Noninvasive Optical Studies of the Brain

2 Noninvasive Optical Studies of the Brain: Contributions From Systemic Physiology Sergio Fantini1, Alexander Ruesch2, Jana M. Kainerstorfer2 1 D E P ART ME NT O F B I O ME DI C AL E NGI NEERING, TUFTS UNIVERSITY, 4 COLBY STREET, ME DFORD, MA 02155, UNITED STATES; 2 DEPART ME NT O F B I O MEDI CAL ENGI NEERI NG, CARNE GIE ME LLON UNIVE RSITY , 5 000 FO RBES AVE NUE , P ITT SBURGH, P A 15 21 3, UNITED STATES

1. Background and Historical Overview of Noninvasive Optical Studies of the Brain The pioneering work of Frans Jo¨bsis in the mid-1970s paved the way for noninvasive optical sensing of cerebral concentration and saturation of hemoglobin, as well as the redox state of cytochrome oxidase [1]. The basic idea is to take advantage of the relatively low absorption of tissue in the red and near-infrared spectral region to achieve a significant optical penetration depth into tissue. Noninvasive optical measurements rely on the fact that light reaching the head surface does penetrate through the intact scalp and skull to illuminate the brain cortex, and some of this penetrating light is able to make it back to the head surface where it can be detected. The low absorption of tissue in this spectral window is balanced by a relatively high scattering of light caused by gradients and discontinuities in the refractive index associated with cellular structures, membranes, organelles, etc. The fact that near-infrared light propagation in tissue is dominated by scattering, i.e., photons experience a large number of scattering events before they may be absorbed or detected, allows for diffuse reflectance measurements from the tissue surface. In the case of brain studies, this means that optical fibers for delivery and collection of light may be placed on the scalp to probe the brain cortex, which is located below the scalp, skull, dura mater, subdural space, arachnoid mater, subarachnoid space, and pia mater, at a depth of about 1e2 cm in adult human subjects [2]. The possibility of performing noninvasive, dynamic optical measurements of the brain that are sensitive to hemodynamic and metabolic changes associated with brain activation led to the introduction of functional near-infrared spectroscopy (fNIRS) in the early 1990s [3e6]. In addition to functional studies of brain activation, several groups Neurophotonics and Biomedical Spectroscopy. https://doi.org/10.1016/B978-0-323-48067-3.00002-0 Copyright © 2019 Elsevier Ltd. All rights reserved.

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26 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

have also explored the potential of noninvasive optical measurements for the diagnosis and characterization of neurological, neurovascular, and psychiatric disorders. For example, explored pathologies include Alzheimer’s disease [7,8], Parkinson’s disease [9,10], epilepsy [11,12], traumatic brain injury and intracranial hemorrhages [13,14], stroke [15], and schizophrenia [16]. We refer the reader to recent review articles that provide excellent overviews of noninvasive optical studies of the brain for functional imaging [17] and clinical applications [18].

1.1

The Diagnostic Optical Window(s) and Key Tissue Chromophores

The basic premise of noninvasive optical studies of tissues is that the intact scalp and skull do not prevent light delivered to the outer scalp surface from reaching the brain cortex and, after having interacted with it, from making it back to the scalp and exiting the tissue where it may be detected. This is possible only in the red and near-infrared spectral region, originally identified with the wavelength range 700e1300 nm by Jo¨bsis [1], and commonly referred to as the diagnostic optical window, because of the relatively low collective absorption of key tissue constituents (mostly hemoglobin and water) in this spectral band. A narrower spectral window at 650e950 nm has become standard in diffuse optical studies of tissue because of readily available light sources and optical detectors in this spectral range. It is worth noting, however, that even beyond this standard optical window, other spectral windows lend themselves to deep optical measurements of the brain, namely 1100e1350 nm (which is in the high-wavelength range of the window originally identified by Jo¨bsis), 1600e1870 nm, and 2100e2300 nm [19], as a result of local minima in the water absorption and a general decreasing trend of the scattering coefficient versus wavelength. These NIR and SWIR windows are currently defined as window #1 denoted as therapeutic window (650 e1050 nm), window #2 (1100 e1350 nm), window # 3 denoted as Golden window (1600 e1870 nm), and window #4 (2100 e2300 nm) [19]. The absorption spectra of relevant tissue chromophores and components in the standard red/near-infrared diagnostic optical window at 650e950 nm (extended to 1050 nm) are illustrated in Fig. 2.1. Specifically, Fig. 2.1 reports the absorption spectra of oxy-hemoglobin (HbO2), deoxy-hemoglobin (Hb), and cytochrome c oxidase (difference of oxidized and reduced states: cytoxy-cytred) [20], as well as water [21], and lipids [22]. The absorption coefficients are reported in reference to a base e exponential decay of optical intensity and for concentrations of 50 mM (HbO2), 50 mM (Hb), 10 mM (cytoxy-cytred), 80% by volume (water), and 50% by volume (lipids). In optical studies of the brain, oxy- and deoxy-hemoglobin concentrations provide indications on the cerebral blood volume and the relative supply (through cerebral blood flow) and demand (through tissue oxidative metabolism) of oxygen at the tissue level. A measure of oxygen consumption at the cellular level is provided by the redox state of cytochrome c oxidase.

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0.4

Absorption coefficient (cm-1)

Hb

Water

0.3

0.2

HbO2 cytoxy-cytred

0.1

Lipids 0 650

700

750

800

850

900

950

1000

1050

Wavelength (nm) FIGURE 2.1 Absorption spectra of relevant chromophores and tissue components in the red/near-infrared diagnostic optical window. cytoxy-cytred, difference of oxidized and reduced forms of cytochrome c oxidase; Hb, deoxyhemoglobin; HbO2, oxy-hemoglobin. Concentrations of the absorbers are given in the text. From data reported in [20] for Hb, HbO2, and cytochrome c oxidase, data reported in [21] for water, and data reported in [22] for lipids.

