We combined frequency-domain near-infrared spectroscopy measures of cerebral hemoglobin oxygenation with diffuse correlation spectroscopy measures of cerebral blood flow index to estimate an index of oxygen metabolism. We tested the utility of this measure as a bedside screening tool to evaluate the health and development of the newborn brain.
Perinatal brain injury remains a significant cause of infant mortality and morbidity, but there is not yet an effective bedside tool that can accurately screen for brain injury, monitor injury evolution, or assess response to therapy. The energy used by neurons is derived largely from tissue oxidative metabolism, and neural hyperactivity and cell death are reflected by corresponding changes in cerebral oxygen metabolism (CMRO2). Thus, measures of CMRO2 are reflective of neuronal viability and provide critical diagnostic information, making CMRO2 an ideal target for bedside measurement of brain health.
Brain-imaging techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT) yield measures of cerebral glucose and oxygen metabolism, but these techniques require the administration of radionucleotides, so they are used in only the most acute cases.
Continuous-wave near-infrared spectroscopy (CWNIRS) provides non-invasive and non-ionizing radiation measures of hemoglobin oxygen saturation (SO2) as a surrogate for cerebral oxygen consumption. However, SO2 is less than ideal as a surrogate for cerebral oxygen metabolism as it is influenced by both oxygen delivery and consumption. Furthermore, measurements of SO2 are not sensitive enough to detect brain injury hours after the insult 1,2, because oxygen consumption and delivery reach equilibrium after acute transients 3. We investigated the possibility of using more sophisticated NIRS optical methods to quantify cerebral oxygen metabolism at the bedside in healthy and brain-injured newborns. More specifically, we combined the frequency-domain NIRS (FDNIRS) measure of SO2 with the diffuse correlation spectroscopy (DCS) measure of blood flow index (CBFi) to yield an index of CMRO2 (CMRO2i) 4,5.
With the combined FDNIRS/DCS system we are able to quantify cerebral metabolism and hemodynamics. This represents an improvement over CWNIRS for detecting brain health, brain development, and response to therapy in neonates. Moreover, this method adheres to all neonatal intensive care unit (NICU) policies on infection control and institutional policies on laser safety. Future work will seek to integrate the two instruments to reduce acquisition time at the bedside and to implement real-time feedback on data quality to reduce the rate of data rejection.
The FDNIRS device is a customized frequency-domain system from ISS Inc. with two identical sets of 8 laser diodes emitting at eight wavelengths ranging from 660 to 830 nm, and two photomultiplier tube (PMT) detectors. Sources and detectors are modulated at 110 MHz and 110 MHz plus 5 kHz, respectively, to achieve heterodyne detection 6. Each laser diode is turned on for 10 msec in sequence, for a 160 msec total acquisition time per cycle. Sources and detectors are coupled to fiber optics and arranged in a row in an optical probe. The arrangement of fibers on the probe is such that it produces four different source-detector separations. By measuring transmitted light (amplitude attenuation and phase shift) at multiple distances, we can quantify the absorption (μa) and scattering (μs’) coefficients of the tissue under observation. From the absorption coefficients at multiple wavelengths, we then estimate the absolute values of oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations 7, cerebral blood volume (CBV) and hemoglobin oxygen saturation (SO2).
The DCS device is a home–built system similar to the one developed by Drs. Arjun Yodh and Turgut Durduran at the University of Pennsylvania 8,9. The DCS system that consists of a solid–state, long coherence laser at 785 nm, four photon-counting avalanche photodiode (APD) detectors (EG&G Perkin Elmer SPCM-AQRH) featuring low dark counts (<50 counts/sec) and a high quantum yield (>40% at 785 nm), and a four channel, 256-bin multi-tau correlator, with 200 nsec resolution. With the DCS we measure microvascular blood flow in cerebral cortex by quantifying the temporal intensity fluctuations of multiply scattered light that arises from Doppler shifts produced by moving red blood cells. The technique, similar to laser Doppler blood flowmetry (i.e. they are Fourier Transform analogs), measures an autocorrelation function of the intensity fluctuations of each detector channel computed by a digital correlator over a delay time range of 200 nsec – 0.5 sec. The correlator computes the temporal intensity auto-correlation of the light re-emerging from tissue. We then fit the diffusion correlation equation to the measured autocorrelation function, acquired sequentially, about once per second, to obtain the blood flow index (CBFi) 10,11. DCS measures of blood flow changes have been extensively validated 12,13. By combining the FDNIRS measures of SO2 with the DCS measures of CBFi, we achieve an estimate of cerebral oxygen metabolism (CMRO2i).
1. Preparation for Bedside Measures
2. FDNIRS Gain Settings and Calibration
3. DCS Settings
4. Data Acquisition
5. Measure of Systemic Parameters
6. Data Analysis
In the past five years we have demonstrated the feasibility and clinical utility of the proposed method. In particular, we have shown CMRO2 to be more representative of brain health and development than SO2.
In a cross-sectional study on more than 50 healthy infants, we found that while CBV is more than double during the first year of life, SO2 remains constant 4 (Figure 5). In a study on 70 healthy newborns we also found that SO2 is constant across brain regions while CMRO2i, CBV and CBF are higher in temporal and parietal regions than in the frontal region (Figure 6)20, which is consistent with PET glucose uptake findings 21. In both of our studies, the constant SO2, within a 60-70 percent range indicates that oxygen delivery closely matches local consumption, while CBV, CBF and CMRO2 are more tightly coupled with neural development.
