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J Cereb Blood Flow Metab. 2012 March; 32(3): 481–488.
Published online 2011 October 26. doi:  10.1038/jcbfm.2011.145
PMCID: PMC3293111

Near-infrared spectroscopy assessment of cerebral oxygen metabolism in the developing premature brain

Abstract

Little is known about cerebral blood flow, cerebral blood volume (CBV), oxygenation, and oxygen consumption in the premature newborn brain. We combined quantitative frequency-domain near-infrared spectroscopy measures of cerebral hemoglobin oxygenation (SO2) and CBV with diffusion correlation spectroscopy measures of cerebral blood flow index (BFix) to determine the relationship between these measures, gestational age at birth (GA), and chronological age. We followed 56 neonates of various GA once a week during their hospital stay. We provide absolute values of SO2 and CBV, relative values of BFix, and relative cerebral metabolic rate of oxygen (rCMRO2) as a function of postmenstrual age (PMA) and chronological age for four GA groups. SO2 correlates with chronological age (r=−0.54, P value [less-than-or-eq, slant]0.001) but not with PMA (r=−0.07), whereas BFix and rCMRO2 correlate better with PMA (r=0.37 and 0.43, respectively, P value [less-than-or-eq, slant]0.001). Relative CMRO2 during the first month of life is lower when GA is lower. Blood flow index and rCMRO2 are more accurate biomarkers of the brain development than SO2 in the premature newborns.

Keywords: brain hemodynamic development, cerebral oxygen consumption, diffuse correlation spectroscopy, frequency-domain near-infrared spectroscopy, premature neonates

Introduction

Premature birth interferes with normal brain maturation, and clinical events and interventions may have additional deleterious effects. Compared with normal term newborns, premature newborns at term equivalent postmenstrual age (PMA) have structural abnormalities on magnetic resonance imaging (Huppi et al, 1998) and magnetic resonance diffusion abnormalities that have been associated with functional impairment (Bassi et al, 2008), and their resting state functional connectivity networks are abnormal (Smyser et al, 2010). Using near-infrared spectroscopy (NIRS), several studies have described alterations in cerebral blood volume (CBV) and oxygenation in preterm newborns (see review in Wolf and Greisen, 2009). However, little is known about baseline cerebral blood flow, oxygenation, and oxygen consumption in the premature newborn's brain. Such information, especially if available at the bedside, would provide valuable insight on early brain development and the impact of premature birth.

Near-infrared spectroscopy is a portable and noninvasive method for interrogating cerebral physiology that uses low-intensity nonionizing radiation. It is thus suitable for use in neonates, whose thin scalps and skulls facilitate light transmission. Contrary to continuous wave NIRS measures of changes in oxy- and deoxy-hemoglobin concentrations (respectively, HbO (oxygenated hemoglobin concentration) and HbR (reduced hemoglobin concentration)) (Wolf and Greisen, 2009; Wyatt et al, 1986), frequency-domain near-infrared spectroscopy (FDNIRS) provides absolute values of HbO and HbR from which absolute values of CBV and hemoglobin oxygen saturation (SO2) can be calculated (Fantini et al, 1995; Zhao et al, 2005). Frequency-domain near-infrared spectroscopy has been successful in measuring the evolution of CBV, SO2, and relative cerebral metabolic rate of oxygen (rCMRO2) over the first year of normal brain development (Franceschini et al, 2007), establishing baseline values of CBV, SO2, and rCMRO2 during the first 6 weeks of life in premature neonates (Roche-Labarbe et al, 2010), and determining the effect of acute brain injury on these parameters (Grant et al, 2009).

Diffusion correlation spectroscopy (DCS) provides a measure of tissue perfusion based on the movement of scatterers (i.e., blood cells) inside the tissue (Boas and Yodh, 1997; Cheung et al, 2001). Diffusion correlation spectroscopy is a valid assessment of cerebral blood flow changes in the adult brain (Durduran et al, 2004; Li et al, 2005) and infants (Buckley et al, 2009; Durduran et al, 2010; Roche-Labarbe et al, 2010) and a safe and reliable alternative to the oxygen bolus (Edwards et al, 1988) or indocyanine green (Patel et al, 1998) methods.

