ArticleLiterature Review

Statistical Analysis of fNIRS Data: A Comprehensive Review.

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Abstract

Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure brain activities using the changes of optical absorption in the brain through the intact skull. fNIRS has many advantages over other neuroimaging modalities such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or magnetoencephalography (MEG), since it can directly measure blood oxygenation level changes related to neural activation with high temporal resolution. However, fNIRS signals are highly corrupted by measurement noises and physiology-based systemic interference. Careful statistical analyses are therefore required to extract neuronal activity-related signals from fNIRS data. In this paper, we provide an extensive reviewof historical developments of statistical analysis for fNIRS signal, which includes motion artifact correction, short source-detector separation correction, principal component analysis (PCA)/independent component analysis (ICA), false discovery rate (FDR), serially-correlated errors, as well as inference techniques such as the standard t-test, F-test, analysis of variance (ANOVA), and statistical parameter mapping (SPM) framework. In addition, to provide a unified view of various existing inference techniques, we explain a linear mixed effect model with restricted maximum likelihood (ReML) variance estimation, and show that most of the existing inference methods for fNIRS analysis can be derived as special cases. Some of the open issues in statistical analysis are also described.

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... Visually, MAs can present as rapid and very large changes in magnitude (spikes) relative to the baseline data. These spikes can be several orders of magnitude larger than the tissue-related hemodynamic changes [57]. Additionally, due to the movement of the source detector pairs on the scalp, the baseline fNIRS signal can shift. ...
... As mentioned above, many fNIRS studies assume the shape of the hemodynamic response with canonical gamma functions. Different convolutions of gamma functions are used to assume the shape of the canonical hemodynamic response [57]. While this method of the GLM is useful if the shape of the response is already known, assuming the shape could potentially lead to modelling errors as the response can change between recordings [72]. ...
... An additional consideration for the GLM is that the researcher can avoid the uncertainty of the differential path length factor (DPF) [57], a term used to correct for the extra distance that NIR light travels in the cortex due to light scatter from biological tissues [73]. The DPF is a highly variable parameter because it can change between different ages and populations of participants [74], as well as between brain regions [75]. ...
Article
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FNIRS pre-processing and processing methodologies are very important—how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.
... They concluded that GLM could be used to analyze the fNIRS data for posterior epileptic activity. In 2014, Tak and Ye [7] systematically reviewed the commonly used fNIRS statistics such as principal component analysis, independent component analysis, false discovery rate, in addition to the inference statistics such as the standard t-test, F-test, analysis of variance, and statistical parameter mapping framework. Eventually, they proposed adopting GLM mixed-effect model with restricted maximum likelihood variance estimation [7]. ...
... In 2014, Tak and Ye [7] systematically reviewed the commonly used fNIRS statistics such as principal component analysis, independent component analysis, false discovery rate, in addition to the inference statistics such as the standard t-test, F-test, analysis of variance, and statistical parameter mapping framework. Eventually, they proposed adopting GLM mixed-effect model with restricted maximum likelihood variance estimation [7]. ...
... Second, as shown in Table 4, the quadratic regression results for the Non-user group indicated that: (1) a U-shaped curve (a > 0) was observed in 14 channels (ch 1,2,4,5,7,8,9,[11][12][13][14][15][16][17], and the quadratic predictor (time) could explained 48.1% to 96.3% of the variance (HbO), R 2 s > .48, Fs > 91.16, ps < .001; ...
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General Linear Modelling (GLM) has been widely employed to estimate the hemodynamic changes evoked by cognitive processing, which are more likely to be nonlinear than linear. First, this study re-analyzed the fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected in the Mixed-Order Design Dimensional Change Card Sort (DCCS) task. The results indicated that the quadratic equation was better than GLM to model HbO changes in this task. Second, analysis of a new set of data indicated that the Habit-DisHabit design of DCCS was more effective in identifying the neural correlates of cognitive shifting than the Mixed-Order Design. Third, this study found that the Non-users were more attentive and engaged than the Heavy-users, with a slower but more steady increase of brain activation in BA8 and BA9.
... 27 Amplitudes of MW oscillations were obtained from [O 2 Hb] by normalizing the band-power (0.07 to 0.14 Hz) with its pulse band-power (0.6 to 2 Hz), and the median value of all longseparation channels was extracted. 27 A GLM 27,43 was applied on the time course of the long-separation channel (i.e., with and without SCR). As an evaluation metric, t-values were obtained. ...
... The t-values give an indication on the signal strength of a fitted hemodynamic response curve in relation to the residuals. The used GLM consisted of a modeled hemodynamic response time course, obtained from the convolution of the boxcar function and the canonical hemodynamic response, 43 its time and dispersion derivatives, and a constant offset, which were fitted into the fNIRS data of each recording channel. The time and dispersion derivatives 44 were included to correct for deviations of the onset and the shape of the hemodynamic response, respectively. ...
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Significance: Functional near-infrared spectroscopy (fNIRS) enables the measurement of brain activity noninvasively. Optical neuroimaging with fNIRS has been shown to be reproducible on the group level and hence is an excellent research tool, but the reproducibility on the single-subject level is still insufficient, challenging the use for clinical applications. Aim: We investigated the effect of short-channel regression (SCR) as an approach to obtain fNIRS measurements with higher reproducibility on a single-subject level. SCR simultaneously considers contributions from long- and short-separation channels and removes confounding physiological changes through the regression of the short-separation channel information. Approach: We performed a test-retest study with a hand grasping task in 15 healthy subjects using a wearable fNIRS device, optoHIVE. Relevant brain regions were localized with transcranial magnetic stimulation to ensure correct placement of the optodes. Reproducibility was assessed by intraclass correlation, correlation analysis, mixed effects modeling, and classification accuracy of the hand grasping task. Further, we characterized the influence of SCR on reproducibility. Results: We found a high reproducibility of fNIRS measurements on a single-subject level ( ICC single = 0.81 and correlation r = 0.81 ). SCR increased the reproducibility from 0.64 to 0.81 ( ICC single ) but did not affect classification (85% overall accuracy). Significant intersubject variability in the reproducibility was observed and was explained by Mayer wave oscillations and low raw signal strength. The raw signal-to-noise ratio (threshold at 40 dB) allowed for distinguishing between persons with weak and strong activations. Conclusions: We report, for the first time, that fNIRS measurements are reproducible on a single-subject level using our optoHIVE fNIRS system and that SCR improves reproducibility. In addition, we give a benchmark to easily assess the ability of a subject to elicit sufficiently strong hemodynamic responses. With these insights, we pave the way for the reliable use of fNIRS neuroimaging in single subjects for neuroscientific research and clinical applications.
... A wearable functional near-infrared spectroscopy (fNIRS) (Brite 24; Artinis Medical Systems Co., Netherlands) was used to measure the concentration changes in oxyhemoglobin (ΔO 2 Hb) and deoxyhemoglobin (ΔHHb) in the PFC. The fNIRS is a non-invasive brain imaging technique that strikes a good trade-off between temporal and spatial resolution (Tak and Ye, 2014). The advantage of the Brite 24 system-which weighs only 300 g-is that it permits the monitoring of ΔO 2 Hb and ΔHHb without imposing constraints on the posture and movement of the subject, and thus is suited for studying cortical hemodynamics during sleep. ...
... The total raw signals measured by the Brite 24 consist of several components, and the ΔO 2 Hb and ΔHHb related to neural activity is only a small portion. Noisy components are those related to breath, heartbeat, and movement, which need to be removed (Tak and Ye, 2014). The fNIRS data preprocessing pipeline is illustrated in Figure 3. ...
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People with mental stress often experience disturbed sleep, suggesting stress-related abnormalities in brain activity during sleep. However, no study has looked at the physiological oscillations in brain hemodynamics during sleep in relation to stress. In this pilot study, we aimed to explore the relationships between bedtime stress and the hemodynamics in the prefrontal cortex during the first sleep cycle. We tracked the stress biomarkers, salivary cortisol, and secretory immunoglobulin A (sIgA) on a daily basis and utilized the days of lower levels of measured stress as natural controls to the days of higher levels of measured stress. Cortical hemodynamics was measured using a cutting-edge wearable functional near-infrared spectroscopy (fNIRS) system. Time-domain, frequency-domain features as well as nonlinear features were derived from the cleaned hemodynamic signals. We proposed an original ensemble algorithm to generate an average importance score for each feature based on the assessment of six statistical and machine learning techniques. With all channels counted in, the top five most referred feature types are Hurst exponent, mean, the ratio of the major/minor axis standard deviation of the Poincaré plot of the signal, statistical complexity, and crest factor. The left rostral prefrontal cortex (RLPFC) was the most relevant sub-region. Significantly strong correlations were found between the hemodynamic features derived at this sub-region and all three stress indicators. The dorsolateral prefrontal cortex (DLPFC) is also a relevant cortical area. The areas of mid-DLPFC and caudal-DLPFC both demonstrated significant and moderate association to all three stress indicators. No relevance was found in the ventrolateral prefrontal cortex. The preliminary results shed light on the possible role of the RLPCF, especially the left RLPCF, in processing stress during sleep. In addition, our findings echoed the previous stress studies conducted during wake time and provides supplementary evidence on the relevance of the dorsolateral prefrontal cortex in stress responses during sleep. This pilot study serves as a proof-of-concept for a new research paradigm to stress research and identified exciting opportunities for future studies.
... Two repeated-measures ANOVAs were performed on D-dependent fNIRS data (O2Hb and HHb mean values) using Group (2: COVID-19, non-COVID-19) as independent between factor and Channel (6: Ch1, Ch2, Ch3, Ch4, Ch5, Ch6) as independent within factor [55,56]. Pairwise comparisons were used to check simple effects for significant interactions, and Bonferroni correction was employed to reduce biases in repeated comparisons. ...
... The ANOVA performed on the D-dependent measures for O2Hb mean values revealed a significant main effect of the Group (F [2,18] = 7.76, p = 0.01, η 2 = 0.398), with participants displaying higher mean values in the COVID-19-related condition compared to the non-COVID-19 condition (Figure 3). factor [55,56]. Pairwise comparisons were used to check simple effects for significant interactions, and Bonferroni correction was employed to reduce biases in repeated comparisons. ...
