1 May 2010 Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects
Author Affiliations +
Abstract
We employ a hybrid diffuse correlation spectroscopy (DCS) and near-infrared spectroscopy (NIRS) monitor for neonates with congenital heart disease (n=33). The NIRS-DCS device measured changes during hypercapnia of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations; cerebral blood flow (rCBFDCS); and oxygen metabolism (rCMRO2). Concurrent measurements with arterial spin-labeled magnetic resonance imaging (rCBFASL-MRI, n=12) cross-validate rCBFDCS against rCBFASL-MRI, showing good agreement (R=0.7, p=0.01). The study demonstrates use of NIRS-DCS on a critically ill neonatal population, and the results indicate that the optical technology is a promising clinical method for monitoring this population.
Durduran, Zhou, Buckley, Kim, Yu, Choe, Gaynor, Spray, Durning, Mason, Montenegro, Nicolson, Zimmerman, Putt, Wang, Greenberg, Detre, Yodh, and Licht: Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects

1.

Introduction

Approximately 30,000 infants are born each year in the United States with congenital heart disease (CHD), with about a third requiring major surgical repair in the first few months of life.1 Recent advances in cardiac surgery for complex CHD have minimized infant mortality. Thus, the current focus in the clinical community is oriented toward preventing neurologic injury and improving neurocognitive outcome in these high-risk babies who grow up to face various medical and academic challenges.2, 3

Two key determinants of neurologic injury in babies with complex CHD are now believed to be damaged cerebral autoregulation4 and low-baseline cerebral blood flow (CBFbl) .5 Cerebral autoregulation6 is loosely defined as the ability of a subject to maintain stable and adequate blood flow to the brain at the microvascular level despite changes in cerebral perfusion pressure. Damaged cerebral autoregulation may complicate the clinical management of sick newborns, where the goal is to minimize periods of hypoxia and hypoperfusion. In fact, damaged cerebral autoregulation implies that the local CBF may be passively dependent on external factors. Therefore, to achieve success with this approach to clinical management, it is desirable to continuously monitor cerebral autoregulation and local CBF at the bedside and optimize patient management accordingly.

Unfortunately, it is very difficult to measure cerebral autoregulation noninvasively at the bedside.7 The neonatal population presents challenges different from those of adults. Traditional modalities for measurement of CBF in adults (PET, SPECT, Xenon CT, ASL-MRI, Doppler ultrasound) often pose safety risks, require patient transport, or are limited to large-vessel measurements.8, 9, 10 Thus, an unfilled niche exists for a safe, noninvasive, continuous, bedside monitor of CBF and related hemodynamics in the infant microvasculature.

In this study, we focus on infants born with CHD. We validate a new diffuse optical diagnostic technique, diffuse correlation spectroscopy (DCS), against a more established modality, arterial spin-labeled magnetic resonance imaging (ASL-MRI), for measurement of cerebral blood flow (CBF). Then, using an all-optical instrument that combines DCS with near-infrared spectroscopy (NIRS), we derive changes in oxy- and deoxyhemoglobin concentrations, CBF, as well as a calculated estimate of cerebral metabolic rate of oxygen extraction (CMRO2) during hypercapnia.

Previously, optical monitoring using near-infrared light (NIRS) has been used for transcranial measurements of total hemoglobin concentration, blood oxygen saturation.11, 12 NIRS is particularly successful in infants due to their thin skulls.13, 14, 15, 16, 17, 18, 19, 20 NIRS has also been used for CBF monitoring using exogenous tracers such as indocyanine green or changes in inspired gases.21, 22 Unfortunately, this approach using tracers is indirect at best and can be limited in certain physiological conditions.23

A recent advance in biomedical optics has been the development and in vivo application of diffuse correlation spectroscopy (DCS).24, 25, 26, 27 DCS measures microvascular blood flow in deep tissue utilizing the temporal intensity fluctuations of multiply scattered light. DCS is based on physical principles somewhat similar to those of NIRS and thus shares advantages such as noninvasiveness and the ability to penetrate to deep tissues. Additionally, DCS provides a direct measure of CBF without the need for exogenous tracers.28, 29, 30 DCS has been validated against other modalities under a variety of conditions,31, 32, 33, 34, 35, 36 and concurrent use of DCS and NIRS in hybrid probes offers potential for continuous noninvasive estimation of CMRO2 .28, 29, 35, 37, 38, 39

However, application of DCS in neonatal populations has been very limited. For example, while NIRS-DCS has been utilized in adults40 and premature infants33, 35 in clinical settings, it has not as yet been validated or even explored as a monitor of cerebral hemodynamics in critically ill neonates with low-baseline CBF.5

For this study, we measure CBF in infants with complex forms of CHD. During the study, we employ increased carbon dioxide (CO2) in the inspired gas mixture as an intervention to study vascular reactivity in the population. Information about vascular reactivity, in turn, permits us to assess the status of cerebral autoregulation.5 Increased CO2 is a potent cerebral arteriolar vasodilator. Healthy response to hypercapnia is characterized by a slight increase in blood pressure, by a drop in vascular resistance, and by an increase in CBF.41 The CBF response to CO2 is a marker for physiologic reserve in the cerebrovascular bed. CO2 reactivity [i.e., change in CBF per change in partial pressure of CO2 (pCO2) ] is of interest, because impaired CO2 reactivity has been associated with poor neurodevelopmental outcome and a higher risk of death in all age groups.42, 43, 44

Neonates with complex forms of CHD are dependent on a patent ductus arteriosus for systemic blood flow, including CBF. In these neonates, management of the delicate balance of pulmonary blood flow and systemic blood flow is critical. Since CO2 is also a potent pulmonary arteriolar vasoconstrictor, its presence can alter this balance by limiting pulmonary flow in favor of systemic circulation. Increased inspired CO2 (FiCO2) has been shown by NIRS to significantly increase mixed venous oxygenation in neonates with hypoplastic left heart syndrome (HLHS)45 and to increase CBF during hypothermic cardiopulmonary bypass.46, 47 Studies from our institution have demonstrated that periventricular leukomalacia (PVL), a form of white matter injury seen in this patient population and in infants born prematurely, occurred in 28% of CHD neonates and was associated with lower baseline CBF values and a smaller change in cerebral blood flow with hypercapnia—i.e., associated with reduced CO2 reactivity.5 We note that while the baseline CBF of term neonates born with CHD as a group (10.2±4.4ml100gmin) also tends to be lower than healthy neonates48 (16.6±5.9ml100gmin) , the key point we stress here is that those with lower CBF among CHD neonates had a higher occurrence of neurological injury.5

Very recently, we have demonstrated in a subpopulation of infants with CHD that neurological injury was associated with decreased blood oxygen saturation and increased time to surgery, thus indicating the potential value for preoperative monitoring.49 NIRS has also been used to follow hemodynamic changes in CHD neonates after the Norwood procedure, suggesting that cerebral hemodynamics were influenced by external interventions and postoperative events.50 The present feasibility study demonstrates potential for relating neurological outcome and cerebral hemodynamics in this early period after surgery by demonstrating the use of an all-optical, bedside monitor during this presurgical period that could safely be deployed at the bedside. We measure hemodynamic CO2 reactivity in response to induced hypercapnia, which, as mentioned earlier, could be related to the neurological outcome (to be demonstrated in a future, larger study). Furthermore, arguably, addition of a CBF measure and calculation of CMRO2 should further enhance this potential by providing a more complete picture of the cerebral oxygen metabolism than currently available.

The present work is the first to report the use of such an all-optical instrument in neonates with complex CHD (n=33) . Furthermore, concurrent measurements with ASL-MRI (rCBFASL-MRI) in 12 (n=12) neonates cross-validate DCS (rCBFDCS) against rCBFASL-MRI . The optical data is compared to literature values of vasoreactivity to hypercapnia, and a calculated index that is approximately proportional to changes in CMRO2 during hypercapnia is determined.

