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Neonatal neuromonitoring is a major clinical focus of near-infrared spectroscopy (NIRS) and there is an increasing interest in measuring cerebral blood flow (CBF) and oxidative metabolism (CMRO2) in addition to the classic tissue oxygenation saturation (StO2). The purpose of this study was to assess the ability of broadband NIRS combined with diffusion correlation spectroscopy (DCS) to measured changes in StO2, CBF and CMRO2 in preterm infants undergoing pharmaceutical treatment of patent ductus arteriosus. CBF was measured by both DCS and contrast-enhanced NIRS for comparison. No significant difference in the treatment-induced CBF decrease was found between DCS (27.9 ± 2.2%) and NIRS (26.5 ± 4.3%). A reduction in StO2 (70.5 ± 2.4% to 63.7 ± 2.9%) was measured by broadband NIRS, reflecting the increase in oxygen extraction required to maintain CMRO2. This study demonstrates the applicability of broadband NIRS combined with DCS for neuromonitoring in this patient population.
© 2015 Optical Society of America
1. Introduction
Preterm infants born at a very low birth weight (≤ 1500 g) are at high risk of brain injury, with 5-10% developing major disabilities such as cerebral palsy and 40-50% having long-term cognitive and behavioral deficits [1]. While the etiologies of the major forms of preterm brain injury, intraventricular haemorrhage and periventricular leukomalacia, are multifactorial, it is believed that the immaturity of the cerebral vasculature coupled with unstable cerebral hemodynamics play an important role in their pathogeneses [2,3]. Cerebral blood flow (CBF) in the premature brain is substantially lower than in adults – roughly 15 ml/100g/min compared to 50 ml/100g/min – and therefore less tolerant to flow disturbances that disrupt oxygen delivery [4]. Unstable cerebral hemodynamics has been found in preterm infants by using near-infrared spectroscopy (NIRS) to monitor fluctuations in cerebral oxy- and deoxy-hemoglobin concentrations ([HbO2] and [Hb], respectively) and tissue oxygenation saturation (StO2), defined as [5–7]. The occurrence of low StO2 has also been shown to precede the confirmation of intraventricular haemorrhage by ultrasound [8]. However, NIRS has yet to be widely accepted into clinical practice, in part because the large intra- and inter-patient variability of StO2 makes it difficult to establish thresholds that would have sufficient predictive value [9]. Consequently, there is a growing interest in alternative methods that can directly measure CBF and the cerebral metabolic rate of oxygen (CMRO2) since these are likely more sensitive markers of brain health than StO2 alone [10]. This has been shown in an animal model of neonatal brain injury in which CMRO2, and not tissue oxygenation, correlated with the duration of cerebral ischemia [11].
There is an increasing interest in using NIRS with diffuse correlation spectroscopy (DCS) since the latter can monitor CBF by measuring light intensity fluctuations caused by the movement of red blood cells [12–14]. Combining this measure with StO2 from NIRS can be used to assess CMRO2. Measurements of CBF and CMRO2 in physiological units – i.e., ml of blood/100g/min for the former and ml of O2/100g/min for the latter – can also be obtained using dynamic contrast-enhanced (DCE) NIRS to calibrate DCS [15,16]. The combination of DCS and NIRS has been used in a number of studies involving newborns [17]. All of these studies used frequency-domain NIRS to measure the absorption coefficient at a few discrete wavelengths in order to calculate HbO2 and Hb. In this study we present an alternative broadband NIRS approach, which provides a number of unique features: Unlike acquiring continuous-wave NIRS at a few discrete wavelengths, broadband acquisition enables the quantification of chromophore concentrations since the effects of scattering can be included in the spectral analysis. Additional chromophores besides HbO2 and Hb, such as indocyanine green (ICG) for DCE NIRS and cytochrome-c-oxidase [18], can be quantified because of their unique spectral features. Finally, differential spectral analysis eliminates DC offsets due to such factors as errors in fiber/tissue coupling [19,20] and avoids errors caused by differences in geometry between a calibration phantom and the neonatal head [21].
One approach for quantifying chromophore concentrations by broadband NIRS is based on the application of the modified Beer-Lawbert law to second derivative spectra [22]. This method has been adapted to DCE NIRS for measuring CBF and CMRO2 [23,24]; however, the lack of any discernable features in the 2nd derivative spectrum of HbO2 prevents the determination of StO2. Recently we developed an alternative approach for measuring chromophore concentrations that is based on the application of the diffusion approximation to first and second derivative spectra [25,26]. The purpose of the current work was twofold: To measure CBF, StO2 and CMRO2 in preterm infants diagnosed with patent ductus arteriosus (PDA) who were undergoing treatment with indomethacin. This treatment group was selected because of the well-known vasoconstricting effects of indomethacin, which provides the opportunity to assess if differential spectroscopy can measure the expected decrease in StO2 required to maintain normal CMRO2 [27]. CBF was measured before and after indomethacin treatment using ICG as an intravascular contrast agent [23]. The second objective was to compare indomethacin-induced CBF decreases measured by DCE NIRS to those measured by DCS. We have previously shown a strong correlation between relative CBF measured by these two techniques in animal experiments [15]. This study will provide the opportunity to assess their agreement in a clinical application.
