2.1.

Animal Preparation

The Institutional Animal Care and Use Committee (IACUC) at the University of California, Irvine (protocol number 2013-3098-01) approved all procedures described in this report. Ten male Wistar rats (weight $\sim 300$ to 400 g) were imaged, and details of the animal preparation procedures are described in our previous publications.^8,12,15,16 Before the experiment, all subjects were endotracheally intubated under isoflurane anesthesia. Each subject had epidural screw electrodes implanted for electrocorticography (ECoG) and a hemicraniectomy (4 mm right-to-left × 6 mm anterior-to-posterior) was performed to enable imaging of a portion of the right sensory and visual cortices. Cannulation of the femoral artery allowed the delivery of drugs, sampling of blood, and monitoring of blood pressure.

2.2.

Cardiac Arrest and Cardiopulmonary Resuscitation

Figure 1 shows the multimodal setup employed in the experiments. At the onset of each experiment, the level of isoflurane was decreased from 2% to 0.5% to 1.0%. Concurrently, the mixture of inhaled gases was altered from $50\%{\mathrm{O}}_2+50\%{\mathrm{N}}_2$ to 100% ${\mathrm{O}}_2$. Two minutes later, to reduce confounding effects of isoflurane on cerebral perfusion and metabolism,¹⁷ the anesthesia was turned off, at which time the subject breathed room air (21% ${\mathrm{O}}_2$). During this same period, 1 mL of $2\mathrm{mg}/\mathrm{kg}$ Vecuronium (a neuromuscular blocker) and 1 mL of heparinized saline were administered intravenously, which led to respiration controlled solely by the ventilator. This stage of the experiment lasted for 3 min, after which the ventilator was turned off to induce asphyxia, leading to progressive hypoxic hypercarbic hypotension. CA was defined as the period over which the pulse pressure was below 10 mmHg and systolic pressure below 30 mmHg. The conditions of these experiments induced pulseless electrical activity, which is common in CA patients in a hospital setting.

Fig. 1

Multimodal platform (not to scale) for LSI and multispectral SFDI of the rat brain. A craniectomy ($\sim 6\mathrm{mm}\times 4\mathrm{mm}$ area) is performed to provide direct access to the brain for optical imaging. For SFDI, LEDs of 655, 730, and 850 nm are sequentially sent into a spatial light modulator that acts as a projector to send spatially modulated patterns of light onto the brain.⁸ A scientific CMOS camera detects the backscattered light. For LSI, an 809-nm laser illuminates the brain with coherent light, and the remitted speckle pattern is captured at 60 fps with a CCD camera.

Forty-five seconds before the end of the CA period, the ventilator was turned on ($\text{respiratory rate}=85\text{breaths}/\min$, $\mathrm{PIP}=17.5$ to $18.5\mathrm{cm}{\mathrm{H}}_2\mathrm{O}$, $\mathrm{PEEP}=3\mathrm{cm}{\mathrm{H}}_2\mathrm{O}$ at 2.5 LPM), and 100% oxygen was delivered. Immediately before the onset of cardiopulmonary resuscitation (CPR), $0.01\mathrm{mg}/\mathrm{kg}$ epinephrine, $1\mathrm{mmol}/\mathrm{kg}$ sodium bicarbonate, and 2 mL of heparinized saline were administered intravenously. Then, CPR was performed via external cardiac massage and terminated upon return of spontaneous circulation (ROSC), as identified from arterial blood pressure measurements. Subsequently, the animal was monitored continuously with arterial blood pressure, optical imaging, and ECoG for an additional $\sim 2\mathrm{h}$, after which the animal was euthanized with pentobarbital. As a quantitative measurement of the information content contained in the ECoG signals, we calculated an entropy-based parameter known as ECoG information quantity (IQ).¹⁸ Recovery of ECoG signal following ROSC was quantified by (1) time to initial resumption (burst) of ECoG activity and (2) ECoG IQ 90 min post-ROSC (as in Ref. 19).

2.3.