2.

Materials and Methods

2.1.

Population

With institutional review board approval, all newborn infants with complex CHD admitted to the cardiac intensive care unit (CICU) at Children’s Hospital of Philadelphia (CHOP) were screened for study inclusion and were approached for participation if the admitting CHD diagnosis was hypoplastic left heart syndrome (HLHS) or transposition of the great arteries (TGA). All patients (n=33) were at full-term age ( 40±4weeks gestation age) with pre- or postnatally diagnosed CHD and were scheduled for surgery with with cardiopulmonary bypass with or without deep hypothermic circulatory arrest. A full baseline neurologic examination was carried out by a child neurologist (DJL) on the day prior to the surgery. Table 1 shows detailed tabulation of the patient characteristics.

Table 1

Tabulation of various characteristics of the subjects. Values are quoted as mean+standard error of the mean. Admission CHD diagnosis of either hypoplastic left heart syndrome (HLHS) or +transposition of the great arteries (TGA) was required for study inclusion.

DiagnosisGenderGestational Age (week)Age at Study (day)Head Circumference (cm)Birth Weight (kg)
TGA6M/5F 39.4±0.2 4.5±0.5 34.8±0.2 3.6±0.1
HLHS13M/9F 38.8±0.2 3.7±0.3 34.3±0.2 3.3±0.1

2.2.

Study Protocol

All procedures were approved by the Children’s Hospital of Philadelphia Institutional Review Board. On the morning of surgery, all patients were transported to the operating room for induction of general anesthesia (fentanyl 510μgkg , pancuronium 0.2mgkg ). Vital signs, including blood pressure, electrocardiogram, transcutaneous oxygen saturations, and end-tidal CO2 (EtCO2) measurements, were monitored during the induction of anesthesia, in transport, and while in the MRI. On arrival at the MRI suite, arterial and venous blood samples were drawn for baseline arterial CO2 (PaCO2) and co-oximetry (quantitative venous and arterial oxygen saturations). The protocol has been previously described,5, 49, 51 and a time line is outlined in Fig. 1 .

Fig. 1

Measurement time line consists of two periods: (1) Baseline, where the subject is breathing room air equivalent mixture with zero CO2 concentration, and (2) Hypercapnia, where CO2 is added to the mixture. There is an intermediate transition period until significant changes are recorded in the EtCO2 measurements. Optical data is obtained continuously throughout the study. Structural MRIs are obtained in both periods prior to ASL-MRI measurements.

037004_1_007003jbo1.jpg

2.3.

Diffuse Optics: Background, Instrumentation, and Analysis

In the near-infrared spectral region, light is multiply scattered as it travels centimeters through deep tissue. Photon absorption in the near-infrared range also occurs mainly due to oxy- and deoxyhemoglobin, water, and lipid. A detectable amount of light scattering comes from red blood cells (RBCs). If photons are scattered from moving RBCs, then the light intensity interference pattern (i.e., the speckle pattern) on the tissue surface will fluctuate in time. The resultant fluctuations of the detected intensity are measured by DCS. NIRS, on the other hand, measures the differential change in the transmitted light intensity at multiple wavelengths due to absorption and scattering, which, in turn, depend on concentrations of oxy- and deoxyhemoglobin ( ΔHbO2 and ΔHb , respectively) among other factors.

The present investigation employs a hybrid instrument combining NIRS and DCS.28, 38 For DCS, we employed a long-coherence-length laser at 785nm . Three lasers ( 690nm , 785nm , 830nm ) modulated at 70MHz were used for NIRS. For DCS, two high-sensitivity avalanche photodiode detectors and a correlator board were used to calculate intensity autocorrelation functions in real time. For NIRS, a homodyne detection scheme with one detector channel was used.

As shown in Fig. 2 , a probe with one source fiber (shared by NIRS and DCS lasers), two detector fibers for DCS, and one detector fiber for NIRS was used. All detectors were placed at 2.5cm away from the source fiber. The probe thickness was 2.5mm , and fibers were 12m long. All materials were thoroughly tested for MRI compatibility. Fiducial markers were placed over the probe to locate fiber positions in the MRI images.

Fig. 2

Measurement schematic showing optical probe details, probe placement, and the concurrent measurement. Typical ASL-MRI images (brighter red/orange indicating higher blood flow) and autocorrelation functions measured by DCS during baseline and hypercapnia are also shown.

037004_1_007003jbo2.jpg

Optical data was analyzed using a semi-infinite, homogeneous medium model that is expected to be fairly accurate given the thin neonate skulls. We have verified this assumption with simulations based on a two-layer model.28 Any variation in the tissue optical properties or tissue dynamics within the probed volume, such as those resulting from a CBF change due to CO2 inhalation, are detectable in the decay rate of the intensity autocorrelation function [g2(τ)] and in the amplitude and phase of the NIRS signal. Figure 2 shows two representative g2(τ) measurements wherein changes in decay time due to increased CBF during hypercapnia are clearly visible. The semi-infinite model was iteratively fit to the measured autocorrelation functions, and a flow-index from the decay time for each hemisphere was extracted every 8s . For NIRS, a modified Beer-Lambert equation with assumed photon path lengths from the literature was used.52, 53, 54 NIRS calculations report HbO2 and Hb concentration changes relative to the baseline (i.e., ΔHbO2 , ΔHb , ΔTHC ). Note that measured changes in absorption from NIRS data were used to improve the DCS fits. DCS flow indices from two detectors were divided by the baseline values and averaged, providing a measure of rCBFDCS versus time. All analysis procedures have been previously described.40

Changes in CMRO2 (rCMRO2) can be calculated from a synthesis of rCBF , ΔHb , and ΔTHC utilizing a relatively simple model.37, 55, 56, 57 In particular, a compartmentalized model of the vasculature is assumed, and an equation that relates these measurable quantities is derived using Fick’s law: CMRO2=OEF×CBF×Ca (Ref. 41). OEF is the normalized oxygen extraction fraction—i.e., the difference between oxygen concentrations in arterial (Ca) and venous ends of the vasculature. Since diffuse optical signal mainly originates from the microvasculature, further assumptions are made to relate the microvascular blood oxygenation to the percentage of blood in the venous and arterial components. These assumptions lead to an equation that has been used28, 29, 37, 38 to estimate rCMRO2 :

1.

rCMRO2=rOEF×rCBF,
=(SaO2StO2SaO2blStO2bl)(γblSaO2blγSaO2)rCBF,
(SaO2StO2SaO2blStO2bl)(SaO2blSaO2)rCBF.
StO2=HbO2(THC) is the microvascular blood oxygen saturation measured by NIRS. Subscript bl is used throughout this paper to indicate baseline values of a parameter. Baseline StO2 (StO2bl) was assumed from literature values for neonates to be 65%.58, 59, 60, 61, 62 THCbl is also assumed from baseline values reported in the literature as 53μM .58, 59, 60, 61, 62 Changes in HbO2 and Hb concentrations were then used to calculate the hypercapnic values for StO2 and THC . Note that we do not estimate THC from systemic measures of hemoglobin concentration, since it has been previously shown that NIRS values in CHD populations may not be directly correlated to systemic measures of hemoglobin.62

Here, γ is the percentage of blood in the venous compartment. In the last step, we assume that γ does not change with hypercapnia in accordance with previous observations of neonates and children with CHD that hypercapnia does not alter the proportion of arterial to venous blood in the brain.63

Last, in order to report a single-relative change per parameter per subject, we have used the EtCO2 data to define a stable baseline and a stable hypercapnic period. The latter was defined as the time period during increased CO2 administration where EtCO2 was at a plateau. All the reported changes in both optical and systemic data are calculated according to these time periods. All % changes are reported as % of baseline.