2. Methods
2.1 Patient population and study design
Preterm infants (gestational age < 30 weeks) with hemodynamically significant PDA were enrolled after obtaining parental consent. Diagnosis of PDA was based on clinical indices (systolic murmur, wide pulse pressure, increased ventilation support, and metabolic acidosis) and confirmed by echocardiography (left atrium/aorta ratio > 1.4, internal ductal diameter > 2 mm, and/or left pulmonary artery end diastolic flow velocity > 0.2 m/s). Infants were excluded if diagnosed with moderate or severe intra-ventricular hemorrhage (grade II-IV, Papile classification), major congenital malformations, or persistent hypotension requiring inotropic support. The study was approved by the Health Sciences Research Ethic Board of Western University, London, Ontario, Canada.
Acquisition of the NIRS and DCS data sets were coordinated around the pharmacological treatment of PDA, which involved the infusion of intravenous indomethacin over 30 min at a dose of 0.2 mg/kg. Optical measurements were acquired from the frontoparietal cortex using a custom-built device to hold the optodes on the infant’s head. Approximately 15 min prior to the start of indomethacin infusion, NIR spectra were acquired to determine baseline StO2, followed by the intravenous injection of ICG to measure CBF. The DCE NIRS procedure consisted of acquiring spectra every 200 ms for approximately 80 s after injecting ICG (0.1 mg/kg, BCD Pharma, ON, Canada) into a pre-existing venous catheter. The time-varying arterial ICG concentration was measured simultaneously using a dye densitometer (DDG-2001 A/K, Nihon Kohden, Tokyo, Japan) attached to a foot. After acquiring the baseline NIRS data, DCS measurements were acquired every 30 s throughout the indomethacin infusion. Following the infusion, the NIRS procedure including the injection of a second bolus of ICG was repeated to measure StO2 and CBF.
Recorded clinical data included arterial oxygen saturation (SaO2), heart rate, blood pressure, total hemoglobin concentration [tHb], mode of ventilation, and IVH grade. SaO2 was recorded pre and post indomethacin from the dye densitometer and [tHb] was determined by acquiring a venous blood sample.
2.2 Instrumentation
Optical measurements were acquired with a combined in-house-developed broadband NIRS and DCS system. The main components of the NIRS system were a 20 W halogen light bulb (Ocean Optics HL-2000-HP), which was band-passed filtered to remove all light outside the NIR range, and a spectrometer (Sciencetech Inc., ON, Canada) coupled to a Peltier-cooled CCD camera (Wright Instruments Ltd, UK) [28]. The dimensions of the CCD chip were 1024 x 124 pixels, and the camera was operated as an array detector by binning the 124 pixels. The spectral range of the spectrometer was 680 to 980 nm with a resolution of 0.38 nm/pixel. The light source of the DCS instrument was a continuous-wave laser (DL785-100-S, CrystaLaser, Reno, NV) emitting at 786 nm. The maximum output power at the end of the fiber was 75 mW, which was attenuated by a variable neutral density filter (NDC-50-4M, Thorlabs). In order not to exceed the ANSI limit, the fiber tip was held 1 cm from the skin surface to irradiant a 3.5-mm diameter spot. A single photon counting module (SPCM-AQR4C, Excelitas, QC, Canada) was used for detection and its output was sent to a correlator board (Flex033LQ-1, Correlator.com, NJ, USA) that computed the normalized intensity autocorrelation function.
The probe holder consisted of two emission optodes, one for DCS and the other for NIRS, and a common detection optode. The NIRS emission optode consisted of a fiber optic bundle (3.5-mm diameter active area, 2-m length, 0.55 numerical aperture, NA) to deliver broadband light, and the DCS optode consisted of a single-mode fiber (SMF-28e + , 2-m length N.A. = 0.14, core = 125 μm) coupled to the laser. The detection optode had a common end in contact with the skin that split into two branches: one contained a single-mode fiber connected to the SPCM and the other a fibre bundle coupled to the entrance slit of the spectrograph. The properties of the two branches were the same as the two emission optodes. The source-detector distance for DCS and NIRS were 2.5 and 3.5 cm, respectively. The former distance was shorter due to the lower signal-to-noise characteristics of DCS [15].