Article
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In pandemic times, taking advantage of COVID-19-elicited emotions in commercials has been a popular tactic employed by corporations to build successful consumer engagement and, hopefully, increase sales. The present study investigates whether COVID-19-related emotional communication affects the consumer’s emotional response and the approach/avoidance motivation toward the brand—measured as a function of brain hemodynamic changes—as well as the purchase intentions. The functional Near-Infrared Spectroscopy (fNIRS) was employed to record neural correlates from the prefrontal cortex while the experimental and control groups were observing respectively COVID-19-related and unrelated advertisements (ads). The hemodynamic patterns suggest that COVID-19-related ads may promote deeper emotional elaboration, shifting consumers’ attention from the semantic meaning to the affective features and perhaps supporting a more favorable brand evaluation. Conversely, purchase intentions were only related to the pre-existing level of brand engagement. The findings suggest that leveraging the negative emotional potential of COVID-19 may not shift the explicit purchase intentions but could nonetheless boost emotional engagement, benefitting the final evaluation of the brand at an implicit level.
... Step Description Function Criteria The General Linear Model is the standard approach for analysing and interpreting hemodynamic responses (Monti, 2011;Pinti et al., 2019). Among the range of possibilities this approach offers, the analysis of variance (ANOVA) is a common technique to determine localised brain activation based on changes in simultaneous HbO and HbR concentrations in repeated measures designs (Tak & Ye, 2014). Although it is common in the related literature to report only HbO, HbR or HbT -i.e., the combination of both -, the hemodynamic is a bidimensional response and both chromophores, HbO and HbR, usually correlate negatively during brain stimulation. ...
Thesis
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Real-time monitoring of drivers’ functional states will soon become a required safety feature for commercially available vehicles with automated driving capability. Automated driving technology aims to mitigate human error from road transport with the progressive automatisation of specific driving tasks. However, while control of the driving task remains shared between humans and automated systems, the inclusion of this new technology is not exempt from other human factors-related challenges. Drivers’ functional states are essentially a combination of psychological, emotional, and cognitive states, and they generate a constant activity footprint available for measurement through neural and peripheral physiology, among other measures. These factors can determine drivers’ functional states and, thus, drivers’ availability to safely perform control transitions between human and vehicle. This doctoral project aims at investigating the potential of electrocardiogram (ECG), electrodermal activity (EDA) and functional near-infrared spectroscopy (fNIRS) as measures for a multimodal driver state monitoring (DSM) system for highly automated driving (i.e., SAE levels 3 and 4). While current DSM systems relying on gaze behaviour measures have proven valid and effective, several limitations and challenges could only be overcome using eye-tracking in tandem with physiological parameters. This thesis investigates whether ECG, EDA and fNIRS would be good candidates for such a purpose. Two driving simulator studies were performed to measure mental workload, trust in automation, stress and perceived risk, all identified as modulators of drivers’ functional states and that could eventually determine drivers’ availability to take-over manual control. The main findings demonstrate that DSM systems should adopt multiple physiological measures to capture changes in functional states relevant for driver readiness. Future DSM systems will benefit from the knowledge generated by this research by applying machine learning methods to these measures for determining drivers’ availability for optimal take-over performance.
... Many novel data analysis methods have been proposed to extract the brain originated, task related data from the collected data. [18][19][20] Still there is no consensus on how to approach the fNIRS data, leading to the unsettling yet quite accurate prediction of Drs. Quaresima and Ferrari: "The prediction of the future directions of fNIRS for assessing brain function during human behavior in natural and social situations is not easy." ...
Article
Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR ( N C R ¯ ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: N C R ¯ can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.
... A set of signals-of-interest (SOIs), i.e., channel-by-chromophore combinations, were selected for each individual based on the participant's localizer run's data of that day. Answers were decoded from time courses of these SOIs with a univariate analysis using a General Linear Model (GLM) approach (Tak and Ye, 2014). The data of the four participants in the lab were analyzed post hoc in simulated real-time, whereas the data of the two participants in the cafeteria were analyzed online (i.e., intrasession). ...
Article
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Severely motor-disabled patients, such as those suffering from the so-called “locked-in” syndrome, cannot communicate naturally. They may benefit from brain-computer interfaces (BCIs) exploiting brain signals for communication and therewith circumventing the muscular system. One BCI technique that has gained attention recently is functional near-infrared spectroscopy (fNIRS). Typically, fNIRS-based BCIs allow for brain-based communication via voluntarily modulation of brain activity through mental task performance guided by visual or auditory instructions. While the development of fNIRS-BCIs has made great progress, the reliability of fNIRS-BCIs across time and environments has rarely been assessed. In the present fNIRS-BCI study, we tested six healthy participants across three consecutive days using a straightforward four-choice fNIRS-BCI communication paradigm that allows answer encoding based on instructions using various sensory modalities. To encode an answer, participants performed a motor imagery task (mental drawing) in one out of four time periods. Answer encoding was guided by either the visual, auditory, or tactile sensory modality. Two participants were tested outside the laboratory in a cafeteria. Answers were decoded from the time course of the most-informative fNIRS channel-by-chromophore combination. Across the three testing days, we obtained mean single- and multi-trial (joint analysis of four consecutive trials) accuracies of 62.5 and 85.19%, respectively. Obtained multi-trial accuracies were 86.11% for visual, 80.56% for auditory, and 88.89% for tactile sensory encoding. The two participants that used the fNIRS-BCI in a cafeteria obtained the best single- (72.22 and 77.78%) and multi-trial accuracies (100 and 94.44%). Communication was reliable over the three recording sessions with multi-trial accuracies of 86.11% on day 1, 86.11% on day 2, and 83.33% on day 3. To gauge the trade-off between number of optodes and decoding accuracy, averaging across two and three promising fNIRS channels was compared to the one-channel approach. Multi-trial accuracy increased from 85.19% (one-channel approach) to 91.67% (two-/three-channel approach). In sum, the presented fNIRS-BCI yielded robust decoding results using three alternative sensory encoding modalities. Further, fNIRS-BCI communication was stable over the course of three consecutive days, even in a natural (social) environment. Therewith, the developed fNIRS-BCI demonstrated high flexibility, reliability and robustness, crucial requirements for future clinical applicability.
... fNIRS is increasingly used for functional brain-imaging research in adults and children, including CI recipients, because it is quiet, non-invasive, and CI-compatible [68][69][70][71][72][73][74][75]. fNIRS uses near infrared light and as such is unaffected by electrical or magnetic artefacts. ...
Article
Outcomes following cochlear implantation vary widely for both adults and children, and behavioral tests are currently relied upon to assess this. However, these behavioral tests rely on subjective judgements that can be unreliable, particularly for infants and young children. The addition of an objective test of outcome following cochlear implantation is therefore desirable. The aim of this scoping review was to comprehensively catalogue the evidence for the potential of functional near infrared spectroscopy (fNIRS) to be used as a tool to objectively predict and measure cochlear implant outcomes. A scoping review of the literature was conducted following the PRISMA extension for scoping review framework. Searches were conducted in the MEDLINE, EMBASE, PubMed, CINAHL, SCOPUS, and Web of Science electronic databases, with a hand search conducted in Google Scholar. Key terms relating to near infrared spectroscopy and cochlear implants were used to identify relevant publications. Eight records met the criteria for inclusion. Seven records reported on adult populations, with five records only including post-lingually deaf individuals and two including both pre- and post-lingually deaf individuals. Studies were either longitudinal or cross-sectional, and all studies compared fNIRS measurements with receptive speech outcomes. This review identified and collated key work in this field. The homogeneity of the populations studied so far identifies key gaps for future research, including the use of fNIRS in infants. By mapping the literature on this important topic, this review contributes knowledge towards the improvement of outcomes following cochlear implantation.
... fNIRS data were processed using the open-source software NIRS-SPM implemented in MATLAB ® (MathWorks Inc., Natick, MA, USA). In statistical parametric mapping (SPM) analysis, a generalized linear model (GLM) with standard hemodynamic response curves was performed to model the hypothesized oxyHb response and examined for significant cortical activation during the experiment [38]. At the group level, statistical analysis was performed based on the individual-level beta-values to identify activated channels (corrected p < 0.001) [39]. ...
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In the elderly, walking while simultaneously engaging in other activities becomes more difficult. This study aimed to examine the changes in cortical activity during walking with aging. We try to reveal the effects of an additional task and increased walking speed on cortical activation in the young-old and the old-old elderly. Twenty-seven young-old (70.2 ± 3.0 years) and 23 old-old (78.0 ± 2.3 years) participated in this study. Each subject completed four walking tasks on the treadmill, a 2 × 2 design; two single-task (ST) walking conditions with self-selected walking speed (SSWS) and fast walking speed (FWS), and two dual-task (DT) walking conditions with SSWS and FWS. Functional near-infrared spectroscopy was applied for measurement of cerebral oxyhemoglobin (oxyHb) concentration during walking. Cortical activities were increased during DT conditions compared with ST conditions but decreased during the FWS compared with the SSWS on the primary leg motor cortex, supplementary motor area, and dorsolateral prefrontal cortex in both the young-old and the old-old. These oxyHb concentration changes were significantly less prominent in the old-old than in the young-old. This study demonstrated that changes in cortical activity during dual-task walking are lower in the old-old than in the young-old, reflecting the reduced adaptive plasticity with severe aging.
... fNIRS is a popular noninvasive neuroimaging method, compatible with HAs and CIs, that evaluates brain activities by detecting nearinfrared (650-950-nm) light absorption on the skull and thus has many advantages over other neuroimaging modalities [125]. In a recent fNIRS study with noise-vocoded speech by Lawrence et al. [57] investigating the relationship between cortical changes and SI in normallyhearing listeners, activation in superior temporal regions was found to increase linearly with SI, which suggested that fNIRS could be employed for SI prediction in normal-hearing listeners. ...
