This protocol demonstrates how to measure resting state functional connectivity in the human prefrontal cortex using a custom-made diffuse correlation spectroscopy instrument. The report also discuss practical aspects of the experiment as well as detailed steps for analyzing the data.
To obtain a comprehensive understanding of the human brain, utilization of cerebral blood flow (CBF) as a source of contrast is desired because it is a key hemodynamic parameter related to cerebral oxygen supply. Resting state low frequency fluctuations based on oxygenation contrast have been shown to provide correlations between functionally connected regions. The presented protocol uses optical diffuse correlation spectroscopy (DCS) to assess blood flow-based resting state functional connectivity (RSFC) in the human brain. Results of CBF-based RSFC in human frontal cortex indicate that intra-regional RSFC is significantly higher in the left and right cortices compared to inter-regional RSFC in both cortices. This protocol should be of interest to researchers who employ multi-modal imaging techniques to study human brain function, especially in the pediatric population.
When the brain is in a resting state, it demonstrates a high synchronization of spontaneous activity in functionally related regions, which can be located close in proximity or from a distance. These in-sync regions are known as functional networks1,2,3,4,5,6,7,8,9. This phenomenon was first uncovered by a functional magnetic resonance imaging (fMRI) study using blood oxygen level-dependent (BOLD) signals that indicate oxygenation levels of the cerebral blood5,10, also known as resting state functional connectivity (RSFC). Abnormalities in RSFC have been associated with brain disorders such as autism11, Alzheimer’s12, and depression13. Thus, RSFC is a valuable tool for studying patients with disorders who have trouble performaning task-based assessments. However, many patients, such as young autistic children, are poor candidates for assessment by fMRI, as it requires remaining still inside of a confined space for extended periods of time14,15. Optical imaging is fast and wearable; thus, it is suitable for a majority of patients, particularly the pediatric population16,17,18,19,20,21,22,23,24. Utilizing these advantages, functional near-infrared spectroscopy (fNIRS), which can quantify hemoglobin concentration and oxygen saturation parameters in the brain, is used to measure RSFC in humans (including the pediatric population4,8,25 and patients with autism11).
Optical diffuse correlation spectroscopy (DCS), a relatively new optical technique, can quantify cerebral blood flow, which is an important parameter that associates oxygen supply with metabolism6,17,26,27,28,29. Optical flow contrast quantified by DCS has been shown to have higher sensitivity in the brain compared to oxygenation contrast30. Thus, utilizing DCS-derived CBF parameters for assessing RSFC is advantageous.
DCS is sensitive to moving blood cells. When diffusing photons scatter from moving blood cells, this causes the intensity of detected light to fluctuate over time. DCS measures a time-based intensity autocorrelation function and its decay rate are dependent on the optical parameters and blood flow. These values are ultimately used to obtain the cerebral blood flow index (CBFi). With faster moving blood cells, the intensity autocorrelation function decays faster. Therefore, information about motion deep beneath the tissue surface can be derived (e.g., in the brain) from measurements of diffusing light fluctuations over time27,31,32,33,34,35. DCS is a technique complementary to the widely known fNIRS that measures blood oxygenation17,36. Since both fNIRS and DCS are optical brain imaging techniques with high temporal resolution in the range of milliseconds, the optical imaging set-ups are far less sensitive to motion artifacts than fMRI. They have also been successfully used for functional brain imaging in pediatric populations, including very young infants16. Previously, superficial blood flow measurements have been used to assess RSFC in preclinical studies in mice37. Here, blood flow parameters are used to quantify RSFC in nine healthy adults as a proof-of-concept study38,39.
In this study, a commercial FD-fNIRS system and custom DCS system is used (see Table of Materials). The DCS that was built in-house is comprised of two 785 nm, 100 mW, long coherence length continuous-wave lasers that are coupled to an FC connector and eight single-photon counting machines (SPCM) connected to an auto-correlator. A custom software graphical user interface (GUI) was also made specifically for this system to display and save the photon counts, autocorrelation curves, and semi-quantitative blood flow of each SPCM channel in real-time. The parts in this system are commonly used for DCS16,17,31,32,40,42,43,44, and the results obtained have also been verified in-house and used in a recent study39.
The protocol was approved by the Institutional Review Board at Wright State University, and informed consent was obtained from each participant prior to the experiment.