1.2

Continuous-Wave and Time-Resolved Approaches to Data Collection

Diffuse optical spectroscopy of tissue has been performed since the early decades of the 20th century using steady-state illumination for continuous-wave (CW) optical density measurements [23]. Time-resolved methods, in either the time-domain or frequencydomain, were introduced in the late 1980s and early 1990s to provide data with richer information content, which eventually led to measurements of both absorption and scattering properties of tissue. In the time domain, pulsed light sources (with pulse duration in the picosecond range) and time-resolved detectors were used to measure the oxygen saturation of hemoglobin in the human brain [24] and the optical pathlength in a rat brain [25]. In the frequency domain, intensity modulated lasers (with modulation frequency typically of the order of 100 MHz) and phase sensitive detection were used to characterize the frequency dependence of phase and modulation amplitude of light transmitted through skeletal muscle [26], perform phase- and modulation-based imaging of a human hand [27], and perform functional studies of the human brain during a finger tapping task [28]. While noninvasive optical brain studies are nowadays most commonly performed in CW, there are several groups employing time-domain or frequency-domain techniques

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to leverage their enhanced spatial, chromophore concentration, and scattering information, which in turn may result in more accurate diagnostic, functional, and physiological information. There are several excellent reviews of fNIRS in CW [29] and in the time domain [30], including a wonderful historical review that describes technological and methodological developments including CW, time-domain, and frequency-domain optical approaches to investigating the brain [17].

1.3

Methods of Data Analysis

The most straightforward, and still widely used, method for the analysis of CW optical data at a single source-detector separation is based on the modified BeereLambert law (mBLL) [31,32], which translates changes in the detected intensity into changes in the tissue optical absorption under the assumption of constant scattering and homogeneous absorption changes. Measurements of absorption dynamics at multiple wavelengths can then be assigned to temporal dynamics in the concentrations of individual chromophores such as oxy-hemoglobin, deoxy-hemoglobin, and cytochrome c oxidase. Timeresolved methods feature the added capability of separately measuring the absorption and scattering properties of tissue, relaxing the mBLL assumption of constant scattering. However, the typical assumption of tissue homogeneity (or, in the case of the mBLL, of homogeneity of tissue absorption changes) is clearly violated in the presence of the highly spatially and optically heterogenous nature of the tissue that is interrogated in noninvasive optical measurements of the brain (scalp, skull, dura mater, subdural space, subarachnoid space, brain cortex). Layered tissue models [33,34], also in conjunction with tomographic spatial reconstructions [35], have been used to take into consideration inhomogeneous tissue properties. For noninvasive optical studies of the brain, there are even more critically important issues than those related to the tissue heterogeneity. Specifically, confounding factors from motion artifacts, extracerebral hemodynamic contributions, spontaneous cerebral hemodynamics, and systemic physiological changes that may occur spontaneously or in response to the experimental protocols must all be considered. We refer the reader to a well-written review of statistical analysis of fNIRS data, which describes the multitude of approaches (adaptive filtering, wavelet-based filtering/detrending, independent component analysis, principal component analysis, noise removal through shortdistance corrections, etc.) to address a number of confounding factors in fNIRS [36]. Our emphasis in this chapter is on contributions of systemic origin (cardiac pulsation, respiration, mean arterial pressure changes, etc.) to optical signals collected from the human brain. Such systemic contributions may be taken as confounding factors, especially in functional brain studies, resulting in efforts to remove them from the optical data. Methods for removal of systemic physiological interference include temporalbased corrections based on band-pass and/or folding average filtering in protocols involving multiple task epochs [37], or spatial-based corrections such as spatial eigenfiltering [38]. Spatial-based corrections are more effective when systemic physiological

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contributions synchronize with brain activation tasks, as was shown to be the case for the heart rate and respiratory rate during a finger tapping task [39,40] There are cases, however, in which systemic contributions to the brain optical signals are not considered to be confounding factors, but rather exploited to investigate spatiotemporal features of spontaneous cerebral hemodynamics [40e42], to study the effect of cerebral blood circulation on functional connectivity studies [43], or to characterize cerebral perfusion and autoregulation [44,45]. These approaches were based on dedicated data analysis schemes that invoke instantaneous phase assessment by Hilbert transform [46], spectral power density/coherence/transfer function analysis [40,44], regressors for generalized linear model (GLM) analysis [47], or mathematical models that relate optical signals to properties of the cerebral vasculature and circulation [48].

1.4

Functional Imaging and Resting-State Functional Connectivity

Soon after the initial demonstration of fNIRS, spatially resolved measurements led to noninvasive functional mapping of the brain [49,50]. The field of NIRS-based functional imaging has grown substantially since the mid 1990s and covers today a variety of research applications including brain development [51], psychiatry [52], cognitive neuroscience [53], humanecomputer interactions [54], etc. In addition to functional activation studies, NIRS techniques have more recently been applied to resting-state functional connectivity studies [55]. In all of these applications, contributions from systemic physiological processes play an important role and should be carefully considered. This chapter is devoted to this question: What are the contributions to cerebral NIRS signals from systemic physiology and how should they be treated for determining the information content of the measured NIRS signals? As mentioned above, depending on the applications, these systemic contributions may be a nuisance or a confounding factor to be avoided or corrected for, or they may be a crucial component of the signals that carries desired functional, physiological, or diagnostic information.

2. Systemic Contributions to Cerebral Optical Signals 2.1

Physiological Sources of Systemic Contributions

Since fNIRS is sensitive to hemodynamic changes, such as blood flow and blood volume changes, the method is inherently sensitive to any signals that modulate these quantities. In this chapter, we will focus on systemic changes, which affect the hemodynamics not only in cerebral tissue, but in any vascularized region of the body.

2.1.1 Heart Rate The most dominant and ubiquitous source of systemic hemodynamic changes is the cardiac pulsation. Every time the heart is pumping blood, arterial blood pressure (ABP) increases, which causes the arteries to expand in diameter, hence arterial blood volume

30 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

increases [56]. Such an increase in blood volume results in a greater hemoglobin concentration in tissue and a higher tissue absorption, hence a reduction in the detected light from the tissue. During diastole, ABP decreases, causing the arteries to constrict and blood volume to decrease. Such periodic ABP changes are seen in the arteries and small arterioles [56] and not in the capillaries and veins, where blood pressure is much lower than in the arteries. Pulse oximetry is measuring these periodic optical changes at two or three wavelengths to extract the arterial oxygen saturation of hemoglobin. Fig. 2.2 shows an example of NIRS measured changes in oxygenated and deoxygenated hemoglobin concentration measured on the forehead of a healthy volunteer with a source-detector distance of 3 cm. The heart rate is seen dominantly in DHbO2 since arteries carry close to 100% oxygenated hemoglobin.

