Near-infrared Spectroscopy Signals Research Articles

Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.

Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.

Cerebral autoregulation metrics correlate with some major morbidity and mortality outcomes after cardiac surgery

Background: Personalized arterial blood pressure management based on cerebral autoregulation has shown clinical relevance in cardiac surgery. Here we explore the associations between lower limit of cerebral autoregulation (LLA) metrics and postoperative outcomes in a heterogenous and high-acuity population of patients undergoing high-risk cardiovascular surgery. Methods: Autoregulation parameters were determined retrospectively in 686 patients undergoing cardiac surgery at UAB Hospital. Cerebral oximetry index (COx) and hemoglobin volume reactivity index (HVx) determine a patient’s unique LLA by examining the correlation between mean arterial pressure (MAP) and near-infrared spectroscopy signals. Logistic regression analyses explored associations of area under the time-pressure curve and duration below LLA with stroke, acute kidney injury, operative mortality, low cardiac output syndrome, and mechanical ventilation lasting more than 48 hours. Any incidence of these outcomes was considered as the primary outcome of interest: major morbidity or operative mortality (MMOM). Results: We explored the association of Area under the time-pressure curve for pressures less than patient-specific Lower Limits of Autoregulation (AUCABP<LLA: and MMOM. Results are reported with odds ratio (OR) and 95% confidence intervals (CI). AUCABP<LLA was not significantly associated with MMOM incidence (HVx: Odds Ratio 1.000, 95% CI 0.970-1.030, p=0.979; COx: OR 1.006, 95% CI 0.990-1.023, p=0.456). However, for components of MMOM, specifically operative mortality (HVx: OR 1.063, 95% CI 1.013-1.115, p=0.013; COx: OR 1.027, 95% CI 1.001-1.052, p=0.038) and low cardiac output syndrome (HVx: OR 1.036, 95% CI 1.004-1.068, p=0.025), there were statistically significant relationships with AUCABP<LLA when controlling for logistic Euroscore and preoperative hemoglobin levels. Conclusions: Area under the time-pressure curve for pressures less than patient-specific LLA was not significantly associated with MMOM incidence. However, we found significant associations between components of MMOM, specifically operative mortality and low cardiac output syndrome, with an AUCABP<LLA and time under LLA. These results suggest that LLA metrics might have utility in predicting specific postoperative outcomes, but their role in predicting overall MMOM may be limited in heterogenous and high-acuity patient populations. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Transmission of slow waves in Masimo O3 near infrared spectroscopy measures

IntroductionCerebral autoregulation (CA) dysfunction is a key complication following brain injury. CA assessment using near-infrared spectroscopy (NIRS) offers a promising alternative to the current non-invasive standard, cerebral blood flow velocity (CBFV) measured with transcranial Doppler. Research questionCan autoregulatory slow waves (frequency range 0.005–0.05 Hz) associated with spontaneous and induced changes in ABP in healthy volunteers be detected by parameters measured with the Masimo O3 NIRS device? MethodsABP, CBFV and Masimo O3 parameters were measured in 10 healthy volunteers at baseline and during ABP oscillations induced by squat/stand manoeuvres. Transmission of slow waves was assessed with power spectral density and coherence analysis in NIRS signals and compared to that of CBFV. ResultsAt baseline, slow waves were detected with sufficient power that substantially exceeded the signals’ measurement resolution in all parameters except cerebral oxygen saturation. During ABP oscillations in the 0.033 Hz range (induced by squat/stand), the power of slow waves increased in all parameters in a similar pattern, with total (cHb) and oxygenated (O2Hb) haemoglobin concentrations most closely mirroring CBFV (median standardised power [first-third quartile], baseline vs squat/stand: CBFV 0.35 [0.28–0.42] vs 0.50 [0.45–0.62], O2Hb 0.47 [0.33–0.68] vs 0.61 [0.59–0.69]). Coherence with ABP increased for both CBFV and NIRS measures from low at baseline (<0.4) to high during induced changes (>0.8). ConclusionSpontaneous fluctuations in ABP can be observed in analysed Masimo O3 metrics to a varying degree. The clinical utility of Masimo O3 signals in CA assessment requires further investigation in brain injury patients.

A Quantitative Assessment of Cerebral Hemodynamic Perturbations Associated with Long R-R Intervals in Atrial Fibrillation: A Pilot-Case-Based Experience.