Combining FDNIRS measures of CBV and SO2 and DCS measures of blood flow index (BFix) allows for reliable calculation of local rCMRO2 in the newborns (Roche-Labarbe et al, 2010). Quantification will improve estimation of normal values and detection of abnormalities in at-risk neonates (Nicklin et al, 2003). Such markers of brain development and detection of deviations from normal can be obtained before the age at which accurate behavioral and neurologic assessments can be performed, potentially providing early biomarkers for adverse outcomes.

Here, we studied premature neonates with no known brain injury to determine the relationship between CBV, SO2, rCMRO2, and BFix, gestational age (GA) and chronological age.

Materials and methods

Subjects

We studied 56 neonates (24 to 37 weeks GA at birth, 25 females) enrolled from the neonatal intensive care units and Well Baby Nurseries at the Massachusetts General Hospital, Brigham and Women's Hospital and Children's Hospital Boston between 2008 and 2010. Subjects were included if they had no diagnosis of brain injury or neurologic issue during or after their hospital stay. They were sorted into four groups: 24 to 27 weeks GA (9 subjects, 55 measurements, 6±3 measurements per infant, APGAR score at 5 minutes=8±0.5, weight at birth=930±180 g), 28 to 30 weeks GA (10 subjects, 65 measurements, 7±1 measurements per infant, APGAR score at 5 minutes=7.4±0.7, weight at birth=1,200±350 g), 31 to 33 weeks GA (18 subjects, 64 measurements, 4±1 measurements per infant, APGAR score at 5 minutes=8.5±0.6, weight at birth=1,730±300 g) and 34 to 37 weeks GA (19 subjects, 44 measurements, 2±1 measurements per infant, APGAR score at 5 minutes=8.4±1, weight at birth=2,180±250 g). Subjects included had a variety of cardiovascular and respiratory conditions representative of the neonatal intensive care unit population (Supplementary Table 1). Each infant was measured once a week from 1 to 15 weeks of age (ages in days were rounded off to the nearest week) while in the hospital. Our Institutional Review Board, the Partners Human Research Committee, approved all the aspects of this study and all parents provided informed consent.

Acquisition

We used a customized FDNIRS instrument from ISS Inc., Champaign, IL, USA (http://www.iss.com/products/oxiplex/), and built a DCS instrument similar to the system developed by Drs Arjun Yodh and Turgut Durduran at the University of Pennsylvania (Carp et al, 2010; Cheung et al, 2001; Durduran et al, 2004). Both instruments are described in detail in Roche-Labarbe et al (2010).

The FDNIRS sources and detector fiber bundles (each 2.5 mm diameter) were arranged in a row in a black rubber probe (5 × 2 × 0.5 cm3) with source-detector distances of 1, 1.5, 2, and 2.5 cm (Figure 1C), adequate for a depth penetration of ~1 cm, which includes the cerebral cortex in neonates (Dehaes et al, 2011; Franceschini et al, 1998). The DCS laser (50 mW power) was coupled to a 62.5-μm diameter multimode optical fiber and diffused at the fiber tip to comply with the American National Standards Institute exposure standards. The detectors were coupled to 5.6 μm single mode optical fibers. The DCS fibers were arranged in a second row parallel to the NIRS bundles, with source-detector distances of 1.5 (one fiber) and 2 cm (three fibers) (Figure 1C). For each measurement, the probe and fibers were placed in a single-use polypropylene sleeve for hygiene reasons (Figure 1A).

Figure 1
(A) Subject during a measurement. (B) Locations of recording on the subject's head. (C) Schema of the probe. DCS, diffusion correlation spectroscopy; FDNIRS, frequency-domain near-infrared spectroscopy.