2.3 Data processing
2.3.1 Quantifying CBF and CMRO2
Cerebral blood flow was determined from the DCE NIRS data by modeling cerebral hemodynamics as a linear, time-invariant system. Under this assumption, the time-varying concentration of ICG in brain measured by NIRS, Cb(t), is related to the arterial concentration measured with the DDG, Ca(t), by:
where * is the convolution operator and R(t) is defined as the impulse residue function. It represents the amount of ICG in brain tissue following an idealized bolus injection of unit concentration at t = 0. The product CBF⋅R(t) was extracted from the Cb(t) and Ca(t) data by deconvolution [23]. The initial height corresponds to CBF since by definition R(t) has an initial value of one.CMRO2 was calculated based on the Fick principle:
where, K is the oxygen carrying capacity of hemoglobin (1.39 ml of O2 per g of Hb) and fv represents the venous fraction of the cerebral blood volume. It relates the measured StO2 to the true venous oxygen saturation and is assumed to be equal to 0.75 [16].2.3.2 Quantifying chromophore concentrations
Photon flux through the head was modeled using the solution to the diffusion approximation for a semi-infinite homogenous medium, which characterizes the flux in terms of the absorption coefficient, μa, and the reduced scattering coefficient, μs´ [29]. This solution is considered reasonable for applications involving preterm infants as signal contamination from extra-cerebral tissues is considerably less than in adults [21]. The adaptation of the diffusion approximation to the analysis of spectra has been previously outlined [25,26]. With this approach, the relationship between μs´ and wavelength is modeled by an approximation to Mie scattering given by [30]:
where A represents the μs´ value at λ = 800 nm. Likewise, μa is defined in terms of the concentration of HbO2, Hb and water:where εi(λ) represents the extinction coefficient of the ith chromophore and WF is the tissue water fraction. In the case of DCE NIRS, Eq. (4) can be expanded to include ε(λ) for ICG and Cb(t).The procedure for estimating the concentration of each chromophore began by applying a wavelet de-noising algorithm to the spectra. The algorithm consists of transforming the Poisson noise in the data into Gaussian noise, removing white noise by wavelet de-noising, and applying an inverse transformation to obtain noise-free spectra [31]. The concentrations of the chromophores were determined by fitting the solution of the diffusion approximation to the first and second derivatives of the measured reflectance, R(λ), using five fitting parameters: [HbO2], [Hb], WF, A and α. The fitting was conducted in three steps. First, WF was determined by fitting the second derivative of R(λ) between 825 and 850 nm, a range selected because it is devoid of hemoglobin second derivative features. Using the extracted value of WF, [Hb] was determined by repeating the fitting of the second derivative spectrum over the range from 700 to 800 nm since it contains the distinct 760 nm Hb feature. The final step was to determine A, α and [HbO2] by fitting the first derivative of R(λ) from 680 to 845 nm with the WF and [Hb] fixed to the values obtained from the previous steps. For DCE NIRS, the scattering parameters and hemoglobin concentrations were set to their pre-injection values so that the ICG concentration was the only fitting parameter. Fitting was conducted using a constrained optimization routine based on fminsearchbnd ( http://www.mathworks.com/ matlabcentral/fileexchange/8277-fminsearchbnd-fminsearchcon) with the upper and lower boundaries set to span published values [26].
DCS data were analyzed by modeling the electric field autocorrelation function with the solution to the diffusion equation for a semi-infinite homogeneous medium [32]. For DCS, the solution depends on the interoptode distance (2.5 cm), the tissue optical properties (µa and µs'), the coherence factor (β) relating the electric field autocorrelation function to the measured intensity autocorrelation function, and a blood flow index, BFi – derived assuming perfusion can be modeled as pseudo-Brownian motion. Each intensity autocorrelation curve was fit over the range of correlation times from 0.3 to 10 μs with β and BFi as the fitting parameters, and µa and µs' set to their values for 785 nm [15,33]. These values were determined from the baseline spectra using Eqs. (3) and (4).
2.3.3 Statistical analysis
A paired t test was used to determine significant differences before and after indomethacin infusion. The analysis was conducted for CBF, StO2, and CMRO2, as well as for measured clinical parameters (SaO2, heart rate, BP, pH, and transcutaneous carbon dioxide). Linear regression analysis was used to investigate the relationship between CBF and BFi measured by DCE NIRS and DCS, respectively. It was also used to investigate if changes in either StO2 or CMRO2 correlated to the indomethacin-induced reduction in CBF. Statistical significance for all tests was based on a P value < 0.05. Data are presented as mean ± SE, unless otherwise stated.
3. Results
Data were acquired from 16 infants. Of these, only DCE NIRS data were acquired in the first five experiments and both DCS and NIRS data were acquired in the last 11. Three sets were excluded because of poor quality data attributed to motion artifacts in the DCS data for two cases and in the arterial ICG concentration curves in one case. Baseline clinical details from the remaining infants are given in Table 1. Seven patients had unilateral grade I IVH, and an additional one had mild ventriculomegaly. Every effort was made to place the NIRS probes on the contralateral side to the abnormality if its location was known at the time of treatment. There was a significant increase in mean arterial blood pressure with treatment (36.9 ± 2.0 to 41.9 ± 2.8 mm Hg), but not heart rate (153 ± 3 to 155 ± 3 beats/min).