Background and Objectives: Atrial fibrillation (AF) results in systemic hemodynamic perturbations which impact cerebral circulation, possibly contributing to the development of dementia. However, evidence documenting effects in cerebral perfusion is scarce. The aim of this study is to provide a quantitative characterization of the magnitude and time course of the cerebral hemodynamic response to the short hypotensive events associated with long R-R intervals, as detected by near-infrared spectroscopy (NIRS). Materials and Methods: Cerebral NIRS signals and arterial blood pressure were continuously recorded along with an electrocardiogram in twelve patients with AF undergoing elective electrical cardioversion (ECV). The top 0.5-2.5% longest R-R intervals during AF were identified in each patient and used as triggers to carry out the triggered averaging of hemodynamic signals. The average curves were then characterized in terms of the latency, magnitude, and duration of the observed effects, and the possible occurrence of an overshoot was also investigated. Results: The triggered averages revealed that long R-R intervals produced a significant drop in diastolic blood pressure (-13.7 ± 6.1 mmHg) associated with an immediate drop in cerebral blood volume (THI: -0.92 ± 0.46%, lasting 1.9 ± 0.8 s), followed by a longer-lasting decrease in cerebral oxygenation (TOI: -0.79 ± 0.37%, lasting 5.2 ± 0.9 s, p < 0.01). The recovery of the TOI was generally followed by an overshoot (+1.06 ± 0.12%). These effects were progressively attenuated in response to R-R intervals of a shorter duration. Conclusions: Long R-R intervals cause a detectable and consistent cerebral hemodynamic response which concerns both cerebral blood volume and oxygenation and outlasts the duration of the systemic perturbation. These effects are compatible with the activation of dynamic autoregulatory mechanisms in response to the hypotensive stimulus.

Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features

In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.

Methodological considerations on near-infrared spectroscopy derived muscle oxidative capacity.

Different strategies for near-infrared spectroscopy (NIRS)-derived muscle oxidative capacity assessment have been reported. This study compared and evaluated (I) approaches for averaging trials; (II) NIRS signals and blood volume correction equations; (III) the assessment of vastus lateralis (VL) and tibialis anterior (TA) muscles in two fitness levels groups. Thirty-six participants [18 chronically trained (CT: 14 males, 4 females) and 18 untrained (UT: 10 males, 8 females)] participated in this study. Two trials of twenty transient arterial occlusions were performed for NIRS-derived muscle oxidative capacity assessment. Muscle oxygen consumption ([Formula: see text]O2m) was estimated from deoxygenated hemoglobin (HHb), corrected for blood volume changes following Ryan (HHbR) and Beever (HHbB) equations, and from oxygen saturation (StO2) in VL and TA. Superimposing or averaging [Formula: see text]O2m or averaging the rate constants (k) from the two trials resulted in equivalent k values [two one-sided tests (TOST) procedure with 5% equivalence margin-P < 0.001]. Whereas HHbR (2.35 ± 0.61min-1) and HHbB (2.34 ± 0.58min-1) derived k were equivalent (P < 0.001), StO2 derived k (2.81 ± 0.92min-1) was greater (P < 0.001) than both. k values were greater in CT vs UT in both muscles (VL: + 0.68min-1, P = 0.002; TA: + 0.43min-1, P = 0.01). Different approaches for averaging trials lead to similar k. HHb and StO2 signals provided different k, although different blood volume corrections did not impact k. Group differences in k were detected in both muscles.

Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.

Sport-related concussion alters cerebral hemodynamic activity during controlled respiration.

Sport-related concussion (SRC) is known to disrupt neurohemodynamic activity, cardiac function, and blood pressure (BP) autoregulation. This study aims to observe changes in cerebrovascular and cardiovascular responses during controlled respiration after sustaining an SRC. University varsity athletes (n = 81) completed a preseason physiological assessment and were followed up within 5 days of sustaining an SRC. During preseason and follow-up assessments, participants' continuous beat-to-beat BP was collected by finger photoplethysmography, and right prefrontal cortex oxygenation was collected using near-infrared spectroscopy (NIRS). Participants completed 5 min of seated rest and 5 min of a 6-breaths per minute controlled breathing protocol (5 s inhale and 5 s exhale; 0.10 Hz). Wavelet transformation was applied to the NIRS and BP signals, separating them into respiratory (0.10-0.6 Hz) and cardiac (0.6-2 Hz) frequency intervals. Of the 81 participants, 74 had a usable BP signal, 43 had usable NIRS signals, and 28 had both usable BP and NIRS signals. Wavelet amplitudes were calculated and coherence between NIRS and BP on the 28 participants were assessed. There was a significant (P < 0.05) decrease in oxygenated hemoglobin amplitude from 0.062 to 0.054 Hz and hemoglobin difference amplitude from 0.059 to 0.051 Hz, both at the respiratory (0.10-0.6 Hz) frequency interval, from preseason to acute SRC, respectively. Therefore, during controlled respiration, there was a reduction in intensity at the respiratory band, suggesting a protective, reduced respiratory contribution to cerebral hemodynamic activity following acute SRC.NEW & NOTEWORTHY This study investigated cerebral hemodynamic activity following sport-related concussion. Prefrontal cortex oxygenation was assessed by near-infrared spectroscopy (NIRS) during a controlled breathing protocol. Wavelet transformation of the NIRS signals showed significant decreases in HbO2 and HbD amplitude at the respiratory frequency interval (0.10-0.6 HZ) from preseason baseline to acute concussion. These results suggest a decreased respiratory contribution to cerebral hemodynamic activity following acute concussion.

