The Quest for Functional Biomarkers in the Prefrontal Cortex Using Functional Near-Infrared Spectroscopy (fNIRS)

6 The Quest for Functional Biomarkers in the Prefrontal Cortex Using Functional Near-Infrared Spectroscopy (fNIRS) Hadis Dashtestani, Rachel Zaragoza, Riley Kermanian, Emma Condy, Afrouz Anderson, Fatima Chowdhry, Nader Shahni Karamzadeh, Helga Miguel, Siamak Aram, Amir Gandjbakhche NA TIONAL INSTI TUTE OF CHILD HE ALTH AND HUMAN DE VELOPMENT, NATIONAL INSTITUTE OF HEALTH, B ETHESDA, MD, UNITED STATES

1. Introduction 1.1

Overview of Cognitive Function in the Prefrontal Cortex

Evolutionary biology has provided important insight into brain development of animals with higher order brain structures. Primates and other mammalian species have different brain regions that vary in size, including the cerebellum, mesencephalon, diencephalon, olfactory cortex, parahippocampal cortex, hippocampus, neocortex, septum, and striatum. Comparative neuroanatomy shows how the brain has evolved over time given different environmental, social, and epigenetic changes [1,2]. Moreover, humans have developed higher order cortical structures that are a direct result of living in more complex environments. Namely, the prefrontal cortex (PFC) is immensely important for executive function and socio-emotional processing [1,3]. The PFC matures much later in development. The human brain develops “lower order” caudal structures first, such as sensorimotor cortices [3]. Subsequently, “higher order” rostral structures start to form in the frontal lobe of the brain where cortical areas develop in order to modulate complex decision-making and goal-oriented behavior. The PFC imposes “top-down” processing, which involves modulating behavior that is guided by internal goals. This type of processing and behavior output is used when sensory inputs, thoughts, and actions are not well established and others are rapidly changing. Under such circumstances, the PFC actively maintains patterns of neural activity needed to represent goals and achieve them. Neurophotonics and Biomedical Spectroscopy. https://doi.org/10.1016/B978-0-323-48067-3.00006-8 Copyright © 2019 Elsevier Ltd. All rights reserved.

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FIGURE 6.1 Lateral (A) medial (midsaggital view) (B) Ventral Orbital (from bellow view) (C) Brodmann areas (BA) (Ridderinkhof, Van Den Wildenberg et al. 2004) [4].

The anatomical subdivision of the PFC includes the cortex of the precentral gyrus, the rostral frontal cortex on the dorsolateral PFC (DLPFC) (Brodmann area [BA] 8, 9, 10, 46), the medial PFC (mPFC) (BA 8, 9, 10, 11, 12), the orbitofrontal cortex (OFC) (BA 11, 12, 47), and ventrolateral PFC (VLPFC) (BA 44, 45) [1,4]. Fig. 6.1 shows the mentioned areas. In front of the central sulcus is the frontal cortex and the PFC is located in front of the premotor and the supplementary motor area (SMA). It consists of 25% of the cerebral cortex in the brain [5]. Functional subdivisions in the PFC have been examined histologically to ascertain their neuronal structure, which has played an important role in understanding these functional areas [1]. The limbic frontal cortex (BA 13) is part of the OFC and is responsible for understanding social stimuli and mediating emotional reactions. BA 13 is connected to the insular, temporal polar, parahippocampal cortices, and basal forebrain structures and the size is similar among humans and apes [1]. The OFC and mPFC is connected to the medial temporal limbic structures, which are responsible for longer term memory, processing affect/emotional states, and motivation. These regions are reciprocally connected to the hippocampus, neocortex, amygdala, and hypothalamus [3]. BA 10, the anterior aspect of the PFC is the association cortex involved

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in higher order cognitive functioning, which assists in planning and decision-making. The frontal pole is found in humans and a variety of primate species [1]. The lateral and mid-dorsal PFC is associated with the sensory neocortex. It receives visual, somatosensory, and auditory information from the occipital, temporal, and parietal cortex. The DLPFC (BA 46) has connections to the motor system. Therefore, it controls behavior and has reciprocal connections to the medial frontal lobe [3]. BA 44 and 45, or the inferior frontal gyrus, is responsible for language production, linguistic motor control, planning, syntax, and phonological processing. This area has been identified in chimpanzees, bonobos, gorillas, and orangutans. While different species of primates have similar volumetric differences to the human brain, human brains have more efficient tracks for transmitting information [1]. The PFC is connected to other PFC regions and interconnection exists between the ventromedial PFC (vmPFC), lateral PFC, and mid-dorsal PFC. Overall, the PFC develops these connections in order to form learned associations. Neurons are activated by visual and auditory stimuli. These activations are modulated by reward cues. The PFC actively maintains patterns of neural activity during tasks in order to take out irrelevant information and form new relevant neural associations [3]. The longitudinal study of the composition of these subdivisions is important for understanding the effect of environmental impact on development of human cognitive and emotional capacities during the late onset of human-specific neuropsychiatric disorders.