Table 1. Clinical Parameters
Figure 1 illustrates tissue ICG concentration curves and DCS intensity autocorrelation curves acquired pre and post indomethacin treatment from one infant. In this example CBF measured by DCE NIRS decreased from 14.1 to 10.4 ml/100g/min with treatment. The reduction in CBF (26%) is reflected by the diminished magnitude of the first-pass of ICG through the brain (Fig. 1). The lower CBF after indomethacin infusion is also reflected by the rightward shift of the post-treatment DCS autocorrelation curve, which resulted in a 33% reduction in the BFi. In this figure each correlation curve represents the average over 30 s.
Fig. 1 (left) Brain ICG concentration curves measured by DCE NIRS before (blue) and after (red) indomethacin infusion and (right) corresponding DCS intensity autocorrelation curves measured pre (blue symbols) and post (red symbols) infusion. The solid lines in the DCS graphs represent the best fit of the diffusion equation with flow modeled as pseudo-Brownian motion.
Figure 2 shows the time course of CBF over the entire 30-min infusion of indomethacin for the same preterm infant whose data were presented in Fig. 1. In this example, the time series of BFi values extracted from the continuous acquisition of intensity autocorrelation curves was converted into units of blood flow using the pre-infusion CBF measurement obtained by DCE NIRS.
Fig. 2 Time course of CBF from one infant during indomethacin infusion. Intensity autocorrelation curves were acquired continuously with a temporal resolution of 30 s. Their corresponding BFi values have been scaled to the baseline CBF value measured by DCE NIRS.
Linear regression analysis was conducted to assess the relationship between the relative change in the BFi caused by indomethacin infusion and the corresponding change in CBF measured by DCE NIRS (Fig. 3). For the 8 complete DCS and NIRS data sets, a significant correlation between the two techniques was found (R2 = 0.68) with a slope of 0.43 and an intercept of 16%. There was no significant difference in the average indomethacin-induced CBF reduction measured by the two techniques: 26.5 ± 4.3% by NIRS and 27.9 ± 2.2% by DCS. Regression analysis was also conducted between absolute CBF and BFi values; however, a significant correlation between the two sets of measurements was not found.
Fig. 3 Correlation between the treatment-induced change in BFi measured by DCS and the corresponding CBF change measured DCE NIRS (N = 8). Each symbol represents data from one of eight infants, the solid line is the best linear fit, and the blue lines are the 95% confidence intervals.
First and second derivatives of typical reflectance spectra acquired from an infant before and after indomethacin infusion are shown in Fig. 4. Each spectrum was wavelet de-noised and averaged over 50 repetitions acquired over 10 s. In this example, the lack of any noticeable changes in the 2nd derivative spectra before and after treatment, particularly between 710 and 760 nm, which contains a Hb feature, was reflected by the similarly in pre and post-treatment [Hb] values: 11.9 and 12.4 g/dL, respectively. In contrast, the treatment-related change in the 1st derivative spectrum resulted in a reduction in [HbO2] from 36.9 to 24.7 g/dL and a corresponding reduction in StO2 from 75.6% to 66.6%.
Fig. 4 First (a) and second (b) spectral derivatives of reflectance spectra acquired before (red) and after (blue) indomethacin infusion. Spectra were de-noised and averaged over a 10-s interval.
Figure 5 presents the baseline absorption spectrum averaged across 13 patients and the corresponding 1st and 2nd derivative spectra. The greater variability in the absorption spectrum compared to the derivative spectra is evident by the larger error bars in the absorption graph and reflects differences in the DC offset between patients.
Fig. 5 Average absorption spectrum (top) and the corresponding 1st and 2nd derivative spectra (bottom) (N = 13). In each graph, the mean value is represented by the red line and the standard deviation by the error bars.
Average values of the fitting parameters extracted from the spectral derivative analysis of the NIRS data are given in Table 2 along with the CBF estimates from DCE NIRS and the CMRO2 values determined from Eq. (2). Values were averaged over the 13 patients for whom NIRS data were acquired pre and post indomethacin infusion. A significant reduction in CBF was observed due to the vasoconstricting properties of indomethacin. The drug also caused a significant increase in [Hb] and significant decreases in [HbO2] and StO2; however, there was no overall effect on CMRO2. Regression analysis revealed a strong linear relationship between relative changes in CBF and StO2 with treatment (R2 = 0.72, p < 0.001), but no relationship between CBF and CMRO2 (R2 = 0.06). Table 2 also includes the estimated water fraction, the scattering power α and A (i.e., the value of μs´ at λ = 800 nm). None of these parameters changed significantly after indomethacin infusion.