1. Subject preparation
- Power up the FD-fNIRS and DCS system to warm up for at least 10 min (see sections 2 and 3 for more details) before starting any measurements of the subject. An example of subject measurement with the compact DCS instrument is shown in Figure 1.
- First, use a tape measure to measure the distance between the nasion to inion on each subject’s head (Figure 2A).
- With the nasion as the starting point, mark the location that is 10% of the distance to the inion with an ink marker. This denotes the point between Fp1 and Fp2 of the EEG 10/20 montage (Figure 2A).
- Using an EEG 10/20 cap (see Table of Materials), adjust the cap so that the marked point is between Fp1 and Fp2.
- Mark the point between Fp1 and F7 (left cortex) and the point between Fp2 and F8 (right cortex). This represents the boundaries between the superior prefrontal cortex and dorsolateral prefrontal cortex (DLFC) and between the DLFC and inferior prefrontal cortex (IFC), respectively, for the left and right hemispheres (Figure 2A).
- Using a 3D-printed probe, place the multi-mode fibers (MMF) on the newly marked points (points “S” on Figure 2C) and connect each to the 785 nm laser light source (Figure 2B,C).
- Place the single-mode fibers (SMFs) 2.75 cm away from the MMF. Two fibers should be placed on the DLFC (locations “DLFC,1” and “DLFC,2”) and one on the IFC (location “IFC”). The placement of the SMF is replicated on each side of the cortex for a total of six SMFs (Figure 2c).
- Place another SMF 1 cm below the MMF at location “Ds” in both sides of the cortex (for the detection of blood flow in the scalp) and connect each of the SMFs to individual single-photon counting machines (Figure 2C).
p class="jove_title">2. FD-fNIRS settings and calibration
- Turn off any lights and turn on the FD-fNIRS system to prepare for calibration.
CAUTION: As a general precaution, do not look directly at the light sources and fiber outputs, as this may cause eye damage. Use an IR sensor card (Table of Materials).
- Avoid unnecessary exposure of the detectors to room light levels to maintain noise-free operation and avoid damage to the detectors.
- Warm the light sources and detectors by powering up the system and letting it run for at least 10 min (preferably, 20 min minimum and 1 h maximum for optimal accuracy and stability) with the light on, modulation on, and detector voltage on.
- Run GUI-based data acquisition software. Adjust the detector gain to achieve an optimal signal with the sensor attached and secured to a calibration phantom (polydimethylsiloxane-based phantom of known optical properties, see Table of Materials) by pressing the “auto-bias” button. If the overvoltage warning flashes, lower the gain.
- After adjusting the detector gain to get the maximum signal, disconnect one of the source fibers from the detector and verify that the direct current (DC) is less than 20 counts per measurement period for the corresponding source fiber. If it is greater than this value, there may be excessive room light leaking into the detector45. If this is the case, the system should be powered down, then any excess light in the room should be blocked/removed and steps 2.4–2.5 repeated.
- Verify the proper signal level from every source and detector. The system defines this as above 100 and below 1,500 counts per measurement cycle.
- Perform calibration by pressing the “Calibrate” button in the GUI. The system will take measurements and apply calibration factors to correctly measure the optical properties of the known phantom. These calibration factors are saved and applied automatically to the in vivo measurements.
- Log the calibration data, which will provide a record of system performance on a standard phantom.
3. DCS settings
CAUTION: As a general precaution, do not look at the light sources and fiber outputs directly to avoid potential eye damage. Use the IR sensor card (see Table of Materials).
- Avoid unnecessary exposure of the detectors (i.e., room light) to obtain accurate raw data for the autocorrelation curves and prevent damage to the detectors.
- Warm up the DCS laser light sources and SPCM (see Table of Materials) by switching them to the “on” position and allowing them to run for at least 10 min (preferably, 20 min minimum and 1 h maximum for optimal accuracy and stability).
- Run GUI-based DCS data acquisition software, which displays the photon counts for each detector and semi-quantitative real-time blood flow values. Adjust the fiber position, angle (fiber face should be perpendicular to the skin surface), and data acquisition timing to obtain a signal of at least 5,000 counts/s (for an adequate signal to noise ratio) and below 1,000,000 counts/s (to avoid damaging detectors) (Figure 3A).
- Verify sufficient photon count levels (from step 3.3) from each detector by checking the photon count level and near real-time autocorrelation curves shown on the monitor.
- Verify sufficient fiber contact without any ambient light leakage by checking the y-intercept of the autocorrelation curve displayed on the monitor. The optimal value is ~1.5 without the use of polarizers (Figure 3B).