Neuroimaging evidence of visual-vestibular interaction accounting for perceptual mislocalization induced by head rotation.

A fleeting flash aligned vertically with an object remaining stationary in the head-centered space would be perceived as lagging behind the object during the observer's horizontal head rotation. This perceptual mislocalization is an illusion named head-rotation-induced flash-lag effect (hFLE). While many studies have investigated the neural mechanism of the classical visual FLE, the hFLE has been hardly investigated. We measured the cortical activity corresponding to the hFLE on participants experiencing passive head rotations using functional near-infrared spectroscopy. Participants were asked to judge the relative position of a flash to a fixed reference while being horizontally rotated or staying static in a swivel chair. Meanwhile, functional near-infrared spectroscopy signals were recorded in temporal-parietal areas. The flash duration was manipulated to provide control conditions. Brain activity specific to the hFLE was found around the right middle/inferior temporal gyri, and bilateral supramarginal gyri and superior temporal gyri areas. The activation was positively correlated with the rotation velocity of the participant around the supramarginal gyrus and negatively related to the hFLE intensity around the middle temporal gyrus. These results suggest that the mechanism underlying the hFLE involves multiple aspects of visual-vestibular interactions including the processing of multisensory conflicts mediated by the temporoparietal junction and the modulation of vestibular signals on object position perception in the human middle temporal complex.

Effects of Age on the Auditory Cortex During Speech Perception in Noise: Evidence From Functional Near-Infrared Spectroscopy.

Age-related speech perception difficulties may be related to a decline in central auditory processing abilities, particularly in noisy or challenging environments. However, how the activation patterns related to speech stimulation in different noise situations change with normal aging has yet to be elucidated. In this study, we aimed to investigate the effects of noisy environments and aging on patterns of auditory cortical activation. We analyzed the functional near-infrared spectroscopy signals of 20 young adults, 21 middle-aged adults, and 21 elderly adults, and evaluated their cortical response patterns to speech stimuli under five different signal to noise ratios (SNRs). In addition, we analyzed the behavior score, activation intensity, oxyhemoglobin variability, and dominant hemisphere, to investigate the effects of aging and noisy environments on auditory cortical activation. Activation intensity and oxyhemoglobin variability both showed a decreasing trend with aging at an SNR of 0 dB; we also identified a strong correlation between activation intensity and age under this condition. However, we observed an inconsistent activation pattern when the SNR was 5 dB. Furthermore, our analysis revealed that the left hemisphere may be more susceptible to aging than the right hemisphere. Activation in the right hemisphere was more evident in older adults than in the left hemisphere; in contrast, younger adults showed leftward lateralization. Our analysis showed that with aging, auditory cortical regions gradually become inflexible in noisy environments. Furthermore, changes in cortical activation patterns with aging may be related to SNR conditions, and that understandable speech with a low SNR ratio but still understandable may induce the highest level of activation. We also found that the left hemisphere was more affected by aging than the right hemisphere in speech perception tasks; the left-sided dominance observed in younger individuals gradually shifted to the right hemisphere with aging.

