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1.IntroductionApproximately 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 .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 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 .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 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 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 is a marker for physiologic reserve in the cerebrovascular bed. reactivity [i.e., change in CBF per change in partial pressure of ] is of interest, because impaired 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 is also a potent pulmonary arteriolar vasoconstrictor, its presence can alter this balance by limiting pulmonary flow in favor of systemic circulation. Increased inspired 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 reactivity.5 We note that while the baseline CBF of term neonates born with CHD as a group also tends to be lower than healthy neonates48 , 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 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 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 . Furthermore, concurrent measurements with ASL-MRI in 12 neonates cross-validate DCS against . The optical data is compared to literature values of vasoreactivity to hypercapnia, and a calculated index that is approximately proportional to changes in during hypercapnia is determined. 2.Materials and Methods2.1.PopulationWith 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 were at full-term age ( 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 1Tabulation 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.
2.2.Study ProtocolAll 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 , pancuronium ). Vital signs, including blood pressure, electrocardiogram, transcutaneous oxygen saturations, and end-tidal 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 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 . 2.3.Diffuse Optics: Background, Instrumentation, and AnalysisIn 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 ( and , 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 . Three lasers ( , , ) modulated at 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 away from the source fiber. The probe thickness was , and fibers were long. All materials were thoroughly tested for MRI compatibility. Fiducial markers were placed over the probe to locate fiber positions in the MRI images. 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 inhalation, are detectable in the decay rate of the intensity autocorrelation function and in the amplitude and phase of the NIRS signal. Figure 2 shows two representative 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 . For NIRS, a modified Beer-Lambert equation with assumed photon path lengths from the literature was used.52, 53, 54 NIRS calculations report and Hb concentration changes relative to the baseline (i.e., , , ). 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 versus time. All analysis procedures have been previously described.40 Changes in can be calculated from a synthesis of , , and 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: (Ref. 41). is the normalized oxygen extraction fraction—i.e., the difference between oxygen concentrations in arterial 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 : is the microvascular blood oxygen saturation measured by NIRS. Subscript is used throughout this paper to indicate baseline values of a parameter. Baseline was assumed from literature values for neonates to be 65%.58, 59, 60, 61, 62 is also assumed from baseline values reported in the literature as .58, 59, 60, 61, 62 Changes in and Hb concentrations were then used to calculate the hypercapnic values for and . Note that we do not estimate 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.62Here, is the percentage of blood in the venous compartment. In the last step, we assume that 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 data to define a stable baseline and a stable hypercapnic period. The latter was defined as the time period during increased administration where 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 ProtocolDue 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 and 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 , matrix, , slice and gap. Eight slices were acquired using a gradient echo-planar imaging (EPI) sequence. A delay time 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 ProtocolMRI-compatible optical probes were designed with -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 was added to the fresh gas mixture to achieve an inspired of 2.7% as measured by capnometry. Continuous optical data was acquired throughout the study. Structural brain MRI sequences were acquired for after the initiation of supplemental 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 . 