Name: cerebral blood flow-based resting state functional connectivity of the human brain using optical diffuse correlation spectroscopy
Uploaded: 2020-05-27T13:30:00.000+00:00
Duration: 7 min 13 s
Description: Watch this Scientific Journal Video about Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy at JoVE.com

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).

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
<ol>
	<li>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.</li>
	<li>First, use a tape measure to measure the distance between the nasion to inion on each subject&#8217;s head (Figure 2A).</li>
	<li>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).</li>
	<li>Using an EEG 10/20 cap (see Table of Materials), adjust the cap so that the marked point is between Fp1 and Fp2.</li>
	<li>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).</li>
	<li>Using a 3D-printed probe, place the multi-mode fibers (MMF) on the newly marked points (points &#8220;S&#8221; on Figure 2C) and connect each to the 785 nm laser light source (Figure 2B,C).</li>
	<li>Place the single-mode fibers (SMFs) 2.75 cm away from the MMF. Two fibers should be placed on the DLFC (locations &#8220;DLFC,1&#8221; and &#8220;DLFC,2&#8221;) and one on the IFC (location &#8220;IFC&#8221;). The placement of the SMF is replicated on each side of the cortex for a total of six SMFs (Figure 2c).</li>
	<li>Place another SMF 1 cm below the MMF at location &#8220;Ds&#8221; 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).</li>
</ol>
p class=&#34;jove_title&#34;&#62;2. FD-fNIRS settings and calibration

<ol>
	<li>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).</li>
	<li>Avoid unnecessary exposure of the detectors to room light levels to maintain noise-free operation and avoid damage to the detectors.</li>
	<li>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.</li>
	<li>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 &#8220;auto-bias&#8221; button. If the overvoltage warning flashes, lower the gain.</li>
	<li>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&#8211;2.5 repeated.</li>
	<li>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.</li>
	<li>Perform calibration by pressing the &#8220;Calibrate&#8221; 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.</li>
	<li>Log the calibration data, which will provide a record of system performance on a standard phantom.</li>
</ol>
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).
<ol>
	<li>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.</li>
	<li>Warm up the DCS laser light sources and SPCM (see Table of Materials) by switching them to the &#8220;on&#8221; position and allowing them to run for at least 10 min (preferably, 20 min minimum and 1 h maximum for optimal accuracy and stability).</li>
	<li>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).</li>
	<li>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.</li>
	<li>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).</li>
	<li>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 (&#964; &#62; 10 ms) (Figure 3C).</li>
</ol>
4. Data collection
<ol>
	<li>Instruct the subject to minimize any movements during the 8 min measurement.</li>
	<li>Turn off the lights and make sure that the subject is seated in a comfortable position with his/her eyes closed.</li>
	<li>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 &#8220;Acquire&#8221; button in the FD-fNIRS acquisition GUI. This data will provide static optical properties, absorption parameters, and scattering parameters (&#956;a,&#160;&#956;&#39;s) that will be used for quantification of the dynamic optical parameter, CBFi17,20.</li>
	<li>After completion of the FD-fNIRS measurements, begin data acquisition on the optical DCS measurements by pressing the &#8220;Run&#8221; 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).</li>
	<li>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.</li>
</ol>
5. Data analysis
<ol>
	<li>For FD-fNIRS data, extract the optical absorption and scattering properties (&#956;a,&#160;&#956;&#39;s) that are processed by the slope method46,47,48,49,50,51,52,53.</li>
	<li>For DCS, since post-processing is needed, import the auto-correlation raw data from each of the eight channels into the data analysis software.</li>
	<li>CBF-related parameter quantification is detailed in recent reviews6,27,54. Briefly, from the normalized intensity autocorrelation function (g2 [r,&#964;]), extract the normalized diffuse electric field temporal autocorrelation function (g1 [r,&#964;]) using the Siegert relation: g2 (r,&#964;) = 1 + &#946; | g 1 (r,&#964;)|2. 
	NOTE: &#946; 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.</li>
	<li>To obtain a blood flow-related parameter (&#945;DB) 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, ko&#160;is the wavenumber of light in the medium, &#945; is a factor proportionate to tissue blood volume fraction, and DB is the effective Brownian coefficient. &#945;DB&#160;can be defined as the blood flow index (BFI)6,54 or CBFi17. Here, CBFi is used.</li>
	<li>Fit the model using the optical parameters obtained from FD-fNIRS. The main parameters to fit for are CBFi and &#946;. 
	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 (&#946; &#60; 0.5) (i.e., in the case of Figure 3B, where the function is ~1.2, &#946; &#60; 0.2, due to room light leakage) or if (2) the autocorrelation curve does not decay to 1 for longer correlation time (&#964; &#62; 10 ms) (i.e., in the case of Figure 3C, where the motion artifact, such head or probe movement, leads to unusable data).</li>
	<li>Detrend the quantified results using a second-order polynomial fit to remove slow drift (Figure 4A).</li>
	<li>Use a zero-phase second-order Butterworth filter with a passband of 0.009&#8211;0.080 Hz to remove any unwanted brain frequencies such as Mayer waves (Figure 4A).</li>
	<li>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).</li>
	<li>Calculate the Pearson&#8217;s correlation coefficient between each pair of channels to identify the resting state functional connectivity between brain regions (Figure 5).</li>
	<li>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.</li>
</ol>