Table 2. NIRS parameters before and after indomethacin infusion (N = 13)
4. Discussion
The aims of this study were to investigate in a clinical cohort the ability of derivative spectroscopy to detect StO2 changes caused by indomethacin treatment and assess the agreement between treatment-related CBF changes measured by DCE NIRS and DCS. Previous studies have shown that infusing indomethacin over relatively short periods (30 min or less) will have measurable effects on cerebral hemodynamic [34,35]. We have previously demonstrated that despite these hemodynamic effects, basal CMRO2 is maintained, even at this early age, by an increase in cerebral oxygen extraction [27]. The reduction in StO2 observed in the current study agrees with the concept of a compensatory adjustment in oxygen extraction, and these results demonstrate the ability of broadband NIRS to track changes in StO2 in a patient population.
One of the main advantages of derivative spectroscopy is the elimination of coupling artifacts at the skin surface that lead to wavelength-independent signal variations, as evident in Fig. 5. Although the general shape of the absorption spectrum was similar across patients, the variability between subjects was large. In contrast, the error bars in the 1st and 2nd derivative plots were considerable smaller, with the remaining variability likely reflecting differences in oxygenation between patients. For example, the largest variation in the 2nd derivative spectrum was evident around 760 nm, corresponding to the well-known Hb feature at this wavelength. A potential disadvantage with derivative spectroscopy is the amplification of spectral noise. In this study, wavelet denoising was applied, which we have previously shown to reduce noise by more than a factor of two without distorting spectra features [26]. This is evident in Fig. 4, which shows the quality of spectra that were obtained within a 10-s sampling interval.
The average baseline measurements of CBF, StO2 and CMRO2 are in good agreement with previous studies. A pre-infusion CBF of 16.5 ± 2.1 ml/100g/min is very close to the expected value around 15 ml/100g/min in preterm infants [4]. Likewise, CMRO2 of 0.90 ± 0.14 ml O2/100g/min is similar to values given in previous NIRS studies: a median of 0.47 ml O2/100g/min was reported by Yoxall and Weindling [36] and 1.03 ml O2/100g/min by Elwell et al. [37]. Finally, StO2 of 70.5 ± 2.4% is within the range reported for stable preterm infants, which is typically between 60% and 75% [9]. Unlike these physiological parameters, the μs´ determined at 800 nm (0.35 ± 0.03 mm−1) was smaller than previously reported. Using time-resolved NIRS, Ijichi et al reported an average value of 0.59 ± 0.12 mm−1 at 795 nm [38]. However the variability in μs´ values is large [30], and the values measured in the current study were similar to what we previously measured in the piglet brain (0.45 mm−1) using the same broadband NIRS approach [25]. A direct comparison against a more established method, such as time-resolved NIRS, would be useful to assess the ability of derivative spectroscopy to accurately measure tissue scattering properties.
A number of studies have combined DCS and NIRS to measure CBF and CMRO2 in infant populations, as summarized by Buckley et al. [17]. Of these, three studies involved other flow techniques and all showed a significant correlation between CBF changes measured by DCS and by transcranial Doppler (R2 = 0.58) [39], perfusion MRI (R2 = 0.49) [40] and phase contrast MRI (R2 = 0.62) [41]. In the current study, the comparison to DCE NIRS avoided potential partial volume errors between perfusion measurements since the two optical techniques interrogated similar brain volumes. Unlike previous studies that measured CBF increases caused by either changes in head elevation or hypercapnia, we investigated the agreement between the techniques for CBF decreases caused by pharmacological therapy for a heart condition common to low birth-weight preterm infants. Despite these differences, a significant correlation between CBF measurements from the two techniques was found (R2 = 0.68) and, more importantly, there was no significant difference in the average treatment-related CBF reduction measured by DCS (27.9 ± 2.2%) and NIRS (26.5 ± 4.3%). These results provide further evidence of the suitability of DCS for measuring CBF in this age group.
Unexpected findings in this study were the regression slope of 0.43 when comparing flow changes measured by DCS and NIRS and the lack of a significant correlation between absolute CBF and BFi values. In a previous study involving newborn piglets, we observed greater agreement between the two techniques, both in terms of relative flow changes (R2 = 0.93) and absolute values (R2 = 0.89) [15]. This discrepancy likely reflects the challenges of conducting a comparison study in a patient population compared to animal experiments. First, the range of flow changes was considerably smaller in this clinical study (roughly 40%) compared to a range from −40% to 120% achieved in the animal experiments by manipulating arterial CO2 tension. Second, patient motion can diminish the quality of both DCS and DCS NIRS data. In particular, measuring the arterial ICG concentration curve by dye densitometry is sensitive to motion as this technique relies on detecting arterial pulsation, similar to pulse oximetry. To minimize artifacts, infants were swaddled to prevent excessive motion of the foot on which the DDG probe was attached, and experiments were delayed if an infant appeared distressed after positioning the probes on the head. Another potential limitation was the use of only one detector for DCS; whereas, multiple detectors are typically used to overcome its poor signal-to-noise ratio compared to NIRS [12]. To compensate, a relatively long collection time (30 s) and a count rate as close to 250 kHz as possible were used. However in some cases, data were acquired with count rates as low as 80 kHz due to factors such as suboptimal contact of the probes on the skin and possible hair contamination. Considering these factors, a regression slope less than unity should not be interpreted as a fundamental discrepancy between the methods, particularly considering the good agreement reported in animal studies.