2.1.2 Respiration Another source of measurable systemic hemoglobin concentration changes is respiration, with its associated changes in intrathoracic pressure and arterial blood pressure. An example of cerebral hemodynamics associated with respiration is shown in Fig. 2.2, where the oscillatory components at 0.1 Hz resulting from paced breathing at that frequency are clearly visible in DHbO2 and DHb.

2.1.3 Fluctuations in ABP Spontaneous fluctuations in ABP can occur as well. One of such fluctuations has been reported numerous times in terms of correlations to cerebral hemodynamics, termed “Mayer waves.” Such waves are defined as spontaneous oscillations in arterial pressure at a frequency of w0.1 Hz, with a significant correlation with oscillations of sympathetic nerve activity. The origin of these oscillations remains a source of debate but they undoubtedly reflect the sympathetic response to perturbations in the overall baroreceptorvascular system [57]. More generally, any changes in ABP will have systemic effects on the arterial components of the vasculature, on the peripheral blood flow, and therefore on measurable hemodynamics in tissue.

ΔHbO2 and ΔHb (μM)

ΔHbO2 ΔHb

Time (s) FIGURE 2.2 Example of NIRS data measured on the forehead during paced breathing. The slow oscillations correspond to the paced breathing response at 0.1 Hz. The faster modulation corresponds to the arterial pulsation associated with the heartbeat, which is clearly visible in DHbO2, but not in DHb.

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2.2

31

Modulation and Perturbations to Systemic Circulatory Quantities

Because of the significant effect of ABP on tissue hemodynamics, modulation and perturbations of ABP can be used to introduce temporally controlled dynamics in blood volume and blood flow throughout the vascular system. An active and controlled form of modulation is advantageous for studying the cerebral response in a systematic fashion. A periodic and controlled modulation of ABP can be achieved via paced breathing. Subjects are typically asked to breath by following a metronome. Paced breathing produces a periodic signal in ABP due to modulating the intrathoracic pressure, leading to periodic changes in cerebral blood flow as well as hemoglobin concentration. An example is seen in Fig 2.2. A feature of paced breathing is that hemodynamic changes are coherent with the periodic breathing pattern and individual hemodynamic signals (for example concentrations of oxy- and deoxy-hemoglobin) also feature a high level of coherence. In addition, subjects can be asked to breathe at a variety of frequencies, which has been used to evaluate a frequency-dependent response of the vasculature and blood perfusion. For the brain, paced breathing at a frequency around w0.1 Hz has been used to study cerebral autoregulation, where the phase difference between ABP oscillations and cerebral blood flow (CBF) is indicative of the state of autoregulation [44,58]. In addition, paced breathing at higher frequencies, leading to hyperventilation-induced hypocapnia, can be used to evaluate the dependence of CBF dynamics on end-tidal carbon dioxide (etCO2). Beside respiration-induced changes, other forms of ABP modulation have been used, including methods based on pneumatic cuffs that are wrapped around the subject’s thighs and that are inflated to pressures greater than the systolic blood pressure to induce arterial occlusions. Arterial occlusions on the legs can be used to modulate ABP in a periodic fashion by inflating and deflating the cuffs periodically [59]. Similar to paced breathing, the hemodynamic response to such periodic ABP perturbations can be measured in the brain and have been used to quantify cerebral autoregulation in healthy volunteers [59,60], as well as patients [61]. Thigh cuff occlusions can also be used to change ABP in a rapid and transient fashion. For this, the thigh cuffs are inflated above systolic pressure, held at that pressure for w2 min, and then quickly released to get arterial blood to rush into the legs and ABP to drop. Aaslid et al. [62] have shown that this method allows for quantification of cerebral autoregulation where the CBF recovery time is indicative of the brain’s autoregulatory capacity. Similarly, passive leg lifting has been used [63] to modulate the amount of blood in the extremities, again inducing ABP dynamics. A more active approach to systemic changes can be taken by asking subjects to perform some form of exercise. In this case, changes in heart rate, respiration rate, and ABP will combine into a set of systemic source of cerebral hemodynamic changes.

32 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

2.3

Optical Sensitivity to Cerebral and Extracerebral Tissues

The above-mentioned physiological sources of vascular changes can be found in any vascularized tissue, including muscle, skin, scalp, and skull bone. One of the major methodological challenges of fNIRS is therefore its high sensitivity to hemodynamic fluctuations in the superficial, extracerebral tissue, which includes the scalp and the skull. The influence of superficial layers has been evaluated numerous times in the context of functional activation, where the main conclusion has been that the effect of superficial systemic interference tends to blur the areas of activation [64], complicating the localization and quantitative assessment of the activated brain area, and lowering the signal-to-noise ratio in fNIRS studies [65e71]. The sensitivity to cerebral tissue is also affected by the so-called partial volume effect, which should be taken into account in the case of brain-specific hemodynamic signals such as those related to cerebral autoregulation or neuronal activation. As discussed in Section 1.3, the common method of data analysis based on the modified BeereLambert law assumes that the scattering properties of the investigated tissue are constant and that absorption changes are homogeneous. This latter assumption is clearly violated in the case of brain-specific hemodynamic changes, or focal metabolic changes associated with brain activation. As a result of the incorrect assumption of homogeneity in hemodynamic changes, the measured absorption changes underestimate the localized absorption changes occurring in brain tissue or in specific cortical areas and may result in an incorrect discrimination of oxy- and deoxy-hemoglobin signals [72e76].