The authors declare no competing financial interests.

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.

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&#8217;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.

<table><tbody><tr><td>3D Printed Probe</td><td>In-house</td><td>N/A</td><td>3D printed PLA probe (Craftbot, Craft unique)</td></tr><tr><td>785nm, 100mW, CW, FC coupled Laser</td><td>CrystaLaser</td><td>DL785-100-S</td><td>DCS component (light source)</td></tr><tr><td>Auto-correlator</td><td>Correlator.com</td><td>Flex05-8ch</td><td>DCS component (output g2 curve to PC)</td></tr><tr><td>Data Acquisition GUI</td><td>In-house</td><td>N/A</td><td>GUI coded in LabVIEW to run the DCS system</td></tr><tr><td>Data analysis software</td><td>In-house</td><td>N/A</td><td>Matlab code used for obtaining RSFC results</td></tr><tr><td>EEG Electrode Cap</td><td>OpenBCI</td><td>N/A</td><td>EEG mesh cap with standard 10/20 positions</td></tr><tr><td>Multi-mode fiber</td><td>OZ Optics</td><td>QMMJ-3,2.5-IRVIS-600/630-3PCBK-3</td><td>DCS component (source fiber)</td></tr><tr><td>Oxiplex calibration phantom</td><td>ISS</td><td>75019, 75020</td><td>Set of 2 PDMS Calibration Phantom</td></tr><tr><td>Oxiplex muscle probe</td><td>ISS</td><td>86010</td><td>4 channel muscle probe</td></tr><tr><td>Oxiplex Oximeter</td><td>ISS</td><td>95205</td><td>FD-fNIRS (690nm, 830nm)</td></tr><tr><td>Power meter</td><td>Thorlabs</td><td>PM100D</td><td>Laser light power adjuster</td></tr><tr><td>Sensor card</td><td>Thorlabs</td><td>F-IRC1-S</td><td>laser IR beam viewer</td></tr><tr><td>Single-mode fiber</td><td>OZ Optics</td><td>SMJ-3S2.5-780-5/125-3PCBK-3</td><td>DCS component (detector fiber)</td></tr><tr><td>Single-Photon Counting Machine</td><td>Excelitas</td><td>SPMC-NIR-1x2-FC</td><td>DCS component (detector)</td></tr></tbody></table>

cerebral blood flow-based resting state functional connectivity of the human brain using optical diffuse correlation spectroscopy

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.