Spatio-temporal scale information fusion of Functional Near-Infrared Spectroscopy signal for depression detection

Depression, a prevalent and debilitating mental health disorder, necessitates early detection to enable effective intervention. Functional Near-Infrared Spectroscopy (fNIRS) has proven to be a promising technique for studying depressive symptoms due to its affordability, portability, high spatial resolution and non-invasiveness. However, existing fNIRS-based depression detection approaches often overlook signal intrinsic properties and intrinsic correlations among different feature types, as well as local spatial geometric structures of data. To overcome these limitations, we suggest a spatio-temporal scale information fusion framework that involves supervised local low-rank correlation (IFLRC) to detect depression. IFLRC incorporates a low-rank constraint, a sparse ℓ2,1-norm term, multiple Laplacian regularizations, and a sparse penalty projection matrix to capture global spatial functional information, handle errors to enhance robustness to noise, preserve local spatial geometric structures, exploit correlations among multiple information sets, and enhance discriminative ability. An iterative algorithm, based on the alternating direction method of multipliers (ADMM), has been developed to solve the suggested objective function. The experimental findings validate the efficacy of IFLRC, yielding favorable detection rates of 95.8%, 87.3%, and 97.2% in response to positive, neutral, and negative emotional stimuli, respectively. These findings exceed existing methods, demonstrating the potential of fNIRS and the effectiveness of IFLRC for detecting depression. IFLRC presents notable advancements in feature extraction and fusion techniques for depression detection. Its capacity to capture spatio-temporal scale information, leverage intrinsic correlations, and preserve local geometric structures translates into improved performance. This framework holds promise for early detection and intervention in depressive disorders, offering valuable insights for clinical applications.

Differences in peripheral microcirculatory blood flow regulation in chronic kidney disease based on wavelet analysis of resting near-infrared spectroscopy

Vascular impairment is closely related to increased mortality in chronic kidney disease (CKD). The objective of this study was to assess impairments in the regulation of peripheral microvascular perfusion in patients with CKD based on time-frequency spectral analysis of resting near-infrared spectroscopy (NIRS) signals. Total hemoglobin (tHb) concentration and tissue saturation index (TSI) signals were collected using NIRS for a continuous 5 mins at 10 Hz from the forearm of 55 participants (34 CKD including 5 with end-stage renal disease, and 21 age-matched control). Continuous wavelet transform-based spectral analysis was used to quantify the spectral amplitude within five pre-defined frequency intervals (I, 0.0095–0.021 Hz; II, 0.021–0.052 Hz; III, 0.052–0.145 Hz; IV, 0.145–0.6 Hz and V, 0.6–2.0 Hz), representing endothelial, neurogenic, myogenic, respiratory and heartbeat activity, respectively. CKD patients showed lower tHb average spectral amplitude within the neurogenic frequency interval compared with controls (p = 0.014), consistent with an increased sympathetic outflow observed in CKD. CKD patients also showed lower TSI average spectral amplitude within the endothelial frequency interval compared with controls (p = 0.046), consistent with a reduced endothelial function in CKD. These findings demonstrate the potential of wavelet analysis of NIRS to provide complementary information on peripheral microvascular regulation in CKD.

Multimodal Assessment of Cognitive Workload Using Neural, Subjective and Behavioural Measures in Smart Factory Settings.

Collaborative robots (cobots) have largely replaced conventional industrial robots in today's workplaces, particularly in manufacturing setups, due to their improved performance and intelligent design. In the framework of Industry 5.0, humans are working alongside cobots to accomplish the required level of automation. However, human-robot interaction has brought up concerns regarding human factors (HF) and ergonomics. A human worker may experience cognitive stress as a result of cobots' irresponsive nature in unpredictably occurring situations, which adversely affects productivity. Therefore, there is a necessity to measure stress to enhance a human worker's performance in a human-robot collaborative environment. In this study, factory workers' mental workload was assessed using physiological, behavioural, and subjective measures. Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals were collected to acquire brain signals and track hemodynamic activity, respectively. The effect of task complexity, cobot movement speed, and cobot payload capacity on the mental stress of a human worker were observed for a task designed in the context of a smart factory. Task complexity and cobot speed proved to be more impactful. As physiological measures are unbiased and more authentic means to estimate stress, eventually they may replace the other conventional measures if they prove to correlate with the results of traditional ones. Here, regression and artificial neural networks (ANN) were utilised to determine the correlation between physiological data and subjective and behavioural measures. Regression performed better for most of the targets and the best correlation (rsq-adj = 0.654146) was achieved for predicting missed beeps, a behavioural measure, using a combination of multiple EEG and fNIRS predictors. The k-nearest neighbours (KNN) algorithm was used to evaluate the accuracy of correlation between traditional measures and physiological variables, with the highest accuracy of 77.8% achieved for missed beeps as the target. Results show that physiological measures can be more insightful and have the tendency to replace other biased parameters.

