1.

Introduction

Optical techniques have been utilized to explore the hemodynamics of the visual cortex of the human brain¹ and that of animals.^{2, 3, 4} Grinvald, ⁵ Swindale, ⁶ and Jancke ⁷ have published several articles on the optical reflectance changes observed in the exposed brain surface of cats during visual stimulation. Here, we examine the visual cortex, specifically areas $17∕18$ of the cat using a spectral technique that enables noninvasive measurements through the skull. Specifically, we determine spectral changes due to changes in concentrations of tissue chromophores and scattering during visual stimulation. We show that the spectral technique is sensitive to changes due to neuronal activation by comparing the visual cortex with an area that lies outside the visual cortex; namely, the frontal lobes, where we expect little or no signal change. This is the first study to employ a spectral technique to separate contributions from different tissue components with high temporal and spatial resolution. Several issues must be considered to interpret our results. Specifically, we consider the modality of light propagation in the tissue, the region of the brain explored by light, the physiological origin of the observed effects, and the potential and limitations of this spectral technique. We discuss our technical approach in the context of light transport in tissue to set the stage for the interpretation of the changes observed in the measured optical parameters. We show that different spectral components associated with known tissue chromophores can be identified and that the time course of the changes in spectral components can also be obtained. Several control measurements were done to validate that the observed effects result from the external visual stimulation.

In particular, we performed control trials without visual stimulation, with other conditions remaining the same. Brain regions outside the cat’s visual cortex were examined, during visual stimulation, to determine if the changes observed were localized to a specific region of the brain or were due to systemic changes in blood flow. The framework for the interpretation of the hemodynamic changes observed is based on the well-established BOLD effect (blood oxygenation level dependence response) from the functional magnetic resonance imaging (fMRI) literature. The BOLD effect is primarily due to neurovascular coupling between the part of the brain that is activated by a given task and the corresponding local increase in blood flow, following stimulation. This increase in flow causes a local decrease in the deoxyhemoglobin (HHb) content which is visible both in the MRI experiments and in the optical counterpart. In addition to changes in concentration of the HHb, the spectral method provides concomitant changes in concentration of other tissue chromophores such as oxyhemoglobin $\left({\mathrm{O}}_2\mathrm{Hb}\right)$ and water. Furthermore, the spectral approach that we developed provides changes in the scattering spectrum during brain activation. As a result of our studies, we have a comprehensive and novel description of changes occurring in specific brain regions due to neuronal activation. Our results confirm the general model of neurovascular coupling, and provide new information with regard to changes in the scattering spectrum and apparent changes in the local water concentration that require a new interpretation model. The time course of the optically detected BOLD effect shows unexpected features such as lack of sustainable blood flow increase and decrease of the amplitude of the effect as the task is repeated many times. These observations about fatigue in the cat brain will be discussed in a separate manuscript.

2.