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 &#177; SD) indicated a higher correlation in the intra-regional region of the left (0.64 &#177; 0.25) and right (0.62 &#177; 0.23) cortices, compared to the inter-regional region of the left (0.32 &#177; 0.32), (0.34 &#177; 0.27) and right (0.34 &#177; 0.29), (0.34 &#177; 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 &#8804;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 &#62;0.8, signifying that there was no significant difference between similar brain regions on either side of the cortex.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60765/60765fig1.jpg" /> 
Figure 1: Experimental set-up. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60765/60765fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60765/60765fig2.jpg" /> 
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). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60765/60765fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60765/60765fig3.jpg" /> 
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 (&#964;). (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. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60765/60765fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60765/60765fig4.jpg" /> 
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). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60765/60765fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60765/60765fig5.jpg" /> 
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 &#177; 0.32), (0.34 &#177; 0.27) and right cortex (0.34 &#177; 0.29), (0.34 &#177; 0.26). Group average for intra-regional region of the left cortex (0.64 &#177; 0.25) and right cortex (0.62 &#177; 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 &#8804; 0.0002, while there was no significant difference between the left and right cortex (t-test = p &#62; 0.8). False discovery rate (FDR) was used for multiple comparisons correction. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60765/60765fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy at JoVE.com

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

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 ...

cerebral-blood-flow-based-resting-state-functional-connectivity-human

Nanyang Technological University

Research

JoVE Journal

Bioengineering

6.5K Views.  Wright State University. Dit protocol maakt gebruik van variabele optische sondes om de bloedstroom van de hersenen te evalueren tijdens de rusttoestand. Een belangrijk voordeel van deze techniek is de draagbaarheid. Waardoor het zeer geschikt is voor nachtbewaking.De draagbare sonde maakt het mogelijk om functionele connectiviteitsmetingen te verkrijgen in natuurlijke omgevingen van de proefpersonen voor zowel diagnostische als therapeutische toepassingen. Dit protocol maakt gebruik van draagbare optische son...

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

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular coupling, fNIRS is able to measure the haemoglobin concentration changes secondary to neuronal activity. Compared to other neuroimaging techniques, fNIRS represents a good compromise in terms of spatial and temporal resolution. Moreover, it is portable, lightweight, less sensitive to motion artifacts and does not impose significant physical restraints. It is therefore appropriate to monitor a wide range of cognitive tasks (e.g., auditory, gait analysis, social interaction) and different age populations (e.g., new-borns, adults, elderly people). The recent development of fiberless fNIRS devices has opened the way to new applications in neuroscience research. This represents a unique opportunity to study functional activity during real-world tests, which can be more sensitive and accurate in assessing cognitive function and dysfunction than lab-based tests. This study explored the use of fiberless fNIRS to monitor brain activity during a real-world prospective memory task. This protocol is performed outside the lab and brain haemoglobin concentration changes are continuously measured over the prefrontal cortex while the subject walks around in order to accomplish several different tasks.

Monitoring brain activity outside the lab without physical constraints presents methodological challenges. A fiberless, wearable functional Near Infrared Spectroscopy (fNIRS) system was used to measure brain activity during an ecological prospective memory task. It was demonstrated that this system could be used to monitor brain activity during non-lab based experiments.

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular ...

Behavior

Non-invasive Optical Measurement of Cerebral Metabolism and Hemodynamics in Infants

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.

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 ...

Medicine

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

The simultaneous evaluation of cerebral hemodynamics and the light scattering properties of in vivo rat brain tissue is demonstrated using a conventional multispectral diffuse reflectance imaging system. This system is constructed from a broadband white light source, a motorized filter wheel with a set of narrowband interference filters, a light guide, a collecting lens, a video zoom lens, and a monochromatic charged-coupled device (CCD) camera. An ellipsoidal cranial window is made in the skull bone of a rat under isoflurane anesthesia to capture in vivo multispectral diffuse reflectance images of the cortical surface. Regulation of the fraction of inspired oxygen using a gas mixture device enables the induction of different respiratory states such as normoxia, hyperoxia, and anoxia. A Monte Carlo simulation-based multiple regression analysis for the measured multispectral diffuse reflectance images at nine wavelengths (500, 520, 540, 560, 570, 580, 600, 730, and 760 nm) is then performed to visualize the two-dimensional maps of hemodynamics and the light scattering properties of the in vivo rat brain.