Laser Speckle Imaging

For LSI, an 809-nm laser with long coherence length (Ondax, Monrovia, California) served as the light source. To increase uniformity of illumination over the imaged region of interest (ROI), a ground-glass diffuser (ThorLabs, Inc., Newton, New Jersey) was placed between the laser and the brain. A CCD camera (Point Grey Research Inc., Richmond, BC, Canada) detected the backscattered light with a 10-ms exposure time, resulting in image acquisition at a frame rate of 60 Hz. Using a $5\times 5$ sliding spatial window filter, the equation $K=\sigma /⟨I⟩$ was employed to calculate the local speckle contrast $K$ at each pixel, where $⟨I⟩$ is the mean intensity within the filter and $\sigma$ is the standard deviation within the filter.²⁰ Then, the speckle flow index (SFI) was determined from the values of $K$ and the exposure time $T$ via a simplified speckle imaging equation $\mathrm{SFI}=1/(2T{K}^2)$.²⁰ Time-resolved SFI curves were generated by taking the mean of the SFI over a selected ROI at each time point.

2.4.

Spatial Frequency-Domain Imaging

For SFDI, light-emitting diodes (LEDs) of three different wavelengths (655, 73, and 850 nm) were used as light sources. The light was directed to a spatial light modulator that projected square-wave patterns onto the brain.⁸ Backscattered light was captured using a scientific complementary metal-oxide-semiconductor (sCMOS) camera (Hamamatsu Photonics). An Arduino Due microcontroller board was used to synchronize the camera acquisition, spatial light modulator, and LEDs. For each wavelength, four patterns were projected onto the tissue in sequence. The first pattern was nonmodulated (i.e., DC illumination), and the three subsequent patterns were modulated at spatial frequency $\sim 0.3{\mathrm{mm}}^{-1}$ with three distinct spatial phases to enable demodulation.²¹ Thus, there were a total of (3 wavelengths × 4 frames) = 12 frames of SFDI data for each measurement time point. The detected square wave pattern could be approximated as a sinusoid, allowing demodulation in the manner described previously by our group.²² With this acquisition scheme, we were able to reconstruct tissue hemodynamics and ${\mathrm{CMRO}}_2$, at an effective imaging rate of $\sim 14\mathrm{Hz}$.

After demodulating the spatially modulated data, the diffuse reflectance at each time point and wavelength was calculated from the raw data via calibration against a tissue-simulating phantom with known optical properties.²¹ The diffuse reflectance maps were then fit with a Monte Carlo model to extract the tissue absorption coefficient ${\mu }_{\mathrm{a}}$ and reduced scattering coefficient ${{\mu }_{\mathrm{s}}}^{\prime }$ at each wavelength.⁸ Next, the average ${{\mu }_{\mathrm{s}}}^{\prime }$ was determined for a selected ROI and a new ${\mu }_{\mathrm{a}}$ determined using diffuse reflectance with the nonmodulated pattern and this average ${{\mu }_{\mathrm{s}}}^{\prime }$. To calculate the concentrations of oxygenated and deoxygenated hemoglobin (${\mathrm{ctHbO}}_2$ and ctHb, respectively) within the tissue, this new ${\mu }_{\mathrm{a}}(\lambda )$ spectrum was fit with the model spectrum ${\mu }_{\mathrm{a}}(\lambda )=2.303({\mathrm{ctHbO}}_2{\epsilon }_{\mathrm{HbO}2}+\mathrm{ctHb}{\epsilon }_{\mathrm{Hb}})$, where ${\epsilon }_{\mathrm{HbO}2}$ and ${\epsilon }_{\mathrm{Hb}}$ were the molar extinction coefficients of oxy- and deoxyhemoglobin, respectively. The total tissue hemoglobin concentration (${\mathrm{ctHb}}_{\mathrm{tot}}$) was calculated by summing ctHb and ${\mathrm{ctHbO}}_2$. The tissue oxygen saturation was determined using the equation ${\mathrm{StO}}_2={\mathrm{ctHbO}}_2/({\mathrm{ctHbO}}_2+\mathrm{ctHb})$.

4.1.