2.4.

MRI Imaging Protocol

Due to various technical problems, either with ASL-MRI or optical data acquisition, high-quality data were acquired concurrently with custom pediatric ASL-MRI sequences in 12 of the 33 neonates.5, 64, 65, 66 Figures 1 and 2 show the time line and representative pre- and during hypercapnia ASL-MRI images. All MRIs were acquired on a Siemens 3.0T Trio at Children’s Hospital of Philadelphia. In particular, MRI sequences included multiplanar reconstructed (MPR) volumetric T1 and T2 SPACE (short for sampling perfection with application-optimized contrasts using different flip-angle evolutions) sequences acquired in the axial plane and later reconstructed in the sagittal and coronal planes. Axial fluid attenuated inversion recovery (FLAIR), susceptibility (both standard echo gradient and susceptibility weighted imaging), and diffusion weighted imaging (DWI) sequences were also acquired. Clinical MRI interpretations were performed by a single pediatric neuroradiologist (RAZ) blinded to the patient’s clinical information. Imaging parameters of the ASL-MRI scan were FOV=20cm , 64×64 matrix, TRTE=300019ms , slice thickness=5mm and 1mm gap. Eight slices were acquired using a gradient echo-planar imaging (EPI) sequence. A delay time (1.2s) was applied between the saturation and excitation pulses to reduce transit artifacts. Because of large voxel sizes of ASL-MRI images of CBF, whole brain averages were used to compare against DCS.

2.5.

Overall Study Protocol

MRI-compatible optical probes were designed with 12-m -long optical fibers mounted on a flexible pad. The probe was placed on the neonate’s forehead (Fig. 2). Concurrent baseline optical and baseline MRI perfusion measurements were obtained (Fig. 1). After completion of the baseline ASL-MRI measurements, supplemental CO2 was added to the fresh gas mixture to achieve an inspired CO2 (FiCO2) of 2.7% as measured by capnometry. Continuous optical data was acquired throughout the study. Structural brain MRI sequences were acquired for 10to15min after the initiation of supplemental CO2 and its equilibration. At the end of this period, a second set of ASL-MRI sequences were run to reflect the hypercarbic CBF. Blood gas samples were then drawn and analyzed to confirm a higher PaCO2 . The hypercarbic gas mixture was discontinued after the completion of the hypercarbic CBF measurement, and the patient was transported back to the operating room directly for the surgery.

2.6.

Statistical Analysis

Data from each subject was collected as a time series and normalized to a stable pre-hypercapnia period. In order to assess the hemodynamic changes during hypercapnia, the time period where end-tidal CO2 was stable was identified, and all optical data during that period were averaged. All data are reported as mean±SEM (standard error of the mean) when averaged over the population and as mean±σ (standard error) when averaged over time for a single subject. Standard box-plots67 were used to visually explore the data.

Pearson’s correlation coefficient (R) and corresponding p -value (with p<0.05 considered as significant) were used to investigate correlations between modalities or parameters. Bland-Altman analysis68 was used to assess agreement between modalities (ASL-MRI and DCS measures of CBF) visually by identifying those measurements that lie outside the two standard-deviation range from the mean difference between results, and the slope is not significantly different from zero (with p<0.05 considered significant). Last, Lin’s concordance correlation coefficient was used to investigate the accuracy of the agreement.69, 70

A student’s t -test was used to asses whether the estimated population mean of a hemodynamic change was significantly different from zero. p<0.05 was considered as the threshold for rejection of the null hypothesis.

3.

Results

Figure 3 shows the time series of FiCO2 , EtCO2 , and the optical data from a representative subject. Increased InCO2 led to increases in EtCO2 , rCBFDCS and ΔHbO2 . Blood pressure and arterial oxygen saturation remained relatively stable.

Fig. 3

Typical time traces of inspired and expired CO2 and cerebral hemodynamics during baseline and hypercapnia.

037004_1_007003jbo3.jpg

Figure 4 shows box-plots of population-averaged optical and MRI data. Significant increases were measured in CBFDCS ( 158±6% , p<0.001 , n=33 ), HbO2 ( 11±1μM , p<0.001 , n=33 ), and THC ( 9±1μM , p<0.001 , n=33 ). On the other hand, Hb decreased ( 2±0.4 , p<0.001 , n=33 ), and CMRO2 was unaltered ( 98±8% , p=0.8 , n=33 ). Concurrent measurements of rCBFASL-MRI ( 190±15% , p<0.001 , n=12 ) and rCBFDCS ( 164±12% , p<0.001 , n=12 ) demonstrated (Fig. 5 ) that rCBFDCS and rCBFASL-MRI showed good correlation ( R=0.7 , p=0.01 , n=12 ) and good agreement (concordance correlation coefficient, Rc=0.6 ). Bland-Altman plots confirmed that all points lie within two standard deviations from the mean difference between the results and that the slope is zero (p=0.33) .

Fig. 4

Box-plots of rCBFASL-MRI , rCBFDCS , rCMRO2 (% change from baseline), ΔTHC , ΔHb , and ΔHbO2 (concentration change from baseline) for whole data set (n=33) . Box-plot (top-left) shows the comparison of ASL and DCS (n=12) . Note that the lines in the boxes correspond to the lower quartile, median, and upper quartile. The mean corresponds to the center of the box. The dashed lines extend out to indicate the rest of the data within the interquartile distance. The crosses are the outliers.

037004_1_007003jbo4.jpg

Fig. 5

Correlation between rCBFDCS and rCBFASL-MRI is shown for each individual subject (n=12) .

037004_1_007003jbo5.jpg

We have also investigated whether NIRS measures of ΔTHC are related to rCBFDCS . As expected, a weaker (R=0.5) , but significant (p=0.007) correlation between the two parameters was observed. The so-called Grubb exponent,71 which is the ratio of THC changes to CBF changes, was 0.39±0.04 , in reasonable agreement with the literature.38, 71, 72 We note that this value depends on the assumed baseline value for THC of 53μM .58, 59, 60, 61, 62

Vascular reactivity, defined as percent change in CBF per mmHg change in PaCO2 , was measured to be 3.1±1.9% CBF change/mmHg CO2 , well within the literature values of 1 to 9% CBF change/mmHg CO2 in sick neonates.43, 73, 74, 75, 76 Vascular reactivity to CO2 was not found to depend on baseline CBFASL-MRI or baseline PaCO2 ( p=0.1 for both). The NIRS data was also in good agreement with previous NIRS studies on hypercapnia-induced changes in neonates.45, 77, 78

4.

Discussion

This work demonstrates the feasibility of the DCS and NIRS hybrid method to measure blood flow, blood oxygenation, and total hemoglobin concentration in neonatal brains. NIRS is widely used, albeit mostly as a research tool11, 12—for example, a series of pioneering studies at our institution45, 61, 77, 79, 80 and others46, 81, 82, 83, 84 showed that NIRS could play a role in monitoring cerebral oxygenation before, during, and after surgical interventions to patients with CHD. The application of DCS represents a new approach, adding noninvasive and direct measures of microvascular CBF to the arsenal of optical tools. Taken together, NIRS and DCS enable estimation of rCMRO2 , thereby providing further insight into brain metabolism in CHD neonates. By studying a population of babies with complex congenital heart defects, we were able to utilize hypercapnia as a challenge to alter hemodynamics and validate DCS against ASL-MRI. If deployed during the presurgical period, these technologies should enable large-scale studies of the relationship between cerebral autoregulation, CO2 reactivity, and other important physiological factors and neurological outcome.

ASL-MRI has recently been commercialized and validated against a large array of techniques.85 ASL-MRI is currently the only modality that can be utilized in neonates for microvascular CBF measurements. ASL-MRI provides full-brain images and can be applied repeatedly with minimal safety concerns. On the other hand, it is not a technology suitable for continuous monitoring, since it requires patient transport away from the safe confines of the intensive care unit.