2.4

Optical Sensitivity to Blood Volume, Blood Flow, and Oxygen Consumption

Optical signals are indicative of the concentration of hemoglobin in tissue. NIRS is known to be sensitive to the microvasculature, as the signal is thought to stem dominantly from blood vessels with a diameter of the order or smaller than 0.06 times the inverse of the absorption coefficient of blood [77], or smaller than w200 mm. The origins of changes in oxygenated and deoxygenated hemoglobin concentrations are blood volume changes (vascular constriction and dilation), blood flow changes, as well as metabolic rate of oxygen changes. We have recently developed a mathematical model, which translates such underlying physiological changes to the measurable hemoglobin concentration changes [48]. While the model was developed for cerebral hemodynamics and the corresponding blood transit times in the microcirculation, the model is applicable to any tissue. In its core, the model describes how changes in blood flow, blood volume, and metabolic rate of oxygen influence the tissue concentrations of oxy- and deoxy-hemoglobin in the arterial, capillary, and venous vascular compartments. The model describes how measurable, dynamic and hence time-dependent quantities of HbO2(t) and Hb(t) can be written as the sum of dynamic blood flow, oxygen

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_ and V, consumption, and blood volume contributions, indicated with subscripts F, O, respectively: HbO2 ðtÞ ¼ HbO2F;O_ ðtÞ þ HbO2V ðtÞ

(2.1)

HbðtÞ ¼ HbF;O_ ðtÞ þ HbV ðtÞ

(2.2)

_ refer to dynamic changes in HbO2(t) and Hb(t) The subscripts “V” and “F; O” resulting from dynamic changes in volume, oxygen consumption and flow, hence due to CBV(t), CBF(t), and CMRO2(t), respectively. Because NIRS is unable to discriminate contributions to the optical signals from CBF and CMRO2 [78], we have consolidated the contributions from flow and oxygen consumption into HbO2F;O_ and HbF;O_ . We have introduced a phasor representation to describe oscillatory hemodynamics at a given frequency [79]. Phasors, notated in bold face, are two-dimensional vectors whose amplitude and phase are those of the oscillatory quantity they represent, and having an implicit harmonic time dependence, eiut, at the angular frequency (u) of interest. We further introduce a compact notation of O(u), D(u), and T(u) for the oxy-, deoxy-, and total hemoglobin concentration phasors, and cbv(u), cbf(u), and cmro2(u) for the volume, flow, and oxygen consumption phasors, where lower case notation indicates dimensionless changes relative to baseline values. With these notations, our model equations are:

 OF;O_ ðuÞ ¼ ctHb

OðuÞ ¼ OV ðuÞ þ OF;O_ ðuÞ

(2.3)

h i ðaÞ ðvÞ OV ðuÞ ¼ ctHb SðaÞ CBV 0 cbv ðaÞ ðuÞ þ SðvÞ CBV 0 cbv ðvÞ ðuÞ

(2.4)

 SðcÞ  ðcÞ ðc Þ S  SðvÞ F ðcÞ CBV 0 H ð v Þ S

ðc Þ LP ðuÞ

  ðv Þ þ SðaÞ  SðvÞ CBV 0 H

ðvÞ LP ðuÞ

 ½cbfðuÞ  cmro2ðuÞ (2.5)

DðuÞ ¼ DV ðuÞ þ DF;O_ ðuÞ

(2.6)

h i    ðaÞ ðvÞ DV ðuÞ ¼ ctHb 1  SðaÞ CBV 0 cbv ðaÞ ðuÞ þ 1  SðvÞ CBV 0 cbv ðvÞ ðuÞ

(2.7)

 DF;O_ ðuÞ ¼ ctHb

  ðaÞ  ðcÞ  SðcÞ  ðcÞ ðcÞ ðcÞ ðvÞ ðvÞ ðvÞ ðvÞ ðuÞ ðuÞ ½cbfðuÞ þ S  S CBV H  S H S F CBV 0 0 LP LP SðvÞ

 cmro2ðuÞ i h ðaÞ ðvÞ TðuÞ ¼ ctHb CBV 0 cbv ðaÞ ðuÞ þ CBV 0 cbv ðvÞ ðuÞ ðcÞ H LP ðuÞ

ðvÞ H LP ðuÞ

(2.8) (2.9)

where and are the complex transfer functions associated with blood circulation in the capillary and venous compartments, ctHb is the hemoglobin concentration in blood, F ðcÞ is the Fa˚hraeus factor that expresses the ratio of capillary-tolarge vessel hematocrit, and the superscripts (a), (c), and (v) for CBV, cbv, and oxygen

34 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

T

D DV DF,O.

OV O

OF,O. FIGURE 2.3 Representative cerebral hemodynamics phasors measured with NIRS in the human brain at a frequency of 0.1 Hz. O, oxy-hemoglobin phasor; D, deoxy-hemoglobin phasor; T, total hemoglobin phasor. Subscripts V and F,O_ indicate contributions from blood volume and flow/oxygen-consumption oscillations.

saturation of hemoglobin S indicate their values associated with the arterial, capillary, and venous compartments, respectively. The total, steady-state blood volume is given by ðaÞ ðcÞ ðvÞ ðaÞ ðcÞ ðvÞ CBV 0 ¼ CBV 0 þ F ðcÞ CBV 0 þ CBV 0 , where CBV 0 , CBV 0 , and CBV 0 correspond to the baseline contributions of blood volume in the arterial, capillary, and venous compartment respectively. Fig. 2.3 shows typical oxy-, deoxy-, and total hemoglobin phasors, including their decomposition into volume and flow/oxygen-consumption contributions, as measured in the human brain.

3. Current Strategies to Minimize Systemic Contributions to Cerebral Optical Signals 3.1

Discrimination or Suppression of Contributions From Superficial Tissue by Hardware

As mentioned above in Section 2.3, one drawback of noninvasively monitoring cerebral tissue with NIRS is the significant sensitivity to extracerebral, superficial tissues (scalp and skull). Traditionally, the head is modeled as a homogenous medium, ignoring any differences in hemodynamic changes between the extracerebral and cerebral tissue. However, it is clear that physiological and functional responses may differ in cerebral and extracerebral tissue. A number of approaches to estimate or suppress the signal contributions from extracerebral hemodynamics have been developed, which include hardware-based solutions. Here, we shall review two methods.

3.1.1 Sensing Extracerebral Tissue With Short Source-Detector Distances Since the depth sensitivity in NIRS scales with source-detector distance [80], short (w5e10 mm) separations are mostly sensitive to hemodynamic changes in the extracerebral, superficial tissue layers. A conceptual layout is seen in Fig. 2.4. The confounding contributions from superficial layers can be significantly reduced by properly taking into account the data collected at such short source-detector distances, for example by using these data as regressors during postprocessing [81e88].

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FIGURE 2.4 Short source-detector distance layout. Short source-detector distances are mostly sensitive to superficial layers. Figure taken from L. Gagnon, M.A. Yücel, R.J. Cooper, Further improvement in reducing superficial contamination in NIRS using double short separation measurements, Neuroimage 85 (January 2014) 127e135.