Optical Imaging Segments Venous Regions to Better Quantify Cerebral Oxygen Extraction

The imaging capability of our device allows the segmentation of an ROI atop a prominent vein, which enables more accurate measurements of the quantity of deoxygenated venous blood and, hence, the quantity of oxygen consumed by the brain. With the use of a larger ROI, the local ${\mathrm{CMRO}}_2$ would be systematically underestimated due to inclusion of the parenchyma in the ROI, as oxygen extraction in the parenchyma is lower than in individual vessels. ${\mathrm{CMRO}}_2$ models of diffuse light transport implicitly assume that the concentration of deoxygenated hemoglobin is that within the veins specifically and not the bulk tissue.³⁰ However, most diffuse optics measurements of ${\mathrm{CMRO}}_2$ are unable to satisfy this condition, as they typically use fiber-based spectroscopic techniques that sample the bulk tissue and thus cannot distinguish between venous and mixed arterial-venous parenchymal regions. In this report, the use of DOI allows us to obtain a spatial map of the tissue properties, enabling use of deoxyhemoglobin concentrations measured in a venous ROI to obtain more accurate quantitative values of ${\mathrm{CMRO}}_2$.

4.2.

Correction of ${\mathrm{CMRO}}_2$ Data for Partial-Volume Effects

Due to the heterogeneity of biological tissues, partial-volume effects are a well-known confounding factor, especially with optical property mapping using a planar wide-field imaging technique such as SFDI.¹⁹ In our work, we require knowledge of ${\mathrm{Hb}}_{\mathrm{v}}$ [Eq. (6)]. However, due to partial volume effects, simple selection of an ROI that is coincident with a venule is insufficient. To address this issue, we employed a partial-volume correction to the ${\mathrm{CMRO}}_2$ equation. To accurately incorporate this scaling term, it is necessary to know the concentration of total hemoglobin (${\mathrm{Hb}}_{\mathrm{bl}}$) within the blood of each animal. In this report, these values were acquired via ABG measurement before CA. A coefficient of variation of 13% in ${\mathrm{Hb}}_{\mathrm{bl}}$ was determined from the measurements. If the variation in ${\mathrm{Hb}}_{\mathrm{bl}}$ among the different subjects was not considered, an additional error of $\sim 12\%$ to 25% in the measured ${\mathrm{CMRO}}_2$ would be achieved due to this within-group variability in ${\mathrm{Hb}}_{\mathrm{bl}}$ values.

4.3.

Contributions of Directed Flow Versus Diffuse Flow

With diffuse optical measurements of blood flow, the model of blood flow typically is assumed to be either diffusion-like (i.e., Brownian motion) or directional (i.e., intravascular).³¹ Here, we assumed that the corrected flow speed was the latter [Eq. (2)]. Previous studies have used the diffusion-like term as the free parameter when fitting for flow speed³² or constrained the fit in a model system such that one could choose to fit for either diffuse or directed flow but not both simultaneously.²⁴ Recently, Postnov et al.³³ used high-speed LSI to map the autocorrelation function pixel-by-pixel in the rodent brain, identifying the dominant type of particle motion at each pixel. They observed that the directed flow term was dominant in large vessels, whereas the diffuse flow term was dominant in the parenchyma.

Here, we could not rigorously solve for the autocorrelation function because the sampling frequency of our LSI data acquisition was too low to perform a method similar to that of Postnov et al.³³ Instead, we used a two-step approach of (1) using SFDI data to account for the effects of optical properties on interpreting the LSI data and (2) fitting the resulting corrected data to a model of directed flow to extract a characteristic flow speed. This method provided characteristic flow speeds that were similar to previously reported values.³⁴

4.4.

Limitations of Zero-Flow Condition

Our current approach for measuring absolute ${\mathrm{CMRO}}_2$ requires temporary induction of a “zero-flow” condition in the brain. In this report, this condition was met using a CA model in rats. However, there is a clear need to investigate alternative approaches for interrogating absolute ${\mathrm{CMRO}}_2$ without creating harmful perturbations. Future work can incorporate techniques such as balloon occlusion tests, as sometimes done in the clinical setting,³⁵ to temporarily induce a zero-flow condition that can be quickly reversed without long-term harm to the animal. However, these procedures may have longer-term effects on the brain, so they would require evaluation within our preclinical model. Although the comparison of absolute ${\mathrm{CMRO}}_2$ calculated with our approach with PET and MRI is encouraging, further comparison work is required with measurements collected from the same animals under identical anesthesia conditions. Future work in this area is warranted. Additional future studies will assess the sensitivity of our ${\mathrm{CMRO}}_2$ method to small perturbations in brain metabolism over time in longitudinal studies. This future investigation will involve comparison of our method with an established modality such as MRI in preclinical chronic imaging experiments.