- Verify that the probe and measurements are not prone to motion artifacts by tightening the elastic band so that it is tight enough to resist motion but loose enough to prevent any discomfort to the subject. The user should also check the autocorrelation curves on the monitor concurrently such that the autocorrelation curve decays to 1 for longer correlation time (τ > 10 ms) (Figure 3C).
4. Data collection
- Instruct the subject to minimize any movements during the 8 min measurement.
- Turn off the lights and make sure that the subject is seated in a comfortable position with his/her eyes closed.
- Perform a baseline FD-fNIRS measurements using by placing the FD-fNIRS system optical probe on the forehead adjacent to the DCS probe. Then, press the “Acquire” button in the FD-fNIRS acquisition GUI. This data will provide static optical properties, absorption parameters, and scattering parameters (μa, μ's) that will be used for quantification of the dynamic optical parameter, CBFi17,20.
- After completion of the FD-fNIRS measurements, begin data acquisition on the optical DCS measurements by pressing the “Run” button in the DCS data acquisition GUI. Collect data for a total of 8 min with a maximum 2 s integration time (less is preferred, depending on the signal-to-noise ratio for each subject).
- If necessary, repeat the experiment within 1 h of the initial experiment or repeat the experiment during a similar time of day to reduce external variations such as fatigue, stimulants, or temperature.
5. Data analysis
- For FD-fNIRS data, extract the optical absorption and scattering properties (μa, μ's) that are processed by the slope method46,47,48,49,50,51,52,53.
- For DCS, since post-processing is needed, import the auto-correlation raw data from each of the eight channels into the data analysis software.
- CBF-related parameter quantification is detailed in recent reviews6,27,54. Briefly, from the normalized intensity autocorrelation function (g2 [r,τ]), extract the normalized diffuse electric field temporal autocorrelation function (g1 [r,τ]) using the Siegert relation: g2 (r,τ) = 1 + β | g 1 (r,τ)|2.
NOTE: β is a constant, proportional to the number of spatial modes detected6,17,27,55,56, ranges from 0 to 1, and obtained by fitting the (normalized) electric field autocorrelation function g1.
- To obtain a blood flow-related parameter (αDB) from the fit, use the analytical solution for g16,27,54 and fit the data to the model or decay rate:
NOTE: In the equation above, ko is the wavenumber of light in the medium, α is a factor proportionate to tissue blood volume fraction, and DB is the effective Brownian coefficient. αDB can be defined as the blood flow index (BFI)6,54 or CBFi17. Here, CBFi is used.
- Fit the model using the optical parameters obtained from FD-fNIRS. The main parameters to fit for are CBFi and β.
NOTE: Figure 3A shows representative data that is sufficient for analysis. DCS data is discarded if (1) the autocorrelation function is significantly lower that 1.5 (β < 0.5) (i.e., in the case of Figure 3B, where the function is ~1.2, β < 0.2, due to room light leakage) or if (2) the autocorrelation curve does not decay to 1 for longer correlation time (τ > 10 ms) (i.e., in the case of Figure 3C, where the motion artifact, such head or probe movement, leads to unusable data).
- Detrend the quantified results using a second-order polynomial fit to remove slow drift (Figure 4A).
- Use a zero-phase second-order Butterworth filter with a passband of 0.009–0.080 Hz to remove any unwanted brain frequencies such as Mayer waves (Figure 4A).
- Use linear regression to obtain the residuals from each channel against the short distance measurement to remove the superficial scalp signals on each side of the cortex (Figure 4B).
- Calculate the Pearson’s correlation coefficient between each pair of channels to identify the resting state functional connectivity between brain regions (Figure 5).
- Transform the correlation value into a z-value using a Fisher Z transformation and perform a t-test to obtain the p-value (Figure 5). Use false discovery rate (FDR) for multiple comparisons correction.
The feasibility of using DCS to measure functional connectivity was successfully demostrated39. The resting state functional connectivity in the prefrontal cortices of nine subjects was measured. The results (mean ± SD) indicated a higher correlation in the intra-regional region of the left (0.64 ± 0.25) and right (0.62 ± 0.23) cortices, compared to the inter-regional region of the left (0.32 ± 0.32), (0.34 ± 0.27) and right (0.34 ± 0.29), (0.34 ± 0.26) cortices. (Figure 5). Power analysis with a power of 0.8 and significance level of 0.05 was also performed, which resulted in a power of 0.82 with sample size of eight (below the number of subjects analyzed in this study).
