Using functional near infrared spectroscopy (fNIRS) to image the structure of the brain and function to explain the various biological or clinical manifestations of autism spectrum disorder (ASD) is one of the main rationales behind localizing brain areas. Mostly for classification of structural and functional disorders and for therapeutic intervention, the patient-friendly NIRS can become an imaging modality of choice. It is an emerging technology for noninvasive measurements of the local changes in cerebral hemodynamic levels associated with brain activity. Due to the low optical absorption of biological tissues at NIR wavelengths (λ=700-1000 nm), NIR light can penetrate deep enough to probe the outer layers of brain (i.e. cortex) up to 2-3 cm deep. The NIR absorption spectrum of the tissue is sensitive to changes in the concentration of major tissue chromophores, such as hemoglobin. Therefore, measurements of temporal variations of backscattered light can capture functionally evoked changes in the outermost cortex and can be used to assess the brain functioning. Cerebral hemodynamics results from dynamic cognitive processes and underlying physiological processes, both of which can be captured by fNIRS. The purpose of our current study is to find a metric to quantify the hemodynamic variations due to cerebral autoregulation (CA) and its relationship with childrens development. Further information about CA can be extracted from hemodynamic oscillations by measuring changes in the concentration of oxy- (HbO) and deoxy-hemoglobin (Hb) using fNIRS in response to functional tasks. Different physiological processes result in hemodynamic oscillations at specified characteristic frequencies. In our study two narrow frequency band, LFO and high frequency band, related to autoregulation and respiration, respectively, have been applied to the temporal HbO and Hb signals. Instantaneous amplitudes and phase of HbO and Hb variations then were obtained from the filtered NIRS using an algorithm, based on an analytic signal continuation approach. The new oxygenation variability index (OV index) was introduced to characterize variations in oxygen saturation (SO_2) at above frequency bands. The OV index is defined as the dimensionless ratio of standard deviation and mean of oxygen saturation data. This index therefore assesses the variability of the oxygen saturation SO_2 during the functional task at a given frequency band. fNIRS data were collected with 17 children (ages 4-8 years), while they performed a standard Go-NoGo task. The results indicate oscillation in level of oxygen saturation (SO2) for both frequency bands although patterns of oscillations and their dispersion (characterized by OV Index) were different. Next the relationship between the OV index and childrens age were evaluated to examine the possible developmental changes in CA. Based on the observation, the OV index increases with the age for children between the ages of 4-6 and decreases with age for children ages 6-8. We hypothesize that this change indicates a developmentally significant change in the process of CA. Similar age-related changes in cerebral hemodynamics have been reported by researchers, using other modalities to measure cerebral blood flow. Though OV indexes are similar during both Go and No-Go portions of the task and correlate with age, there was no relationship between this index and age during the Rest. Our method allows us to quantify the mechanism of CA and eventually to measure the relationship between developmental changes in CA and subsequently brain function and its impairments. Analysis of hemodynamic oscillations originating from CA does not provide a full picture of this complicated phenomenon. However, using fNIRS, the suggested approach might allow one to link changes in cerebral hemodynamics with developmental and functional changes. This non-invasive and patient-friendly method can be used to monitor brain development and possibly detect impairments of CA in both typical and atypical groups such as ASD and ADHD. Due to neuro-vascular coupling, local changes in oxyhemoglobin and deoxyhemoglobin levels can serve as an indirect measure of brain activity. At first approximation, these levels are proportional to the intensity of the brain activity. To probe changes in Oxy- and Deoxy-hemoglobin concentrations in the cortex that are caused by brain activity, related to chosen basic tasks, the data are collected at two wavelengths. To assess the brain activation in children of 4-8 years, we have used such tests as standard GO/NO-Go, developed to examine the effects of response inhibition and error processing. The NIRS signal is acquired, while children are performing the GO/NO-GO task. The NIRS sensor, placed on the childs forehead, covered Brodmann areas 9, 10 of the prefrontal cortex (PFC). Initial results of fNIRS assessment of the hemodynamic changes in the cortex indicate that mean activation levels (based on changes in oxy-hemoglobin) obtained from left and right prefrontal cortex during both GO and NO-GO trials are much higher in the case of typical child, compared to that of ASD. This fact indicates the hypo-activation of prefrontal cortex in the ASD group. Studies of resting state and task-based functional connectivity aiming to identify brain regions similar in functional behavior have received increased attention over the past few years. Aside from healthy populations, different patient groups, including patients with ASD, TBI have been the subject of functional connectivity (FC) studies. These studies have identified different connectivity networks in patient groups compared to healthy population. Different imaging modalities have been employed to investigate the brains functional connectivity. We attempt to elucidate features of FC by studying both hemodynamic and neural responses of the brain using different modalities. We recorded hemodynamic activity during the Go/No-Go task from 11 right-handed subjects with probes placed bilaterally over prefrontal areas. Using the data, we presented a reliable detection of fast optical signal (FOS) concurrently with electroencephalogram (EEG) during a Go/No-Go task. According to NIRS the hemodynamic responses showed higher task-related activation (an increase/decrease in oxygenated/deoxygenated hemoglobin, respectively) in the right versus left hemisphere. We have also employed a new approach to trace the dynamic patterns of human brain task-based functional connectivity with EEG. The EEG signals of 5 healthy subjects were recorded while they performed an auditory oddball and a visual modified oddball tasks. To capture the dynamic patterns of functional connectivity during the execution of each task, EEG signals are segmented into duration that correspond to the temporal windows of previously well-studied event-related potentials (ERPs). For each task, the proposed approach was able to establish a unique sequence of dynamic pattern (observed in all 5 subjects) for brain functional connectivity. We have also introduced and validated a novel time series feature extraction technique, Relative Brain Signature (RBS) that can be applied on ERP signals to provide an effective dimensionality reduction, which does not require the typical channel selection procedure and accounts for all the ERP signals. Unlike common feature extraction techniques, RBS technique obtains information from subjects ERP by considering the status of their relationship to the given population of the study. RBS combines vector space analysis and orthogonal subspace projection to generate feature vectors that signify the corresponding population of the subjects. The proposed technique can be used to identify the biomarkers related to a specific population and to localize functional biomarkers.
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