To verify that CMRO2i is a better screening tool than SO2 in detecting neonatal brain injury, we measured brain injured infants during the acute phase 5, and (in a few infants) during the chronic phase several months after injury. Results in Figure 7 show how SO2 is not significantly altered by brain injury in both early (1-15 days after insult) and chronic (months after injury) stages, while CMRO2i is significantly different than normal during both the acute and chronic stages. Specifically, CMRO2i is elevated during the acute phase because of seizure activity after brain injury, and lower than normal during the chronic phase due to neuronal loss.
Infants with hypoxic ischemic injuries are currently treated with therapeutic hypothermia (TH) to lower brain metabolism and reduce damage after the hypoxic insult. Therapeutic hypothermia is maintained for three days and we have been able to monitor 11 infants during treatment (Figure 8). We found that CMRO2i significantly decreases to levels below normal during TH, and this decrease seems to be related to response to therapy and developmental outcome. These preliminary results suggest that the FDNIRS-DCS method may be able to guide and optimize hypothermia therapy.
Figure 1. Picture of the cart with the FDNIRS and DCS devices. The two instruments are compact enough to fit on a small cart that can be moved to the infant’s bedside in the NICU.
Figure 2. (A) Optical probe configuration. (B) The measurement location scheme. (C) A photo of a typical FDNIRS-DCS measurement on an infant.
Figure 3. Representative examples of good and bad fit of measured (A) absorption coefficients and the hemoglobin fit (B) scattering coefficients and the linear fit. P-value > 0.02 refers to a bad fit. Click here to view larger figure.
Figure 4. A representative example of good and bad fit of an autocorrelation function of the intensity fluctuations computed by a correlator over a delay time range of 200 nsec – 0.5 sec. In the bad fit figure the tail of the fitting curve differs from 1 by more than 0.02 and the variation of the 3 first points is more than 0.1. Click here to view larger figure.
Figure 5. Changes in CBV and SO2 across frontal, temporal and parietal cortical regions in infants from birth to one year of age.
Figure 6. CBF, SO2, CBV and CMRO2i of the frontal, temporal and parietal regions in 70 healthy newborns.
Figure 7. Examples of abnormal oxygen consumption and normal SO2 after brain injury in infants. Brain injury is marked by changes in CMRO2 with respect to normal while SO2 is not significantly different from normal. Please note that in these two figures, CMRO2 was calculated using the Grubb relationship, because the DCS measure was not available at the time of those measurements.
Figure 8. rCMRO2 of 11 infants during therapeutic hypothermia vs. age-matched healthy controls. Oxygen metabolism is strongly reduced in all infants with hypothermia therapy.
We demonstrated a quantitative measurement of cerebral hemodynamic and metabolism with FDNIRS and DCS in the neonatal population. The probe configuration is optimized for measuring neonatal cerebral cortex 14. Blood flow changes measured by DCS have been extensively validated against other techniques in animal and human studies 22,23. By using a direct DCS measure of blood flow, we are able to reduce the variance in the calculation of CMRO2i 24. The variance from repeated measures was also smaller than the changes between brain regions and with age 20.
From our previous results, CBFi and CMRO2i showed significant changes with PMA in healthy preterm neonates. The measure of CMRO2i is better able to detect brain damage than the measure of SO2. This suggests that combined measures of vascular and metabolic parameters serve as more robust biomarkers of neonatal brain health and development than oxygen saturation alone. Technical improvements will focus on the integration of two instruments to reduce acquisition time 35-40% per session and the implementation of real-time feedback on data quality to reduce the frequency of discarded measures. In the near future, this system can be delivered to clinical end-users as a novel bedside monitor of altered cerebral oxygen metabolism. By measuring trajectories of CMRO2 over time may also increase clinical significance and predict outcomes. This tool could ultimately make a significant contribution towards improved management of neonatal brain injury.
The authors have nothing to disclose.
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 Boston Children’s Hospital for their help and support. In particular we thank Linda J. Van Marter, Robert M. Insoft, Jonathan H. Cronin, Julianne Mazzawi, and Steven A. Ringer. The authors also thank Marcia Kocienski-Filip, Yvonne Sheldon, Alpna Aggarwall, Maddy Artunguada and Genevieve Nave for their assistance during measurements. This project is supported by NIH R01-HD042908, R21- HD058725, P41- RR14075 and R43-HD071761. Marcia Kocienski-Filip and Yvonne Sheldon are supported by the Clinical Translational Science Award UL1RR025758 to Harvard University and Brigham and Women’s Hospital from the National Center for Research Resources. 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.
Equipment | Company | Catalogue number | Comments (optional) |
Imagent | ISS | FDNIRS | |
DCS laser fibers | Thorlabs | FT400 | DCS component |
DCS detector fiber | Thorlabs | 780HP | DCS component |
DCS laser | CrystaLaser | DL785-070-S | DCS component |
DCS detector | Pacer International | SPCM-AQRH-14-FC | DCS component |
DCS Correlator | Correlator.com | Flex05-8ch | DCS component |
Pronto co-oximeter | Masimo | HGB and SaO2 monitor | |
NOVA | OPHIR | 7Z01500 | Laser power meter |
Sensor card | Newport | F-IRC1-S | IR viewer |
Neutral Density filter | Kodak | NT54-453 |