Diffuse optical technologies in general, and DCS in particular, provide a promising alternative for continuous and bedside hemodynamic cerebral monitoring. Currently, DCS has been limited to measurements of relative CBF in cortex from few positions on the head. On the other hand, DCS accuracy does not depend on baseline CBF, and the method does not require risky patient transport and appears capable of continuous monitoring for hours and even days. Therefore, DCS could provide complementary information to that available from ASL-MRI—e.g., DCS has the potential to study larger populations and healthy babies with minimal risk (e.g., without injection of a contrast agent, patient transfer, or anesthesia) in order to ferret out subtle differences between healthy and diseased response.

A somewhat surprising, although not entirely unexpected, result of this study is the demonstrated ability of these sick babies to respond to increased CO2 . Our data agree with existing neonate data in the literature,43, 73, 74, 75, 76 but generally, these other studies measuring CO2 reactivity were performed on other sick neonates with various clinical conditions, since it is ethically difficult to justify the use of methods requiring contrast agents or anesthesia to measure local CBF and also the artificial induction of hypercapnia. Thus, we are unable to compare our data to the responses of healthy neonates. In the future, it should be possible to derive healthy neonate responses, since our optical methods are noninvasive and can be deployed at the bedside. We note that in comparison to healthy adults,86 our data show a wider range.

The NIRS data were also in agreement with the literature of hypercapnia-related changes in neonates with CHD45, 77, 78 and shows a well-behaved spread. ΔTHC was previously shown to be correlated to blood flow changes measured by Xenon-CT in neonates during hypercapnia.43 However, in comparison, we have observed only a weak correlation of ΔTHC with rCBFDCS .

Combined NIRS and DCS use is beneficial for two reasons: DCS analysis is improved by incorporating NIRS changes, and a more complete picture of cerebral well-being is derived by measuring CBF in combination with NIRS-measured cerebral blood volume and oxygenation without the need for external tracers, without relying on assumptions that translate total hemoglobin concentration to cerebral blood volume, and without assumptions about how cerebral blood volume is then related to CBF. The combined data can be used to measure changes in cerebral metabolic rate of oxygen;28, 29, 37, 38 in fact, it has recently been demonstrated that NIRS and DCS combination provides a better estimate of changes in CMRO2 in premature infants than NIRS alone.35

Historically, it has often been assumed that CMRO2 does not change during hypercapnia.87 In fact, unchanging CMRO2 is assumed during hypercapnia and is used in functional MRI (fMRI) studies to calibrate the blood oxygen level dependent (BOLD) signals for measurement of CMRO2 during a functional task.88, 89 However, several studies indicate increased or even reduced CMRO2 during hypercapnia in both healthy and disease states, leading to a continuing debate (see Ref. 90 and references therein). Very recently, for example, it was shown that spontaneous neuronal activity was reduced during hypercapnia, hinting at the possibility that CMRO2 may be reduced during hypercapnia.91 During surgical procedures with cardiopulmonary bypass where brain CO2 levels are managed according to different pH strategies, it was shown that different responses of CMRO2 to hypercapnia can be observed.92 During this artificially lowered baseline CMRO2 state, hypercapnia led to reductions in CMRO2 in one group but not in the other. In head injury patients, it was shown that CMRO2 was dependent on cerebral CO2 levels.93 Cerebral maturity is a known confounding factor, and studies have shown a direct correlation with cerebral CO2 and CMRO2 in immature animals.94 In immature rats, CBF was improved in response to hypercapnia during hypoxic-ischemic conditions. Presumably, the oxygen delivery was also improved under these conditions.95 This led to observed increases in glucose utilization and oxidative metabolism, and it was suggested that CMRO2 was lowered during the hypoxic-ischemic baseline as a neuroprotective action. In fact, it has been suggested that mild hypercapnia may be permissible for intensive care management of neonates in order to improve cerebral blood flow, oxygen delivery, oxygen consumption, and neurological outcome.96 Overall, the CMRO2 response to hypercapnia is very complex, and its measurement throughout the last decades relied on a multitude of modalities with their own strengths and weaknesses.

Our observations are in general agreement with the assumption of unchanged CMRO2 . A weakness of our study was our reliance on literature data and other estimates for baseline values of StO2 and THC, which have influenced our findings. To establish the effect of these assumptions on our estimates of rCMRO2 , the baseline values were varied over a large range by assuming StO2bl to be correlated to baseline arterial concentration measured by co-oximetry for each neonate. We observed that small changes (<10±4%) in rCMRO2 cannot be ruled out. Despite this weakness, the hybrid DCS and NIRS instrumentation is a relatively simpler and inexpensive device that could be readily deployed in hospital wards and clinics. From a diffuse optical technology development standpoint, further studies with an NIRS device capable of measuring absolute baseline values and potential studies to validate the assumptions that go into rCMRO2 calculations are now in place.

Last, we note that, to the best of our knowledge, no studies have been carried out on infants wherein anesthesia was varied and CO2 administration was repeated. Since it is expected that the relationship of hemodynamic response to CO2 and anesthesia is a mechanism that is species, age, and clinical condition dependent, it may be inaccurate to extrapolate from studies on animals.97, 98 Therefore, in our conclusions, we rely on the fact that all infants were anesthetized in the same manner. Our work is accurate for comparison to other studies with this limitation.

5.

Conclusion

We have recruited a cohort of neonates with complex congenital heart defects to study the cerebrovascular reactivity to increased CO2 (hypercapnia). By employing a hybrid diffuse correlation spectroscopy and near-infrared spectroscopy, we have measured changes during hypercapnia of oxyhemoglobin, deoxyhemoglobin, and total hemoglobin concentrations, cerebral blood flow (rCBF), and oxygen metabolism. In a subpopulation, we were able to obtain concurrent ASL-MRI data to validate the optical measurements of rCBF.

We have shown that rCBF measurements by both modalities exhibit reasonable agreement. Hence, we have provided validation that diffuse correlation spectroscopy provides reliable measurements of changes in CBF in neonates. Furthermore, this population of neonates were shown to retain their cerebrovascular reactivity to hypercapnia. Combination of NIRS and DCS allowed us to study cerebral oxygen metabolism, which was unaltered in response to hypercapnia. Overall, the study demonstrates the potential to use hybrid diffuse optical probes on a critically ill neonatal population.

Acknowledgments

This study was supported by NIH Grant Nos. HL-57835, NS-60653, NS-45839, RR-02305, EB-007610, HL-077699, HD-26979, and NS-52380; Thrasher Research Fund (NR 0016); Fundació Cellex Barcelona; and June and Steve Wolfson Family Foundation. We acknowledge invaluable assistance from Dalton Hance and staff of the MRI facilities at Children’s Hospital of Philadelphia.