Light Transport in Tissues

Light transport in tissue is primarily governed by two processes; absorption and scattering. In the near-IR (NIR) region $\left(650\text{to}1150\mathrm{nm}\right)$ , in the so-called “therapeutic window,” scattering is the dominant process for most tissues. For example, the reduced scattering coefficient ${\mu }_{s^{\prime }}$ of the gray matter in the human brain ranges from $20\text{to}30{\mathrm{cm}}^{-1}$ , while the absorption coefficient ${\mu }_a$ is⁸ about $0.25{\mathrm{cm}}^{-1}$ . In fleshy tissue, HHb, ${\mathrm{O}}_2\mathrm{Hb}$ , and water are the main absorbing species⁹ in the NIR. The large value of the reduced scattering coefficient of the brain enables the use of the diffusion approximation to the Boltzmann transport equation to describe the propagation of light and to quantify the absorption and scattering properties of the tissue when the distance traveled by the light between the source and the detector is^{10, 11, 12} larger than $1\text{to}2\mathrm{cm}$ . However, for smaller distances (in the nondiffusive regime) a different approach must be employed to recover the optical parameters of tissues. Current diffuse optical techniques such as NIR spectroscopy (NIRS) use light at a few wavelengths chosen such that one can determine the concentrations of the HHb and ${\mathrm{O}}_2\mathrm{Hb}$ . By using amplitude modulated light at moderately high frequencies $\left(100\text{to}400\mathrm{MHz}\right)$ , Gratton showed that multidistance frequency-domain NIRS can be used as a noninvasive technique to study the hemodynamics of the muscle and brain.¹³ It has been well documented¹⁴ that the frequency-domain approach of light propagation in tissues provides high temporal resolution and a high SNR. In this paper, we explore a new spectral technique that employs a tungsten lamp source to study the hemodynamics of the tissue, specifically the capillaries where the oxygen exchange takes place. We applied the spectral technique to detect changes in scattering and absorption in the visual cortex of a cat’s brain due to the hemodynamic signal arising from neuronal activation evoked by visual stimulation. Note that we cannot apply the light transport formulation in the diffusion approximation for this experiment, because the anatomical structure of the cat’s brain (namely, its small size) is such that there are insufficient scattering events when the light travels from the source fiber to the detector fiber. Hence, the photons are not fully randomized in direction before arriving at the detector. The spectral approach has been extensively used to study tissue spectral properties.^{15, 16, 17} However, it has not been applied to study rapid changes in optical properties of brain tissue during visual stimulation. We are using a fast time acquisition (up to $200\text{spectra}∕\mathrm{s}$ ) to detect changes in cerebral hemodynamics.

3.

Spectral Approach

In the NIR region, of the electromagnetic spectrum, the major chromophores found in tissue such as HHb, ${\mathrm{O}}_2\mathrm{Hb}$ , fat, water, and cytochrome have well-defined absorption spectra (Fig. 1 ). To have all of the individual spectra on the same graph, they are not plotted on the same scale. HHb and ${\mathrm{O}}_2\mathrm{Hb}$ are in units of ${\mathrm{mm}}^{-1}{\mathrm{mM}}^{-1}$ , while water and fat are in units of inverse millimeters. The scattering spectrum in Fig. 1 follows the ${\lambda }^n$ wavelength dependence with $n=4$ . In the fit of the spectral changes the parameter $n$ was variable.

Fig. 1

Comparison of experimental and theoretical fit and the tissue chromophores used in the calculation.

In the spectral approach, we use a large number of wavelength points; hence, we can determine the absorbance of tissue components such as HHb, ${\mathrm{O}}_2\mathrm{Hb}$ , fat, and water and their spectral shifts with high precision. Assuming that we know the spectrum of the individual tissue components, we can construct a linear combination of basis spectra to fit the overall tissue absorbance spectrum:

4.

Methods—Cat Protocol

4.1.

Preparation of the Cat

These experiments were performed on one adult female cat. All procedures were in accordance with U.S. Public Health Service Policy and protocols approved by the University of Illinois Institutional Animal Care and Use Committee. All surgery was performed aseptically and under general anesthesia. A goldplated ring was implanted under the conjunctiva of one eye to enable eye movements to be monitored using the double magnetic induction method.¹⁸ The scalp and muscle overlying the calvarium were removed, and an aluminum fixture surrounding this area, bonded to the skull to provide support for a protective cap that was also used to immobilize the cat’s head during the experiment. A mixture of clear acrylic cement and antibiotics $\sim 1\mathrm{mm}$ thick was then placed in lieu of the removed tissue to provide a permanent protective barrier.¹⁹ Metal tubes (15 gauge, thin-wall hypodermic tubing, with an inner diameter of $1.52\mathrm{mm}$ and an outer diameter of $1.83\mathrm{mm}$ ), $7\mathrm{mm}$ in height, were then embedded in this mixture above the region corresponding to the visual cortex, area $17∕18$ , and the frontal lobes, area 4. Figure 2 shows schematically the location of the metal tubes.²⁰ The tubes were in direct contact with the acrylic mixture above the bone. For simplicity, a grid system was implemented to indicate the position of each tube; this is used to identify the source and detector locations. The “a” row was located above the frontal lobes and each tube was numbered in sequence from 1 to 5. The “b” row was located in the parasagittally, roughly over the border between areas 17 and 18 of the visual cortex in the right hemisphere. The “c” row was located in a coronal plane that cut across the region of area 17 near the surface of the brain, as well as across the entire coronal extent of areas 18 and 19 of the visual cortex (Fig. 2). The intersection of the “b” and “c” rows is roughly at the center of the representation of the area centralis. The distance between adjacent tubes was $2\mathrm{mm}$ , with the exception of c4 and c5, where the “c” row intersects the “b” row. The tubes served as holders for the source and detector optical fibers during our measurements.