Frequency-domain near-infrared spectroscopy and DCS measurements were obtained in sequence from seven areas of the head (Figure 1B). The optical probe was held in each location for up to three times 10 seconds of data acquisition. Repositioning the probe compensated for local inhomogeneities such as hair and superficial large vessels to ensure that the measurement was representative of the underlying brain region. The total number of positions and repetitions depended on the cooperation of the subject and the presence of other medical devices on the head. Total examination time was ~45 minutes. Hemoglobin counts were extracted from clinical reports and arterial oxygenation (SaO2) was obtained from routine monitors at the time of the measurement session.

Near-Infrared Spectroscopy Data Processing

Amplitude and phase data collected at each wavelength allows the calculation of average absorption and scattering coefficients using the multidistance frequency-domain method (Fantini et al, 1995). An automated data analysis routine includes data quality assessment and data rejection based on previously established statistical criteria (Roche-Labarbe et al, 2010). Oxygenated hemoglobin concentration and HbR were derived by fitting the absorption coefficient at our wavelengths with the hemoglobin spectra using the extinction coefficients reported in the literature (Wray et al, 1988) and a 75% concentration of water (Wolthuis et al, 2001). Total hemoglobin concentration HbT=HbO+HbR (μmol) and SO2=HbO/HbT (%). Cerebral blood volume in mL/100 g was calculated using standard equations (Franceschini et al, 2007; Takahashi et al, 1999) and hemoglobin concentration in the blood (HGB) from clinical charts. For 20% of measurements, HGB was not available, in which case standard normal values for HGB for age were used (de Alarcón and Werner, 2005).

Diffusion Correlation Spectroscopy Data Processing

Diffusion correlation spectroscopy data comprise a set of intensity autocorrelation curves (over a delay time range of 200 nanoseconds ~1 second in our case) acquired sequentially at 1 Hz. Following the diffusion correlation equations (Boas and Yodh, 1997; Cheung et al, 2001; Culver et al, 2003; Durduran et al, 2004), a BFix was derived fitting the normalized intensity temporal autocorrelation profile of the diffusively reflected light to the measured temporal autocorrelation function (Boas et al, 1995; Boas and Yodh, 1997; Cheung et al, 2001). To maximize accuracy, we used the actual optical absorption and scattering coefficients at 785 nm interpolated from the FDNIRS measurements. We rejected measurements that do not met objective criteria (Roche-Labarbe et al, 2010).

Relative CMRO2 from combined DCS and FDNIRS measures was calculated as the ratio between the subject's values and the average of all the first week's values (indicated by the subscript 0) of the 34 to 37 weeks GA group using the following equation:

equation image

Statistical analysis

In each infant for each measurement session and for each measured parameter (HbO, HbR, HbT, SO2, CBV, BFix, and rCMRO2), results were averaged over all positions. We verified that results are consistent when only one location is considered. We averaged data sets to obtain one time point per week (ages in days were rounded off to the nearest week, for example, 3 days becomes week 0, 12 days becomes week 2). Given our small sample size, confidence intervals were calculated using Student's tables.

We calculated linear regression, correlation coefficients (r), coefficient of determination (R2), and significance levels between each measured parameter and HGB, chronological age, or PMA for all time points. Finally, we calculated linear regression between each measured parameter and chronological age for the first 31 days of life (4.4 weeks), then between each measured parameter and PMA for time points taken between 34 and 38.4 weeks PMA. These periods correspond to the overlapping measurements among all four groups. We performed t-tests for independent subjects with Bonferroni adjustment of significance levels on the slopes and intercepts (for each element in Table 2; i.e., 42 elements).

Results

Figure 2 illustrates the weekly average of FDNIRS/DCS measured and derived parameters as a function of age for the four GA groups with confidence intervals. Figure 3 illustrates the same parameters as a function of PMA. For the four GA groups, Figure 2 shows that HbO, HbT, and SO2 decrease with chronological age, HbR and CBV are constant with age, and BFix and rCMRO2 increase with age. Figure 3 shows that the decrease in HbO, HbT, and SO2 of the four groups is independent of PMA, while the increase in BFix and rCMRO2 is associated with PMA (scatterplots of individual measurements as Supplementary Figures 1 and 2).