References

1. Centers for Disease Control and Protection, “Improved national prevalence estimates for 18 selected major birth defects—United States, 1999–2001,” Morbidity Mortality Weekly Report 54, 1301–1305 (2005). Google Scholar

2.  S. P. Miller and P. S. McQuillen, “Neurology of congenital heart disease: insight from brain imaging,” Arch. Dis. Child Fetal Neonatal Ed.1359-2998 92, F435–437 (2007). 10.1136/adc.2006.108845 Google Scholar

3.  A. J. Marelli, A. S. Mackie, R. Ionescu-Ittu, E. Rahme, and L. Pilote, “Congenital heart disease in the general population: changing prevalence and age distribution,” Circulation0009-7322 115, 163–172 (2007). 10.1161/CIRCULATIONAHA.106.627224 Google Scholar

4.  M. T. Donofrio, Y. A. Bremer, R. M. Schieken, C. Gennings, L. D. Morton, B. W. Eidem, F. Cetta, C. B. Falkensammer, J. C. Huhta, and C. S. Kleinman, “Autoregulation of cerebral blood flow in fetuses with congenital heart disease: the brain sparing effect,” Pediatr. Cardiol.0172-0643 24, 436–443 (2003). 10.1007/s00246-002-0404-0 Google Scholar

5.  D. J. Licht, J. Wang, D. W. Silvestre, S. C. Nicolson, L. M. Montenegro, G. Wernovsky, S. Tabbutt, S. M. Durning, D. M. Shera, J. W. Gaynor, T. L. Spray, R. R. Clancy, R. A. Zimmerman, and J. A. Detre, “Preoperative cerebral blood flow is diminished in neonates with severe congenital heart defects,” J. Thorac. Cardiovasc. Surg.0022-5223 128, 841–849 (2004). 10.1016/S0022-5223(04)01066-9 Google Scholar

6.  O. B. Paulson, S. Strandgaard, and L. Edvinsson, “Cerebral autoregulation,” Cerebrovasc Brain Metab. Rev.1040-8827 2, 161–192 (1990). Google Scholar

7.  R. B. Panerai, “Assessment of cerebral pressure autoregulation in humans—a review of measurement methods,” Physiol. Meas0967-3334 19, 305–338 (1998). 10.1088/0967-3334/19/3/001 Google Scholar

8.  M. Wintermark, M. Sesay, E. Barbier, K. Borbely, W. P. Dillon, J. D. Eastwood, T. C. Glenn, C. B. Grandin, S. Pedraza, J. F. Soustiel, T. Nariai, G. Zaharchuk, J. M. Caille, V. Dousset, and H. Yonas, “Comparative overview of brain perfusion imaging techniques,” Stroke0039-2499 36, 83–99 (2005). 10.1161/01.STR.0000177884.72657.8b Google Scholar

9.  A. Zauner, W. P. Daugherty, M. R. Bullock, and D. S. Warner, “Brain oxygenation and energy metabolism: part I—biological function and pathophysiology,” Neurosurgery0148-396X 51, 289–302 (2002). 10.1097/00006123-200208000-00003 Google Scholar

10.  A. Zauner and J. P. Muizelaar, “Measuring cerebral blood flow and metabolism,” Chapter 11 in Head Injury, pp. 217–227, Chapman and Hall, London (1997). Google Scholar

11.  A. Villringer and B. Chance, “Non-invasive optical spectroscopy and imaging of human brain function,” Trends Neurosci.0166-2236 20, 435–442 (1997). 10.1016/S0166-2236(97)01132-6 Google Scholar

12.  E. M. C. Hillman, “Optical brain imaging in vivo: techniques and applications from animal to man,” J. Biomed. Opt.1083-3668 12 051402 (2007). 10.1117/1.2789693 Google Scholar

13.  J. E. Brazy, D. V. Lewis, M. H. Mitnick, and F. F. Jobsis, “Noninvasive monitoring of cerebral oxygenation in preterm infants: preliminary observations,” Pediatrics0031-4005 75, 217–225 (1985). Google Scholar

14.  J. E. Brazy and D. V. Lewis, “Changes in cerebral blood volume and cytochrome AA3 during hypertensive peaks in preterm infants,” J. Pediatr. 108, 983–987 (1986). 10.1016/S0022-3476(86)80944-1 Google Scholar

15.  H. U. Bucher, A. D. Edwards, A. E. Lipp, and G. Duc, “Comparison between near infrared spectroscopy and 133Xenon clearance for estimation of cerebral blood flow in critically ill preterm infants,” Pediatr. Res.0031-3998 33, 56–59 (1993). 10.1203/00006450-199301000-00012 Google Scholar

16.  M. Cope, The Development of a Near-Infrared Spectroscopy System and Its Application for Noninvasive Monitoring of Cerebral Blood and Tissue Oxygenation in the Newborn Infant, University College London, London (1991). Google Scholar

17.  M. Cope and D. T. Delpy, “System for long-term measurement of cerebral blood flow and tissue oxygenation on newborn infants by infra-red transillumination,” Med. Biol. Eng. Comput.0140-0118 26, 289–294 (1988). 10.1007/BF02447083 Google Scholar

18.  D. T. Delpy, M. C. Cope, E. B. Cady, J. S. Wyatt, P. A. Hamilton, P. L. Hope, S. Wray, and E. O. Reynolds, “Cerebral monitoring in newborn infants by magnetic resonance and near infrared spectroscopy,” Scand. J. Clin. Lab Invest Suppl.0085-591X 188, 9–17 (1987). Google Scholar

19.  S. R. Hintz, D. A. Benaron, A. M. Siegel, D. K. Stevenson, and D. A. Boas, “Bedside functional imaging of the premature infant brain during passive motor activation,” J. Perinat. Med.0300-5577 29(4), 335–343 (2001). 10.1515/JPM.2001.048 Google Scholar

20.  J. C. Hebden, “Advances in optical imaging of the newborn infant brain,” Psychophysiology0048-5772 40, 501–510 (2003). 10.1111/1469-8986.00052 Google Scholar

21.  A. D. Edwards, C. Richardson, M. Cope, J. S. Wyatt, D. T. Delpy, and E. O. R. Reynolds, “Cotside measurement of cerebral blood flow in ill newborn infants by near infrared spectroscopy,” Lancet0140-6736 332, 770–771 (1988). 10.1016/S0140-6736(88)92418-X Google Scholar

22.  W. M. Kuebler, “How NIR is the future in blood flow monitoring,” J. Appl. Physiol.8750-7587 104, 905–906 (2008). 10.1152/japplphysiol.00106.2008 Google Scholar

23.  A. J. Wolfberg and A. J. du Plessis, “Near-infrared spectroscopy in the fetus and neonate,” Clin. Perinatol.0095-5108 33, 707–728 (2006). 10.1016/j.clp.2006.06.010 Google Scholar

24.  D. A. Boas, L. E. Campbell, and A. G. Yodh, “Scattering and imaging with diffusing temporal field correlations,” Phys. Rev. Lett.0031-9007 75, 1855–1858 (1995). 10.1103/PhysRevLett.75.1855 Google Scholar

25.  D. A. Boas and A. G. Yodh, “Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,” J. Opt. Soc. Am. A0740-3232 14, 192–215 (1997). 10.1364/JOSAA.14.000192 Google Scholar

26.  D. J. Pine, D. A. Weitz, P. M. Chaikin, and E. Herbolzheimer, “Diffusing-wave spectroscopy,” Phys. Rev. Lett.0031-9007 60, 1134–1137 (1988). 10.1103/PhysRevLett.60.1134 Google Scholar

27.  G. Maret and P. E. Wolf, “Multiple light scattering from disordered media. The effect of Brownian motion of scatterers,” Z. Phys. B0340-224X 65, 409–413 (1987). 10.1007/BF01303762 Google Scholar

28.  T. Durduran, “Non-invasive measurements of tissue hemodynamics with hybrid diffuse optical methods,” Ph.D. Dissertation, Univ. of Pennsylvania (2004). Google Scholar

29.  T. Durduran, G. Yu, M. G. Burnett, J. A. Detre, J. H. Greenberg, J. Wang, C. Zhou, and A. G. Yodh, “Diffuse optical measurements of blood flow, blood oxygenation, and metabolism in human brain during sensorimotor cortex activation,” Opt. Lett.0146-9592 29, 1766–1768 (2004). 10.1364/OL.29.001766 Google Scholar

30.  J. Li, G. Dietsche, D. Iftime, S. E. Skipetrov, G. Maret, T. Elbert, B. Rockstroh, and T. Gisler, “Noninvasive detection of functional brain activity with near-infrared diffusing-wave spectroscopy,” J. Biomed. Opt.1083-3668 10, 044002 (2005). 10.1117/1.2007987 Google Scholar