To test if there was a significant difference between inter-regional RSFC and intra-regional RSFC, the correlation value was transformed into a z-value using a Fisher Z transformation, then a t-test was performed to compare inter- and intra-regional RSFC of both cortices. This resulted in p-values of ≤0.0002, signifying a significant difference that has been demonstrated in previous fNIRS studies8,25 (Figure 5). To determine if there was any difference between symmetrical brain regions (left and right cortices), a t-test was performed. This resulted in p-values of >0.8, signifying that there was no significant difference between similar brain regions on either side of the cortex.
Figure 1: Experimental set-up. Please click here to view a larger version of this figure.
Figure 2: Probe schematic and placement. (A) Placement of the probes as shown on the EEG 80-20 system map. (B) An example of the 3D-printed probe with optical fibers worn by the subject. (C) The CAD model of the location of the detectors (D) and sources (S) in the dorsolateral frontal cortex (DLFC) and inferior frontal cortex (IFC). Please click here to view a larger version of this figure.
Figure 3: Representative sample of data using detectors in the same region at the same source-detector separation. Shown is an autocorrelation curve (g2) with respect to the lag time (τ). (A) Data when the probe has sufficient contact, showing high counts and a good fit to the analytical model. (B) Data (exaggerated) with ambient light leaking into the probe as observed by a lower y-intercept (beta). This is usually due to a combination of poor contact and strong background light, requiring adjustments to be made. (C) Data (exaggerated) with a motion artifact while the g2 curve is being averaged. Please click here to view a larger version of this figure.
Figure 4: Analysis of representative data obtained from one subject. (A) A plot of the power spectrum after each of the processing steps. (B) An example showing the time series of the normalized blood flow signal on one of the channels before and after regression of the short distance channel (scalp signal). Please click here to view a larger version of this figure.
Figure 5: Resting state functional connectivity in prefrontal cortices of all subjects. Group average for inter-regional region (DLFC1-IFC and DLFC2-IFC) of the left cortex (0.32 ± 0.32), (0.34 ± 0.27) and right cortex (0.34 ± 0.29), (0.34 ± 0.26). Group average for intra-regional region of the left cortex (0.64 ± 0.25) and right cortex (0.62 ± 0.23). Error bar indicates SD across all subjects. The t-test shows the difference between intra- and inter-regional RSFC of both cortices is significant with p ≤ 0.0002, while there was no significant difference between the left and right cortex (t-test = p > 0.8). False discovery rate (FDR) was used for multiple comparisons correction. Please click here to view a larger version of this figure.
To determine whether CBF as measured by DCS accurately detected RSFC, two areas of the brain with known RSFC properties were examined. Functional connectivity between DLFC regions and between DLFC and IFC is assumed to exist57,58,59. Connectivity between two sites within the left and right DLFC was chosen, because the intra-regional connectivity is usually higher. Also, connectivity between the IFC and the DLFC was chosen, as the inter-regional connectivity is known to be weaker.
The DCS technique showed high connectivity within the DLFC areas but lower connectivity between the IFC and DLFC areas, which is consistent with similar studies performed with other methods such as fMRI. These results demonstrate the potential of DCS as a non-invasive means to assess RSFC in humans. When combined with other imaging modalities such as fNIRS, accurate characterization of neuronal diseases such as autism becomes viable. Although concurrent measurements of fNIRS and DCS remain a challenge, several approaches to this problem have been explored19,20,21,23,27,28,60,61,62,63,64,65. In a pilot study, the isolated, lighter DCS probes were chosen for better contact. In the future, the probe design can be improved, fNIRS fibers can be inserted next to DCS fibers, and light sources can be sequentially illuminated as previously demonstrated. In summary, DCS will serve as a complement to other techniques and become a useful tool for non-invasive assessment of brain function in young and disabled patients.
The authors declare no competing financial interests.