3.1.2 Pressure Applied to the Head Another method, which has been recently introduced in the field of diffuse correlation spectroscopy (DCS) for CBF measurements, is based on collecting short and long sourcedetector distance data under normal conditions and during externally applied pressure to the scalp to suppress extracerebral blood flow [89]. In this case, a two-layer model of the head, together with the dual measurements at short or long distances, and with or without externally applied pressure, results in a DCS flow index that is representative of brain flow that is corrected for contributions from extracerebral flow.

3.2

Digital Signal Processing Schemes

Time-series analysis techniques have also been used to filter out superficial and systemic contributions in the NIRS signal. Filtering schemes in functional brain mapping measurements inherently assume that the origin of the physiological noise is of systemic nature, such as heart rate, respiration, and ABP fluctuations. The most straightforward method to remove such fluctuations in the NIRS signals is low- or band-pass filtering, which removes specific frequency content from the measured signals. Low-pass filters can be used to remove the fast cardiac oscillations and high-pass filters can be used to remove blood pressure oscillations and Mayer waves that are generally found between 0.08 and 0.12 Hz. However, respiration-induced changes cannot always be eliminated with such basic filters, since the respiration frequency generally overlaps with the hemodynamic response in functional activation tasks [90]. More complex filtering methods include the use of wavelets [91,92] and adaptive filters to remove systemic physiology from specific frequency bands while preserving the evoked response.

36 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

4. Optical Techniques That Take Advantage of Systemic Effects on Cerebral Perfusion 4.1

Cerebral Pulse Oximetry

Pulse oximetry was developed by Takuo Aoyagi in 1972 and evolved to the most used optical technique in clinics since the 1980s [93]. It measures a pulsatile oxygen saturation (SpO2), which is a measure of arterial saturation (SaO2), or the percentage of oxygenated hemoglobin in the arteries. It provides information about the supply of oxygen to peripheral tissues and the efficiency of oxygenation of hemoglobin in the pulmonary alveoli [94]. The key to pulse oximetry is to relate changes in blood volume during a cardiac stroke to the amount of oxygen in the arterial blood. Since oxygenated and deoxygenated blood has different absorption spectra, they are distinguishable (see Fig. 2.1). To extract SaO2, an extremity (typically a finger or toe) is illuminated with light at two wavelengths, one in the red and one in the near-infrared region, and the transmitted light is captured by a detector. The pulsatile component is a measure of the amplitude of intensity oscillations at the heartbeat frequency. Calculating SaO2 from pulsatile intensities at two wavelengths can be achieved by applying the modified BeereLambert law, which yields the pulsatile amplitudes of oxy- and deoxy-hemoglobin concentrations, DHbO2 and DHb, respectively. However, the ratio of the differential path length factors at the two wavelengths needs to be known. In the absence of this information, commercial pulse oximeters are based on an empirical calibration. Essential for pulse oximetry is the change in absorbance (A) during one heartbeat. The highly dominant change in absorbance that can be seen in the optical signal occurs as a result of arterial blood volume change. Since all other components to the attenuation of the optical signal are fairly constant over time, changes in absorbance at the heart rate are mainly affected by the arterial, pulsatile blood volume, which is the key to calculating SaO2. According to the BeereLambert law, an equation describing the attenuation of light traveling through nonscattering media, absorbance is defined as the extinction coefficient (ε) multiplied by the concentration of absorber (C) and the optical pathlength (L). More formally:   X Iout ¼ L$ A ¼ log ε i $ Ci ; Iin i

(2.10)

where i denotes the different molecular absorber and Iin , Iout are the incident and transmitted intensities. Given the two strongest absorbers in the arteries, oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb), the change in absorbance during a cardiac pulse becomes: DA ¼ ðSaO2 $ εHbO2 þ ð1  SaO2 ÞεHb Þ $ D½HbTa $ L

(2.11)

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FIGURE 2.5 If SaO2 is large, the alternation (AC) in the red (gray in print version) light intensity is smaller due to a permanent higher amount of HbO2, while the magnitude at lower SaO2 increases, accommodated with a decrease of the average of the signal (DC). The trends for near-infrared light behave vice versa. Figure taken from P.D. Mannheimer, The light-tissue interaction of pulse oximetry, Anesth Analg 105 (Suppl. 6) (2007).

SaO2 takes a value between 0 and 1, and D½HbTa denotes the change in hemoglobin concentration in the arteries during one pulse. The effect of high and low SaO2 on the optical signal can be seen in Fig. 2.5, in which the alternating component (AC) of the red light is reduced in height because of a high HbO2 concentration. While trending to lower SaO2, the AC component increases while the mean value (DC) decreases. The opposite trend can be seen for near-infrared light. The modulation ratio (AC/DC) between the two wavelength of light is correlated with the BeereLambert law and can be used to calculate the saturation of arterial blood oxygenation by the following equation:  ðSpO2 $ εHbO2 þ ð1  SpO2 ÞεHb Þ $ D½HbTa $ L l1 ½dlogðIout Þ=dtl1 ½AC=DCl1 R¼ z ¼ ½dlogðIout Þ=dtl2 ½AC=DCl2 ðSpO2 $ εHbO2 þ ð1  SpO2 ÞεHb Þ $ D½HbTa $ L l2

(2.12)

A solution for SaO2 in terms of the optical signals exists [95,96]. R describes the modulation ratio of the two detected signals. However, the solution given above cannot be used to calculate SaO2 directly. While the BeereLambert law is used in commercial pulse oximeters, it does not account for scattering changes that affect the measured light intensity by introducing a distribution of optical path lengths and by scattering light away from the detector. This problem could be addressed by using the modified BeereLambert law, derived from the diffusion equation, which corrects the calculation by adding a differential path length factor (DPF), which is the ratio of the mean optical pathlength to the geometrical source-detector distance. However, the ratio of the DPFs at the two wavelengths needs to be known. In commercial pulse oximeters, this information is typically not known and the problem is addressed by empirical calibration of R based on healthy volunteers [95]. Different SaO2 values from blood drawn from the volunteers within a physiological range are matched to the corresponding measurement

38 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

of R by the pulse oximeter. To obtain SaO2, a mathematical model based on the calibration values and dependent on R can be formulated [95]. SaO2 ¼