Figure 2
Measured and derived parameters as a function of chronological age. Confidence intervals are displayed where two or more values were averaged. Filled markers were used when at least two values were averaged, empty markers represent individual values. ...
Figure 3
Measured and derived parameters as a function of postmenstrual age (PMA). Postmenstrual age for each group is calculated starting from the group's median gestational age (GA). Confidence intervals are displayed where two or more values were averaged. ...

Table 1 presents the results of linear regressions of the measured and derived optical parameters with HGB, age, and PMA across groups at all time points. To specifically illustrate differences among groups, Table 2 presents the results of the t-test on slopes and intercepts of the measured and derived parameters during the first month of life. Differences in slope, as seen on SO2, reflect different rates of progression between GA groups, while differences in intercepts for similar slopes, as seen on rCMRO2, reflect different absolute values between GA groups. Because the differences in CBV intercepts are due to the large variance in the 24 to 27 group in the first weeks of life (Figure 2), which is confirmed by the absence of correlation with any factor (Table 1), they do not reflect true intergroup differences. t-Test on slopes and intercepts between groups at same PMA showed no significant or near significant differences. Figure 4 represents the trends (slopes and intercepts) of SO2 and rCMRO2 maturation during the first month of life as a function of GA at birth (other box plots as Supplementary Figure 3). SO2 decrease is steeper and occurs at an earlier PMA in subjects born at a lower GA (slopes are different). Relative CMRO2 during the first month of life is proportional to GA at birth (slopes are comparable but intercepts are proportional to GA).

Figure 4
Boxplots of slope and intercept of oxygen saturation (SO2) and relative cerebral metabolic rate of oxygen (rCMRO2) during the first month of life. The dot is the mean, the box is the standard error, the whiskers depict the 0.95 confidence interval. Legend: ...
Table 1
Correlation coefficient and significance (*) in individual measurements
Table 2
t-Tests on slopes and intercepts for the first month of life

Discussion

We (1) provided absolute values of HbO, HbR, HbT, SO2, and CBV and relative values of BFix and rCMRO2 as a function of gestational and chronological age; (2) found that SO2 correlates with chronological age but not with PMA; (3) BFix and rCMRO2 correlate better with PMA than with chronological age; and (4) rCMRO2 during the first month of life is lower when GA at birth is lower.

SO2 is not correlated with PMA but varies with chronological age and HGB, suggesting that it depends on systemic changes and does not reflect changes associated with brain development. This is consistent with the findings that SO2 correlates with the heart and respiratory rate and with arterial SO2 in newborns (Tina et al, 2009). SO2 undergoes a dip around 6 to 8 weeks of life, probably due to the transition from fetal to adult hemoglobin (Franceschini et al, 2007; Roche-Labarbe et al, 2010). This decrease is steeper and occurs at an earlier PMA in subjects born at a lower GA. This is because SO2 immediately after birth is higher when GA is lower (Tina et al, 2009), and because SO2 is highly dependent on HGB, which starts decreasing at birth regardless of GA and decreases faster in more premature infants (de Alarcón and Werner, 2005). These findings question the relevance of SO2 as a measure of brain health and brain development in newborns, and are consistent with the results showing that SO2 is not sensitive to evolving acute brain injury in newborns (Grant et al, 2009). The focus on SO2 may explain why many early NIRS studies yielded inconsistent results, which hampered the implementation of NIRS in clinical settings (Greisen, 2006; Nicklin et al, 2003). Those using NIRS to evaluate hemodynamics in infants have typically used SO2 rather than other parameters, and SO2 is sensitive to transient hemodynamic changes (Huang et al, 2004; Naulaers et al, 2004; Petrova and Mehta, 2006; Toet et al, 2005). However, SO2 is the parameter least sensitive to development or the evolution of injury.

Blood flow index and rCMRO2 correlate better with PMA and are less dependent on chronological age than SO2, suggesting that they are more sensitive to hemodynamic and metabolic changes associated with early brain development. Relative CMRO2 during the first month of life is proportional to GA at birth, which is consistent with the correlation between GA at birth and spontaneous neuronal activity transients measured with electrophysiology (André et al, 2010). This is also consistent with the findings that fractional tissue oxygen extraction during the first 6 hours of life is higher when GA at birth is higher (Tina et al, 2009).