Simultaneous Evaluation of Cerebral Hemodynamics and Light Scattering Properties of the In Vivo Rat Brain Using Multispectral Diffuse Reflectance Imaging

The simultaneous evaluation of cerebral hemodynamics and the light scattering properties of in vivo rat brain tissue is demonstrated using a conventional multispectral diffuse reflectance imaging system.

The simultaneous evaluation of cerebral hemodynamics and the light scattering properties of in vivo rat brain tissue is demonstrated using a conventional ...

Neuroscience

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Task-based functional magnetic resonance imaging bears great potential to understand how our brain reacts to various types of stimulation; however, this is often achieved without considering the dynamic facet of functional processing, and analytical outputs typically account for merged influences of task-driven effects and underlying spontaneous fluctuations of brain activity. Here, we introduce a novel methodological pipeline that can go beyond these limitations: the use of a sliding-window analytical scheme permits tracking of functional changes over time, and through cross-subject correlational measurements, the approach can isolate purely stimulus-related effects. Thanks to a rigorous thresholding process, significant changes in inter-subject functional correlation can be extracted and analyzed.
On a set of healthy subjects who underwent naturalistic audio-visual stimulation, we demonstrate the usefulness of the approach by tying the unraveled functional reconfigurations to particular cues of the movie. We show how, through our method, one can capture either a temporal profile of brain activity (the evolution of a given connection), or focus on a spatial snapshot at a key time point. We provide a publicly available version of the whole pipeline, and describe its use and the influence of its key parameters step by step.

The goal of the described approach is to determine at what moments of the paradigm (temporal perspective), and between which regions (spatial perspective), significant reconfigurations in functional connectivity occur on functional magnetic resonance imaging recordings during which a time-locked stimulus is played.

Task-based functional magnetic resonance imaging bears great potential to understand how our brain reacts to various types of stimulation; however, this ...

Mapping Regional Homogeneity and Functional Connectivity of the Visual Cortex in Resting-State fMRI

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, has been used to evaluate synchronous neuronal activity changes in retinitis pigmentosa (RP). The purpose of this study is to describe our method for analysis of intra- and interregional synchronizations of changes in neuronal activity in RP patients. The advantages of the combined ReHo and FC method are that it is both noninvasive and sufficiently sensitive to investigate changes in cerebral synchronous neuronal activity changes in vivo. Here, 16 RP patients and 14 healthy controls closely matched in age, sex, and education underwent resting-state fMRI scans. Two sample t-tests were conducted to compare ReHo and FC across groups. Our results showed that visual network disconnection and reorganization of the retino-thalamocortical pathway and dorsal visual stream occurred in the RP patients. Here, we describe the details of this method, its use, and the impact of its key parameters in a step-by-step manner.

We present a protocol for analyzing functional magnetic resonance imaging data to investigate spontaneous neural activity alterations in retinitis pigmentosa patient using a combined regional homogeneity and functional connectivity method.

A combined regional homogeneity (ReHo) and functional connectivity (FC) method, a type of noninvasive functional magnetic resonance imaging (fMRI) method, ...

Cerebrovascular Reactivity Measurement with Functional Near Infrared Spectroscopy

Cerebrovascular reactivity (CVR) is the capacity of blood vessels in the brain to alter cerebral blood flow (either with dilation or constriction) in response to chemical or physical stimuli. The amount of reactivity in the cerebral microvasculature depends on the integrity of the capacitance vasculature and is the primary function of endothelial cells. CVR is, therefore, an indicator of the microvasculature&#8217;s physiology and overall health. Imaging methods that can measure CVR are available but can be costly, and require magnetic resonance imaging centers and technical expertise. In this study, we used fNIRS technology to monitor changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the cerebral microvasculature to assess the CVR of 15 healthy controls (HC) in response to a vasoactive stimulus (inhaled 5% carbon dioxide or CO2). Our results suggest that this is a promising imaging technology that offers a non-invasive, accurate, portable, and cost-effective method of mapping cortical CVR and associated microvasculature function, resulting from a traumatic brain injury or other conditions associated with cerebral microvasculopathy.