5. Conclusion
This study demonstrated the ability of a combined broadband NIRS and DCS system to measure changes in CBF and StO2 associated with indomethacin treatment of PDA. In addition, it was demonstrated that CMRO2 did not change with treatment, reflecting the concurrent increase in cerebral oxygen extraction. Compared to other NIRS techniques, broadband NIRS has the advantage that it can account for additional chromophores such as ICG, which can be used with DCE NIRS to calibrate DCS, and potential blood-breakdown contaminates associated with intraventricular haemorrhage [42]. The ability to measure absolute blood flow and oxygen utilization accurately with this combined method could help improve the outcome of patients at risk of brain injury by early identification of clinically significant hemodynamic events.
Acknowledgments
This study was supported by the Canadian Institutes of Health Research (CIHR), the Children’s Health Research Institute, and a personnel award to K St. Lawrence from the Heart and Stroke Foundation, Ontario Provincial Office. The authors would like to the families who graciously consented to be included in this research.
References and links
1. R. E. Behrman and A. S. Bulter, Preterm Birth: Causes, Consequences, and Prevention (National Academies Press, 2007).
2. J. J. Volpe, H. C. Kinney, F. E. Jensen, and P. A. Rosenberg, “The developing oligodendrocyte: key cellular target in brain injury in the premature infant,” Int. J. Dev. Neurosci. 29(4), 423–440 (2011). [CrossRef] [PubMed]
3. H. Bassan, “Intracranial hemorrhage in the preterm infant: understanding it, preventing it,” Clin. Perinatol. 36(4), 737–762 (2009). [CrossRef] [PubMed]
4. G. Greisen, “Cerebral blood flow in preterm infants during the first week of life,” Acta Paediatr. Scand. 75(1), 43–51 (1986). [CrossRef] [PubMed]
5. J. S. Soul, P. E. Hammer, M. Tsuji, J. P. Saul, H. Bassan, C. Limperopoulos, D. N. Disalvo, M. Moore, P. Akins, S. Ringer, J. J. Volpe, F. Trachtenberg, and A. J. du Plessis, “Fluctuating pressure-passivity is common in the cerebral circulation of sick premature infants,” Pediatr. Res. 61(4), 467–473 (2007). [CrossRef] [PubMed]
6. M. Tsuji, J. P. Saul, A. du Plessis, E. Eichenwald, J. Sobh, R. Crocker, and J. J. Volpe, “Cerebral intravascular oxygenation correlates with mean arterial pressure in critically ill premature infants,” Pediatrics 106(4), 625–632 (2000). [CrossRef] [PubMed]
7. F. Y. Wong, T. S. Leung, T. Austin, M. Wilkinson, J. H. Meek, J. S. Wyatt, and A. M. Walker, “Impaired autoregulation in preterm infants identified by using spatially resolved spectroscopy,” Pediatrics 121(3), e604–e611 (2008). [CrossRef] [PubMed]
8. S. Noori, M. McCoy, M. P. Anderson, F. Ramji, and I. Seri, “Changes in cardiac function and cerebral blood flow in relation to peri/intraventricular hemorrhage in extremely preterm infants,” J. Pediatr. 164(2), 264–270 (2014). [CrossRef] [PubMed]
9. F. van Bel, P. Lemmers, and G. Naulaers, “Monitoring neonatal regional cerebral oxygen saturation in clinical practice: value and pitfalls,” Neonatology 94(4), 237–244 (2008). [CrossRef] [PubMed]
10. D. A. Boas and M. A. Franceschini, “Haemoglobin oxygen saturation as a biomarker: the problem and a solution,” Philos. Trans. A. 369(1955), 4407–4424 (2011).
