Analysis Of fNIRS Data Research Articles

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting-state functional near-infrared spectroscopy data.

Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near-infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting-state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN-based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting-state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head-up tilt test.

Hyperscanning fNIRS data analysis using multiregression dynamic models: an illustration in a violin duo.

Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context. In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together. This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players. The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player.

Impact of averaging fNIRS regional coherence data when monitoring people with long term post-concussion symptoms.

Functional near-infrared spectroscopy (fNIRS), with its measure of delta hemoglobin concentration, has shown promise as a monitoring tool for the functional assessment of neurological disorders and brain injury. Analysis of fNIRS data often involves averaging data from several channel pairs in a region. Although this greatly reduces the processing time, it is uncertain how it affects the ability to detect changes post injury. We aimed to determine how averaging data within regions impacts the ability to differentiate between post-concussion and healthy controls. We compared interhemispheric coherence data from 16 channel pairs across the left and right dorsolateral prefrontal cortex during a task and a rest period. We compared the statistical power for differentiating groups that was obtained when undertaking no averaging, vs. averaging data from 2, 4, or 8 source detector pairs. Coherence was significantly reduced in the concussion group compared with controls when no averaging was undertaken. Averaging all 8 channel pairs before undertaking the coherence analysis resulted in no group differences. Averaging between fiber pairs may eliminate the ability to detect group differences. It is proposed that even adjacent fiber pairs may have unique information, so averaging must be done with caution when monitoring brain disorders or injury.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear. Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering. Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics. Significance. The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Identification of Functional Cortical Plasticity in Children with Cerebral Palsy Associated to Robotic-Assisted Gait Training: An fNIRS Study.

Cerebral palsy (CP) is a non-progressive neurologic condition that causes gait limitations, spasticity, and impaired balance and coordination. Robotic-assisted gait training (RAGT) has become a common rehabilitation tool employed to improve the gait pattern of people with neurological impairments. However, few studies have demonstrated the effectiveness of RAGT in children with CP and its neurological effects through portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS). The aim of the study is to evaluate the neurophysiological processes elicited by RAGT in children with CP through fNIRS, which was acquired during three sessions in one month. The repeated measure ANOVA was applied to the β-values delivered by the General Linear Model (GLM) analysis used for fNIRS data analysis, showing significant differences in the activation of both prefrontal cortex (F (1.652, 6.606) = 7.638; p = 0.022), and sensorimotor cortex (F (1.294, 5.175) = 11.92; p = 0.014) during the different RAGT sessions. In addition, a cross-validated Machine Learning (ML) framework was implemented to estimate the gross motor function measure (GMFM-88) from the GLM β-values, obtaining an estimation with a correlation coefficient r = 0.78. This approach can be used to tailor clinical treatment to each child, improving the effectiveness of rehabilitation for children with CP.

Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data.

Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields. We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength). We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions. PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics. This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.

Effects of Baduanjin imagery and exercise on cognitive function in the elderly: A functional near-infrared spectroscopy study.

