{Coherence}}_{\mathrm{ST}}$ (inter-measurement adjusted for variability in ABP) in VLF and LF illustrating the amount of concordance. Error bars represent $1.96\times$ SE of each infant’s estimated ${\text{Coherence}}_{\mathrm{ST}}$ [●]. The shaded areas indicate concordance between estimated ${\text{Coherence}}_{\mathrm{ST}}$ in VLF and LF.

4.1.

Repeatability as a Measure of Precision

We set out to find a method that could provide a more precise detection of cerebral autoregulation based on coherence analysis of spontaneous variations in ABP and cerebral NIRS and used repeatability as a proxy for precision. We based this assumption on the fact that we only included clinically stable infants. Furthermore, as cerebral autoregulation reflects the overall activity of millions of vascular smooth muscle cells, it may be assumed that changes in autoregulatory capacity occur gradually. In an experimental study, autoregulation was abolished by $20\min$ of severe hypoxia and recovered over $5–7\mathrm{h}$ .¹³ Hence, because the infants we studied were clinically stable, we assumed that cerebral autoregulation stayed reasonably stable between two successive $10\text{-}\min$ measurements. This was in accordance with the lack of clear evidence of drift in coherence over time in individual infants.

4.2.

Adjustment for Variability in Blood Pressure

Contrary to our expectations, we found that the mean coherence $\left({\mathrm{Coh}}_{\text{mean}}\right)$ was best to discriminate among infants (i.e., had the highest precision). Thus, in other words, adjusting for variability in ABP within each measurement did not increase the signal-to-noise ratio. The same tendency was seen in our simulation study where fewer measurements (less monitoring time) were needed to discriminate among infants based on the mean coherence $\left({\mathrm{Coh}}_{\text{mean}}\right)$ . We ascribe the lower precision of the ${\mathrm{Coh}}_{\mathrm{MAPwmean}}$ and ${\mathrm{Coh}}_{\mathrm{MAPmax}}$ compared to ${\mathrm{Coh}}_{\text{mean}}$ to the effect of the signal-to-noise ratio in MAP. The noise is transferred to the power spectrum by the Fourier transform and gives rise to uncertainty in determining the weighting and the frequency with maximum power, respectively.

However, in line with our hypothesis, precision improved when we adjusted for the varying degree of variability in ABP between measurements in each infant (intermeasurement ABP weighting). Thus, in other words, weighting measurements with large fluctuations in ABP in favor of those with small actually increased the signal-to-noise ratio. Unfortunately, a power study using simulated data sets could not be conducted to explore how this weighting would affect the monitoring time because it would require simulating an ABP signal.

To our knowledge, no previous studies have investigated the influence of variability in ABP on estimated coherence between spontaneous fluctuations in ABP and cerebral NIRS measurements. Our results, however, are in line with previous studies of cerebral autoregulation based on transfer function analysis between Doppler measurements of cerebral blood flow velocity and spontaneous variations in ABP.^{14, 15} It has been proposed to set a minimum variability in ABP for a more precise estimation of cerebral autoregulation from spontaneous fluctuations in ABP.^{5, 14} However, this approach will result in data loss, because in practice, the variability in ABP can be quite low over a considerable length of time. It is important to use data as effectively as possible when research involves vulnerable patients, such as premature infants. Hence, our approach with weighting coherence with variability in ABP in each measurement seems relevant for research studies comparing cerebral autoregulation among vulnerable patients.

4.3.

Frequency-Dependent Functional Relationship

Simulations to estimate the statistical power of the various signal analytical approaches showed a remarkable difference in statistical power in the two frequency ranges. Statistical power was almost three times bigger in LF compared to VLF. From a practical point of view, this indicates that a three-times-longer recording time is required in VLF compared to LF. We speculate that this finding might be attributed to the fact that other regulatory mechanisms for cerebral hemodynamics have more influence in the VLF band (i.e., $0.003–0.4\mathrm{Hz}$ , $25–300\mathrm{s}\text{cycle}$ length) compared to the LF band (i.e., $0.04–0.1\mathrm{Hz}$ , $10–25\mathrm{s}\text{cycle}$ length). This will result in a lower signal-to-noise ratio. Because 91% of our infants were breathing spontaneously, irregular fluctuations in ${\mathrm{P}}_{\mathrm{a}}\mathrm{C}{\mathrm{O}}_2$ might have contributed to the lower statistical power in the VLF band.

The lack of concordance between VLF and LF (Fig. 5) implies that they might represent different aspects or time scales of cerebral autoregulation. Because the autoregulatory ability is controlled by vascular smooth muscle cells, we speculate that an injurious stimulus initially reduces the response time and then eventually destroys the ability to react to changes in blood pressure. Consequently, an infant might actually have a bad autoregulation at higher frequencies and a good one at lower frequencies (upper left quadrant on Fig. 5). However, a higher amount of physiological noise in VLF compared to LF could also contribute to the lack of concordance by falsely indicating a good autoregulation at low frequencies. Still, further analyses are needed to clarify if the two frequency bands contribute with shared or different information.

4.4.

Nature of Cerebral Autoregulation

Our results indicated a range of levels of coherence, even in these relatively healthy preterm infants. Furthermore, the drift in individual infants was minimal. In an earlier work by Soul,⁸ who used the same technique to detect cerebral autoregulation in preterm infants, data were analyzed and interpreted differently. The authors computed a pressure-passive index (i.e., percentage of all $10\text{-}\min$ measurements that were classified as coherent) to characterize autoregulation in each infant. The computation of Soul’s study was based on an assumption of autoregulation being “on” or “off” according to the mean coherence being below or above the threshold of statistical significance. Our data show that single values of coherence from $10\text{-}\min$ measurements have an imprecision that exceeds the variability among infants. Consequently, we believe that the precision of this measuring method is too low to get a meaningful estimate from a single measurement. Similarly, using the maximum value of coherence (i.e., picking a single measurement among several) to characterize each infant’s level of autoregulation is not an effective nor unbiased way of data analysis. In two previous studies, this was done and the maximum coherence was associated with mortality⁷ and occurrence of IVH in premature infants.⁹

An imperfect cerebral autoregulation (i.e., coherence exceeding the limit of statistical significance) was detected in approximately one-half of our stable infants, and no correlation was found to disease (i.e., IVH, neonatal death, or use of Dopamine). The fact that an imperfect cerebral autoregulation did not correlate with disease was unexpected. However, the present study was not powered to test for this correlation. The proportion of infants with an imperfect cerebral autoregulation is in line with earlier studies using the same technique to study “dynamic” cerebral autoregulation in premature infants.^{7, 9} Contrarily, studies using a “static” measuring approach have, in general, shown an intact cerebral autoregulation in stable premature infants and an impaired autoregulation in sick premature infants. The discrepancy between studies based on continuous cerebral NIRS and studies based on single measurements of cerebral blood flow by means of either xenon-133 clearance technique^{16, 17} or cerebral NIRS^{18, 19} may be apparent. The conclusion that autoregulation is present in stable premature infants in the latter studies came from a statistically insignificant relation between arterial blood flow and cerebral blood flow. In fact, the 95%-confidence limit was $+2\mathrm{\%}∕\mathrm{mm}$ Hg (i.e., compatible with an imperfect autoregulation that would give rise to a statistically significant coherence with the techniques discussed here).

4.5.

Strength and Limitations