11. K. M. Tichauer, D. Y. L. Wong, J. A. Hadway, R. J. Rylett, T.-Y. Lee, and K. St Lawrence, “Assessing the severity of perinatal hypoxia-ischemia in piglets using near-infrared spectroscopy to measure the cerebral metabolic rate of oxygen,” Pediatr. Res. 65(3), 301–306 (2009). [CrossRef] [PubMed]
12. T. Durduran and A. G. Yodh, “Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement,” Neuroimage 85(Pt 1), 51–63 (2014). [CrossRef] [PubMed]
13. T. Durduran, G. Yu, M. G. Burnett, J. A. Detre, J. H. Greenberg, J. Wang, C. Zhou, and A. G. Yodh, “Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation,” Opt. Lett. 29(15), 1766–1768 (2004). [CrossRef] [PubMed]
14. N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. A. Franceschini, “Noninvasive optical measures of CBV, StO(2), CBF index, and rCMRO(2) in human premature neonates’ brains in the first six weeks of life,” Hum. Brain Mapp. 31(3), 341–352 (2010). [CrossRef] [PubMed]
15. M. Diop, K. Verdecchia, T.-Y. Lee, and K. St Lawrence, “Calibration of diffuse correlation spectroscopy with a time-resolved near-infrared technique to yield absolute cerebral blood flow measurements,” Biomed. Opt. Express 2(7), 2068–2081 (2011). [CrossRef] [PubMed]
16. K. Verdecchia, M. Diop, T.-Y. Lee, and K. St Lawrence, “Quantifying the cerebral metabolic rate of oxygen by combining diffuse correlation spectroscopy and time-resolved near-infrared spectroscopy,” J. Biomed. Opt. 18(2), 27007 (2013). [CrossRef] [PubMed]
17. E. M. Buckley, A. B. Parthasarathy, P. E. Grant, A. G. Yodh, and M. A. Franceschini, “Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects,” Neurophotonics 1(1), 011009 (2014). [CrossRef] [PubMed]
18. G. Bale, S. Mitra, J. Meek, N. Robertson, and I. Tachtsidis, “A new broadband near-infrared spectroscopy system for in-vivo measurements of cerebral cytochrome-c-oxidase changes in neonatal brain injury,” Biomed. Opt. Express 5(10), 3450–3466 (2014). [CrossRef] [PubMed]
19. H. Dehghani, F. Leblond, B. W. Pogue, and F. Chauchard, “Application of spectral derivative data in visible and near-infrared spectroscopy,” Phys. Med. Biol. 55(12), 3381–3399 (2010). [CrossRef] [PubMed]
20. O. Pucci, V. Toronov, and K. St Lawrence, “Measurement of the optical properties of a two-layer model of the human head using broadband near-infrared spectroscopy,” Appl. Opt. 49(32), 6324–6332 (2010). [CrossRef] [PubMed]
21. M. Dehaes, P. E. Grant, D. D. Sliva, N. Roche-Labarbe, R. Pienaar, D. A. Boas, M. A. Franceschini, and J. Selb, “Assessment of the frequency-domain multi-distance method to evaluate the brain optical properties: Monte Carlo simulations from neonate to adult,” Biomed. Opt. Express 2(3), 552–567 (2011). [CrossRef] [PubMed]
22. S. J. Matcher, M. Cope, and D. T. Delpy, “Use of the water absorption spectrum to quantify tissue chromophore concentration changes in near-infrared spectroscopy,” Phys. Med. Biol. 39(1), 177–196 (1994). [CrossRef] [PubMed]
23. D. W. Brown, P. A. Picot, J. G. Naeini, R. Springett, D. T. Delpy, and T. Y. Lee, “Quantitative near infrared spectroscopy measurement of cerebral hemodynamics in newborn piglets,” Pediatr. Res. 51(5), 564–570 (2002). [CrossRef] [PubMed]
24. K. M. Tichauer, J. A. Hadway, T.-Y. Lee, and K. St Lawrence, “Measurement of cerebral oxidative metabolism with near-infrared spectroscopy: a validation study,” J. Cereb. Blood Flow Metab. 26(5), 722–730 (2006). [CrossRef] [PubMed]
25. H. Z. Yeganeh, V. Toronov, J. T. Elliott, M. Diop, T.-Y. Lee, and K. St Lawrence, “Broadband continuous-wave technique to measure baseline values and changes in the tissue chromophore concentrations,” Biomed. Opt. Express 3(11), 2761–2770 (2012). [CrossRef] [PubMed]
26. M. Diop, E. Wright, V. Toronov, T.-Y. Lee, and K. St Lawrence, “Improved light collection and wavelet de-noising enable quantification of cerebral blood flow and oxygen metabolism by a low-cost, off-the-shelf spectrometer,” J. Biomed. Opt. 19(5), 057007 (2014). [CrossRef] [PubMed]
27. R. Arora, M. Ridha, D. S. C. Lee, J. Elliott, H. C. Rosenberg, M. Diop, T.-Y. Lee, and K. St Lawrence, “Preservation of the metabolic rate of oxygen in preterm infants during indomethacin therapy for closure of the ductus arteriosus,” Pediatr. Res. 73(6), 713–718 (2013). [CrossRef] [PubMed]