When subjects from various GA groups increase in chronological age, rCMRO2 shows an increase over time, which may reflect increasing oxygen requirements due to synaptic development. We did not find rCMRO2 differences between GA groups at the same PMA, suggesting that synaptic production is appropriate for PMA, regardless of GA at birth. This is consistent with primate studies showing that premature birth does not affect the rate of synaptic production in the visual cortex: synaptogenesis correlates with PMA but not chronological age despite increased sensory stimulation (Bourgeois et al, 1989). The consistency of rCMRO2 with PMA regardless of GA is also consistent with angiogenesis being driven by intrinsic mechanisms associated with synaptogenesis, with no direct effect of outside stimulation (Fonta and Imbert, 2002). However, it conflicts with reports of thinner cortex associated with premature birth (Nagy et al, 2010).

Slopes of BFix were similar among GA groups, but so were intercepts, perhaps due to competitive influences of HGB decreasing (BFix was more influenced by HGB than rCMRO2) and neuronal activity increasing BFix. Overall, these results that agree with the findings in brain-injured neonates (Grant et al, 2009), suggest that rCMRO2 is a better indicator of brain health and developmental stage in infants than SO2.

Oxygenated hemoglobin concentration, HbR, and HbT constitute the output of most NIRS systems and are provided for comparison purposes. Cerebral blood volume does not show any consistent behavior during the first weeks of life (Franceschini et al, 2007; Roche-Labarbe et al, 2010). Cerebral blood volume was higher in the 24 to 27 group compared with the other groups during the first 3 weeks of life (different intercepts), but the large variance in that group and the absence of correlation with any factor (HGB, age, or PMA) suggests that this may be an artifact due to small sample size in that group at that age.

Because premature infants are often treated with medication or require respiratory assistance, we included them in analyses. The variety of cardiac and respiratory conditions among subjects probably contributed to the intragroup variability, particularly in the lower GA group. Caffeine, commonly administered to premature infants with apnea, stimulates neurons and decreases cerebral blood flow, therefore uncoupling cerebral blood flow and rCMRO2, but its effects on baseline CBV, SO2, and rCMRO2 remain controversial (Chen and Parrish, 2009; Perthen et al, 2008). Ventilation modes are also suspected to affect brain hemodynamics, although results are still inconsistent (Milan et al, 2009).

Combined FDNIRS and DCS offers an effective and quantitative bedside method to monitor CBV, SO2, and BFix as well as rCMRO2 in the premature brain. Quantitative values facilitate both individual follow-up and comparison among patients. Blood flow index and rCMRO2 appear to be more accurate biomarkers of newborn brain development than SO2.

Acknowledgments

The authors thank all the families for their participation in this study and the nurses, physicians, and staff in the Neonatal ICU, the Special Care Nursery, Pediatric Neurology, and the maternity units at Massachusetts General Hospital, Brigham and Women's Hospital and Children's Hospital Boston for helping with recruitment and data collection. In particular, the authors thank Marcia Kocienski-Filip, Maddy Artunduaga, Elizabeth A Warren, Sarah M Barnett, Kalpathy S Krishnamoorthy, Linda J Van Marter, Robert M Insoft, Jonathan H Cronin, Julianne Mazzawi, and Steven A Ringer. The authors also thank David A Boas for his advice regarding data analysis. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Center for Research Resources or the National Institutes of Health.

Notes

The authors declare no conflict of interest.

Footnotes

Supplementary Information accompanies the paper on the Journal of Cerebral Blood Flow & Metabolism website (http://www.nature.com/jcbfm)

This work was supported by NIH Grant R01 HD042908, P41-RR14075, and R21-HD058725 and by Clinical Translational Science Award UL1RR025758 to Harvard University and Brigham and Women's Hospital from the National Center for Research Resources.

Supplementary Material

Supplementary Figure 1

Supplementary Figure 2

Supplementary Figure 3

Supplementary Figure Legends

Supplementary Information

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