The authors would like to acknowledge financial support from the Ohio Third Frontier to the Ohio Imaging Research and Innovation Network (OIRAIN, 667750), and the National Natural Science Foundation of China (No. 81771876).
|3D Printed Probe||In-house||N/A||3D printed PLA probe (Craftbot, Craft unique)|
|785nm, 100mW, CW, FC coupled Laser||CrystaLaser||DL785-100-S||DCS component (light source)|
|Auto-correlator||Correlator.com||Flex05-8ch||DCS component (output g2 curve to PC)|
|Data Acquisition GUI||In-house||N/A||GUI coded in LabVIEW to run the DCS system|
|Data analysis software||In-house||N/A||Matlab code used for obtaining RSFC results|
|EEG Electrode Cap||OpenBCI||N/A||EEG mesh cap with standard 10/20 positions|
|Multi-mode fiber||OZ Optics||QMMJ-3,2.5-IRVIS-600/630-3PCBK-3||DCS component (source fiber)|
|Oxiplex calibration phantom||ISS||75019, 75020||Set of 2 PDMS Calibration Phantom|
|Oxiplex muscle probe||ISS||86010||4 channel muscle probe|
|Oxiplex Oximeter||ISS||95205||FD-fNIRS (690nm, 830nm)|
|Power meter||Thorlabs||PM100D||Laser light power adjuster|
|Sensor card||Thorlabs||F-IRC1-S||laser IR beam viewer|
|Single-mode fiber||OZ Optics||SMJ-3S2.5-780-5/125-3PCBK-3||DCS component (detector fiber)|
|Single-Photon Counting Machine||Excelitas||SPMC-NIR-1x2-FC||DCS component (detector)|
- Cohen, A. L., et al. Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage. 41, (1), 45-57 (2008).
- Pizoli, C. E., et al. Resting state activity in development and maintenance of normal brain function. Proceedings of the National Academy of Sciences of the United States of America. 108, (28), 11638-11643 (2011).
- Duan, L., Zhang, Y. J., Zhu, C. Z. Quantitative comparison of resting state functional connectivity derived from fNIRS and fMRI: A simultaneous recording study. NeuroImage. 60, (4), 2008-2018 (2012).
- White, B. R., et al. Resting state functional connectivity in the human brain revealed with diffuse optical tomography. NeuroImage. 47, (1), 148-156 (2009).
- Biswal, B., Yetkin, F. Z., Haughton, V. M., Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic resonance in medicine official journal of the Society of Magnetic Resonance in Medicine/Society of Magnetic Resonance in Medicine. 34, (4), 537-541 (1995).
- Mesquita, R. C., et al. Direct measurement of tissue blood flow and metabolism with diffuse optics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering. (2011).
- Zhang, H., et al. Test-retest assessment of independent component analysis-derived resting state functional connectivity based on functional near-infrared spectroscopy. NeuroImage. 55, (2), 607-615 (2011).
- Lu, C. M., et al. Use of fNIRS to assess resting state functional connectivity. Journal of Neuroscience Methods. 186, (2), 242-249 (2010).
- Zhang, Y. -J., et al. Detecting Resting state Functional Connectivity in the Language System using Functional Near-Infrared Spectroscopy. Journal of Biomedical Optics. 15, (4), 047003 (2010).
- Fransson, P. Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting state default mode of brain function hypothesis. Human Brain Mapping. (2005).
- Li, J., et al. Characterization of autism spectrum disorder with spontaneous hemodynamic activity. Biomedical Optics Express. (2016).
- Sheline, Y. I., Raichle, M. E. Resting state functional connectivity in preclinical Alzheimer's disease. Biological Psychiatry. (2013).
- Mulders, P. C., van Eijndhoven, P. F., Schene, A. H., Beckmann, C. F., Tendolkar, I. Resting state functional connectivity in major depressive disorder: A review. Neuroscience and Biobehavioral Reviews. (2015).
- Kiviniemi, V., et al. Slow vasomotor fluctuation in fMRI of anesthetized child brain. Magnetic Resonance in Medicine. (2000).
- Fransson, P., et al. Resting state networks in the infant brain. Proceedings of the National Academy of Sciences of the United States of America. (2007).
- Durduran, T., Yodh, A. G. Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement. Neuroimage. 85, 51-63 (2014).
- Buckley, E. M., Parthasarathy, A. B., Grant, P. E., Yodh, A. G., Franceschini, M. A. Diffuse correlation spectroscopy for measurement of cerebral blood flow: future prospects. Neurophotonics. 1, (1), 011009 (2014).
- Buckley, E. M., et al. Cerebral hemodynamics in preterm infants during positional intervention measured with diffuse correlation spectroscopy and transcranial Doppler ultrasound. Optics Express. (2009).
- Dehaes, M., et al. Cerebral oxygen metabolism in neonatal hypoxic ischemic encephalopathy during and after therapeutic hypothermia. Journal of Cerebral Blood Flow. (2014).