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 AnalysisData 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 was stable was identified, and all optical data during that period were averaged. All data are reported as (standard error of the mean) when averaged over the population and as (standard error) when averaged over time for a single subject. Standard box-plots67 were used to visually explore the data. Pearson’s correlation coefficient and corresponding -value (with 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 considered significant). Last, Lin’s concordance correlation coefficient was used to investigate the accuracy of the agreement.69, 70 A student’s -test was used to asses whether the estimated population mean of a hemodynamic change was significantly different from zero. was considered as the threshold for rejection of the null hypothesis. 3.ResultsFigure 3 shows the time series of , , and the optical data from a representative subject. Increased led to increases in , and . Blood pressure and arterial oxygen saturation remained relatively stable. Figure 4 shows box-plots of population-averaged optical and MRI data. Significant increases were measured in ( , , ), ( , , ), and THC ( , , ). On the other hand, Hb decreased ( , , ), and was unaltered ( , , ). Concurrent measurements of ( , , ) and ( , , ) demonstrated (Fig. 5 ) that and showed good correlation ( , , ) and good agreement (concordance correlation coefficient, ). 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 . We have also investigated whether NIRS measures of are related to . As expected, a weaker , but significant correlation between the two parameters was observed. The so-called Grubb exponent,71 which is the ratio of THC changes to CBF changes, was , in reasonable agreement with the literature.38, 71, 72 We note that this value depends on the assumed baseline value for THC of .58, 59, 60, 61, 62 Vascular reactivity, defined as percent change in CBF per mmHg change in , was measured to be CBF change/mmHg , well within the literature values of 1 to 9% CBF change/mmHg in sick neonates.43, 73, 74, 75, 76 Vascular reactivity to was not found to depend on baseline or baseline ( for both). The NIRS data was also in good agreement with previous NIRS studies on hypercapnia-induced changes in neonates.45, 77, 78 4.DiscussionThis 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 , 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, 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 . Our data agree with existing neonate data in the literature,43, 73, 74, 75, 76 but generally, these other studies measuring 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. 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 with . 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 in premature infants than NIRS alone.35 Historically, it has often been assumed that does not change during hypercapnia.87 In fact, unchanging is assumed during hypercapnia and is used in functional MRI (fMRI) studies to calibrate the blood oxygen level dependent (BOLD) signals for measurement of during a functional task.88, 89 However, several studies indicate increased or even reduced 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 may be reduced during hypercapnia.91 During surgical procedures with cardiopulmonary bypass where brain levels are managed according to different pH strategies, it was shown that different responses of to hypercapnia can be observed.92 During this artificially lowered baseline state, hypercapnia led to reductions in in one group but not in the other. In head injury patients, it was shown that was dependent on cerebral levels.93 Cerebral maturity is a known confounding factor, and studies have shown a direct correlation with cerebral and 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 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 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 . A weakness of our study was our reliance on literature data and other estimates for baseline values of and THC, which have influenced our findings. To establish the effect of these assumptions on our estimates of , the baseline values were varied over a large range by assuming to be correlated to baseline arterial concentration measured by co-oximetry for each neonate. We observed that small changes in 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 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 administration was repeated. Since it is expected that the relationship of hemodynamic response to 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.ConclusionWe have recruited a cohort of neonates with complex congenital heart defects to study the cerebrovascular reactivity to increased (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. AcknowledgmentsThis 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. ReferencesCenters 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
S. P. Miller and P. S. McQuillen,
“Neurology of congenital heart disease: insight from brain imaging,”
Arch. Dis. Child Fetal Neonatal Ed., 92 F435
–437