Fig. 2

Bird’s eye view of placement of tubes on the calvarium of the cat with cortices labeled superimposed on a view of the dorsal surface of the cat brain.

5.

Experimental Procedure

6.

Results

For all of the different source-detector (S-D) configurations, data were processed as described in the previous section to detect both the changes due to the hemodynamic signal and to the fast (millisecond) neuronal signal. However, due to the volume of information that was obtained, only certain aspects of the hemodynamic signal are presented in this paper. First, we present maps of the raw data matrix after folding followed by the separation of this matrix into the weighted contributions of scattering, water, ${\mathrm{O}}_2\mathrm{Hb}$ , and HHb as functions of time. The maps show the average change for all of the wavelengths as a function of time for the folded data. Finally a comparison of the optical BOLD effect seen as a function of S-D configuration is shown. Although we show results for only a few experiments, the results are highly reproducible for any given S-D combinations. For locations in the visual cortex, the optical BOLD effect is observed during visual stimulation and not seen in the absence of the stimulation. Similarly for S-D configurations located outside of the visual cortex (frontal lobes), the Optical BOLD effect was not observed for NVS or VS trials.

6.1.

Raw Data

Maps displaying the raw data matrix of each S-D pair over the visual cortex, areas 17 and 18 show a significant change in the shorter wavelengths for the first $3\mathrm{s}$ of the visual stimulation [Fig. 3(a) and 3(c) ]. The data matrix for the control configuration that was positioned over the frontal lobes, where we did not anticipate any changes, remains relatively constant during the same activation period [Fig. 3(e)]. For all of the configurations during NVS trials, the changes in wavelengths as a function of time were also minimal [Fig. 3(b), 3(d), 3(f)].

Fig. 3

Raw data matrix indicating source and detector location and trial type: (a) b4b6 VS, (b) b4b6 NVS, (c) b4c6 VS, (d) b4c6 NVS, (e) a2a4 VS, and (f) a2a4 NVS.

6.2.

Spectral Deconvolution

The raw data matrix was decomposed for each time bin (linked to trial length) using four spectral components: ${\mathrm{O}}_2\mathrm{Hb}$ , HHb, water, and scattering. Figures 4(a), 4(b), 4(c), 4(d), 4(e), 4(f) show three different S-D locations for VS and NVS trials, one from the parasagittal row of tubes over the area $17∕18$ border [b4b6; Figs. 4(a) and 4(b)], one roughly in a coronal plane over areas 17 and 18 [b4c6; Figs. 4(c) and 4(d)], and one over the frontal lobes [a2a4; Figs. 4(e) and 4(f)]. There is an optically detected BOLD effect due to the activation of the visual cortex in which the ${\mathrm{O}}_2\mathrm{Hb}$ starts to increase after $1\text{to}2.5\mathrm{s}$ after visual stimulation. This delay is dependent on the area investigated. Differences in the values and time courses of the signals due to changes in ${\mathrm{O}}_2\mathrm{Hb}$ and HHb are detected between regions in the visual cortex which differ by about $2\mathrm{mm}$ . Additionally, significant changes are seen due to scattering and water. The change due to scattering also has a different time course with respect to the other components. For S-D configurations that correspond to areas in the visual cortex, a maximum decrease (trough) in scattering is seen at roughly the same time as the ${\mathrm{O}}_2\mathrm{Hb}$ reaches a maximum in the optical BOLD effect during VS trials. The water component is seen to decrease at the onset of stimulation and tracks the HHb changes with some time delay [Figs. 4(a) and 4(b)]. Control experiments [Figs. 4(e) and 4(f)] show that there are no major changes in the ${\mathrm{O}}_2\mathrm{Hb}$ and HHb (BOLD effect) that track with the visual stimuli for the S-D pair located over the frontal lobes, and the contributions from these components are one order of magnitude smaller than the signal obtained from the VS trials [Figs. 4(a) and 4(c)]. The signals due to the changes in water and scattering are of the same order of magnitude, but they do not appear to be correlated to visual stimulation. In addition, control experiments show minimal signal changes in the absence of visual stimulation [Figs. 4(b), 4(d), 4(f)].