31.  G. Yu, T. Durduran, C. Zhou, H. W. Wang, M. E. Putt, M. Saunders, C. M. Seghal, E. Glatstein, A. G. Yodh, and T. M. Busch, “Noninvasive monitoring of murine tumor blood flow during and after photodynamic therapy provides early assessment of therapeutic efficacy,” Clin. Cancer Res.1078-0432 11, 3543–3552 (2005). 10.1158/1078-0432.CCR-04-2582 Google Scholar

32.  G. Yu, T. Floyd, T. Durduran, C. Zhou, J. J. Wang, J. A. Detre, and A. G. Yodh, “Validation of diffuse correlation spectroscopy for muscle blood flow with concurrent arterial-spin-labeling perfusion,” Opt. Express1094-4087 15, 1064–1075 (2007). 10.1364/OE.15.001064 Google Scholar

33.  E. M. Buckley, N. M. Cook, T. Durduran, M. N. Kim, C. Zhou, R. Choe, G. Yu, S. Shultz, 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. Express1094-4087 17, 12571–12581 (2009). 10.1364/OE.17.012571 Google Scholar

34.  C. Zhou, S. Eucker, T. Durduran, G. Yu, J. Ralston, S. H. Friess, R. N. Ichord, S. S. Margulies, and A. G. Yodh, “Diffuse optical monitoring of hemodynamic changes in piglet brain with closed head injury,” J. Biomed. Opt.1083-3668 14, 034015 (2009). 10.1117/1.3146814 Google Scholar

35.  N. Roche-Labarbe, S. A. Carp, A. Surova, M. Patel, D. A. Boas, P. E. Grant, and M. Franceschini, “Noninvasive optical measures of CBV, StO2, CBF index, and rCMRO2 in human premature neonates’ brains in the first six weeks of life (p NA),” Hum. Brain Mapp1065-9471 31(3), 341–352 (2009). Google Scholar

36.  M. N. Kim, T. Durduran, S. Frangos, B. L. Edlow, E. M. Buckley, E. M. Heather, C. Zhou, G. Yu, R. Choe, M. E, R. L. Wolf, J. H. Woo, M. S. Grady, J. H. Greenberg, J. M. Levine, A. G. Yodh, J. A. Detre, and W. A. Kofke, “Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults,” Neurocritical Care 12(2), 173–180 (2009). 10.1007/s12028-009-9305-x Google Scholar

37.  J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab.0271-678X 23, 911–924 (2003). 10.1097/01.WCB.0000076703.71231.BB Google Scholar

38.  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.0031-9155 46, 2053–2065 (2001). 10.1088/0031-9155/46/8/302 Google Scholar

39.  C. Zhou, G. Yu, D. Furuya, J. H. Greenberg, A. G. Yodh, and T. Durduran, “Diffuse optical correlation tomography of cerebral blood flow during cortical spreading depression in rat brain,” Opt. Express1094-4087 14, 1125–1144 (2006). 10.1364/OE.14.001125 Google Scholar

40.  T. Durduran, C. Zhou, B. L. Edlow, G. Yu, R. Choe, M. N. Kim, B. L. Cucchiara, M. E. Putt, Q. Shah, S. E. Kasner, J. H. Greenberg, A. G. Yodh, and J. A. Detre, “Transcranial optical monitoring of cerebrovascular hemodynamics in acute stroke patients,” Opt. Express1094-4087 17, 3884–3902 (2009). 10.1364/OE.17.003884 Google Scholar

41.  S. S. Kety and C. F. Schmidt, “The nitrous oxide method for the quantitative determination of cerebral blood flow in man: theory, procedure, and normal values,” J. Clin. Invest.0021-9738 27, 476–483 (1948). 10.1172/JCI101994 Google Scholar

42.  S. Ashwal, W. Stringer, L. Tomasi, S. Schneider, J. Thompson, and R. Perkin, “Cerebral blood flow and carbon dioxide reactivity in children with bacterial meningitis,” J. Pediatr. 117, 523–530 (1990). 10.1016/S0022-3476(05)80683-3 Google Scholar

43.  O. Pryds, G. Greisen, L. L. Skov, and B. Friis-Hansen, “Carbon dioxide–related changes in cerebral blood volume and cerebral blood flow in mechanically ventilated preterm neonates: comparison of near-infrared spectrophotometry and 133Xenon clearance,” Pediatr. Res.0031-3998 27, 445–449 (1990). 10.1203/00006450-199005000-00006 Google Scholar

44.  S. Ashwal, R. M. Perkin, J. R. Thompson, L. G. Tomasi, D. van Stralen, and S. Schneider, “CBF and CBF/PCO2 reactivity in childhood strangulation,” Pediatr. Neurol.0887-8994 7, 369–374 (1991). 10.1016/0887-8994(91)90068-V Google Scholar

45.  S. Tabbutt, C. Ramamoorthy, L. M. Montenegro, S. M. Durning, C. D. Kurth, J. M. Steven, R. I. Godinez, T. L. Spray, G. Wernovsky, and S. C. Nicolson, “Impact of inspired gas mixtures on preoperative infants with hypoplastic left heart syndrome during controlled ventilation,” Circulation0009-7322 104, I-159–164 (2001). 10.1161/hc37t1.094818 Google Scholar

46.  F. Kern, R. Ungerleider, T. Quill, B. Baldwin, W. White, J. Reves, and W. Greeley, “Cerebral blood flow response to changes in arterial carbon dioxide tension during hypothermic cardiopulmonary bypass in children,” J. Thorac. Cardiovasc. Surg.0022-5223 101, 618–622 (1991). Google Scholar

47.  J. A. Stockwell, R. F. Goldstein, R. M. Ungerleider, F. H. Kern, J. N. Meliones, and W. J. Greeley, “Cerebral blood flow and carbon dioxide reactivity in neonates during venoarterial extracorporeal life support,” Crit. Care Med.0090-3493 24, 155–162 (1996). 10.1097/00003246-199601000-00025 Google Scholar

48.  M. J. Miranda, K. Olofsson, and K. Sidaros, “Noninvasive measurements of regional cerebral perfusion in preterm and term neonates by magnetic resonance arterial spin labeling,” Pediatr. Res.0031-3998 60, 359–363 (2006). 10.1203/01.pdr.0000232785.00965.b3 Google Scholar

49.  C. J. Petit, J. J. Rome, G. Wernovsky, S. E. Mason, D. M. Shera, S. C. Nicolson, L. M. Montenegro, S. Tabbutt, R. A. Zimmerman, and D. J. Licht, “Preoperative brain injury in transposition of the great arteries is associated with oxygenation and time to surgery, not balloon atrial septostomy,” Circulation0009-7322 119, 709–716 (2009). 10.1161/CIRCULATIONAHA.107.760819 Google Scholar

50.  J. Li, G. Zhang, H. Holtby, A. Guerguerian, S. Cai, T. Humpl, C. A. Caldarone, A. N. Redington, and G. S. Van Arsdell, “The influence of systemic hemodynamics and oxygen transport on cerebral oxygen saturation in neonates after the Norwood procedure,” J. Thorac. Cardiovasc. Surg.0022-5223 135, 83–90.e2 (2008). 10.1016/j.jtcvs.2007.07.036 Google Scholar

51.  D. J. Licht, D. M. Shera, R. R. Clancy, G. Wernovsky, L. M. Montenegro, S. C. Nicolson, R. A. Zimmerman, T. L. Spray, J. W. Gaynor, and A. Vossough, “Brain maturation is delayed in infants with complex congenital heart defects,” J. Thorac. Cardiovasc. Surg.0022-5223 137, 529–537 (2009). 10.1016/j.jtcvs.2008.10.025 Google Scholar