Presented here is a protocol for imaging and measurement of cerebrovascular reactivity in humans with functional Near Infrared Spectroscopy (fNIRS). fNIRS is a novel imaging modality that captures the concentration changes of hemoglobin species in the brain&#8217;s outermost cortex under specific stimuli.

Cerebrovascular reactivity (CVR) is the capacity of blood vessels in the brain to alter cerebral blood flow (either with dilation or constriction) in ...

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical treatment. Among the many neuroimaging technologies used to measure brain activity, functional near-infrared spectroscopy (fNIRS) enables the evaluation of dynamic cortical activities by measuring the local hemoglobin levels similar to functional magnetic resonance imaging (fMRI). Also, due to lesser physical restriction in fNIRS, multiple variants of sensorimotor tasks can be evaluated. Many laboratories have developed several methods for fNIRS data analysis; however, despite the fact that the general principles are the same, there is no universally standardized method. Here, we present the qualitative and comparative analytic methods of data obtained from a multi-channel fNIRS experiment using a block design. For qualitative analysis, we used a software for NIRS as a mass-univariate approach based on the generalized linear model. The NIRS-SPM analysis shows qualitative results for each session by visualizing the activated area during the task. In addition, the non-invasive three-dimensional digitizer can be used to estimate the fNIRS channel locations relative to the brain. To corroborate the NIRS-SPM findings, the amplitude of the changes in hemoglobin levels induced by the sensorimotor task can be statistically analyzed by comparing the data obtained from two different sessions (before and after intervention) of the same study subject using a multi-channel hierarchical mixed model. Our methods can be used to measure the pre- vs. post-intervention analysis in a variety of neurological disorders such as movement disorders, cerebrovascular diseases, and neuropsychiatric disorders.

We describe the analysis of continuous-wave functional near-infrared spectroscopy experiment using a block design with a sensorimotor task. To increase the reliability of the data analysis, we used the qualitative general linear model-based statistical parametric mapping and the comparative hierarchical mixed models for multi-channels.

Neuroimaging studies play a pivotal role in the evaluation of pre- vs. post-interventional neurological conditions such as in rehabilitation and surgical ...

Real-Time Monitoring of Neurocritical Patients with Diffuse Optical Spectroscopies

Neurophysiological monitoring is an important goal in the treatment of neurocritical patients, as it may prevent secondary damage and directly impact morbidity and mortality rates. However, there is currently a lack of suitable non-invasive, real-time technologies for continuous monitoring of cerebral physiology at the bedside. Diffuse optical techniques have been proposed as a potential tool for bedside measurements of cerebral blood flow and cerebral oxygenation in case of neurocritical patients. Diffuse optical spectroscopies have been previously explored to monitor patients in several clinical scenarios ranging from neonatal monitoring to cerebrovascular interventions in adults. However, the feasibility of the technique to aid clinicians by providing real-time information at the bedside remains largely unaddressed. Here, we report the translation of a diffuse optical system for continuous real-time monitoring of cerebral blood flow, cerebral oxygenation, and cerebral oxygen metabolism during intensive care. The real-time feature of the instrument could enable treatment strategies based on patient-specific cerebral physiology rather than relying on surrogate metrics, such as arterial blood pressure. By providing real-time information on the cerebral circulation at different time scales with relatively cheap and portable instrumentation, this approach may be especially useful in low-budget hospitals, in remote areas and for&#160;monitoring in open fields (e.g., defense and sports).

Presented here is a protocol for non-invasively monitoring cerebral hemodynamics of neurocritical patients in real-time and at the bedside using diffuse optics. Specifically, the proposed protocol uses a hybrid diffuse optical systems to detect and display real-time information on cerebral oxygenation, cerebral blood flow and cerebral metabolism.