- Lin, P. Y., et al. Non-invasive optical measurement of cerebral metabolism and hemodynamics in infants. Journal of Visualized Experiments. (2013).
- Lin, P. Y., et al. Regional and hemispheric asymmetries of cerebral hemodynamic and oxygen metabolism in newborns. Cerebral Cortex. 23, (2), (2013).
- Busch, D. R., et al. Cerebral Blood Flow Response to Hypercapnia in Children with Obstructive Sleep Apnea Syndrome. Sleep. (2016).
- Durduran, T., et al. Cerebral oxygen metabolism (CMRO2) reactivity to hypercapnia in neonates with severe congenital heart defects measured with diffuse optics. Journal of Cerebral Blood Flow and Metabolism. (2009).
- Durduran, T., et al. Optical measurement of cerebral hemodynamics and oxygen metabolism in neonates with congenital heart defects. Journal of Biomedical Optics. (2010).
- Mesquita, R. C., Franceschini, M. A., Boas, D. A. Resting state functional connectivity of the whole head with near-infrared spectroscopy. Biomedical optics express. 1, (1), 324-336 (2010).
- Boas, D. A., Franceschini, M. A. Haemoglobin oxygen saturation as a biomarker: The problem and a solution. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering. (2011).
- Durduran, T., Yodh, A. G. Diffuse correlation spectroscopy for non-invasive, micro-vascular cerebral blood flow measurement. NeuroImage. (2014).
- Yu, G. Diffuse Correlation Spectroscopy (DCS): A Diagnostic Tool for Assessing Tissue Blood Flow in Vascular-Related Diseases and Therapies. Current Medical Imaging Reviews. (8), 194-210 (2012).
- Yu, G., Durduran, T., Zhou, C., Cheng, R., Yodh, A. G. Near-Infrared Diffuse Correlation Spectroscopy for Assessment of Tissue Blood Flow. Handbook of Biomedical Optics. 195-216 (2011).
- Selb, J., et al. Sensitivity of near-infrared spectroscopy and diffuse correlation spectroscopy to brain hemodynamics: simulations and experimental findings during hypercapnia. Neurophotonics. 1, (1), (2014).
- Cheung, C., et al. In vivo cerebrovascular measurement combining diffuse near-infrared absorption and correlation spectroscopies. Physics in Medicine and Biology. 46, (8), 2053-2065 (2001).
- Mesquita, R. C., et al. Direct measurement of tissue blood flow and metabolism with diffuse optics. Philos Trans A Math Phys Eng Sci. 369, (1955), 4390-4406 (2011).
- Maret, G., Wolf, P. E. Multiple Light Scattering from Disordered Media. The Effect of Brownian Motion of Scatterers. Z. Phys. B - Condensed Matter. 65, 409-413 (1987).
- Yu, G. Q. Near-infrared diffuse correlation spectroscopy in cancer diagnosis and therapy monitoring. Journal of Biomedical Optics. 17, (1), (2012).
- Carp, S. A., Dai, G. P., Boas, D. A., Franceschini, M. A., Kim, Y. R. Validation of diffuse correlation spectroscopy measurements of rodent cerebral blood flow with simultaneous arterial spin labeling MRI; towards MRI-optical continuous cerebral metabolic monitoring. Biomedical Optics Express. 1, (2), 553-565 (2010).
- Roche-Labarbe, N., et al. Near-infrared spectroscopy assessment of cerebral oxygen metabolism in the developing premature brain. Journal of Cerebral Blood Flow. (2012).
- Bergonzi, K. M., Bauer, A. Q., Wright, P. W., Culver, J. P. Mapping functional connectivity using cerebral blood flow in the mouse brain. J Cereb Blood Flow Metab. 35, (3), 367-370 (2015).
- Poon, C. S., Li, J., Kress, J., Rohrbach, D. J., Sunar, U. Resting state Functional Connectivity measured by Diffuse Correlation Spectroscopy. Optics InfoBase Conference Papers. (2018).
- Li, J., Poon, C. -S., Kress, J., Rohrbach, D. J., Sunar, U. Resting state functional connectivity measured by diffuse correlation spectroscopy. Journal of Biophotonics. 11, (2), (2018).
- Yu, G. Q. Near-infrared diffuse correlation spectroscopy in cancer diagnosis and therapy monitoring. J Biomed Opt. 17, (1), (2012).