Article
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Speech intelligibility (SI) measurement has attracted great attention in the speech communication community over the last decade. It is a critical consideration for speech enhancement, coding, and transmission, as well as for diagnostics. In particular, nonintrusive SI measures that are realistically applicable without reference speech signals have been growing rapidly. This paper gives a review of methodology of nonintrusive SI measures and aims to show how nonintrusive SI metrics perform relative to intrusive ones, as well as their potential in future speech communication applications. In addition, this paper provides a systematic classification of historical and recently introduced methods in a comprehensive framework with critical comments and comparisons of their advantages and limitations. It considers an extensive and up-to-date bibliography to provide a suitable background and overview of recent advancements. The current SI metric development status is presented in the context of an organized framework with associated analyses and examples of the utility of SI metrics in physiological research and clinical applications. Finally, this paper discusses important emergent and potential future research directions.
... Functional connectivity (FC) is a temporal correlation in brain activity between different brain regions, which is an effective indicator of an individual's brain network [20][21][22]. In a recent study, FC gained interest as a neurological biomarker (neuromarker) because its variance was largely determined by stable individual properties, rather than trial variations [23][24][25]. ...
Article
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Detecting Alzheimer's disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named "refined network," in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening.
... The existent inference frameworks in adult fNIRS analysis involve the use of modelling techniques that assume that signal data coming from all subjects share standard attributes. Typically, these models are based on the assumption that a canonical haemodynamic response function generated in response to a specific stimulus can be represented as a linear combination of several sources (regressors) 10 . Similarly, priors-based modelling, such as seed-based functional connectivity analysis, is heavily dependent on the choice of the channels to be used as a seed 11 . ...
Article
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In the last decades, non-invasive and portable neuroimaging techniques, such as functional near infrared spectroscopy (fNIRS), have allowed researchers to study the mechanisms underlying the functional cognitive development of the human brain, thus furthering the potential of Developmental Cognitive Neuroscience (DCN). However, the traditional paradigms used for the analysis of infant fNIRS data are still quite limited. Here, we introduce a multivariate pattern analysis for fNIRS data, xMVPA, that is powered by eXplainable Artificial Intelligence (XAI). The proposed approach is exemplified in a study that investigates visual and auditory processing in six-month-old infants. xMVPA not only identified patterns of cortical interactions, which confirmed the existent literature; in the form of conceptual linguistic representations, it also provided evidence for brain networks engaged in the processing of visual and auditory stimuli that were previously overlooked by other methods, while demonstrating similar statistical performance. Andreu-Perez et al developed a multivariate pattern analysis for fNIRS data (xMVPA), which is powered by eXplainable Artificial Intelligence (XAI). They demonstrated its application in the context of investigating visual and auditory processing in six-month-old infants and showed that it provided insight into patterns of cortical networks.
... Appropriate wavelengths are chosen to estimate cerebral blood oxygen saturation from the light intensity attenuation at two or more wavelengths. Variations in the HbR and HbO 2 content are calculated according to the absorption law [56]. Finally, the cerebral blood oxygen saturation values are estimated using the predictive model. ...
Article
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In recent years, cerebral blood oxygen saturation has become a key indicator during the perioperative period. Cerebral blood oxygen saturation monitoring is conducive to the early diagnosis and treatment of cerebral ischemia and hypoxia. The present study discusses the three most extensively used clinical methods for cerebral blood oxygen saturation monitoring from different aspects: working principles, relevant parameters, current situations of research, commonly used equipment, and relative advantages of different methods. Furthermore, through comprehensive comparisons of the methods, we find that near-infrared spectroscopy (NIRS) technology has significant potentials and broad applications prospects in terms of cerebral oxygen saturation monitoring. Despite the current NIRS technology, the only bedside non-invasive cerebral oxygen saturation monitoring technology, still has many defects, it is more in line with the future development trend in the field of medical and health, and will become the main method gradually.
... In comparison with the simple optical topography (OT) implementation -a direct extension to fNIRS, this configuration enables three-dimensional (3D) model-based reconstruction of hemodynamic changes in the cerebral-cortex (CC) layer at an improved quantitative accuracy and spatial resolution, and has a promising prospect in both task-evoked and resting-state brain functional investigations [6]- [9]. Nevertheless, as common adversities in fNIRS regime, the brain signals in HD-DOT are inevitably contaminated by the superficial physiological interferences driven by blood pressure fluctuations in the scalp (SC) layer, related to heart rate, respiration and low-frequency oscillations, etc. [2], [3], [10]. Besides, the random interferences, originating from photon-shot and instrumental noises etc., also have confounding effects on the extraction of the intrinsic neural responses due to weakness of the activated hemodynamics (relative to the baseline) in the CC-layer [2], [3]. ...
Article
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High-density (HD) diffuse optical tomography (DOT), as an advanced modality of functional near-infrared spectroscopy, is finding increasing applications in neuroimaging regime. However, it is a primary challenge that the superficial physiological interferences usually significantly contaminate the functional activation reconstruction. In addition, the random noises, majorly the photon-shot and instrumental ones, also cast negative influences on the measurements, and further distort the reconstructed image. To mitigate the adversities, we herein propose a combined scheme of two-layer semi-three-dimensional (S3D) reconstruction and multi-wavelength image fusion, which leverages a mathematical model with explicit physical significance, to suppress the physiological interferences and random noises in HD-DOT reconstruction, respectively. The approach is purely data-driven without additional auxiliary measurement, and comprised of two steps: First, the absorption perturbations are topographically reconstructed over both the scalp (SC) and cerebral-cortex (CC) layers using the two-layer S3D scheme, of which the superficial interferences are estimated from the SC reconstruction and adaptively filtered out from the CC one; Second, the interference-suppressed multi-wavelength CC-images are decomposed using the discrete wavelet transform, and fused at multi-resolutions into a mask for further removal of the random noises. We comprehensively validate the proposed scheme using simulations and phantom experiments, and demonstrate its sound effectiveness in suppressing the physiological interferences and random noises. The performance improvement rather than by more cycles or longer sampling time offers additional payoff: shorter measurement time or higher temporal resolution.
... Finally, after signal preprocessing, the majority of fNIRS studies apply statistical analysis methods to detect brain activation (e.g., stimulation vs. baseline) for an effect of interest, or to test activation differences between experimental groups or conditions (Ye et al., 2009;Hassanpour et al., 2014;Tak and Ye, 2014;Santosa et al., 2019). This step is crucial for drawing neuroscientific inferences, and for this reason recent publications have outlined various statistical challenges of task-based and resting-state fNIRS data analysis (Barker et al., 2013;Huppert, 2016;Santosa et al., 2017;Blanco et al., 2018). ...
Thesis
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Language acquisition is mediated by maturational and experiential mechanisms. It is a remarkably complex process, yet infants show incredible language learning capacities. In a bilingual context this process is even more challenging, since bilingual infants benefit from less day-to-day experience with each language. In addition, they need to perform specific computations such as separating their languages or storing the information of two linguistic inputs. Learning two languages, however, does not negatively affect language acquisition: bilingual infants follow a similar pace to their monolingual peers when main developmental milestones are considered. It has been suggested that bilingualism might elicit cognitive adaptations that allow infants to cope with the increased complexity of their linguistic environment. Distinctive attention allocation skills or an increased perceptual sensitivity are examples of the proposed adaptations. Whether bilingual infants’ success is also supported by modulations in the underlying functional systems in charge of these linguistic processes is the question this thesis aims to unravel. This question is addressed using a functional brain imaging technique especially suitable for infant populations: functional near-infrared spectroscopy (fNIRS). This neuroimaging technique offers the potential to study neural activity non- invasively based on cerebral hemodynamics. Because fNIRS is a relatively novel technique to measure infants functional brain activity, the thesis also contains a major methodological component. Particular focus is dedicated to data quality assessment and signal processing. A series of fNIRS experiments are presented to investigate whether bilingualism might be one factor eliciting experience-induced neural adaptations in 4-month-old infants. First, the brain’s functional organization is examined through resting-state functional connectivity. This approach represents a viable strategy to link brain function and cognition, and it offers the potential to simultaneously examine various functional systems. Likewise, functional network activity can be modulated by different prenatal and postnatal conditions. Studying functional connectivity with fNIRS arises some methodological challenges that are inherent to this imaging technique. In particular, whether the fNIRS data preprocessing pipeline should include a step to deal with signal autocorrelation. The second study of this thesis addresses the influence of this step for functional connectivity analyses from a theoretical and empirical point of view. A third study investigates functional differences that might emerge during spoken language processing. Monolingual and bilingual infants’ brain responses to speech stimuli are measured to examine the brain areas in support of this cognitive process. The results of these experiments are presented. Investigating the impact of bilingual exposure on how the brain works, prior to infants even beginning to babble, has remarkable theoretical implications for the field of language acquisition, which had long suspected that brain reorganization for linguistic exposure may begin in-utero, but certainly in the first months of life. This thesis also provides several methodological advancements confirming the suitability of fNIRS imaging for accurately and reliably assessing brain function in developmental populations. The importance of the theoretical and methodological implications of the findings of this thesis are discussed, as is the relevance of transparent and replicable research methodologies for future works in developmental cognitive neuroscience.
... Correlation-based signal improvement (Cui et al., 2010) was used to correct artifacts caused by head movement. The wavelet-minimum description length and hemodynamic response functions methods were used to remove the signal drift caused by physiological and machine noises Tak and Ye, 2014). For each individual participant, the average HbO concentration change of 16 channels during the task period, including the 3-min closed-eye rest, 3-min-and-51-s emotioninducing film task, and 5-min drawing task, was extracted. ...
Article
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Differences in emotion experience and emotion expression between patients with schizophrenia and the healthy population have long been the focus of research and clinical attention. However, few empirical studies have addressed this topic using art-making as a tool of emotion expression. This study explores the differences in brain mechanism during the process of expressing anger between patients with schizophrenia and healthy participants using pictographic psychological techniques. We used functional near-infrared spectroscopy to fully detect changes in frontal cortex activity among participants in two groups—schizophrenia and healthy—during the process of experiencing and expressing anger. The results showed that there were no differences in the experience of anger between the two groups. In the process of anger expression, the dorsolateral prefrontal cortex, frontal pole, and other regions showed significant negative activation among patients with schizophrenia, which was significantly different from that of the healthy group. There were significant differences between patients with schizophrenia and the healthy group in the drawing features, drawing contents, and the ability to describe the contents of their drawings. Moreover, the effect size of the latter was greater than those of the former two. In terms of emotion expression, the drawing data and brain activation data were significantly correlated in each group; however, the correlation patterns differed between groups.