ðk1  k2 ÞR ðk3  k4 ÞR

(2.13)

The calibration variables ki are found by a best-fit approach. The strong light scattering in tissue also has an advantage in such that reflectancebased pulse oximeters are possible. Reflectance measurements are based on the equations mentioned above, but can be used on various body parts, including the head, which enables access to the brain. The pulse amplitude (AC) of the optical signal on the fingertip can be determined as approximately 3% of the baseline intensity, while the amplitude on the forehead only shows an approximated 1% change [96]. A pulsatile signal can easily be measured above the eyebrows due to a high vascular density [97]. The translation from the ratio-of-ratios R to SaO2 is achieved again by empirical calibration. Cerebral pulse oximetry has also been performed with frequency-domain, multiwavelength spectroscopy to quantify the amplitude of pulsatile components of oxy- and deoxy-hemoglobin concentrations in the human prefrontal cortex [98]. In this case, no calibration measurements are needed, resulting in absolute cerebral pulse oximetry.

4.2

Venous Oxygen Saturation Measurements and Spiroximetry

Aside from the arterial oxygen saturation, efforts have been made to calculate the venous oxygen saturation noninvasively. If SaO2 is measured with pulse-oximetry, describing the oxygenation level of arterial blood delivered to peripheral tissues, and venous oxygen saturation (SvO2) is measured simultaneously, describing the oxygenation level of drained blood after oxygen delivery to tissue, their difference, i.e., the oxygen extraction fraction, can be calculated. The oxygen extraction fraction represents the efficiency of oxygen uptake in the capillaries and delivery to the cells. It furthermore describes the demand of oxygen from the cells. This clinically highly relevant value is therefore the dominant motivation for measuring SvO2. Different approaches have been made to measure SvO2 noninvasively, all of which follow the general principle used in pulse-oximetry, i.e., the separation of the specific compartment of interest from the summation of contributors to the optical signal. The compartment of interest for SaO2 is the arterial blood volume change during a cardiac stroke. For SvO2, a change in venous blood volume is needed. Such a change can be archived in limbs by venous occlusions, leading to a pooling of venous blood downstream. Comparing measurements during and after occlusion will provide the necessary compartment separation, given that the thicker artery walls are not affected by a venous occlusion [95,99]. This approach can be applied to extremities like the fingers, arms and legs, where occlusion is applicable by inflation of pneumatic cuffs. For cerebral measurements, instead of a venous occlusion, a head tilting protocol can be used. By headdown tilting of 15 degrees, a venous pooling and therefore a venous blood volume

Chapter 2
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39

change in the cerebral veins is induced, which can be used to separate venous from arterial contribution to the optical signal [100]. Depending on the medical condition and clinical procedure, application of aforementioned methods with external perturbations can be complicated. A more applicable method to single out the venous compartment is given by respiratory oscillations. While the influence of the cardiac pulse can be comprehended easily, the influence of respiration is slightly more complex. During inspiration, the thoracic diaphragm flattens, which causes a reduction in pressure in the thorax and an increase in pressure in the abdomen. This pressure change not only leads to an intake of air into the lungs but also affects the right ventricle and atrium of the heart as well as larger veins, including the vena cava superior collecting the blood from the head and upper extremities. The negative pressure translates to a pressure gradient in the thoracic veins and actively draws blood from the periphery, including the cerebral veins. The correlation of respiration and optical signal change can be seen in Fig. 2.6. The compliance of veins is approximately 20 times higher than arterial compliance because of a reduced amount of smooth muscle and therefore much thinner walls. The pressure change in the thorax has thus a 20 times higher effect on veins. Inspiration will ultimately cause a decrease in venous blood volume and expiration translates to a recovery of blood volume in the cerebral venous system. Because of venous valves, no active increase in venous blood volume during expiration is expected. When this approach, based on respiratory 0.004

Red PPG

Normalized PPG signal

0.002 0.000 –0.002 –0.004 0.006

Infrared PPG

0.004 0.002 0.000 –0.002 –0.004

Pressure (cmH2O)

–0.006

25

Airway Pressure

15 5 –5 –15 –25

0

5

10

15

20

25

30

Time (s)

FIGURE 2.6 Sample of photoplethysmographic (PPG) signal of a human subject measured on the right index finger during paste breathing of 12 breaths per minute. The red and near-infrared signal shows the effect of respiration shaping the baseline, with cardiac pulsation on top. The bottom graph shows airway pressure measured with a mouthpiece through which the subject had to breathe [102].

40 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

oscillation, is used in combination with NIRS to measure SvO2, it is referred to as spiroximetry [101]. Eq. (2.12) can in theory be used to calculate the ratio of ratios at the respiration frequency. An empirical calibration would then again be used to translate R into SvO2. However, this simple approach may not be sufficient for accurate venous saturation measurements. While arterial saturation can be assumed as constant throughout the body, venous saturation is influenced by many factors, including the localized oxygen demand and uptake efficiency in the capillaries. It is not always possible to drain venous blood from the exact same location at which the optical probe was placed, especially during cerebral hemodynamic assessment. Belhaj et al. showed that SvO2 measurements on the fingertips are only poorly correlated with co-oximetry measurements from venous blood drawn from the dorsum of the same hand [102]. In an occlusion-based approach on the hand and fingertip, Nitzan et al. [99] used two relatively close wavelengths (767 and 811 nm) to assume a similar path length for both. The assumption can be used to neglect the influence of light scattering variations between the two wavelengths and therefore disables the necessity for a calibration. Drawbacks are the reduced sensitivity due to the very similar influence on both wavelength and the location close to the isosbestic point for hemoglobin (w800 nm), a specific wavelength at which oxygenated and deoxygenated hemoglobin are equally strong absorber. To overcome the restricted calibration possibilities, many groups started to use nearinfrared spectroscopy (NIRS) in combination with different mathematical models besides the pulse-based approach. Absorption coefficient changes can be calculated via the modified BeereLambert law: Dma ðl; tÞ ¼ 

1 DIðl; tÞ ; $ LðlÞ I0 ðlÞ

(2.14)

where Dma ðl; tÞ is the change in absorption coefficient at a specific wavelength, I0 ðlÞ is the incident light intensity, DIðl; tÞ is the change in detected light intensity, and L ¼ L $ DPF is the mean optical path length. The differential path length factor (DPF) is a dimensionless correction term, which accounts for the increased path length that a photon has to travel from the illumination point to the collection point due to scattering. The changes in absorption coefficient for the two wavelength of light can then be translated into DHbO2 and DHb, which represent the changes in oxy- and deoxyhemoglobin concentration, respectively. Because of the lack of physiological specificity, the acquired data represents a mixture of hemoglobin from veins, arteries, and capillaries. It has been shown that the respiration-induced amplitudes in DHbO2 and DHb are correlated to the venous oxyhemoglobin saturation in the following way [101,103]: SvO2 ¼

A½DHbO2 resp A½DHbresp þ A½DHbO2 resp

.