(2007). https://doi.org/10.1136/adc.2006.108845 1359-2998 Google Scholar
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,”
Circulation, 115 163
–172
(2007). https://doi.org/10.1161/CIRCULATIONAHA.106.627224 0009-7322 Google Scholar
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., 24 436
–443
(2003). https://doi.org/10.1007/s00246-002-0404-0 0172-0643 Google Scholar
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., 128 841
–849
(2004). https://doi.org/10.1016/S0022-5223(04)01066-9 0022-5223 Google Scholar
O. B. Paulson, S. Strandgaard, and L. Edvinsson,
“Cerebral autoregulation,”
Cerebrovasc Brain Metab. Rev., 2 161
–192
(1990). 1040-8827 Google Scholar
R. B. Panerai,
“Assessment of cerebral pressure autoregulation in humans—a review of measurement methods,”
Physiol. Meas, 19 305
–338
(1998). https://doi.org/10.1088/0967-3334/19/3/001 0967-3334 Google Scholar
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,”
Stroke, 36 83
–99
(2005). https://doi.org/10.1161/01.STR.0000177884.72657.8b 0039-2499 Google Scholar
A. Zauner, W. P. Daugherty, M. R. Bullock, and D. S. Warner,
“Brain oxygenation and energy metabolism: part I—biological function and pathophysiology,”
Neurosurgery, 51 289
–302
(2002). https://doi.org/10.1097/00006123-200208000-00003 0148-396X Google Scholar
A. Zauner and J. P. Muizelaar,
“Measuring cerebral blood flow and metabolism,”
Head Injury, 217
–227 Chapman and Hall, London (1997). Google Scholar
A. Villringer and B. Chance,
“Non-invasive optical spectroscopy and imaging of human brain function,”
Trends Neurosci., 20 435
–442
(1997). https://doi.org/10.1016/S0166-2236(97)01132-6 0166-2236 Google Scholar
E. M. C. Hillman,
“Optical brain imaging in vivo: techniques and applications from animal to man,”
J. Biomed. Opt., 12 051402
(2007). https://doi.org/10.1117/1.2789693 1083-3668 Google Scholar
J. E. Brazy, D. V. Lewis, M. H. Mitnick, and F. F. Jobsis,
“Noninvasive monitoring of cerebral oxygenation in preterm infants: preliminary observations,”
Pediatrics, 75 217
–225
(1985). 0031-4005 Google Scholar
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). https://doi.org/10.1016/S0022-3476(86)80944-1 Google Scholar
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., 33 56
–59
(1993). https://doi.org/10.1203/00006450-199301000-00012 0031-3998 Google Scholar
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
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., 26 289
–294
(1988). https://doi.org/10.1007/BF02447083 0140-0118 Google Scholar
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., 188 9
–17
(1987). 0085-591X Google Scholar
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., 29
(4), 335
–343
(2001). https://doi.org/10.1515/JPM.2001.048 0300-5577 Google Scholar
J. C. Hebden,
“Advances in optical imaging of the newborn infant brain,”
Psychophysiology, 40 501
–510
(2003). https://doi.org/10.1111/1469-8986.00052 0048-5772 Google Scholar
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,”
Lancet, 332 770
–771
(1988). https://doi.org/10.1016/S0140-6736(88)92418-X 0140-6736 Google Scholar
W. M. Kuebler,
“How NIR is the future in blood flow monitoring,”
J. Appl. Physiol., 104 905
–906
(2008). https://doi.org/10.1152/japplphysiol.00106.2008 8750-7587 Google Scholar
A. J. Wolfberg and A. J. du Plessis,
“Near-infrared spectroscopy in the fetus and neonate,”
Clin. Perinatol., 33 707
–728
(2006). https://doi.org/10.1016/j.clp.2006.06.010 0095-5108 Google Scholar
D. A. Boas, L. E. Campbell, and A. G. Yodh,
“Scattering and imaging with diffusing temporal field correlations,”
Phys. Rev. Lett., 75 1855
–1858
(1995). https://doi.org/10.1103/PhysRevLett.75.1855 0031-9007 Google Scholar
D. A. Boas and A. G. Yodh,
“Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation,”
J. Opt. Soc. Am. A, 14 192
–215
(1997). https://doi.org/10.1364/JOSAA.14.000192 0740-3232 Google Scholar
D. J. Pine, D. A. Weitz, P. M. Chaikin, and E. Herbolzheimer,
“Diffusing-wave spectroscopy,”
Phys. Rev. Lett., 60 1134
–1137
(1988). https://doi.org/10.1103/PhysRevLett.60.1134 0031-9007 Google Scholar
G. Maret and P. E. Wolf,
“Multiple light scattering from disordered media. The effect of Brownian motion of scatterers,”
Z. Phys. B, 65 409
–413
(1987). https://doi.org/10.1007/BF01303762 0340-224X Google Scholar
T. Durduran,
“Non-invasive measurements of tissue hemodynamics with hybrid diffuse optical methods,”
(2004). Google Scholar
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., 29 1766
–1768
(2004). https://doi.org/10.1364/OL.29.001766 0146-9592 Google Scholar
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., 10 044002
(2005). https://doi.org/10.1117/1.2007987 1083-3668 Google Scholar
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., 11 3543
–3552
(2005). https://doi.org/10.1158/1078-0432.CCR-04-2582 1078-0432 Google Scholar
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. Express, 15 1064
–1075
(2007). https://doi.org/10.1364/OE.15.001064 1094-4087 Google Scholar
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. Express, 17 12571
–12581