Fig. 4

Spectral deconvolution, ${\mathrm{O}}_2\mathrm{Hb}$ , and HHb: (a) b4b6 VS (b), b4b6 NVS, (c) b4b6 VS, (d) b4b6 NVS, (e) a2a4 VS, and (f) a2a4 NVS.

6.3.

Optical BOLD Effect as a Function of S-D Locations

Figures 5(a) and 5(b) show the optically detected BOLD effect for several S-D locations, two parallel to the $17∕18$ border (b4b6, b3b5), and one S-D pair located over the frontal lobes (a2a4), and three that roughly straddled this border orthogonally (b4c6, b4c5, and c5b5), respectively. The signals for the two S-D configurations parallel to the $17∕18$ border show similar temporal behavior. However, for the configuration b4b6 the amplitude of the signal is much larger (about a factor of 4) than for any other configuration. For the orthogonal S-D pairs, it was observed that the amplitude of the signal due to the optical BOLD effect was approximately the same. However, there were different delays among the different pairs. In one configuration, b4c6, an initial dip is seen lasting for $\sim 2\mathrm{s}$ . The initial dip was not observed at all locations, indicating that its magnitude is not constant everywhere.

Fig. 5

Optically detected BOLD effect at different locations in the visual cortex. For each curve, positive line indicates ${\mathrm{O}}_2\mathrm{Hb}$ and negative line indicates HHb (a) for parasagittal S-D pairs, which were roughly parallel to the $17∕18$ border, and (b) for S-D pairs roughly orthogonal to the $17∕18$ border.

7.

Discussion

This work is the first to determine changes of the spectral components in the mammalian (cat) brain due to external visual stimuli. Previous work focused on the determination of the reflectance signal in the near-IR with the purpose of imaging columnar neuronal organization,²¹ and the optically detected BOLD effect was not determined in those studies. Furthermore, the fast dynamics were obtained using long-integration, high-sensitivity cameras in which the acquisition was triggered at different times after visual stimulation rather than utilizing rapid spectral acquisition, which was synchronous with the visual stimulation as in the present approach. The spatial resolution that is obtained with our instrument is of the order of millimeters, which when compared to that obtained using multidistance NIRS in human subjects (centimeters) still gives us a relatively high spatial resolution. One concern is that the radiation emitted by the tungsten lamp will be unstable as it is temperature dependent. However, before any analysis was done, the data was block averaged and folded across trials as is standard in similar studies. Hence, any random fluctuations that are present would be removed before the spectral deconvolution is performed. We have observed changes in the ${\mathrm{O}}_2\mathrm{Hb}$ and HHb signals during visual stimulation reminiscent of the fMRI BOLD effect. The origin of the BOLD effect is attributed to an increase in blood flow following neuronal activity in one part of the brain. This causes a decrease of the HHb level because HHb is washed out. In the fMRI signal, only the decrease of the HHb concentration is measured, whereas with the optical technique, we have access to both ${\mathrm{O}}_2\mathrm{Hb}$ and HHb. Upon visual stimulation, we can clearly see the decrease in the HHb and the quasisimultaneous increase in the ${\mathrm{O}}_2\mathrm{Hb}$ . Careful examination of the data in Figs. 5(a) and 5(b) shows that there is a delay between the two signals. A similar delay between the increase of the ${\mathrm{O}}_2\mathrm{Hb}$ and decrease of HHB was also observed in optical experiments in humans and was attributed to oxygen consumption.^{22, 23} A striking feature of our experiments is that the optical BOLD effect starts to decrease after a few seconds [Fig. 5(a)], although the stimulation continued throughout the trial.