2. Functional Near-Infrared Spectroscopy Many functional neuroimaging studies have sought to determine the associations between functional activation patterns and various regions of the brain. Functional magnetic resonance imaging (fMRI) is the most frequently used imaging modality, which has been used over the decades to address this issue. Particularly, fMRI measures blood oxygenation changes followed by neural activities, which is called blood-oxygen-level-dependent (BOLD) signal. While previous studies have used fMRI to examine PFC brain activation, functional near-infrared spectroscopy (fNIRS) is an emerging technology that can be used as an alternative to fMRI. fNIRS is a noninvasive, safe, portable, low-cost method to monitor hemodynamic response, or brain activation, which can translate laboratory work to clinical applications.

2.1

Principles of fNIRS

Neurovascular coupling is increase in cerebral blood flow (CBF) in response to activation of some neurons. This process ensures that there would be enough CBF at the region of activation. Thus, cortical regions that are activated will use higher values of CBF [6], which has been related to increase in oxygen consumption by neurons [7]. Therefore, by measuring local hemodynamic changes over time, for example during the execution of a functional task, it is possible to indirectly measure neural activity, similar to fMRI-BOLD signal [8].

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FIGURE 6.2 Absorption spectra of HbO, HbR, and water in NIRS system.

Detector

Source

FIGURE 6.3 Photon diffusion path from location of source to detector, described as “banana shape.”

When photons going through the tissue, they would get absorbed and also scattered several times. Typically, near-infrared light (700e900 nm wavelength) is sent through the scalp by light sources and the backscattered light is measured at the site of the detectors. The major absorbing chromophores in this range are HbO and HbR [8]. Fig. 6.2 shows the absorption spectra of HbO, HbR, and water in the NIRS system [6]. The probabilistic trajectory of photons from source to detector has been described by “diffusion theory” as a “banana shape” [9] (Fig. 6.3). It is worth noting that the distance between source and detector, source-detector separation, is an important definitive factor in effective light penetration depth. For example, if the source-detector separation distance is too short, the light would not penetrate deep enough to probe the cortex, whereas too large distance does not allow collecting sufficient backscattered photons. The optimal distance between source and detector for brain study is between 2 and 3 cm and provides the spatial resolution of 0.5e1 cm and penetration depth of w5e8 mm in brain tissue [6]. As it mentioned earlier, changes in intensity of the backscattered light could be a representative of changes in oxyhemoglobin due to functional tasks. Then, modified BeereLambert equation is used to convert raw fNIRS signals to HbO and HbR [10].

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FNIRS has biochemical specificity, which is why its temporal resolution in the millisecond range. It has the potential to measure intracellular and intravascular events [11]. fNIRS has lower spatial resolution and lower signal-to-noise ratio than fMRI, though [8,11]. Therefore, it can be used to assess neurocognitive processes associated with brain disorders such as those with Alzheimer’s, Parkinson’s, epilepsy, traumatic brain injury (TBI), schizophrenia, and various mood disorders [8,12e16].