... The device also has an embedded tri-axis accelerometer to record head movements that might disturb the tissue-optode contact. The fNIRS has many advantages over other brain monitoring techniques [15]. Compared to EEG, fNIRS has better tolerance to motion artefacts and allows higher spatial resolution. ...
Conference Paper
It is well-known that stress affects sleep quality, suggesting abnormal brain activity during sleep when people are stressed. However, no study has examined where brain processes stress during sleep. This study aims to explore the associations between bedtime stress and the hemodynamics in the prefrontal cortex (PFC) during the first sleep cycle under free-living conditions. Stress biomarkers including salivary cortisol and secretory immunoglobulin A (sIgA) were measured using the SOMA Dual Analyte LFD test kits on the experiment nights between 22:00-23:00 to control the circadian oscillation of the stress-related hormones. Perceived stress level was rated on a 1-10 Likert scale right after the collection of the salivary samples. The hemodynamics of the pre-frontal cortex (PFC) was measured using a wearable functional near-infrared spectroscopy (fNIRS) device. Correlation analysis with statistical test was performed to examine the associations between different stress indicators and a set of time-domain and frequency-domain features derived from the hemodynamic responses. Significantly positive linear correlations were observed in the standard deviation, skewness, and kurtosis between the average concentration change of oxyhemoglobin and that of deoxyhemoglobin in the whole region of interest. Stress was found to correlate to the hemodynamics in the mid-DLPFC, the caudal-DLPFC, and the left RLPFC. The relationships between stress and these PFC subregions depends on the stress indicator adopted. Our finding provides supplementary support to the role of the PFC in processing stress. The preliminary results also shed light on the development of stress response markers in brain activity that can be measured with wearable brain-computer interface technologies.
... Body movement during tasks can be reduced using body supports such as a chin or arm rest. Furthermore, various analytical methods have been developed to reduce physiological artifacts (Scholkmann et al., 2014;Tak and Ye, 2014), including independent component analysis (Kohno et al., 2007), spatial filtering (Zhang et al., 2016), and the hemodynamic modality separation method (Yamada et al., 2012). For the hemodynamic modality separation method, a free data analysis package is currently available (The National Institute of Advanced Industrial Science and Technology, 2019). ...
Article
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This perspective article discusses the importance of evidence-based psychotherapy and highlights the usefulness of near-infrared spectroscopy (NIRS) in assessing the effects of psychotherapeutic interventions as a future direction of clinical psychology. NIRS is a safe and non-invasive neuroimaging technique that can be implemented in a clinical setting to measure brain activity via a simple procedure. This article discusses the possible benefits and challenges of applying NIRS for this purpose, and the available methodology based on previous studies that used NIRS to evaluate psychotherapeutic effects. Furthermore, this perspective article suggests alternative methodologies that may be useful, namely, the single- and multi-session evaluations using immediate pre- and post-intervention measurements. These methods can be used to evaluate state changes in brain activity, which can be derived from a single session of psychotherapeutic interventions. This article provides a conceptual schema important in actualizing NIRS application for evidence-base psychotherapy.
... However, available datasets from other functional and structural neural modalities have not been provided for investigations until now. For example, the fNIRS modality is one of the most inexpensive and most accurate procedures to diagnose a variety of neurological disorders (Tak and Ye, 2014;Peng et al., 2014). The lack of available fNIRS datasets for the diagnosis of epileptic seizures has given rise to confined research in this field. ...
Preprint
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Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional and structural neuroimaging modalities. AI encompasses a variety of areas, and one of its branches is deep learning (DL). Not long ago, and before the rise of DL algorithms, feature extraction was an essential part of every conventional machine learning method, yet handcrafting features limit these models' performances to the knowledge of system designers. DL methods resolved this issue entirely by automating the feature extraction and classification process; applications of these methods in many fields of medicine, such as the diagnosis of epileptic seizures, have made notable improvements. In this paper, a comprehensive overview of the types of DL methods exploited to diagnose epileptic seizures from various neuroimaging modalities has been studied. Additionally, rehabilitation systems and cloud computing in epileptic seizures diagnosis applications have been exactly investigated using various modalities.
... HOMER-3, NIRS-SPM, and Hitachi POTATo) that offer graphical user interfaces and open-source MATLAB functions if one wants to develop custom code, as well as a wide array of data analysis pipelines (Huppert et al. 2009;Sutoko et al. 2016;Ye et al. 2009). Data analysis most often includes converting light attenuation to HbO 2 and HHb concentration values, band-pass filtering, detection and removal of motion artifacts, modeling of the hemodynamic response functions using general linear modeling, correcting stimulation period data for any drifts from baseline, spatial registration to determine brain regions underlying data channels, visual analysis of all processed data, analysis of video data to confirm task compliance, and finally, statistical analysis with appropriate corrections for multiple comparisons (see statistical analysis details in Tak & Ye, 2014). However, several technical challenges continue to persist in the field of fNIRS that must be acknowledged (discussed in detail in Yücel et al. 2017). ...
Article
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During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.
... systemic components resulted in a substantial improvement in the detection rate. 41,[51][52][53] Although it is common to use just the first or second PCs as regressors, 2 we observed that including all components resulted in the best performance, consistent with Huppert. 74 Including either all PCA components, the mean, or all individual short channels simplifies analysis as these approaches do not require a specific selection criterion, making them easy to implement, describe, and replicate. ...
Article
Significance: Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool in auditory research, but the range of analysis procedures employed across studies may complicate the interpretation of data. Aim: We aim to assess the impact of different analysis procedures on the morphology, detection, and lateralization of auditory responses in fNIRS. Specifically, we determine whether averaging or generalized linear model (GLM)-based analysis generates different experimental conclusions when applied to a block-protocol design. The impact of parameter selection of GLMs on detecting auditory-evoked responses was also quantified. Approach: 17 listeners were exposed to three commonly employed auditory stimuli: noise, speech, and silence. A block design, comprising sounds of 5 s duration and 10 to 20 s silent intervals, was employed. Results: Both analysis procedures generated similar response morphologies and amplitude estimates, and both indicated that responses to speech were significantly greater than to noise or silence. Neither approach indicated a significant effect of brain hemisphere on responses to speech. Methods to correct for systemic hemodynamic responses using short channels improved detection at the individual level. Conclusions: Consistent with theoretical considerations, simulations, and other experimental domains, GLM and averaging analyses generate the same group-level experimental conclusions. We release this dataset publicly for use in future development and optimization of algorithms.
... neuronal vs systemic), (ii) spatial locations (axially for tissue layers i.e. cerebral vs extracerebral or laterally i.e. left vs right hemispheres), and (iii) experimental or task related elements (i.e. evoked vs resting state or with vs. without stressors) [14][15][16][17][18] . Therefore, extraction of the signal of interest requires application of appropriate signal processing techniques, which first entails thorough characterization and analysis of signal components arising because of the task being studied. ...
Article
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Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within the VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.
... fNIRS measurements contain the hemoglobin response to brain activation as well as confounding signals arising from head movement and systemic physiological changes [7,8]. Confounding signals typically exhibit (i) abrupt signal changes such as spikes, (ii) baseline shift, (iii) periodic variation of time series, and (iv) low-frequency drift. ...
Article
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Objective: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN). Approach: The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts. Main results: Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contains variants of shapes and amplitudes of HRF. Significance: The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.
... The same montage has been used previously by Ludyga et al. (2019a). Spacers were used to keep the inter-optode distance constant at 3cm, which is considered the best compromise between high light penetration depth and sufficient signal-to-noise ratio (Ferrari and Quaresima, 2012;Tak and Ye, 2014). A black overcap was used to minimize the impact of ambient light. ...
Book
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The main objective of this Research Topic was to gather studies that shed more light on the benefits of physical exercise in the neurophysiological system, from childhood to old age and from the field of health to sports or professional performance. For example, we consider important those studies that deepen into the epigenetic mechanisms involved in the aging process and their modulation through physical exercise, improving prevention and treatment therapies, and those that contributes to better understand how physical activity improves brain functions (e.g., increased hippocampal), or what effect cognitive loads cause in variables such as heart rate variability or brain waves. We also consider it particularly interesting to show studies that can reflect how physical exercise can be a good preventive strategy to avoid or counteract neurodegenerative diseases, such as Alzheimer, and consequently, increase the time and quality of life. Thus, some of the topics of interest for this Research Topic are studies that contemplate the latest advances on neurophysiological and epigenetic effects of physical exercise on the aging, or beneficial effects of the practice of physical activity and sport on anti-aging and neuroprotective mechanisms. Equally relevant aspects to consider are the effects of physical exercise to prevent neurodegenerative diseases, the relationship between physical exercise practice and improvement of brain functions, the effects of cognitive loads at the neurophysiological level, or the neurophysiological system behavior related to sports or professional performance.
... A General Linear Model (GLM) analysis performed on round-level fNIRS HbO and HbR data obtained 'beta' values, describing the goodness-of-fit of observed brain activity to an expected hemodynamic response function (HRF). For detailed information about fNIRS analyses using the GLM, please see Barker et al. (2013), Tak and Ye (2014), and Yücel et al. (2021). The GLM was fit to each NIRS channel individually per participant, resulting in one beta value for each roundparticipant-channel combination. ...