Compressibility Analysis of Functional Near-Infrared Spectroscopy Signals in Children With Attention-Deficit/Hyperactivity Disorder.

Functional near- infrared spectroscopy (fNIRS) as an emerging optical neuroimaging technique has attracted the interest and attention of many investigators. With the growth of fNIRS data volume, effective data compression methods are urgent. Compressive sensing (CS) has been demonstrated a promising tool to deal with biomedical data. However, whether the compressibility of fNIRS data can discriminate different brain states is unclear. In this study, the fNIRS signals from fifteen attention-deficit/hyperactivity disorder (ADHD) children and fifteen typically developing (TD) children were recorded during an N-back task and a Go/NoGo task respectively. A block sparse Bayesian learning-based CS method was used to reconstruct the compressed fNIRS data. To assess the performance of the CS method, we adopted two metrics, structural similarity index (SSIM) and mean squared error (MSE), both of them effective in evaluating the compressibility of fNIRS data. Then, the two metrics were analyzed to discriminate the brain states of ADHD children and TD children during the two tasks using the multivariate pattern analysis (MVPA) method. As indicated by the results, the CS method could reconstruct the compressed fNIRS data with a high reconstruction quality at different compression ratio ([Formula: see text] and [Formula: see text]). Furthermore, the MVPA method could distinguish different brain states with high accuracy, and identify that the prefrontal cortex is a key brain region for distinguishing ADHD vs. TD or N-back vs. Go/NoGo. These findings indicated that CS is very promising for the storage and transmission of massive fNIRS data, and the compressibility of fNIRS data is a potential biomarker for the diagnosis of ADHD.

A Wireless 2-Channel Layered EMG/NIRS Sensor System for Local Muscular Activity Evaluation.

A wireless 2-channel layered sensor system that enables electromyography (EMG) and near-infrared spectroscopy (NIRS) measurements at two local positions was developed. The layered sensor consists of a thin silver electrode and a photosensor consisting of a photoemitting diode (LED) or photodiode (PD). The EMG and NIRS signals were simultaneously measured using a pair of electrodes and photosensors for the LED and PD, respectively. Two local muscular activities are presented in detail using layered sensors. In the experiments, EMG and NIRS signals were measured for isometric constant and ramp contractions at each forearm using layered sensors. The results showed that local muscle activity analysis is possible using simultaneous EMG and NIRS signals at each local position.

Motivation-related influences on fNIRS signals during walking exercise: a permutation entropy approach.

Cortical activity is typically indexed by analyzing functional near-infrared spectroscopy (fNIRS) signals in terms of the mean (e.g., mean oxygenated hemoglobin; HbO). Entropy approaches have been proposed as useful complementary methods for analyzing fNIRS signals. Entropy methods consider the regularity of a time series, and in doing so, may provide additional insights into the underlying dynamics of brain activity. Recent research using fNIRS found that non-disabled adults exhibit widespread increases in cortical activity and walk faster when under "extra motivation" conditions (e.g., verbal encouragement, lap timer) compared to trials without such motivators ("standard motivation"). This ancillary analysis of that study aimed to assess the extent to which fNIRS permutation entropy (PE) was affected by motivational conditions and explained variance in self-reported motivation. No regional PE differences were found between different motivational conditions. However, a greater difference in PE between motivational conditions (higher in standard, lower in extra motivation) in the anterior prefrontal cortex (aPFC) was associated with greater self-determined motivation. PE was also higher (less regular) in the primary sensorimotor cortex lower limb area compared to all other cortical areas analyzed, except the dorsal premotor cortex, regardless of motivational condition. This study provides early evidence to suggest that while different motivational environments during walking activity influence the magnitude of fNIRS signals, they may not influence the regularity of cortical signals. However, the magnitude of PE difference between motivational conditions was related to self-determined motivation in the aPFC, and this is an area warranting further investigation.

Lateralization difference in functional activity during Stroop tasks: a functional near-infrared spectroscopy and EEG simultaneous study.