2.2

Functional Biomarkers

A biomarker is a quantifiable property that indicates normal anatomical or physiological processes, pathogenic processes, or a response to a therapeutic drug [17]. Functional biomarkers in the brain utilize functional imaging methods such as fMRI, EEG, or fNIRS to establish a standard model that is used as a baseline for identifying irregularities. Biomarkers are employed for a variety of reasons, including to reduce unexplained variance in order to increase statistical power [18]. In fact, some biomarkers may be better suited for predicting the clinical effects of drug therapy than traditional clinical measures, especially at early disease stages [18]. When a biomarker can be effectively used in place of a clinical evaluationdthat is, if its progression reflects the progression of its intended clinical targetdit is considered a surrogate endpoint that can predict the clinical outcomes of therapeutic intervention [17e19]. Exciting research aimed at developing functional biomarkers is being conducted for a wide variety of disorders. For example, Damoiseaux et al. [19] sought a functional biomarker that could be used to classify between normally aging and patients with Alzheimer’s disease (AD). Resting-state fMRI, which is known to have irregular patterns in patients with AD [20], was performed to establish a baseline and was subsequently repeated 2e4 years later in order to track the progression of the disease and changes in functional connectivity. The study found that patients with AD had a significantly larger decrease in functional connectivity than control subjects. More importantly, they concluded that AD symptom progression and decreases in functional connectivity progressed hand-in-hand, indicating that functional connectivity decline could serve as an important functional biomarker for Alzheimer’s disease. In another study [21], researchers examined activation patterns in patients with TBI. Subjects performed a working memory task and were imaged using fMRI. Although patients with TBI performed the task with a nearly identical success rate as the control group, they had significantly lower activation in the dorsolateral prefrontal cortex (DLPFC) and greater activation in brain regions outside the prefrontal cortex (PFC) than did the controls. The authors hypothesized that the brain compensated for injury by increasing activation in other regions of the brain. Additionally, because the PFC is the typical region-of-interest in working memory tasks, it is believed that this additional activation could be used as a functional biomarker for TBI [21]. Similarly, Zhang et al. [22] found decreased functional connectivity in the Default Mode Network (DMN) and increased activation in the right parietal cortex, right DLPFC, and hippocampus after

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physical activity in patients with TBI compared with control subjects. These studies indicate regions and tasks that may be useful in developing functional biomarkers for traumatic brain injury. An innovative approach for discovering useful biomarkers includes the use of classification techniques that can efficiently and accurately categorize sets of data into subgroups based on defined characteristics. This powerful technique can take inputs of many features that may not be relevant to classifying on their own, but which may become relevant when combined with other unique features [23]. A demonstration of this technique was shown by Drysdale et al. [24] in their study on depression. This study proposed using fMRI to define depression subtypes based on functional connectivity abnormalities in the frontostriatal and limbic systems. Using a support vector machine (SVM) classifier, the researchers found four depression subtypes, each of which was found to correlate with specific clinical-symptom profiles. Moreover, the classifier could successfully predict the efficacy of various treatment options based on the patient’s subtype. All the above studies can show the importance and usefulness of functional biomarkers in both diagnostic stage and between treatment sessions. However, fMRI limitations such as its cost and unique environment can be a real obstacle for patients with special conditions. Therefore, the need for a more affordable, portable and patientfriendly neuroimaging modality such as fNIRS can be sensed especially when quantitative assessment of brain neural activity in an unusual environment or group of subjects is critical.

2.3

Role of Prefrontal Cortex in TBI and Toddler Population

Prefrontal cortex development begins in the womb and continues throughout adolescence and adulthood. The brain matures during childhood by reducing synaptic and neuronal density, promoting the growth of dendrites, and increasing white matter volume. This allows the formation of distributed neural networks that can be used for complex cognitive processing [25]. Behavioral performance on various cognitive tasks improves over development; however, intercorrelations of performance between various tasks weaken [25]. Generally, there is age-related improvement in cognitive function. For instance, working memory (WM) emerges at age 4 and improves substantially between the ages of 5e7 years old. Previous studies have found that visual memory span increases substantially from 5 to 11 years old, where there is better visuospatial WM at 8 years old compared to 5 years old [26,27]. Furthermore, there is a concurrent increase in inhibitory control performance. Though generally there is an increase in cognitive performance observed over development, the separable aspects of cognition show differential patterns of development over time. For example, working memory and inhibition show distinct developmental trajectories [28]. The PFC of very young children is developing which allows the potential for flexible change [25,29,30]. Progressive and regressive

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changes occur throughout the course of PFC development in order to obtain specialized focal regions. Performance of executive function tasks involving inhibitory control, processing speed, and working memory improves from middle childhood and beyond. However, abstract information that is processed through the integration of somatic and affective inputs in the ventromedial PFC (vmPFC) does not show substantial improvement until puberty. Identity is established throughout adolescence which allows the formulation of higher order thoughts that occur about self-reflection, social reputation, and other complex topics [31]. Motor development and cognitive development are interrelated. When cognitive development is affected, then motor development is subsequently affected. The PFC and the cerebellum reach maturity much later in life and many cognitive tasks require motor control. Therefore, understanding neural basis of different PFC functions can greatly enhance early detection and diagnosis of brain disorders. Studies involving toddlers are very limited since fMRI studies are restricted to the sleep studies [32]. Toddler imaging studies have focused on infants and preschoolers. However, we have focused on more challenging period of 18e36 months old as it will be discussed in Section 2.5. This age range becomes especially important since developmental delays are mostly noted during this period and tools for measuring neural correlates of potential delays are needed. Furthermore, it has been shown that PFC has abnormal growth along with unusual neural activities in different brain disorders [33]. In another study, fNIRS was used to extract the most informative biomarkers through advanced machine learning techniques, which will be presented in Section 2.6.