- Li, J., et al. Measurements of human motor and visual activities with diffusing-wave spectroscopy. Novel Optical Instrumentation for Biomedical Applications II. 5864, 58640 (2005).
- Wang, D., et al. Fast blood flow monitoring in deep tissues with real-time software correlators. Biomedical Optics Express. 7, (3), 776 (2016).
- Boas, D. A., Yodh, A. G. Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation. Journal of the Optical Society of America a-Optics Image Science and Vision. 14, (1), 192-215 (1997).
- Diop, M., Lee, T. -Y., St. Lawrence, K. Continuous monitoring of absolute cerebral blood flow by combining diffuse correlation spectroscopy and time-resolved near-infrared technology. Spie. 7896, 78960 (2011).
- Medical, I. ISS Oxiplex Manual. (2008).
- Fantini, S., et al. Quantitative optical monitoring of the hemoglobin concentration and saturation in the piglet brain. Biomedical Optical Spectroscopy and Diagnostics. (2000).
- Hueber, D. M., et al. Non-invasive and quantitative near-infrared haemoglobin spectrometry in the piglet brain during hypoxic stress, using a frequency-domain multidistance instrument. Physics in Medicine and Biology. (2001).
- Zhang, J., et al. Application of I&Q detection system in scouting the curative effect of neck squamous cell carcinoma. Optical Tomography and Spectroscopy of Tissue V. (2003).
- Zhao, J., Ding, H. S., Hou, X. L., Le Zhou, C., Chance, B. In vivo determination of the optical properties of infant brain using frequency-domain near-infrared spectroscopy. Journal of Biomedical Optics. (2005).
- Tu, T., Chen, Y., Zhang, J., Intes, X., Chance, B. Analysis on performance and optimization of frequency-domain near-infrared instruments. Journal of Biomedical Optics. (2002).
- Choe, R., et al. Transabdominal near infrared oximetry of hypoxic stress in fetal sheep brain in utero. Proceedings of the National Academy of Sciences of the United States of America. (2003).
- Sunar, U., et al. Noninvasive diffuse optical measurement of blood flow and blood oxygenation for monitoring radiation therapy in patients with head and neck tumors: a pilot study. Journal of Biomedical Optics. 11, (6), (2006).
- Sunar, U., et al. Hemodynamic responses to antivascular therapy and ionizing radiation assessed by diffuse optical spectroscopies. Optics Express. (2007).
- Durduran, T., Choe, R., Baker, W. B., Yodh, A. G. Diffuse Optics for Tissue Monitoring and Tomography T. Rep Prog Phys. 73, (7), (2010).
- Boas, D. A., Campbell, L. E., Yodh, A. G. Scattering and imaging with diffusing temporal field correlations. Physical Review Letters. (1995).
- Boas, D. A., Yodh, A. G. Spatially varying dynamical properties of turbid media probed with diffusing temporal light correlation. Journal of the Optical Society of America A. (1997).
- Chuang, C. -C., Sun, C. -W. Gender-related effects of prefrontal cortex connectivity: a resting state functional optical tomography study. Biomedical Optics Express. 5, (8), 2503 (2014).
- Okamoto, M., et al. Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI. NeuroImage. (2004).
- Koessler, L., et al. Automated cortical projection of EEG sensors: Anatomical correlation via the international 10-10 system. NeuroImage. (2009).
- Farzam, P., et al. Shedding light on the neonatal brain: Probing cerebral hemodynamics by diffuse optical spectroscopic methods. Scientific Reports. (2017).
- Shang, Y., Li, T., Yu, G. Clinical applications of near-infrared diffuse correlation spectroscopy and tomography for tissue blood flow monitoring and imaging. Physiological Measurement. (2017).
- Mesquita, R. C., et al. Direct measurement of tissue blood flow and metabolism with diffuse optics. Philos Trans A Math Phys Eng Sci. 369, (1955), 4390-4406 (2011).
- Durduran, T., et al. Diffuse optical measurement of blood flow, blood oxygenation, and metabolism in a human brain during sensorimotor cortex activation. Optics Letters. (2004).
- Kim, M. N., et al. Noninvasive measurement of cerebral blood flow and blood oxygenation using near-infrared and diffuse correlation spectroscopies in critically brain-injured adults. Neurocritical Care. (2010).
- Irwin, D., et al. Influences of tissue absorption and scattering on diffuse correlation spectroscopy blood flow measurements. Biomedical Optics Express. (2011).