(2.15)

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41

here A½DHbO2 resp denotes the amplitude of respiration-induced changes in oxygenated hemoglobin. Accordingly, A½DHbresp shows the changes in deoxygenated hemoglobin. The study by Lynch et al. was performed on ventilated pulmonary hypertension pediatric patients [103]. The measured SvO2 values using NIRS were compared to venous blood drawn from a catheterized vena cava. Both restrictions, ventilation and a different position of the catheter compared to the NIRS probe, lead to small deviations between NIRS and co-oximetry. An animal (piglets) and human (healthy subjects) spiroximetry study found a good agreement between NIRS and invasive measurements of SvO2 in piglets over the range 20%e95% and found a rest value of SvO2 of about 75% in the human thigh muscle [101].

4.3

Cerebral Autoregulation Assessment

Cerebral autoregulation is the mechanism of the brain to maintain cerebral blood flow constant despite changes in cerebral perfusion pressure. Cerebral autoregulation is mediated by vascular reactivity, which is the diameter change in resistance vessels in response to changes in mean arterial pressure (MAP). The mechanism of autoregulation is valid for a certain range of cerebral perfusion pressure (CPP) namely 50e150 mmHg in humans [104e106], where CPP is the pressure difference between mean arterial blood pressure and intracranial pressure (ICP), CPP ¼ MAP-ICP. Within this range, changes in CPP lead to vasomotor adjustments in cerebral vascular resistance, thus allowing CBF to remain relatively constant. Lassen constructed an autoregulation curve, which shows a CBF plateau in the aforementioned CPP range [104], as seen in Fig. 2.7A. Vascular reactivity is also an indicator of intact autoregulation, where changes in MAP are analyzed in terms of effect on ICP. If MAP changes are compensated by ICP changes, the vasculature is pressure active and autoregulation intact. This idea has been used to develop the so-called pressure reactivity index, PRx, which is the correlation coefficient between ICP and MAP as seen in Fig. 2.7B. The optimal perfusion pressure CPPopt is such that MAP and ICP are uncorrelated. While blood flow, autoregulation, and vascular reactivity are related, they are not the same [107] and measurement methods for

(B)

LLA

CPP [mmHg]

ULA

0.4 0.2 0 -0.2

Optimal perfusion pressure CPPopt

Dynamic Autoregulation

CPP

Intact autoregulation

(C) Pressure reactivity

PRx

CBF

Static Autoregulation

Intact autoregulation

CBF

(A)

CPP [mmHg]

impaired autoregulation

Time (s)

FIGURE 2.7 Three classes of autoregulation assessment. (A) Static autoregulationdLassen’s curve, showing the lower (LLA) and upper limit (ULA) of autoregulation. (B) Pressure reactivity given by the correlation coefficient between ICP and MAP, (C) Dynamic autoregulation, where CBF response to pressure changes is indicative of autoregulation.

42 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

assessing either are based on assumptions, which may not be valid and generalizable for all diseases. In addition to PRx and static assessments, dynamic autoregulation is based on evaluating how fast the brain reacts to and counteracts pressure changes (Fig. 2.7C). The quicker the blood flow recovers, the better the system is autoregulating. Since ICP is not accessible in most patient populations, MAP is used as a surrogate for CPP. Aaslid et al. introduced a thigh cuff-based method of inducing fast MAP changes, which is the most frequently used method to induce rapid blood pressure perturbations [62]. Another way of measuring dynamic autoregulation, instead of inducing rapid MAP changes, is based on studying CBF responses to slow oscillations in MAP. Such oscillations can be induced at a specific frequency by a number of protocols including paced breathing [44,58], headup-tilting [108], and periodic thigh cuff inflation [59], with oscillations typically being induced around 0.1 Hz. The measurement of dynamic CA can then be performed by transfer function analysis (TFA) where beat-to-beat MAP measurements are used as input and CBF measurements as output [106,109,110]. TFA is based on analysis of the coherence, gain, and phase differences between MAP and CBF as a function of frequency. Similar to the rapid change in MAP with thigh cuffs, the phase differences, which are related to the time delay, between MAP and CBF found with TFA, have been found to be a good indicator of autoregulation efficiency. While transcranial Doppler (TCD) is the most commonly used method for assessing dynamic changes in CBF, the bedside long-term use is not rapidly given. NIRS, measuring hemodynamic changes in the microvasculature, has been proposed as a surrogate and alternative method for measuring CBF dynamics. Examples of static autoregulation assessment with NIRS have dominantly been demonstrated in animal studies since it requires CPP to change outside the autoregulatory range [111]. The pressure reactivity index with NIRS was successfully implemented in [112e114], termed “HVx” or “THx.” Dynamic autoregulation has been assessed with NIRS in healthy volunteers as well as patients [45,61].

4.4

Intracranial Pressure Assessment

Since the NIRS signals are sensitive to CPP, rather than just MAP, conceptually NIRS is sensitive to ICP changes. The idea of exploring the ability of NIRS, which is sensitive to CBV, for monitoring ICP changes has been explored before. For instance, Lee et al. [114] used a NIRS sensor for measuring relative changes in hemoglobin concentration in piglet brains while gradual hypertension was induced. Results from this study indicated that changes in HbT are correlated with ICP changes at frequencies below 0.02 Hz as measured by high coherence between the two measures. Subsequently, it has been proposed that a PRx index could be derived based on NIRS measures, termed “HVx.” The reason for high correlation between the PRx and HVx is that it has been hypothesized that very low-frequency changes, Lundberg B-waves, are associated with cerebral blood volume changes [114e116]. Using NIRS combined with ICP and MAP sensing, reports in

Chapter 2
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43

the literature suggest that changes in HbT are reflective of changes in ICP, at least at 0.05 Hz and lower [112e114,117,118]. While reports suggest a high correlation between ICP and hemoglobin concentration changes, the correlation is not always high and a mismatch between the signals has been reported [117,118]. While these studies suggest there being a vascular influence to observed variations in ICP in head injured patients, the origin of these changes needs to be understood better before NIRS can be used to noninvasively measure ICP.