(2009). https://doi.org/10.1364/OE.17.012571 1094-4087 Google Scholar
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., 14 034015
(2009). https://doi.org/10.1117/1.3146814 1083-3668 Google Scholar
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 Mapp, 31
(3), 341
–352
(2009). 1065-9471 Google Scholar
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). https://doi.org/10.1007/s12028-009-9305-x Google Scholar
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., 23 911
–924
(2003). https://doi.org/10.1097/01.WCB.0000076703.71231.BB 0271-678X Google Scholar
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 2053
–2065
(2001). https://doi.org/10.1088/0031-9155/46/8/302 0031-9155 Google Scholar
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. Express, 14 1125
–1144
(2006). https://doi.org/10.1364/OE.14.001125 1094-4087 Google Scholar
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. Express, 17 3884
–3902
(2009). https://doi.org/10.1364/OE.17.003884 1094-4087 Google Scholar
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., 27 476
–483
(1948). https://doi.org/10.1172/JCI101994 0021-9738 Google Scholar
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). https://doi.org/10.1016/S0022-3476(05)80683-3 Google Scholar
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., 27 445
–449
(1990). https://doi.org/10.1203/00006450-199005000-00006 0031-3998 Google Scholar
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., 7 369
–374
(1991). https://doi.org/10.1016/0887-8994(91)90068-V 0887-8994 Google Scholar
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,”
Circulation, 104 I-159
–164
(2001). https://doi.org/10.1161/hc37t1.094818 0009-7322 Google Scholar
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., 101 618
–622
(1991). 0022-5223 Google Scholar
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., 24 155
–162
(1996). https://doi.org/10.1097/00003246-199601000-00025 0090-3493 Google Scholar
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., 60 359
–363
(2006). https://doi.org/10.1203/01.pdr.0000232785.00965.b3 0031-3998 Google Scholar
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,”
Circulation, 119 709
–716
(2009). https://doi.org/10.1161/CIRCULATIONAHA.107.760819 0009-7322 Google Scholar
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., 135 83
–90.e2
(2008). https://doi.org/10.1016/j.jtcvs.2007.07.036 0022-5223 Google Scholar
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., 137 529
–537
(2009). https://doi.org/10.1016/j.jtcvs.2008.10.025 0022-5223 Google Scholar
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., 316 143
–153
(1992). 0065-2598 Google Scholar
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., 40 295
–304
(1995). https://doi.org/10.1088/0031-9155/40/2/007 0031-9155 Google Scholar
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., 43 1771
–1782
(1998). https://doi.org/10.1088/0031-9155/43/6/028 0031-9155 Google Scholar
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., 17 64
–72
(1997). https://doi.org/10.1097/00004647-199701000-00009 0271-678X Google Scholar
K. J. Friston, A. Mechelli, R. Turner, and C. J. Price,
“Nonlinear responses in fMRI: the balloon model, Volterra kernels, and other hemodynamics,”
Neuroimage, 12 466
–477
(2000). https://doi.org/10.1006/nimg.2000.0630 1053-8119 Google Scholar
A. Gjedde,
“The relation between brain function and cerebral blood flow and metabolism,”
Cerebrovascular Disease, 23
–40 Lippincott-Raven, Philadelphia
(1997). Google Scholar
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., 58 568
–573
(2005). https://doi.org/10.1203/01.PDR.0000175638.98041.0E 0031-3998 Google Scholar
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., 61 546
–551
(2007). 0031-3998 Google Scholar
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., 10 024028
(2005). https://doi.org/10.1117/1.1891345 1083-3668 Google Scholar
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., 72 187
–192
(2001). https://doi.org/10.1016/S0003-4975(01)02632-7 0003-4975 Google Scholar
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., 190 260
–263
(2005). https://doi.org/10.1016/j.amjsurg.2005.05.023 0002-9610 Google Scholar
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,”
Anesthesiology, 93 947
–953
(2000). https://doi.org/10.1097/00000542-200010000-00012 0003-3022 Google Scholar
J. Wang and D. J. Licht,
“Pediatric perfusion MR imaging using arterial spin labeling,”
Neuroimaging Clin. N. Am., 16 149
–167
(2006). https://doi.org/10.1016/j.nic.2005.10.002 1052-5149 Google Scholar
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. Imaging, 24 249
–254
(2006). https://doi.org/10.1016/j.mri.2005.10.031 0730-725X Google Scholar
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 Imaging, 18 404
–413
(2003). https://doi.org/10.1002/jmri.10372 1053-1807 Google Scholar
J. W. Tukey, Exploratory Data Analysis, Addison-Wesley, Reading, MA
(1977). Google Scholar
J. M. Bland and D. G. Altman,
“Comparing methods of measurement: why plotting difference against standard method is misleading,”