Conflict monitoring and processing is an important part of the human cognitive system, it plays a key role in many studies of cognitive disorders. Based on a Chinese word-color match Stroop task, which included incongruent and neutral stimuli, the Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) signals were recorded simultaneously. The Pearson correlation coefficient matrix was calculated to analyze brain connectivity based on EEG signals. Granger Causality (GC) method was employed to analyze the effective connectivity of bilateral frontal lobes. Wavelet Transform Coherence (WTC) was used to analyze the functional connectivity of the bilateral hemisphere and ipsilateral hemisphere. Results indicated that brain connectivity analysis on EEG signals did not show any significant lateralization, while fNIRS analysis results showed the frontal lobes especially the left frontal lobe play the leading role in dealing with conflict tasks. The human brain shows leftward lateralization while processing the more complicated incongruent stimuli. This is demonstrated by the higher functional connectivity in the left frontal lobe and the information flow from the left frontal lobe to the right frontal lobe. Our findings in brain connectivity during cognitive conflict processing demonstrated that the dual modality method combining EEG and fNIRS is a valuable tool to excavate more information through cognitive and physiological studies.

Exploring the feasibility of tensor decomposition for analysis of fNIRS signals: a comparative study with grand averaging method.

The analysis of functional near-infrared spectroscopy (fNIRS) signals has not kept pace with the increased use of fNIRS in the behavioral and brain sciences. The popular grand averaging method collapses the oxygenated hemoglobin data within a predefined time of interest window and across multiple channels within a region of interest, potentially leading to a loss of important temporal and spatial information. On the other hand, the tensor decomposition method can reveal patterns in the data without making prior assumptions of the hemodynamic response and without losing temporal and spatial information. The aim of the current study was to examine whether the tensor decomposition method could identify significant effects and novel patterns compared to the commonly used grand averaging method for fNIRS signal analysis. We used two infant fNIRS datasets and applied tensor decomposition (i.e., canonical polyadic and Tucker decompositions) to analyze the significant differences in the hemodynamic response patterns across conditions. The codes are publicly available on GitHub. Bayesian analyses were performed to understand interaction effects. The results from the tensor decomposition method replicated the findings from the grand averaging method and uncovered additional patterns not detected by the grand averaging method. Our findings demonstrate that tensor decomposition is a feasible alternative method for analyzing fNIRS signals, offering a more comprehensive understanding of the data and its underlying patterns.

Using cross-correlation analysis of multi-channel near infrared spectroscopy to assess the hemodynamic response to cupping therapy.

Cupping therapy is a common intervention for the management of musculoskeletal impairment. Previous studies have demonstrated that cupping therapy can improve muscle hemodynamic responses using single-channel near-infrared spectroscopy (NIRS). However, the effects of cupping therapy on spatial hemodynamic responses as well as the correlation between oxyhemoglobin and deoxy-hemoglobin are largely unknown. The cross-correlation function (CCF) algorithm was used to determine the correlation between time-series NIRS signals from inside and outside the cup as well as time-series oxyhemoglobin and deoxy-hemoglobin under 4 cupping intensities, including -225 and -300 mmHg for 5 and 10 min. The main finding was that the maximum CCF values of oxyhemoglobin was significantly higher than those in deoxy-hemoglobin (p < 0.05). Furthermore, it was found that there was a correlation between deoxy-hemoglobin with a longer duration and a larger magnitude of negative pressure. This is the first study investigating time-series hemodynamic responses after cupping therapy using cross-correlation function analysis of multi-channel NIRS signals.

A model of neurovascular coupling and its application to cortical spreading depolarization

Cortical spreading depolarization (CSD) is a neuropathological condition involving propagating waves of neuronal silence, and is related to multiple diseases, such as migraine aura, traumatic brain injury (TBI), stroke, and cardiac arrest, as well as poor outcome of patients. While CSDs of different severity share similar roots on the ion exchange level, they can lead to different vascular responses (namely spreading hyperemia and spreading ischemia). In this paper, we propose a mathematical model relating neuronal activities to predict vascular changes as measured with near-infrared spectroscopy (NIRS) and fMRI recordings, and apply it to the extreme case of CSD, where sustained near-complete neuronal depolarization is seen. We utilize three serially connected models (namely, ion exchange, neurovascular coupling, and hemodynamic model) which are described by differential equations. Propagating waves of ion concentrations, as well as the associated vasodynamics and hemodynamics, are simulated by solving these equations. Our proposed model predicts vasodynamics and hemodynamics that agree both qualitatively and quantitatively with experimental literature. Mathematical modeling and simulation offer a powerful tool to help understand the underlying mechanisms of CSD and help interpret the data. In addition, it helps develop novel monitoring techniques prior to data collection. Our simulated results strongly suggest that fMRI is unable to reliably distinguish between spreading hyperemia and spreading ischemia, while NIRS signals are substantially distinct in the two cases.