4.5

Coherent Hemodynamics Spectroscopy (CHS)

A new technique recently developed by us, coherent hemodynamics spectroscopy (CHS) [48,61], specifically aims at exploiting the physiological information content of the cerebral microvascular response to perturbations of systemic origin. Even under baseline conditions, spontaneous oscillations like the ones described in this chapter result in fluctuating cerebral hemodynamics. However, the physiological origin of baseline cerebral hemodynamics is unknown and variable. For example, one study reported that systemic contributions from heart rate and arterial blood pressure may typically account for no more than half of the overall variability exhibited by low-frequency cerebral hemodynamics [119]. Therefore, low-frequency cerebral hemodynamic oscillations result from multiple physiological sources, some of them systemic (heart rate, arterial pressure, respiration, etc.) and some of them local (vasomotion, vascular reactivity, autoregulatory effects, focal metabolism, etc.). The idea of CHS is to restrict the investigation to cerebral hemodynamics that are coherent with a specific systemic source, which is therefore the main driving force of the observed hemodynamics. One may consider a variety of systemic perturbations, and we have recently focused on changes in mean arterial pressure (MAP). The application of the CHS model [48], which leads to the equations reported in Section 2.4, allows for the translation of multifrequency measurements of amplitude and phase of coherent hemodynamic oscillations (CHS spectra) into several physiological quantities. These include microvascular blood transit times, microvascular blood volume, and measures of autoregulation. An example of CHS spectra (in the frequency rage 0.04e0.20 Hz) of relative amplitudes and phases of D versus O, and O versus T, and their best fits with the model, are shown in Fig. 2.8. In this example, cerebral hemodynamics

FIGURE 2.8 CHS spectra of the phase (Arg) and amplitude (j.j) of coherent cerebral hemoglobin concentrations as a function of frequency. Bold face indicates phasor notation. D, deoxy-hemoglobin concentration; O, oxy-hemoglobin concentration; T, total hemoglobin concentration. Figure taken from K. T. Tgavalekos, A. Sassaroli, X. Cai, J. Kornbluth, and S. Fantini, Coherent hemodynamics spectroscopy: Initial applications in the neurocritical care unit, Proc. SPIE 10059 (2017) 100591F.

44 NEUROPHOTONICS AND BIOMEDICAL SPECTROSCOPY

oscillations coherent with MAP were induced with a protocol based on cyclic inflation (to a pressure of 200 mmHg) and deflation of pneumatic thigh cuffs [120]. The physiological quantities associated with the fits in Fig. 2.8 are the capillary blood transit time (0.63  0.06 s), venous blood transit time (6.0  0.2 s), capillary-to-total blood volume fraction (0.39  0.06), and autoregulation cutoff frequency (0.07  0.02 s). CHS is an example of an application where systemic contributions to cerebral hemodynamics are enhanced and carefully measured and characterized, rather than being considered as a confound to be suppressed or corrected for. The coherence aspect of CHS is there to identify the specific driving force for the observed hemodynamics or to guarantee that reliable amplitude and phase values may be assigned to the observed hemodynamics.

5. Conclusions 5.1

The Power of NIRS and fNIRS

Despite intrinsic limitations in penetration depth and spatial resolution, near-infrared spectroscopy offers important advantages for brain studies. First, it is noninvasive and poses no or minimal risk to the subject. Second, it requires relatively simple and compact instrumentation that allows for bedside or home use. Third, it is relatively insensitive to subject movement, so that it can be used to investigate human subjects in everyday settings. Fourth, it is highly sensitive to cerebral perfusion and oxygenation, which are directly relevant to brain activation and brain health. These desirable features account for the growing number of applications of cerebral NIRS and the continuing expansion of this research field. Confounding factors associated with motion artifacts, improper optical coupling between tissue and optical probes, and contamination from extracerebral tissue contributions must all be minimized or corrected for cerebral NIRS to be truly sensitive to brain tissue. This chapter was devoted to systemic contributions to cerebral NIRS signals, which may be friend or foe depending on the application and the nature of the study. One fundamental strength of cerebral NIRS is its sensitivity to both the oxygenated and reduced species of hemoglobin, HbO2 and Hb, respectively. For example, this is not the case for functional MRI, a most successful hemodynamic-based, noninvasive neuroimaging modality, which is mostly sensitive to paramagnetic deoxy-hemoglobin. Because the relative concentrations of HbO2 and Hb, as well as their relative temporal dynamics, yield important information on the dynamic interplay of cerebral blood volume, blood flow, and metabolic rate of oxygen, the ability to measure both hemoglobin species can be of paramount importance for functional and diagnostic brain studies.

Chapter 2
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5.2

45

Clinical Potential

The ability of NIRS to provide a safe, noninvasive real-time monitor of cerebral oxygenation, perfusion, autoregulation, microvascular viability, and functional activity at the bedside is of highly significant clinical potential. On the basis of these capabilities, it is easy to envision diagnostic and clinical applications for NIRS-based monitors in home, doctor’s office, and intensive care unit environments. The attainment of this significant clinical potential, however, requires the collection of reliable and reproducible NIRS data that properly accounts for confounding factors and leverages the valuable intrinsic sources of contrast available to NIRS. In this chapter, we have considered the role of systemic physiological contributions to cerebral NIRS signals and described the importance of taking them into consideration to enhance the information content of the detected data. We think that this is crucial toward the development of mature NIRS techniques for advanced research use and clinical applications.

Acknowledgments JMK acknowledges support from the US National Institutes of Health (Grant no. R21-EB024675) and the American Heart Association, grant no. 17SDG33700047/Kainerstorfer/2017. AR is supported by the Center for Machine Learning and Health Fellowship, School of Computer Science, CMU. SF acknowledges support from the US National Institutes of Health (Grants no. R01-NS095334 and R21-EB020347).

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