Another novel outcome of these experiments is that the water signal, as well as the scattering signal, changes during stimulation. The changes in the water component are quite surprising, since we expect no net change of the tissue water content during the short time of visual stimulation. For the water content in the tissue to change rapidly (in seconds), the water should be replaced by something else, which is not plausible. To explain the decrease of the water content during the optical BOLD effect we must consider the origin of the optical signal in the tissue. The tissue is highly heterogeneous from both the physiological and the optical points of view. There are tissue regions that are optically opaque, such as relatively large blood vessels. If, as we expect, these vessels change diameter to accommodate an increase of blood flow following neuronal stimulation, then this opaque optical compartment will increase. The net effect of the increase in the opaque compartment results in an effective decrease of all the spectral components, including the ${\mathrm{O}}_2\mathrm{Hb}$ and HHb. However, these tissue chromophores undergo additional physiological changes due to the exchange of ${\mathrm{O}}_2$ and $\mathrm{C}{\mathrm{O}}_2$ in the tissue, which results in a net increase of the ${\mathrm{O}}_2\mathrm{Hb}$ signal (washout effect). We propose that this optical effect caused by the presence of the opaque blood vessels is at the origin of the apparent decrease of the water component. Following this reasoning, the water component could be used to assess the extent of vasodilation due to stimulation. With regard to the scattering signal, it should also decrease due to this optical effect. However, there are also changes in the size of the microcapillaries, larger blood vessels, and possible changes at the cellular and subcellular levels. Therefore, the scattering signal will not necessarily track the water signal.

All data presented in Figs. 5(a) and 5(b) refer to only the first 10 trials of a series of 100 trials. As the trials continued, there was a gradual change of the shape of the optical BOLD effect. This fatigue effect will be discussed in a separate manuscript.

We observed that the optical BOLD effect is strongly dependent on the location in the visual cortex. Locations $2\mathrm{mm}$ apart gave significant differences both in the amplitude and in the time course of the signals.

We observed several differences between the optical BOLD effect for the cat, when compared with similar experiments in humans. It is premature to discuss the differences, when only one animal has been studied. Furthermore, we cannot state unequivocally that these results are “cat invariant.” However, additional cats are being trained to repeat the experiments, and to assess if there are differences among individual animals. Certainly the size of the cat’s brain could generate hemodynamic responses that differ from those in humans.

One consideration for this discussion is whether or not the spectral approach could be extended to studies of the human brain. First, the size of the skull will bring the regime of light propagation into the diffusive regime. However, the spectral approach is independent of the modality of light propagation. Two factors must be considered for the application of this spectral approach in humans. First, with the present experimental apparatus, the SNR is insufficient to observe the dynamic changes of the tissue chromophores at the distance of centimeters. For the slow hemodynamic signal, using more sensitive detectors and more efficient monochromators, we should have enough light for an accurate determination of spectral components. Second, for the fast signal due to neuronal activation, where a very short integration time is required, the amount of light necessary could be critical. Hence, additional work is required for the improvement of the technical aspects of this work.

In summary, we developed a technique that is independent of the light modality (diffusive or nondiffusive), where a broadband spectral approach is used to determine the individual NIR spectrum of tissue components. For the application in a mammalian brain, we have examined the behavior of the scattering, ${\mathrm{O}}_2\mathrm{Hb}$ and HHb (BOLD effect) simultaneously with other tissue components such as water content. The technique has proven to have a high temporal and spatial resolution to adequately determine the localized hemodynamics. The behavior of water during stimulation has not been discussed in previous literature. Our proposed model satisfactorily accounts for the apparent change in the water content, which can be used to better qualify the role of water in vascular dynamics.