2.4

fNIRS Data Acquisition

To measure fNIRS signals, an fNIR Model 1000 (fNIR Devices LLC, Potomac, MD, USA) was utilized in the two studies that we will be discussing in the following sections. The system has four sources and 10 detectors, with a source-detector separation of 2.5 cm, for a total of 16 channels of HbO and HbR. The sampling frequency is 2 Hz. The optode arrangement can be seen in Fig. 6.4.

2.5

Prefrontal Hemodynamics in Toddlers at Rest: A Pilot Study of Developmental Variability

Due to the level of comfort and increased tolerance to head motion, it is clear that fNIRS provides opportunities to study children who may not comply with fMRI studies.

FIGURE 6.4 Functional near-infrared spectroscopy (fNIRS) channel arrangement. There are 4 sources and 10 detectors forming 16 source/detector pairs with 2.5 cm distance from each other.

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Anderson et al. [34] set out to capture prefrontal hemodynamics data from one of the most developmentally interesting age groups, but also one of the hardest to get to comply with neuroimaging. 29 subjects, 18e36 months old, including those with language delay were included in this study. Given the difficulties due to language delay as well as the attention span of the toddlers, the vanilla baseline paradigm was used to better homogenize participants’ experience while maintaining attention during data collection. Subjects watched two brief (50s) clips from the TV show “Sesame Street.” They did not consider the specific stimuli within the video clips, but added them to keep the toddlers attention. This allowed data collection with minimal motion artifact from a previously inaccessible patient population. The temporal resolution of their fNIRS device allows the acquisition of recognizable hemodynamic responses in as little as 8s, meaning there was more than sufficient data available. The problem remained that there was a lack of stimulus aimed at PFC activation. However, many studies in the neuroimaging field have measured and analyzed a “baseline” or “vanilla baseline.” Between the ages of 18e43 months, they hypothesized that there is such a difference in hemodynamic development that there could be significant differences in prefrontal activation even in the absence of a task, or “at rest.” The metrics they used to analyze the baseline data were the lateralization index and the oxygen variability (OV) index [35]. The OV index is a measure of the variability of oxygen saturation at the frequency band related to the cerebral autoregulation. Cerebral autoregulation maintains cerebral blood flow by means of vasomotion and is a necessary process for precise regulation of cerebral hemodynamics. This process is related to brain function in typical development and varies depending on age and physiological development. The lateralization index is a measure of changes in oxyhemoglobin based on area under the curve of the hemodynamic response between the right and left hemispheres. Their findings indicate positive correlation between and composite-DQ as measure of development. Moreover, toddler with lower composite-DQ showed higher discrepancy between left and right PFC activation. The study showed that fNIRS can be reliably used in toddler population with developmental conditions. Moreover, OV index would be used as a metric to investigate the developments in toddlers.

2.6

A Machine Learning Approach to Identify Functional Biomarkers in Human Prefrontal Cortex for Individuals With Traumatic Brain Injury Using Functional Near-Infrared Spectroscopy

Studies have shown that patients with TBI often have impaired executive functioning abilities, a finding which is believed to be caused by damage to the PFC [36,37]. Functional imaging, including fMRI, EEG, and fNIRS, can be used to explore patients with TBI as they undergo executive function tasks. Although fMRI is the traditional imaging modality used for studying patients with TBI, it is expensive and cannot be moved [38]. fNIRS, with its relative low cost and ease-of-application, can be reliably used to study patients with TBI. Although several TBI experiments using fNIRS have been