Article
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Intelligent agents are rapidly evolving from assistants into teammates as they perform increasingly complex tasks. Successful human-agent teams leverage the computational power and sensory capabilities of automated agents while keeping the human operator's expectation consistent with the agent's ability. This helps prevent over-reliance on and under-utilization of the agent to optimize its effectiveness. Research at the intersection of human-computer interaction, social psychology, and neuroergonomics has identified trust as a governing factor of human-agent interactions that can be modulated to maintain an appropriate expectation. To achieve this calibration, trust can be monitored continuously and unobtrusively using neurophysiological sensors. While prior studies have demonstrated the potential of functional near-infrared spectroscopy (fNIRS), a lightweight neuroimaging technology, in the prediction of social, cognitive, and affective states, few have successfully used it to measure complex social constructs like trust in artificial agents. Even fewer studies have examined the dynamics of hybrid teams of more than 1 human or 1 agent. We address this gap by developing a highly collaborative task that requires knowledge sharing within teams of 2 humans and 1 agent. Using brain data obtained with fNIRS sensors, we aim to identify brain regions sensitive to changes in agent behavior on a long- and short-term scale. We manipulated agent reliability and transparency while measuring trust, mental demand, team processes, and affect. Transparency and reliability levels are found to significantly affect trust in the agent, while transparency explanations do not impact mental demand. Reducing agent communication is shown to disrupt interpersonal trust and team cohesion, suggesting similar dynamics as human-human teams. Contrasts of General Linear Model analyses identify dorsal medial prefrontal cortex activation specific to assessing the agent's transparency explanations and characterize increases in mental demand as signaled by dorsal lateral prefrontal cortex and frontopolar activation. Short scale event-level data is analyzed to show that predicting whether an individual will trust the agent, with data from 15 s before their decision, is feasible with fNIRS data. Discussing our results, we identify targets and directions for future neuroergonomics research as a step toward building an intelligent trust-modulation system to optimize human-agent collaborations in real time.
... When a region of the brain is triggered by external stimuli (e.g., language input), it starts to absorb more energy and oxygen which is transferred by oxygenated hemoglobin (Pfeifer et al., 2018). There are several neuroimaging techniques that can be used in listening assessment research, notably functional near-infrared spectroscopy (fNIRS), which is a non-invasive and user-friendly optical neuroimaging technology to measure changes in hemodynamics and oxygenation in the brain cortex (Scholkmann et al., 2014;Tak and Ye, 2014;Pfeifer et al., 2018;Sulpizio et al., 2018). The brain cortex in the left hemisphere plays an essential role in language (and listening) comprehension. ...
Article
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This study aims to investigate whether and how test takers' academic listening test performance is predicted by their metacognitive and neurocognitive process under different test methods conditions. Eighty test takers completed two tests consisting of while-listening performance (WLP) and post-listening performance (PLP) test methods. Their metacognitive awareness was measured by the Metacognitive Awareness Listening Questionnaire (MALQ), and gaze behavior and brain activation were measured by an eye-tracker and functional near-infrared spectroscopy (fNIRS), respectively. The results of automatic linear modeling indicated that WLP and PLP test performances were predicted by different factors. The predictors of WLP test performance included two metacognitive awareness measures (i.e., person knowledge and mental translation) and fixation duration. In contrast, the predictors of the PLP performance comprised two metacognitive awareness measures (i.e., mental translation and directed attention), visit counts, and importantly, three brain activity measures: the dmPFC measure in the answering phase, IFG measure in the listening phase, and IFG measure in the answering phase. Implications of these findings for language assessment are discussed.
... Functional near-infrared spectroscopy (fNIRS) is a noninvasive, optical neuroimaging modality that measures and maps the tissue concentrations changes of oxy-and deoxyhemoglobin (ΔHbO 2 and HHb), providing an estimate of the brain hemodynamic and oxygenation changes secondary to neuronal activation. Theoretically, brain activation in fNIRS is considered as a statistically significant increase in HbO 2 and a decrease in HHb, 1 based on the principle of neurovascular coupling, where brain activation is marked by an initial dip in HHb followed by a large increase of HbO 2 in the blood to support the increased neurons' metabolic demand. This increase in blood volume, which is composed of an increase in HbO 2 and a smaller decrease in HHb, is called the hemodynamic response. ...
Article
Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated ( HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on HbO 2 only. Previous work has shown that HbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both HbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of HbO 2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combine HbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [ HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on HbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using HbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.
... Using wavelengths either side of the isosbestic point therefore increases the likelihood that the matrix in Equation (1.20) will be well-conditioned. Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique which uses the principles discussed in this chapter to measure changes in the concentration of the haemoglobins in the brain which, due to the haemodynamic response function, are markers of functional activation (Ferrari & Quaresima, 2012;Tak & Ye, 2014). An array consisting of sources and detectors of near-infrared light (embedded within a cap or headband -see Figure 1.3) is placed in contact with the scalp surface. ...
Thesis
This thesis describes the development and application of age-appropriate structural priors to improve the localisation accuracy of diffuse optical tomography (DOT) approaches in infants aged from birth to two years of age. Knowledge of the target cranial anatomy, known as a structural prior, is required to produce three-dimensional images localising concentration changes to the cortex. A structural prior would ideally be subject-specific, i.e. derived from structural magnetic resonance imaging (MRI) data from each specific subject. Requiring a structural scan from every infant participant, however, is not feasible and undermines many of the benefits of DOT. A review was conducted to catalogue available infant structural MRI data, and selected data was then used to produce structural priors for infants aged 1- to 24-months. Conventional analyses using functional near-infrared spectroscopy (fNIRS) implicitly assume that head size and array position are constant across infants. Using DOT, the validity of assuming these parameters constant in a longitudinal infant cohort was investigated. The results show that this assumption is reasonable at the group-level in infants aged 5- to 12-months but becomes less valid for smaller group sizes. A DOT approach was determined to illicit more subtle effects of activation, particularly for smaller group sizes and expected responses. Using state-of-the-art MRI data from the Developing Human Connectome Project, a database of structural priors of the neonatal head was produced for infants aged pre-term to term-equivalent age. A leave-one-out approach was used to determine how best to find a match between a given infant and a model from the database, and how best to spatially register the model to minimise the anatomical and localisation errors relative to subject-specific anatomy. Model selection based on the 10/20 scalp positions was determined to be the best method (of those based on external features of the head) to minimise these errors.
... Taking average values during task blocks as data has been a frequently used approach in previous fNIRS studies for statistical analysis Pelicioni et al., 2020;Yang et al., 2020). This method is recommended because it is less prone to being affected by artifacts (Vitorio et al., 2017) and is considered to be an appropriate method to use, with no assumptions of the exact shape or timing of the course of hemoglobin concentration changes (Tak and Ye, 2014). Thus, appropriate signal processing approaches and analyses of fNIRS data are necessary to guarantee accurate estimation of cortical activation. ...
Article
Active exercise for upper limb training has been widely used to improve hemiplegic upper limb function, and its effect may be boosted by extrinsic visual feedback. The passive movement of the hemiplegic upper limb is also commonly used. We conducted a functional near-infrared spectroscopy experiment to compare cortical activation during the following three conditions: active left upper limb movement (on the hemiplegic sides in stroke patients), with or without extrinsic motor performance visual feedback (LAV, LAnV), and passive left upper limb movement (hemiplegic sides in stroke patients) (LP) in stroke patients and healthy controls. Twenty patients with right hemispheric stroke and 20 healthy controls were recruited for this study. Hemodynamic changes were detected during left upper limb movements (on the hemiplegic sides in stroke patients) under the above three conditions in the sensorimotor cortex (SMC), supplementary motor area (SMA), and premotor cortex (PMC). There was no significant difference in the level of cortical activation between patients with stroke and healthy subjects during the three conditions. Both the LAV and LAnV induced significantly higher activation in the contralateral SMA and PMC than in the LP. Extrinsic visual feedback led to additional activation in the contralateral PMC and SMA, but this was not statistically significant. Our study indicates that active upper-limb movement appears to induce higher cortical activation than that elicited by passive movement in both stroke patients and the healthy population. Extrinsic motor performance in the form of visual feedback provided during active movement may facilitate sensorimotor areas over the contralateral hemisphere.
... Individual channels with substantial amounts of observable noise were excluded from further processing; seven parieto-occipital channels and all four frontal channels were retained for each participant. Principle components analysis (PCA) filtering removed spatial eigenvector components accounting for 80% of the covariance across the data, which is associated with the presence of motion artefacts [55]. Approximately one component (or fewer) was removed from each data set. ...
Article
Full-text available
EEG, fMRI and TMS studies have implicated the extra-striate cortex, including the Lateral Occipital Cortex (LOC), in the processing of visual mirror symmetries. Recent research has found that the sustained posterior negativity (SPN), a symmetry specific electrophysiological response identified in the region of the LOC, is generated when temporally displaced asymmetric components are integrated into a symmetric whole. We aim to expand on this finding using dynamic dot-patterns with systematically increased intra-pair temporal delay to map the limits of temporal integration of visual mirror symmetry. To achieve this, we used functional near-infrared spectroscopy (fNIRS) which measures the changes in the haemodynamic response to stimulation using near infrared light. We show that a symmetry specific haemodynamic response can be identified following temporal integration of otherwise meaningless dot-patterns, and the magnitude of this response scales with the duration of temporal delay. These results contribute to our understanding of when and where mirror symmetry is processed in the visual system. Furthermore, we highlight fNIRS as a promising but so far underutilised method of studying the haemodynamics of mid-level visual processes in the brain.
... 11,12 Discriminant analysis with statistical approaches was able to reveal significant discrepancies in the probability distributions of signal patterns between people with MDD (MDDs) and healthy controls (HCs), however, it remains insufficient to use fNIRS to establish the diagnosis of MDD. 13 In recent years, machine learning (ML) has been explored from classification to treatment outcomes prediction for psychiatric disorders due to its ability in automatically learning from empirical data to recognize complex patterns. 14,15 Despite early concept studies did not include external validation due to the lack of an independent dataset, 16,17 the preliminary results still indicated that machine learning is a promising analysis tool for discovering biomarkers and elucidating pathophysiology of MDD. ...