52.  P. van der Zee, M. Cope, S. R. Arridge, M. Essenpreis, L. A. Potter, A. D. Edwards, J. S. Wyatt, D. C. McCormick, S. C. Toth, E. O. R. Reynolds, and D. T. Delpy, “Experimentally measured optical pathlengths for the adult’s head, calf, and forearm and the head of the newborn infant as a function of interoptode spacing,” Adv. Exp. Med. Biol.0065-2598 316, 143–153 (1992). Google Scholar

53.  A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, L. Tyszczuk, M. Cope, and D. T. Delpy, “Optical pathlength measurements on adult head, calf, and forearm and the head of the newborn infant using phase-resolved optical spectroscopy,” Phys. Med. Biol.0031-9155 40, 295–304 (1995). 10.1088/0031-9155/40/2/007 Google Scholar

54.  M. Kohl, C. Nolte, H. R. Heekeren, S. Horst, U. Scholz, H. Obrig, and A. Villringer, “Determination of the wavelength dependence of the differential pathlength factor from near-infrared pulse signals,” Phys. Med. Biol.0031-9155 43, 1771–1782 (1998). 10.1088/0031-9155/43/6/028 Google Scholar

55.  R. B. Buxton and L. R. Frank, “A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation,” J. Cereb. Blood Flow Metab.0271-678X 17, 64–72 (1997). 10.1097/00004647-199701000-00009 Google Scholar

56.  K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, “Nonlinear responses in fMRI: the balloon model, Volterra kernels, and other hemodynamics,” Neuroimage1053-8119 12, 466–477 (2000). 10.1006/nimg.2000.0630 Google Scholar

57.  A. Gjedde, “The relation between brain function and cerebral blood flow and metabolism,” in Cerebrovascular Disease, H Hunt Batjer, L. R. Caplan, L. Friberg, R. G. Greenlee Jr., T. A. Kropitnik Jr., and W. L. Young, Eds., pp. 23–40, Lippincott-Raven, Philadelphia (1997). Google Scholar

58.  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.0031-3998 58, 568–573 (2005). 10.1203/01.PDR.0000175638.98041.0E Google Scholar

59.  M. A. Franceschini, S. Thaker, G. Themelis, K. K. Krishnamoorthy, H. Bortfeld, S. G. Diamond, D. A. Boas, K. Arvin, and P. E. Grant, “Assessment of infant brain development with frequency-domain near-infrared spectroscopy,” Pediatr. Res.0031-3998 61, 546–551 (2007). Google Scholar

60.  J. Zhao, H. S. Ding, X. L. Hou, C. L. Zhou, and B. Chance, “In vivo determination of the optical properties of infant brain using frequency-domain near-infrared spectroscopy,” J. Biomed. Opt.1083-3668 10, 024028 (2005). 10.1117/1.1891345 Google Scholar

61.  C. D. Kurth, J. L. Steven, L. M. Montenegro, H. M. Watzman, J. W. Gaynor, T. L. Spray, and S. C. Nicolson, “Cerebral oxygen saturation before congenital heart surgery,” Ann. Thorac. Surg.0003-497572, 187–192 (2001). 10.1016/S0003-4975(01)02632-7 Google Scholar

62.  K. N. Fenton, K. Freeman, K. Glogowski, S. Fogg, and K. F. Duncan, “The significance of baseline cerebral oxygen saturation in children undergoing congenital heart surgery,” Am. J. Surg.0002-9610 190, 260–263 (2005). 10.1016/j.amjsurg.2005.05.023 Google Scholar

63.  H. M. Watzman, C. D. Kurth, L. M. Montenegro, J. Rome, J. M. Steven, and S. C. Nicolson, “Arterial and venous contributions to near-infrared cerebral oximetry,” Anesthesiology0003-3022 93, 947–953 (2000). 10.1097/00000542-200010000-00012 Google Scholar

64.  J. Wang and D. J. Licht, “Pediatric perfusion MR imaging using arterial spin labeling,” Neuroimaging Clin. N. Am.1052-5149 16, 149–167 (2006). 10.1016/j.nic.2005.10.002 Google Scholar

65.  J. Wang, D. J. Licht, D. W. Silvestre, and J. A. Detre, “Why perfusion in neonates with congenital heart defects is negative—technical issues related to pulsed arterial spin labeling,” Magn. Reson. Imaging0730-725X 24, 249–254 (2006). 10.1016/j.mri.2005.10.031 Google Scholar

66.  J. Wang, D. J. Licht, G.-H. Jahng, C.-S. Liu, J. T. Rubin, J. Haselgrove, R. A. Zimmerman, and J. A. Detre, “Pediatric perfusion imaging using pulsed arterial spin labeling,” J. Magn. Reson Imaging1053-1807 18, 404–413 (2003). 10.1002/jmri.10372 Google Scholar

67.  J. W. Tukey, Exploratory Data Analysis, Addison-Wesley, Reading, MA (1977). Google Scholar

68.  J. M. Bland and D. G. Altman, “Comparing methods of measurement: why plotting difference against standard method is misleading,” Lancet0140-6736 346, 1085–1087 (1995). 10.1016/S0140-6736(95)91748-9 Google Scholar

69.  L. I. Lin, “A concordance correlation coefficient to evaluate reproducibility,” Biometrics0006-341X 45, 255_268 (1989). 10.2307/2532051 Google Scholar

70.  L. I. K. Lin, “A note on the concordance correlation coefficient,” Biometrics0006-341X 56, 324–325 (2000). 10.1111/j.0006-341X.2000.00775.x Google Scholar

71.  R. L. J. Grubb, M. Raichle, J. O. Eichling, and M. M. Ter-Pogossian, “The effects of changes in PaCO2 on cerebral blood volume, blood flow, and vascular mean transit time,” Stroke0039-2499 5, 630–639 (1974). Google Scholar

72.  T. S. Leung, M. M. Tachtsidis, I. Tisdall, C. Pritchard, M. Smith, and C. E. Elwell, “Estimating a modified Grubb’s exponent in healthy human brains with near-infrared spectroscopy and transcranial Doppler,” Physiol. Meas0967-3334 30, 1–12 (2009). 10.1088/0967-3334/30/1/001 Google Scholar

73.  O. Pryds, G. Greisen, H. Lou, and B. Friis-Hansen, “Heterogeneity of cerebral vasoreactivity in preterm infants supported by mechanical ventilation,” J. Pediatr. 115, 638–645 (1989). 10.1016/S0022-3476(89)80301-4 Google Scholar

74.  M. I. Levene, D. Shortland, N. Gibson, and D. H. Evans, “Carbon dioxide reactivity of the cerebral circulation in extremely premature infants: effects of postnatal age and indomethacin,” Pediatr. Res.0031-3998 24, 175–179 (1988). 10.1203/00006450-198808000-00007 Google Scholar

75.  G. Greisen and W. Trojaborg, “Cerebral blood flow, PaCO2 changes, and visual evoked potentials in mechanically ventilated preterm infants,” Acta Paediatr. Scand.0001-656X 76, 394–400 (1987). 10.1111/j.1651-2227.1987.tb10488.x Google Scholar

76.  O. Baenziger, M. Moenkhoff, C. G. Morales, K. Waldvogel, M. Wolf, H. Bucher, and S. Fanconi, “Impaired chemical coupling of cerebral blood flow is compatible with intact neurological outcome in neonates with perinatal risk factors,” Biol. Neonate0006-3126 75, 9–17 (1999). 10.1159/000014072 Google Scholar

77.  C. Ramamoorthy, S. Tabbutt, C. D. Kurth, J. M. Steven, L. M. Montenegro, S. Durning, G. Wernovsky, J. W. Gaynor, T. L. Spray, and S. C. Nicolson, “Effects of inspired hypoxic and hypercapnic gas mixtures on cerebral oxygen saturation in neonates with univentricular heart defects,” Anesthesiology0003-3022 96, 283–288 (2002). 10.1097/00000542-200202000-00010 Google Scholar