28. M. Diop, J. T. Elliott, K. M. Tichauer, T.-Y. Lee, and K. St Lawrence, “A broadband continuous-wave multichannel near-infrared system for measuring regional cerebral blood flow and oxygen consumption in newborn piglets,” Rev. Sci. Instrum. 80(5), 054302 (2009). [CrossRef] [PubMed]
29. S. Fantini, M. A. Franceschini, and E. Gratton, “Semi-infinite-geometry boundary problem for light migration in highly scattering media: a frequency-domain study in the diffusion approximation,” J. Opt. Soc. Am. B 11(10), 2128 (1994). [CrossRef]
30. S. L. Jacques, “Optical properties of biological tissues: a review,” Phys. Med. Biol. 58(11), R37–R61 (2013). [CrossRef] [PubMed]
31. M. Diop and K. St Lawrence, “Deconvolution method for recovering the photon time-of-flight distribution from time-resolved measurements,” Opt. Lett. 37(12), 2358–2360 (2012). [CrossRef] [PubMed]
32. C. Cheung, J. P. Culver, K. Takahashi, J. H. Greenberg, and A. G. Yodh, “In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies,” Phys. Med. Biol. 46(8), 2053–2065 (2001). [CrossRef] [PubMed]
33. L. Dong, L. He, Y. Lin, Y. Shang, and G. Yu, “Simultaneously extracting multiple parameters via fitting one single autocorrelation function curve in diffuse correlation spectroscopy,” IEEE Trans. Biomed. Eng. 60(2), 361–368 (2013). [CrossRef] [PubMed]
34. J. Patel, I. Roberts, D. Azzopardi, P. Hamilton, and A. D. Edwards, “Randomized double-blind controlled trial comparing the effects of ibuprofen with indomethacin on cerebral hemodynamics in preterm infants with patent ductus arteriosus,” Pediatr. Res. 47(1), 36–42 (2000). [CrossRef] [PubMed]
35. C. Hammerman, J. Glaser, M. S. Schimmel, B. Ferber, M. Kaplan, and A. I. Eidelman, “Continuous versus multiple rapid infusions of indomethacin: effects on cerebral blood flow velocity,” Pediatrics 95(2), 244–248 (1995). [PubMed]
36. C. W. Yoxall and A. M. Weindling, “Measurement of cerebral oxygen consumption in the human neonate using near infrared spectroscopy: cerebral oxygen consumption increases with advancing gestational age,” Pediatr. Res. 44(3), 283–290 (1998). [CrossRef] [PubMed]
37. C. E. Elwell, J. R. Henty, T. S. Leung, T. Austin, J. H. Meek, D. T. Delpy, and J. S. Wyatt, “Measurement of CMRO2 in neonates undergoing intensive care using near infrared spectroscopy,” Adv. Exp. Med. Biol. 566, 263–268 (2005). [CrossRef] [PubMed]
38. S. Ijichi, T. Kusaka, K. Isobe, K. Okubo, K. Kawada, M. Namba, H. Okada, T. Nishida, T. Imai, and S. Itoh, “Developmental changes of optical properties in neonates determined by near-infrared time-resolved spectroscopy,” Pediatr. Res. 58(3), 568–573 (2005). [CrossRef] [PubMed]
39. E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Schultz, C. M. Sehgal, D. J. Licht, P. H. Arger, M. E. Putt, H. H. Hurt, and A. G. Yodh, “Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound,” Opt. Express 17(15), 12571–12581 (2009). [CrossRef] [PubMed]
40. T. Durduran, C. Zhou, E. M. Buckley, M. N. Kim, G. Yu, R. Choe, J. W. Gaynor, T. L. Spray, S. M. Durning, S. E. Mason, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, M. E. Putt, J. Wang, J. H. Greenberg, J. A. Detre, A. G. Yodh, and D. J. Licht, “Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects,” J. Biomed. Opt. 15(3), 037004 (2010). [CrossRef] [PubMed]
41. V. Jain, E. M. Buckley, D. J. Licht, J. M. Lynch, P. J. Schwab, M. Y. Naim, N. A. Lavin, S. C. Nicolson, L. M. Montenegro, A. G. Yodh, and F. W. Wehrli, “Cerebral oxygen metabolism in neonates with congenital heart disease quantified by MRI and optics,” J. Cereb. Blood Flow Metab. 34(3), 380–388 (2014). [CrossRef] [PubMed]
42. J. Kishimoto, M. Diop, P. McLachlan, S. de Ribaupierre, D. S. C. Lee, and K. St. Lawrence, “Evidence of ventricular contamination of the optical signal in preterm neonates with post hemorrhagic ventricle dilation,” in Optical Tomography and Spectroscopy of Tissue XI, B. J. Tromberg, A. G. Yodh, E. M. Sevick-Muraca, R. R. Alfano, ed. (Proc. of SPIE, 2015), p. 931908.