Article
Full-text available
Background Early diagnosis of major depressive disorder (MDD) could enable timely interventions and effective management which subsequently improve clinical outcomes. However, quantitative and objective assessment tools for the suspected cases who present with depressive symptoms have not been fully established. Methods Based on a large-scale dataset (n = 363 subjects) collected with functional near-infrared spectroscopy (fNIRS) measurements during the verbal fluency task (VFT), this study proposed a data representation method for extracting spatiotemporal characteristics of NIRS signals, which emerged as candidate predictors in a two-phase machine learning framework to detect distinctive biomarkers for MDD. Supervised classifiers (e.g., support vector machine (SVM), k-nearest neighbors (KNN)) cooperated with cross-validation were implemented to evaluate the predictive capability of selected features in a training set. Another test set that was not involved in developing the algorithms enabled the independent assessment of the model's generalization. Findings For the classification with the optimal fusion features, the SVM classifier achieved the highest accuracy of 75.6% ± 4.7% in the nested cross-validation, and the correct prediction rate of 78.0% with a sensitivity of 75.0% and a specificity of 81.4% in the test set. Moreover, the multiway ANOVA test on clinical and demographic factors confirmed that twenty out of 39 optimal features were significantly correlated with the MDD-distinctive consequence. Interpretation The abnormal prefrontal activity of MDD may be quantified as diminished relative intensity and inappropriate activation timing of hemodynamic response, resulting in an objectively measurable biomarker for assessing cognitive deficits and screening MDD at the early stage. Funding This study was funded by NUS iHeathtech Other Operating Expenses (R-722-000-004-731).
... where ∆µ a,j represents the absorption change in layer j (of N) and L j is the mean partial pathlength (MPPL) of photons in the same layer. If the absorption process during a measurement at wavelength λ is supposed to be ruled only by HbO and HbR concentration changes, then for layer j it is possible to write [9]: ...
Article
Full-text available
Functional near infrared spectroscopy (fNIRS) is a valuable tool for assessing oxy- and deoxyhemoglobin concentration changes (Δ[HbO] and Δ[HbR], respectively) in the human brain. To this end, photon pathlengths in tissue are needed to convert from light attenuation to Δ[HbO] and Δ[HbR]. Current techniques describe the human head as a homogeneous medium, in which case these pathlengths are easily computed. However, the head is more appropriately described as a layered medium; hence, the partial pathlengths in each layer are required. The current way to do this is by means of Monte Carlo (MC) simulations, which are time-consuming and computationally expensive. In this work, we introduce an approach to theoretically calculate these partial pathlengths, which are computed several times faster than MC simulations. Comparison of our approach with MC simulations show very good agreement. Results also suggest that these analytical expressions give much more specific information about light absorption in each layer than in the homogeneous case.
... Thus, based on its advantages and fewer limitations on subjects, especially allowing variance in motion artifacts, it has a wide range of use in patient groups like infants and neurological patients. fNIRS signals contain various types of noise [8][11]- [13], including those of instrumental due to interfaces in a surrounding environment like external light and devices, experimental noise, motion artifacts, and contact pressure between scalp and optode that would change due to movement and physiological noise [8], [10], [11], [14]. fNIRS also has several limitations like depth sensitivity and coverage surface [6], which cause this technology is not appropriate to detecting deep brain structures and cortical neuronal systems. ...
Conference Paper
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In brain-computer interface (BCI) systems, finding an optimal channel set to decrease the cost of computation and portability of the signal acquisition system to achieve higher classification accuracy is vital. This study presents a multiobjective meta-heuristic algorithm to select optimal channels in the multi-channel fNIRS signals. We proposed non-dominated sorting multi-objective Genetic Algorithm (NSGA-II) to perform channel selection. We will find an optimal solution to the channel selection problem in brain-computer interface (BCI) systems to obtain the best channels in the multi-channel fNIRS signal dataset.Toward the classification of the fNIRS signals, a preprocessing task should be applied, followed by selecting the channels set and extracting related features of every trial. In the next step, in order to apply the feature selection method, the mRmR algorithm was applied. Classification accuracy with 10-fold cross-validation is then performed as an objective for the presented algorithm to select the best accuracy and best channel set. Finally, the results illustrate that the proposed selected method obtained an average of 27.25 best optimal channels per subject. Moreover, classification results are 67.9±11% for the subjects. It was found that the LDA classification method resulted in the best performance compared to other methods.
... Channels with low amplitude were excluded from group processing. Signals were then processed by the principal component analysis method to remove any systematic artifacts according to methods described in previous literature (Tak & Ye, 2014). The motion artifact threshold of more than 15 standard deviations from the mean were identified and replaced with spline interpolation based on preceding and subsequent segment of the signals. ...
Article
We investigated prefrontal cortex (PFC) hemodynamic response, through functional near infrared spectroscopy (fNIRS) during executive function (EF) processing in response to acute high intensity intermittent exercise (HIIE) in young adults. We also assessed the associated sex differences in the cognitive scores and related PFC hemodynamic functions in response to HIIE. 49 young healthy adult participants (32 women, 17 men) were randomly assigned to either control or HIIE intervention groups. HIIE group participants performed 4 × 4 minutes of HIIE on cycle ergometer with 3 minutes of active recovery between the bouts; control group relaxed for the time equivalent to intervention. fNIRS data was collected during the performance of the EF tests including Color Word Stroop Test (CWST) and Trail Making Test (TMT) in pre and post sessions in both the groups. Results indicated a significant change in the hemodynamic response in the form of increased oxygenated and decreased deoxygenated hemoglobin in the PFC areas specific to the EF tasks, with improved CWST and TMT scores in response to HIIE intervention. PFC activation was different in men and women in response to HIIE, however similar scores of task performance were observed in men and women during the performance of executive functions in response to HIIE. The study concludes that an acute HIIE session improves executive function which is associated with an increase activation of PFC. Sex differences exist in the activation of PFC in response to HIIE during EF processing. Our study adds to the current evidence regarding exercise and cognition.
... 31 Random fields have been used for human task-based imaging with fNIRS. 27,32 Application of these techniques to resting-state functional connectivity data is difficult, as the statistical space (the correlation matrix) is now NðN − 1Þ, where N is the dimensionality of the images. 15,33 The relevant parameter is the covariance of different regions in the correlation matrix; any individual column or row may be a random field, whereas the entire matrix may have a different excursion set. ...
Article
Significance: Resting-state functional connectivity imaging in mice with optical intrinsic signal (OIS) imaging could provide a powerful translational tool for developing imaging biomarkers in preclinical disease models. However, statistical interpretation of correlation coefficients is hampered by autocorrelations in the data. Aim: We sought to better understand temporal and spatial autocorrelations in optical resting-state data. We then adapted statistical methods from functional magnetic resonance imaging to improve statistical inference. Approach: Resting-state data were obtained from mice using a custom-built OSI system. The autocorrelation time was calculated at each pixel, and z scores for correlation coefficients were calculated using Fisher transforms and variance derived from either Bartlett's method or xDF. The significance of each correlation coefficient was determined through control of the false discovery rate (FDR). Results: Autocorrelation was generally even across the cortex and parcellation reduced variance. Correcting variance with Bartlett's method resulted in a uniform reduction in z scores, with xDF preserving high z scores for highly correlated data. Control of the FDR resulted in reasonable thresholding of the correlation coefficient matrices. The use of Bartlett's method compared with xDF results in more conservative thresholding and fewer false positives under null hypothesis conditions. Conclusions: We developed streamlined methods for control of autocorrelation in OIS functional connectivity data in mice, and Bartlett's method is a reasonable compromise and simplification that allows for accurate autocorrelation correction. These results improve the rigor and reproducibility of functional neuroimaging in mice.
... The fNIRS signal consists of both ∆HbO and ∆HbR regarding artifacts and heartbeat(1-1.5HZ) ), in addition to the respiration (0.2-0.5HZ) in trials [3], [4], [15]. Each trial is filtered by the Butterworth filter with degree 3 and frequency domain [0.01,0.2] ...
Conference Paper
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Brain-Computer-Interface systems were invented in the last decade to record brain signals and then control a system that behaves and conveys with a biosignal recording device and the brain. Its major objective is to aid individuals who suffer from behavioral infirmity. The focus of this research is to analyze the cortical surface of the brain's hemodynamic response using functional near-infrared spectroscopy signals (fNIRS). It is utilized in a variety of cognitive neuroscience and behavioral rehabilitation treatments. Additionally, it was applied to classify thirty participants who volunteered to do a task divided into three classes. The primary task is to classify multi-class fNIRS signals using various classification methods and then compare the results.We utilized classification methods for each of the 30 subjects,followed by the voting and stacking procedures as part of an ensemble learning method. The averaged results for all subjects reached 64.813 percent, while ensemble learning using the voting method reached 66.416 percent. Following that, ensemble learning using the stacking method combined with the ANFIS kernel reached 60.6616 percent. Finally, the findings suggest that it may improve accuracy and reduce standard deviation depending on the Ensemble Learning approach used. It asserts that when the variance of the predictions was reduced, the classification model produced better results.
... neuronal vs systemic), (ii) spatial locations (axially for tissue layers i.e. cerebral vs extracerebral or laterally i.e. left vs right hemispheres), and (iii) experimental or task related elements (i.e. evoked vs resting state or with vs. without stressors) [14][15][16][17][18] . Therefore, extraction of the signal of interest requires application of appropriate signal processing techniques, which first entails thorough characterization and analysis of signal components arising as a result of the task being studied. ...
Preprint
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Functional near infrared spectroscopy (fNIRS) measurements are confounded by signal components originating from multiple physiological causes, whose activities may vary temporally and spatially (across tissue layers, and regions of the cortex). Furthermore, the stimuli can induce evoked effects, which may lead to over or underestimation of the actual effect of interest. Here, we conducted a temporal, spectral, and spatial analysis of fNIRS signals collected during cognitive and hypercapnic stimuli to characterize effects of functional versus systemic responses. We utilized wavelet analysis to discriminate physiological causes and employed long and short source-detector separation (SDS) channels to differentiate tissue layers. Multi-channel measures were analyzed further to distinguish hemispheric differences. The results highlight cardiac, respiratory, myogenic, and very low frequency (VLF) activities within fNIRS signals. Regardless of stimuli, activity within VLF band had the largest contribution to the overall signal. The systemic activities dominated the measurements from the short SDS channels during cognitive stimulus, but not hypercapnic stimulus. Importantly, results indicate that characteristics of fNIRS signals vary with type of the stimuli administered as cognitive stimulus elicited variable responses between hemispheres in VLF band and task-evoked temporal effect in VLF, myogenic and respiratory bands, while hypercapnic stimulus induced a global response across both hemispheres.