78.  J. S. Wyatt, A. D. Edwards, M. Cope, D. T. Delpy, D. C. McCormick, A. Potter, and E. O. Reynolds, “Response of cerebral blood volume to changes in arterial carbon dioxide tension in preterm and term infants,” Pediatr. Res.0031-3998 29, 553–557 (1991). 10.1203/00006450-199106010-00007 Google Scholar

79.  T. Durduran, C. Zhou, M. N. Kim, E. M. Buckley, G. Yu, R. Choe, S. M. Durning, S. Mason, L. M. Montenegro, S. C. Nicholson, R. A. Zimmerman, J. J. Wang, J. A. Detre, A. G. Yodh, and D. J. Licht, “Validation of diffuse correlation spectroscopy for non-invasive, continuous monitoring of CBF in neonates with congenital heart defects,” in Annu. Meeting American Neurological Association, Abstract 299, American Neurological Association, Salt Lake City, UT (2008). Google Scholar

80.  C. D. Kurth, J. Steven, S. Nicolson, and M. Jacobs, “Cerebral oxygenation during cardiopulmonary bypass in children,” J. Thorac. Cardiovasc. Surg.0022-5223 113, 71–79 (1997). 10.1016/S0022-5223(97)70401-X Google Scholar

81.  P. Fallon, I. Roberts, F. J. Kirkham, M. J. Elliott, A. Lloyd-Thomas, R. Maynard, and A. D. Edwards, “Cerebral hemodynamics during cardiopulmonary bypass in children using near-infrared spectroscopy,” Ann. Thorac. Surg.0003-4975 56, 1473–1477 (1993). Google Scholar

82.  T. Takami, H. Yamamura, K. Inai, Y. Nishikawa, Y. Takei, A. Hoshika, and M. Nakazawa,“Monitoring of cerebral oxygenation during hypoxic gas management in congenital heart disease with incrased pulmonary blood flow,” Pediatr. Res.0031-3998 58, 521–524 (2005). 10.1203/01.pdr.0000176913.41568.9d Google Scholar

83.  P. S. McQuillen, A. J. Barkovich, S. E. G. Hamrick, M. Perez, P. Ward, D. V. Glidden, A. Azakie, T. Karl, and S. P. Miller, “Temporal and anatomic risk profile of brain injury with neonatal repair of congenital heart defects,” Stroke0039-2499 38, 736–741 (2007). 10.1161/01.STR.0000247941.41234.90 Google Scholar

84.  I. Roberts, P. Fallon, F. Kirkham, P. Kirshbom, C. Cooper, M. Elliott, and A. Edwards, “Measurement of cerebral blood flow during cardiopulmonary bypass with near-infrared spectroscopy,” J. Thorac. Cardiovasc. Surg.0022-5223 115, 94–98 (1998). 10.1016/S0022-5223(98)70447-7 Google Scholar

85.  E. T. Petersen, I. Zimine, Y.-C. L. Ho, and X. Golay, “Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques,” Br. J. Radiol.0007-1285 79, 688–701 (2006). 10.1259/bjr/67705974 Google Scholar

86.  L. Sokoloff, “The effects of carbon dioxide on the cerebral circulation,” Anesthesiology0003-3022 21, 664–673 (1960). 10.1097/00000542-196011000-00010 Google Scholar

87.  B. K. Siesjo, “Carbon dioxide in brain energy metabolism,” in Brain Energy Metabolism, pp. 131–150, Wiley, New York (1978). Google Scholar

88.  R. D. Hoge, J. Atkinson, B. Gill, G. R. Crelier, S. Marrett, and G. B. Pike, “Investigation of BOLD signal dependence on cerebral blood flow and oxygen consumption: the deoxyhemoglobin dilution model,” Magn. Reson. Med.0740-3194 42, 849–863 (1999). 10.1002/(SICI)1522-2594(199911)42:5<849::AID-MRM4>3.0.CO;2-Z Google Scholar

89.  T. L. Davis, K. K. Kwong, R. M. Weisskoff, and B. R. Rosen, “Calibrated functional MRI: mapping the dynamics of oxidative metabolism,” Proc. Natl. Acad. Sci. U.S.A.0027-8424 95, 1834–1839 (1998). 10.1073/pnas.95.4.1834 Google Scholar

90.  B. Siesjo, “Cerebral metabolic rate in hypercarbia-A controversy; editorial view,” Anesthesiology0003-3022 52, 461–465 (1980). Google Scholar

91.  A. C. Zappe, K. Uludag, A. Oeltermann, K. Ugurbil, and N. K. Logothetis, “The influence of moderate hypercapnia on neural activity in the anesthetized nonhuman primate,” Cereb. Cortex1047-3211 18, 2666–2673 (2008). 10.1093/cercor/bhn023 Google Scholar

92.  D. S. Prough, A. T. Rogers, D. M. S. Stump, G. P. Gravlee, and C. Taylor, “Hypercarbia depresses cerebral oxygen consumption during cardiopulmonary bypass,” Stroke0039-2499 21, 1162–1166 (1990). Google Scholar

93.  W. D. Obrist, G. L. Clifton, C. S. Robertson, and T. W. Langfitt, “Cerebral metabolic changes induced by hyperventilation in acute head injury,” in Cerebral Vascular Disease, J. S. Meyer, K Lechaer, M. Reivich, and E. O. Ott, Eds., pp. 251–255, Elsevier Science Publishers, New York (1987). Google Scholar

94.  J. H. Reuter and T. A. Disney, “Regional cerebral blood flow and cerebral metabolic rate of oxygen during hyperventilation in the newborn dog,” Pediatr. Res.0031-3998 20, 1102–1106 (1986). 10.1203/00006450-198611000-00008 Google Scholar

95.  R. C. Vannucci, R. M. Brucklacher, and S. J. Vannucci, “Effect of carbon dioxide on cerebral metabolism during hypoxia-ischemia in the immature rat,” Pediatr. Res.0031-3998 42, 24–29 (1997). 10.1203/00006450-199707000-00005 Google Scholar

96.  R. P. Jankov and A. K. Tanswell, “Hypercapnia and the neonate,” Acta Paediatr.0803-5253 97, 1502–1509 (2008). 10.1111/j.1651-2227.2008.00933.x Google Scholar

97.  A. Dahan and L. Teppema, “Influence of low-dose anaesthetic agents on ventilatory control: where do we stand?,” Br. J. Anaesth.0007-0912 83, 204–209 (1999). Google Scholar

98.  B. K. Siesjo, Brain Energy Metabolism, John Wiley & Sons Ltd (1978). Google Scholar

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Turgut Durduran, Turgut Durduran, Chao Zhou, Chao Zhou, Erin M. Buckley, Erin M. Buckley, Meeri N. Kim, Meeri N. Kim, Guoqiang Yu, Guoqiang Yu, Regine Choe, Regine Choe, J. William Gaynor, J. William Gaynor, Thomas L. Spray, Thomas L. Spray, Suzanne M. Durning, Suzanne M. Durning, Stefanie E. Mason, Stefanie E. Mason, Lisa M. Montenegro, Lisa M. Montenegro, Susan C. Nicolson, Susan C. Nicolson, Robert A. Zimmerman, Robert A. Zimmerman, Mary E. Putt, Mary E. Putt, Jiongjiong Wang, Jiongjiong Wang, Joel H. Greenberg, Joel H. Greenberg, John A. Detre, John A. Detre, Arjun G. Yodh, Arjun G. Yodh, Daniel J. Licht, Daniel J. Licht, } "Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects," Journal of Biomedical Optics 15(3), 037004 (1 May 2010). https://doi.org/10.1117/1.3425884 . Submission:
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