Neurophysiological monitoring is an important goal in the treatment of neurocritical patients, as it may prevent secondary damage and directly impact ...

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional magnetic resonance imaging (fMRI), which makes it highly suitable for conducting naturalistic studies of brain function and for use with developmental and clinical populations. Both fNIRS and fMRI methodologies detect changes in cerebral blood oxygenation during functional brain activation, and prior studies have shown high spatial and temporal correspondence between the two signals. There is, however, no quantitative comparison of the two signals collected simultaneously from the same subjects with whole-head fNIRS coverage. This comparison is necessary to comprehensively validate area-level activations and functional connectivity against the fMRI gold standard, which in turn has the potential to facilitate comparisons of the two signals across the lifespan. We address this gap by describing a protocol for simultaneous data collection of fMRI&#160;and fNIRS signals that: i) provides whole-head fNIRS coverage; ii) includes short-distance measurements for regression of the non-cortical, systemic physiological signal; and iii) implements two different methods for optode-to-scalp co-registration of fNIRS measurements. fMRI and fNIRS data from three subjects are presented, and recommendations for adapting the protocol to test developmental and clinical populations are discussed. The current setup with adults allows scanning sessions for an average of approximately 40 min, which includes both functional and structural scans. The protocol outlines the steps required to adapt the fNIRS equipment for use in the magnetic resonance (MR) environment, provides recommendations for both data recording and optode-to-scalp co-registration, and discusses potential modifications of the protocol to fit the specifics of the available MR-safe fNIRS system. Representative subject-specific responses from a flashing-checkerboard task illustrate the feasibility of the protocol to measure whole-head fNIRS signals in the MR environment. This protocol will be particularly relevant for researchers interested in validating fNIRS signals against fMRI across the lifespan.

We present a method for simultaneously collecting fMRI and fNIRS signals from the same subjects with whole-head fNIRS coverage. The protocol has been tested with three young adults and can be adapted for data collection for developmental studies and clinical populations.

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging methodology, more robust to motion and more cost-effective than functional ...

MRI-Arterial Spin Labeling Integration to Detect and Monitor Cerebral Blood Flow 

Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling

Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately detect early signs of reversible tissue damage, underlining the need for innovative diagnostic tools that can quantify changes in cerebral blood flow (CBF) with high specificity and sensitivity.&#160;This study integrates three-dimensional arterial spin labeling (3D-ASL) with structural MRI to develop comprehensive CBF atlases that cover all main functional regions of the brain. This innovative magnetic resonance imaging- arterial spin labeling (MRI-ASL) methodology provides a rapid and noninvasive means of quantifying region-specific CBF, offering a detailed view of CBF levels across different functional regions.The comparison between chronic cerebral ischemia (CCI) patients and healthy subjects revealed significantly diminished CBF across the cerebral functional regions in the constructed CBF atlases for the former. This approach not only allows for the efficient identification of CCI by analyzing concurrent decreases in CBF across critical areas relative to healthy distributions but also enables the tracking of treatment responses and rehabilitation progress through longitudinal CBF atlases.The CBF atlas developed using the MRI-ASL technique represents a novel advancement in the field of cerebral diagnostics and patient care. By comparing regional CBF levels against normative standards, this method enhances diagnostic capabilities, enabling clinicians to provide personalized care to patients with cerebral conditions.

Author Spotlight: Noninvasive Cerebral Blood Flow Determination in Human Functional Brain Region for Diagnosis of Neurological Disorders

This study integrated magnetic resonance imaging- arterial spin labeling images to derive cerebral blood flow (CBF) atlas for cerebral functional regions. Comparing typical healthy and chronic cerebral ischemia CBF atlases revealed significant differences in regional CBF distributions, enabling rapid, noninvasive assessments of functional CBF to assist in diagnosis and evaluate therapeutics.

Cerebral conditions often require precise diagnosis and monitoring, necessitating advanced imaging techniques. Current modalities may not adequately ...

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

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