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Background Observation of real-time neural characteristics during gameplay would provide distinct evidence for discriminating the currently controversial diagnosis of internet gaming disorder (IGD), and elucidate neural mechanisms that may be involved in addiction. We aimed to provide preliminary findings on possible neural features of IGD during real-time internet gaming using functional near-infrared spectroscopy (fNIRS). Methods Prefrontal cortical activations accompanying positive and negative in-game events were investigated. Positive events: (1) participant’s champion slays or assists in slaying an opponent without being slain. (2) the opposing team’s nexus is destroyed. Negative events: (1) participant’s champion is slain without slaying or assisting in slaying any opponent. (2) the team’s nexus is destroyed. Collected data were compared between the IGD group and control group, each with 15 participants. Results The IGD group scored significantly higher than the CTRL group on the craving scale. Following positive events, the IGD group displayed significantly stronger activation in the DLPFC. Following negative events, the IGD group displayed significantly weaker activation in the lateral OFC. Discussion and Conclusions Individuals scoring high on the IGD scale may crave for more internet gaming after encountering desired events during the game. Such observations are supported by the correlation between the craving scale and DLPFC activation. The IGD group may also show diminished punishment sensitivity to negative in-game experiences rendering them to continue playing the game. The present study provides preliminary evidence that IGD may demonstrate neural characteristics observed in other addictive disorders and suggests the use of fNIRS in behavioral addiction studies.
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Functional near-infrared spectroscopy (fNIRS) is an increasingly used technology for imaging neural correlates of cognitive processes. However, fNIRS signals are commonly impaired by task-evoked and spontaneous hemodynamic oscillations of non-cerebral origin, a major challenge in fNIRS research. In an attempt to isolate the task-evoked cortical response, we investigated the coupling between hemodynamic changes arising from superficial and deep layers during mental effort. For this aim, we applied a rhythmic mental arithmetic task to induce cyclic hemodynamic fluctuations suitable for effective frequency-resolved measurements. Twenty university students aged 18-25 years (eight males) underwent the task while hemodynamic changes were monitored in the forehead using a newly developed NIRS device, capable of multi-channel and multi-distance recordings. We found significant task-related fluctuations for oxy- and deoxy-hemoglobin, highly coherent across shallow and deep tissue layers, corroborating the strong influence of surface hemodynamics on deep fNIRS signals. Importantly, after removing such surface contamination by linear regression, we show that the frontopolar cortex response to a mental math task follows an unusual inverse oxygenation pattern. We confirm this finding by applying for the first time an alternative method to estimate the neural signal, based on transfer function analysis and phasor algebra. Altogether, our results demonstrate the feasibility of using a rhythmic mental task to impose an oscillatory state useful to separate true brain functional responses from those of non-cerebral origin. This separation appears to be essential for a better understanding of fNIRS data and to assess more precisely the dynamics of the neuro-visceral link.
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Previous studies revealed a close relationship between retrieval ability and creative thinking; however, it is still unclear what processes of creative thinking are influenced by retrieval ability. This study applied a novel task paradigm to distinguish between different processes of creative thinking. We used functional near-infrared spectroscopy (fNIRS) to explore the differences of cortical activation and functional connectivity in the prefrontal cortex (PFC), temporoparietal junction (TPJ) and temporal cortex between high (HRA) and low (LRA) retrieval ability groups during creating original ideas (CO) and recalling original ideas (RO) tasks. The behaviour results revealed that in the CO task, the HRA group performed better than the LRA group on fluency, flexibility, and originality. Importantly, the fNIRS results further indicated that the HRA group exhibited higher activation of the l-TPJ, l-STG, l-MTG, r-FPC, r-DLPFC than the LRA group during the CO task. Moreover, the HRA group exhibited higher activation of the bilateral TPJ, l-STG, l-MTG, r-DLPFC, and r-FPC in the CO task than in the RO task, and the LRA group exhibited higher activation of the l-STG in the CO task than in the RO task. The functional connectivity between the PFC and IFG, TPJ, and MTG of the HRA group was significantly stronger than that of the LRA group in both the CO and RO tasks. The findings suggest that high retrieval ability could facilitate the generation of creative ideas by facilitating the retrieval of novel information and suppression of common information compared to low retrieval ability. This study provides neural evidence for the effect of different levels of retrieval ability on creative thinking.
Chapter
Advancements in Microelectromechanical systems (MEMS) have enabled the manufacture of affordable and efficient wearable devices. In sensor-based gait analysis, motion and biofeedback sensor devices are easily attached to different parts of the body. Instrumentation of gait using different sensor technologies enables researchers and clinicians to capture high-resolution quantitative motion data within and beyond the lab. Integration of advanced sensor technologies provides objective and rater-independent multimodal outcomes that complement established clinical examination. Multi-modal data capture in ecologically valid, patient-relevant habitual settings opens new possibilities to monitor fluctuating and rare incidents by informing different aspects of impaired gait. Interconnected device communication and the Internet of Things (IoT) provide the infrastructural platform to enable remote gait assessment. However, an extended period of motion data recorded by different sensor technologies results in a vast amount of unlabelled data. Computational methods and artificial intelligence techniques (e.g., data mining) provide opportunities to manage data collected in unsupervised environments. Although technological advancement and algorithms promote remote gait assessment, more work needs to be done in terms of analytical and clinical validation to achieve robust and reliable gait analysis tools that contribute to better rehabilitation and treatment.
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Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging technique that may serve as a methodological tool for studying how sociocultural contexts can shape the human brain and impact cognition and behavior. The use of fNIRS in community-based research may (a) advance theoretical knowledge in psychology and neuroscience, particularly regarding underrepresented ethnic-racial communities; (b) increase diversity in samples; and (c) provide neurobiological evidence of sociocultural factors supporting human development. The review aims to introduce the use of fNIRS, including its practicalities and limitations, to new adopters inquiring how sociocultural inputs affect the brain. The review begins with an introduction to cultural neuroscience, and a review on the use of fNIRS follows. Next, benefits and guidelines to the design of fNIRS research in naturalistic environments (in the community or in the field) using a cultural lens are discussed. Strengths-based and community-based approaches in cultural neuroscience are recommended throughout. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Neurovascular coupling is a key physiological mechanism that occurs in the healthy human brain, and understanding this process has implications for understanding the aging and neuropsychiatric populations. Combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has emerged as a promising, noninvasive tool for probing neurovascular interactions in humans. However, the utility of this approach critically depends on the methodological quality used for multimodal integration. Despite a growing number of combined EEG–fNIRS applications reported in recent years, the methodological rigor of past studies remains unclear, limiting the accurate interpretation of reported findings and hindering the translational application of this multimodal approach. To fill this knowledge gap, we critically evaluated various methodological aspects of previous combined EEG–fNIRS studies performed in healthy individuals. A literature search was conducted using PubMed and PsycINFO on June 28, 2021. Studies involving concurrent EEG and fNIRS measurements in awake and healthy individuals were selected. After screening and eligibility assessment, 96 studies were included in the methodological evaluation. Specifically, we critically reviewed various aspects of participant sampling, experimental design, signal acquisition, data preprocessing, outcome selection, data analysis, and results presentation reported in these studies. Altogether, we identified several notable strengths and limitations of the existing EEG–fNIRS literature. In light of these limitations and the features of combined EEG–fNIRS, recommendations are made to improve and standardize research practices to facilitate the use of combined EEG–fNIRS when studying healthy neurovascular coupling processes and alterations in neurovascular coupling among various populations.
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Fair distribution of resources matters to both individual interests and group harmony during social cooperation. Different allocation rules, including equity- and equality-based rules, have been widely discussed in reward allocation research; however, it remains unclear whether and how individuals’ cooperative manner, such as interpersonal coordination, influence their subsequent responsibility attribution and reward allocation. Here, 46 dyads conducted a time estimation task—either synergistically (the coordination group) or solely (the control group)—while their brain activities were measured using a functional near-infrared spectroscopy hyperscanning approach. Dyads in the coordination group showed higher behavioral synchrony and higher interpersonal brain synchronization (IBS) in the dorsal lateral prefrontal cortex (DLPFC) during the time estimation task than those in the control group. They also showed a more egalitarian tendency of responsibility attribution for the task outcome. More importantly, dyads in the coordination group who had higher IBS in the dorsal medial prefrontal cortex (DMPFC) were more inclined to make egalitarian reward allocations, and this effect was mediated by responsibility attribution. Our findings elucidate the influence of interpersonal coordination on reward allocation and the critical role of the prefrontal cortex in these processes.
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Functional Near-Infrared Spectroscopy (fNIRS) captures activations and inhibitions of cortical areas and implements a viable approach to neuromonitoring in clinical research. Compared to more advanced methods, continuous wave fNIRS (CW-fNIRS) is currently used in clinics for its simplicity in mapping the whole sub-cranial cortex. Conversely, it often lacks hardware reduction of confounding factors, stressing the importance of a correct signal processing. The proposed pipeline includes movement artifact reduction (MAR), bandpass filtering (BPF), and principal component analysis (PCA). Eight MAR algorithms were compared among 23 young adult volunteers under motor-grasping task. Single-subject examples are shown followed by the percentage in energy reduction (ERD%) statistics by single steps and cumulative values. The block average of the hemodynamic response function was compared with generalized linear model fitting. Maps of significant activation/inhibition were illustrated. The mean ERD% of pre-processed signals concerning the initial raw signal energy reached 4%. A tested multichannel MAR variant showed overcorrection on 4-fold more expansive windows. All of the MAR algorithms found similar activations in the contralateral motor area. In conclusion, single channel MAR algorithms are suggested followed by BPF and PCA. The importance of whole cortex mapping for fNIRS integration in clinical applications was also confirmed by our results.
