Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Chien Poon; Ben Rinehart; Jun Li; Ulas Sunar

doi:10.3791/60765

JoVE Journal > Bioengineering

Bioengenharia

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published: May 27, 2020

doi:

10.3791/60765

Chien Poon¹, Ben Rinehart¹, Jun Li^1,2, Ulas Sunar¹

¹Department of Biomedical, Industrial and Human Factors Engineering,Wright State University, ²South China Academy of Advanced Optoelectronics,South China Normal University

Summary

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.

Abstract

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.

Introduction

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.

Protocol

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 “D_s” 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 (μ_a, 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 (μ_a, 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 (αD_B) from the fit, use the analytical solution for g₁⁶^,²⁷^,⁵⁴ and fit the data to the model or decay rate:
EQUATION ONE
NOTE: In the equation above, k_o is the wavenumber of light in the medium, α is a factor proportionate to tissue blood volume fraction, and D_B is the effective Brownian coefficient. αD_Bcan 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.

Representative Results

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.

Discussion

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.

Declarações

The authors have nothing to disclose.

Acknowledgements

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).

Materials

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-1×2-FC	DCS component (detector)

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Poon, C., Rinehart, B., Li, J., Sunar, U. Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy. J. Vis. Exp. (159), e60765, doi:10.3791/60765 (2020).