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conducted in the past, almost all of them used a univariate statistical analysis to differentiate between patients with TBI and healthy controls [39e42]. Each of these studies used a single feature from the hemodynamic signal to identify differences in the hemodynamic responses of healthy controls and patients with TBI. Nevertheless, by using only a single feature and thus performing a univariate statistical analysis, the studies ignored other hemodynamic signatures that may serve as useful functional biomarkers for TBI. On the other hand, Karamzadeh et al. [43] designed a method using several features and a multivariate statistical analysis to successfully classify between patients with TBI and healthy controls [43]. Using a multivariate statistical analysis makes it possible to generalize the method for future use on new subjects. Karamzadeh et al. [43] analyzed data from 31 healthy controls and 30 patients with TBI in their study. Subjects performed a task with an experimental condition (complexity task) and a control condition (font task). In the complexity task, which was originally developed for an fMRI study by Krueger et al. [44], subjects were asked to decide if they thought an action was complex or not; for example, subjects were asked their opinion on “stirring a cup of coffee” and “planning a wedding.” This task was chosen because it is known to cause activation in the PFC [45,46]. For the control condition (font task), subjects were asked if the font-type of the instructions and the task were the same. Karamzadeh et al. [43] filtered the data with a cutoff frequency of 0.1 Hz using a lowpass Butterworth IIR filter of order 10. The filtered data were then linearly detrended. In order to reduce motion artifacts and emphasize effects of the hemodynamic response, trials were only considered if HbO and HbR were negatively correlated. In addition, to ensure the selection of true hemodynamic response to the presented stimulus was captured, only trials in which HbO was greater than HbR (on average) were considered. Furthermore, trials with negative HbO values were discarded. Finally, HbO and HbR values of high complexity trials were block averaged by channel. Eleven features for each channel to be used in the multivariate statistical analysis were extracted: mean value of the HbO signal, variance of the HbO signal, left slope of the activity curve, right slope of the activity curve, Kurtosis value of the HbO signal, skewness value of the HbO signal, area under the activity curve, full width half maximum of the activity curve, peak amplitude of the activity curve, activity starting time, and discrete Fourier transform coefficients of the HbO signal. Fig. 6.5 depicts some of the mentioned hemodynamic features extracted for the analysis. The authors used the wrapper feature selection method, which searches all possible feature combinations, in order to determine the optimal combination of features for classification [23]. The decision tree algorithm was used to classify the feature sets, with 70% of the subjects used as “training” data and the remaining 30% used as “test” data [47]. The generalization performance was assessed by dividing the set into training and test data and performing classification 1000 times. In this study, the authors sought innovative criteria that could be used as functional biomarkers for TBI. They found an optimum set of three features (area under the activity curve, discrete Fourier transform coefficients of the HbO signal, and full

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FIGURE 6.5 The red (dark gray in print version) signal is the HbO signal and other hemodynamic features are visualized.

width half maximum of the activity curve) that had an average classification accuracy of 85%. Fig. 6.6 shows the average activity map of the brain using left slope and variance of the HbO signal. This study demonstrates the power of machine learning techniques and their ability to aid in the discovery and development of functional biomarkers.

3. Conclusion It has been shown that the prefrontal cortex (PFC) is responsible for a variety of complex actions and behaviors. Its functions include processing internal goals, personality expression, decision-making, and short-term memory processes. Advanced functional imaging such as fNIRS makes it possible to reliably measure activation patterns in the prefrontal cortex (PFC), which have traditionally been measured by functional magnetic resonance imaging (fMRI). However, fMRI requires specialized personnel, is stationary, and is relatively expensive. In comparison, functional nearinfrared spectroscopy (fNIRS) offers a portable, noninvasive, and inexpensive alternative for functional imaging. Compared to fMRI, fNIRS provides data with relatively greater temporal resolution but lower spatial resolution, which allows for better analysis of taskbased studies. Typically, near-infrared light (700e900 nm wavelength) is sent through the scalp; the major chromophores in this range are oxy-hemoglobin (HbO) and deoxyhemoglobin (HbR). By measuring HbO and HbR over time, it is possible to measure neural activity, similar to fMRI-BOLD. In fact, it has been found that fNIRS and fMRI-BOLD

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FIGURE 6.6 The average brain activity obtained from left slope and variance of the HbO signal for healthy (A) and traumatic brain injury (TBI) (B) participants.

measurements are significantly correlated [48]. With advancements in feature extraction, and further proof of method compared to other imaging techniques, fNIRS has gained significant traction over the past two decades in the scientific community. Functional biomarkers derived from fNIRS data have been developed in order to more thoroughly investigate the PFC’s role in both typical subjects and subjects suffering from neurological disorders. By utilizing multivariate machine-learning techniques such as feature extraction, more functional biomarkers may be uncovered. Specifically, multivariate analysis allows for features that are individually irrelevant in characterizing a group to become relevant when used in conjunction with other features of the fNIRS signal. In this context, this interesting and viable approach constructs a single or

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multidimensional feature space from hemodynamic responses consisting of ranked hemodynamic features according to their contribution to the specific class of study. Future research should elaborate on fNIRS applications in the assessment of functional biomarkers in neurological disorders such as TBI.

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