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Functional near‐infrared spectroscopy (fNIRS) is a noninvasive optical brain‐imaging technique that detects changes in hemoglobin concentration in the cerebral cortex. fNIRS devices are safe, silent, portable, robust against motion artifacts, and have good temporal resolution. fNIRS is reliable and trustworthy, as well as an alternative and a complement to other brain‐imaging modalities, such as electroencephalography or functional magnetic resonance imaging. Given these advantages, fNIRS has become a well‐established tool for neuroscience research, used not only for healthy cortical activity but also as a biomarker during clinical assessment in individuals with schizophrenia, major depressive disorder, bipolar disease, epilepsy, Alzheimer's disease, vascular dementia, and cancer screening. Owing to its wide applicability, studies on fNIRS have increased exponentially over the last two decades. In this study, scientific publications indexed in the Web of Science databases were collected and a bibliometric‐type methodology was developed. For this purpose, a comprehensive science mapping analysis, including top‐ranked authors, journals, institutions, countries, and co‐occurring keywords network, was conducted. From a total of 2310 eligible documents, 6028 authors and 531 journals published fNIRS‐related papers, Fallgatter published the highest number of articles and was the most cited author. University of Tübingen in Germany has produced the most trending papers since 2000. USA was the most prolific country with the most active institutions, followed by China, Japan, Germany, and South Korea. The results also revealed global trends in emerging areas of research, such as neurodevelopment, aging, and cognitive and emotional assessment.
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Functional near-infrared spectroscopy is a noninvasive optical imaging technique to register brain activity. It utilizes near-infrared light to evaluate the oxygenated (HbO) and deoxygenated hemoglobin concentration. Here, we used HbO and HbR to analyze the oxygen saturation and electromyographic signals to study muscle activity during the single left- and right-hand movements. Sixteen right-handed volunteers participated in the experiment. During the active phase of the experiment, the subject was asked to perform movements with his left or right hand according to the screen instructions. There were 40 total hand movement trials (20 for each hand) that were performed in random order. The oxygen saturation increased contralaterally, peaking at about 6 s post-command onset and then decreased, reaching baseline level at 12 s. The maximal amplitudes appeared in the primary motor (M1) cortex in the hemisphere contralateral to the performing limb. In the left hemisphere, the right hand induced a higher response than the left hand. In the right hemisphere, the response amplitude remains similar for both hands. We hypothesized that the right hand being a dominant hand in the group may require additional neuronal recruitment in the contralateral M1 cortex.
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Rostral PFC (area 10) activation is common during prospective memory (PM) tasks. But it is not clear what mental processes these activations index. Three candidate explanations from cognitive neuroscience theory are: (i) monitoring of the environment; (ii) spontaneous intention retrieval; (iii) a combination of the two. These explanations make different predictions about the temporal and spatial patterns of activation that would be seen in rostral PFC in naturalistic settings. Accordingly, we plotted functional events in PFC using portable fNIRS while people were carrying out a PM task outside the lab and responding to cues when they were encountered, to decide between these explanations. Nineteen people were asked to walk around a street in London, U.K. and perform various tasks while also remembering to respond to prospective memory (PM) cues when they detected them. The prospective memory cues could be either social (involving greeting a person) or non-social (interacting with a parking meter) in nature. There were also a number of contrast conditions which allowed us to determine activation specifically related to the prospective memory components of the tasks. We found that maintaining both social and non-social intentions was associated with widespread activation within medial and right hemisphere rostral prefrontal cortex (BA 10), in agreement with numerous previous lab-based fMRI studies of prospective memory. In addition, increased activation was found within lateral prefrontal cortex (BA 45 and 46) when people were maintaining a social intention compared to a non-social one. The data were then subjected to a GLM-based method for automatic identification of functional events (AIDE), and the position of the participants at the time of the activation events were located on a map of the physical space. The results showed that the spatial and temporal distribution of these events was not random, but aggregated around areas in which the participants appeared to retrieve their future intentions (i.e., where they saw intentional cues), as well as where they executed them. Functional events were detected most frequently in BA 10 during the PM conditions compared to other regions and tasks. Mobile fNIRS can be used to measure higher cognitive functions of the prefrontal cortex in “real world” situations outside the laboratory in freely ambulant individuals. The addition of a “brain-first” approach to the data permits the experimenter to determine not only when haemodynamic changes occur, but also where the participant was when it happened. This can be extremely valuable when trying to link brain and cognition.
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Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.
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The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics. We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques. Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.
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Near infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI) both allow non-invasive monitoring of cerebral cortical oxygenation responses to various stimuli. To compare these methods in elderly subjects and to determine the effect of age on cortical oxygenation responses, we determined motor-task-related changes in deoxyhemoglobin concentration ([HHb]) over the left motor cortex in six healthy young subjects (age 35 ± 9 years, mean ± SD) and five healthy elderly subjects (age 73 ± 3 years) by NIRS and blood-oxygen-level-dependent (BOLD) fMRI simultaneously. The motor-task consisted of seven cycles of 20-sec periods of contralateral finger-tapping at a rate as fast as possible alternated with 40-sec periods of rest. Time-locked averages over the seven cycles were used for further analysis. Task-related decreases in [HHb] over the motor cortex were measured by NIRS, with maximum changes of −0.83 ± 0.38 μmol/L (P < 0.01) for the young and −0.32 ± 0.17 μmol/L (P < 0.05) for the elderly subjects. The BOLD-fMRI signal increased over the cortex volume under investigation with NIRS, with maximum changes of 2.11 ± 0.72% (P < 0.01) for the young and 1.75 ± 0.71% (P < 0.01) for the elderly subjects. NIRS and BOLD-fMRI measurements showed good correlation in the young (r = −0.70, r2 = 0.48, P < 0.001) and elderly subjects (r = −0.82, r2 = 0.67, P < 0.001). Additionally, NIRS measurements demonstrated age-dependent decreases in task-related cerebral oxygenation responses (P < 0.05), whereas fMRI measurements demonstrated smaller areas of cortical activation in the elderly subjects (P < 0.05). These findings demonstrate that NIRS and fMRI similarly assess cortical oxygenation changes in young subjects and also in elderly subjects. In addition, cortical oxygenation responses to brain activation alter with aging. Hum. Brain Mapping 16:14–23, 2002. © 2002 Wiley-Liss, Inc.
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Background: This study assesses the utility of a hybrid optical instrument for noninvasive transcranial monitoring in the neurointensive care unit. The instrument is based on diffuse correlation spectroscopy (DCS) for measurement of cerebral blood flow (CBF), and near-infrared spectroscopy (NIRS) for measurement of oxy- and deoxy-hemoglobin concentration. DCS/NIRS measurements of CBF and oxygenation from frontal lobes are compared with concurrent xenon-enhanced computed tomography (XeCT) in patients during induced blood pressure changes and carbon dioxide arterial partial pressure variation. Methods: Seven neurocritical care patients were included in the study. Relative CBF measured by DCS (rCBF(DCS)), and changes in oxy-hemoglobin (DeltaHbO(2)), deoxy-hemoglobin (DeltaHb), and total hemoglobin concentration (DeltaTHC), measured by NIRS, were continuously monitored throughout XeCT during a baseline scan and a scan after intervention. CBF from XeCT regions-of-interest (ROIs) under the optical probes were used to calculate relative XeCT CBF (rCBF(XeCT)) and were then compared to rCBF(DCS). Spearman's rank coefficients were employed to test for associations between rCBF(DCS) and rCBF(XeCT), as well as between rCBF from both modalities and NIRS parameters. Results: rCBF(DCS) and rCBF(XeCT) showed good correlation (r (s) = 0.73, P = 0.010) across the patient cohort. Moderate correlations between rCBF(DCS) and DeltaHbO(2)/DeltaTHC were also observed. Both NIRS and DCS distinguished the effects of xenon inhalation on CBF, which varied among the patients. Conclusions: DCS measurements of CBF and NIRS measurements of tissue blood oxygenation were successfully obtained in neurocritical care patients. The potential for DCS to provide continuous, noninvasive bedside monitoring for the purpose of CBF management and individualized care is demonstrated.
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Near infrared spectroscopy (NIRS) is rapidly gaining popularity for functional brain imaging. It is well suited to studies of patients or children; however, in these populations particularly, motion artifacts can present a problem. Here, we propose the use of imaging channels with negligible distance between light source and detector to detect subject motion, without the need for an additional motion sensor. Datasets containing deliberate motion artifacts were obtained from three subjects. Motion artifacts could be detected in the signal from the co-located channels with a minimum sensitivity of 0.75 and specificity of 0.98. Five techniques for removing motion artifact from the functional signals were compared, namely two-input recursive least squares (RLS) adaptive filtering, wavelet-based filtering, independent component analysis (ICA), and two-channel and multiple-channel regression. In most datasets, the median change in SNR across all channels was the greatest using ICA or multiple-channel regression. RLS adaptive filtering produced the smallest increase in SNR. Where sharp spikes were present, wavelet filtering produced the largest SNR increase. ICA and multiple-channel regression are promising ways to reduce motion artifact in functional NIRS without requiring time-consuming manual techniques.
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The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses – the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferroni-type procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
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In this paper, we formulate diffuse optical tomography (DOT) problems as a source localization problem and propose a MUltiple SIgnal Classification (MUSIC) algorithm for functional brain imaging application. By providing MUSIC spectra for major chromophores such as oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), we are able to investigate the spatial distribution of brain activities. Moreover, the false discovery rate (FDR) algorithm can be applied to control the family-wise error in the MUSIC spectra. The minimum distance between the center of mass in DOT and the Montreal Neurological Institute (MNI) coordinates of target regions in experiments was between approximately 6 and 18 mm, and the displacement of the center of mass in DOT and fMRI ranged between 12 and 28 mm, which demonstrate the legitimacy of the DOT-based imaging. The proposed brain mapping method revealed its potential as an alternative algorithm to monitor the brain activation.
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In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.
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The blood-oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigated the spatial and temporal characteristics of the individual noise processes by conducting concurrent near-infrared spectroscopy (NIRS) and fMRI studies on six subjects during a resting state acquisition. Three time series corresponding to LFO, respiration, and cardiac pulsation were extracted by frequency from the NIRS signal (which has sufficient temporal resolution to critically sample the cardiac waveform) and used as regressors in a BOLD fMRI analysis. Our results suggest that LFO and cardiac signals modulate the BOLD signal independently through the circulatory system. The spatiotemporal evolution of the LFO signal in the BOLD data correlates with the global cerebral blood flow. Near-infrared spectroscopy can be used to partition these contributing factors and independently determine their contribution to the BOLD signal.
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