Lancet, 346 1085
–1087
(1995). https://doi.org/10.1016/S0140-6736(95)91748-9 0140-6736 Google Scholar
L. I. Lin,
“A concordance correlation coefficient to evaluate reproducibility,”
Biometrics, 45 255_268
(1989). https://doi.org/10.2307/2532051 0006-341X Google Scholar
L. I. K. Lin,
“A note on the concordance correlation coefficient,”
Biometrics, 56 324
–325
(2000). https://doi.org/10.1111/j.0006-341X.2000.00775.x 0006-341X Google Scholar
R. L. J. Grubb, M. Raichle, J. O. Eichling, and M. M. Ter-Pogossian,
“The effects of changes in on cerebral blood volume, blood flow, and vascular mean transit time,”
Stroke, 5 630
–639
(1974). 0039-2499 Google Scholar
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. Meas, 30 1
–12
(2009). https://doi.org/10.1088/0967-3334/30/1/001 0967-3334 Google Scholar
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). https://doi.org/10.1016/S0022-3476(89)80301-4 Google Scholar
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., 24 175
–179
(1988). https://doi.org/10.1203/00006450-198808000-00007 0031-3998 Google Scholar
G. Greisen and W. Trojaborg,
“Cerebral blood flow, changes, and visual evoked potentials in mechanically ventilated preterm infants,”
Acta Paediatr. Scand., 76 394
–400
(1987). https://doi.org/10.1111/j.1651-2227.1987.tb10488.x 0001-656X Google Scholar
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. Neonate, 75 9
–17
(1999). https://doi.org/10.1159/000014072 0006-3126 Google Scholar
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,”
Anesthesiology, 96 283
–288
(2002). https://doi.org/10.1097/00000542-200202000-00010 0003-3022 Google Scholar
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., 29 553
–557
(1991). https://doi.org/10.1203/00006450-199106010-00007 0031-3998 Google Scholar
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,”
Annu. Meeting American Neurological Association, American Neurological Association, Salt Lake City, UT
(2008). Google Scholar
C. D. Kurth, J. Steven, S. Nicolson, and M. Jacobs,
“Cerebral oxygenation during cardiopulmonary bypass in children,”
J. Thorac. Cardiovasc. Surg., 113 71
–79
(1997). https://doi.org/10.1016/S0022-5223(97)70401-X 0022-5223 Google Scholar
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., 56 1473
–1477
(1993). 0003-4975 Google Scholar
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., 58 521
–524
(2005). https://doi.org/10.1203/01.pdr.0000176913.41568.9d 0031-3998 Google Scholar
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,”
Stroke, 38 736
–741
(2007). https://doi.org/10.1161/01.STR.0000247941.41234.90 0039-2499 Google Scholar
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., 115 94
–98
(1998). https://doi.org/10.1016/S0022-5223(98)70447-7 0022-5223 Google Scholar
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., 79 688
–701
(2006). https://doi.org/10.1259/bjr/67705974 0007-1285 Google Scholar
L. Sokoloff,
“The effects of carbon dioxide on the cerebral circulation,”
Anesthesiology, 21 664
–673
(1960). https://doi.org/10.1097/00000542-196011000-00010 0003-3022 Google Scholar
B. K. Siesjo,
“Carbon dioxide in brain energy metabolism,”
Brain Energy Metabolism, 131
–150 Wiley, New York
(1978). Google Scholar
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., 42 849
–863
(1999). https://doi.org/10.1002/(SICI)1522-2594(199911)42:5<849::AID-MRM4>3.0.CO;2-Z 0740-3194 Google Scholar
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., 95 1834
–1839
(1998). https://doi.org/10.1073/pnas.95.4.1834 0027-8424 Google Scholar
B. Siesjo,
“Cerebral metabolic rate in hypercarbia-A controversy; editorial view,”
Anesthesiology, 52 461
–465
(1980). 0003-3022 Google Scholar
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. Cortex, 18 2666
–2673
(2008). https://doi.org/10.1093/cercor/bhn023 1047-3211 Google Scholar
D. S. Prough, A. T. Rogers, D. M. S. Stump, G. P. Gravlee, and C. Taylor,
“Hypercarbia depresses cerebral oxygen consumption during cardiopulmonary bypass,”
Stroke, 21 1162
–1166
(1990). 0039-2499 Google Scholar
W. D. Obrist, G. L. Clifton, C. S. Robertson, and T. W. Langfitt,
“Cerebral metabolic changes induced by hyperventilation in acute head injury,”
Cerebral Vascular Disease, 251
–255 Elsevier Science Publishers, New York
(1987). Google Scholar
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., 20 1102
–1106
(1986). https://doi.org/10.1203/00006450-198611000-00008 0031-3998 Google Scholar
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., 42 24
–29
(1997). https://doi.org/10.1203/00006450-199707000-00005 0031-3998 Google Scholar
R. P. Jankov and A. K. Tanswell,
“Hypercapnia and the neonate,”
Acta Paediatr., 97 1502
–1509
(2008). https://doi.org/10.1111/j.1651-2227.2008.00933.x 0803-5253 Google Scholar
A. Dahan and L. Teppema,
“Influence of low-dose anaesthetic agents on ventilatory control: where do we stand?,”
Br. J. Anaesth., 83 204
–209
(1999). 0007-0912 Google Scholar
B. K. Siesjo, Brain Energy Metabolism, John Wiley & Sons Ltd(1978). Google Scholar
|