ObjectiveCognitive function is essential in ensuring the quality of life of the elderly. This study aimed to investigate the effects of Baduanjin imagery and Baduanjin movement (a traditional Chinese health exercise, TCHE) on cognitive function in the elderly using functional near-infrared spectroscopy (fNIRS).Methods72 participants with a mean age of 66.92 years (SD = 4.77) were recruited for this study. The participants were randomly assigned to three groups: the Baduanjin imagery, the Baduanjin exercise, and the Control. Stroop task was used to record the accuracy and reaction times, and a near-infrared spectral brain imaging system was used to monitor the brain's oxy-hemoglobin concentration responses.Results(1) For the reaction times of Stroop incongruent tasks, the main effect of the test phase (F = 114.076, p < 0.001) and the interaction effect between test phase and group (F = 10.533, p < 0.001) were all significant. The simple effect analysis further demonstrated that the reaction times of the Baduanjin imagery group and Baduanjin exercise group in the post-test was faster than that in the pre-test (ps < 0.001); (2) Analysis of fNIRS data showed the significant interaction effect (F = 2.554, p = 0.013) between the test phase and group in the left dorsolateral prefrontal cortex. Further analysis showed that, during the post-test incongruent tasks, the oxy-Hb variations were significantly higher in participants of the Baduanjin imagery group (p = 0.005) and Baduanjin exercise group (p = 0.002) than in the control group; For the right inferior frontal gyrus, the interaction between the test phase and group was significant (F = 2.060, p = 0.044). Further analysis showed that, during the post-test incongruent tasks, the oxy-Hb variations were significantly higher in participants of the Baduanjin imagery group than in the control group (p = 0.001).ConclusionBaduanjin imagery and exercise positively affect cognitive performance; Baduanjin imagery and exercise activated the left dorsolateral prefrontal cortex; Baduanjin imagery activated the right inferior frontal gyrus, while Baduanjin exercise could not.

Applications of graph theory to the analysis of fNIRS data in hyperscanning paradigms.

Hyperscanning is a promising tool for investigating the neurobiological underpinning of social interactions and affective bonds. Recently, graph theory measures, such as modularity, have been proposed for estimating the global synchronization between brains. This paper proposes the bootstrap modularity test as a way of determining whether a pair of brains is coactivated. This test is illustrated as a screening tool in an application to fNIRS data collected from the prefrontal cortex and temporoparietal junction of five dyads composed of a teacher and a preschooler while performing an interaction task. In this application, graph hub centrality measures identify that the dyad's synchronization is critically explained by the relation between teacher's language and number processing and the child's phonological processing. The analysis of these metrics may provide further insights into the neurobiological underpinnings of interaction, such as in educational contexts.

Multivariate Pattern Analysis of Entropy estimates in Fast- and Slow-Wave Functional Near Infrared Spectroscopy: A Preliminary Cognitive Stress study.

Functional near infrared spectroscopy (fNIRS) is a modality that can measure shallow cortical brain signals and also contains pulsatile oscillations that originate from heartbeat dynamics. In particular, while fNIRS slow waves (0 Hz to 0.6 Hz) refer to the standard hemodynamic signal, fast-wave (0.8 Hz to 3 Hz) fNIRS signals refer to cardiac oscillations. Using a cognitive stress experiment paradigm with mental arithmetic, the aim of this study was to assess differences in cortical activity when using slow-wave or fast-wave fNIRS signals. Furthermore, we aimed to see whether fNIRS fast and slow waves provide different information to discriminate mental arithmetic tasks from baseline. We used data from 10 healthy subjects from an open dataset performing mental arithmetic tasks and assessed fNIRS signals using mean values in the time domain, as well as complexity estimates including sample, fuzzy, and distribution entropy. A searchlight representational similarity analysis with pairwise t-test group analysis was performed to compare the representational dissimilarity matrices of each searchlight center. We found significant representational differences between fNIRS fast and slow waves for all complexity estimates, at different brain regions. On the other hand, no statistical differences were observed for mean values. We conclude that entropy analysis of fNIRS data may be more sensitive than traditional methods like mean analysis at detecting the additional information provided by fast-wave signals for discriminating mental arithmetic tasks and warrants further research.

The Immediate and Sustained Effects of Exercise-Induced Hemodynamic Response on Executive Function During Fine Motor-Cognitive Tasks Using Functional Near-Infrared Spectroscopy.

Several studies have shown that acute exercise has a small positive effect on cognitive performance. However, it is still unclear what type of exercise has a sustained impact on cognitive performance during post-exercise recovery. Therefore, the purpose of our study was to investigate cognitive performance at the behavioral level, and their neural correlates after a 10-minute post-exercise recovery period with two different types of exercise intervention (high-intensity interval exercise (HIIE) vs. Moderate-intensity continuous exercise (MCE)). A total of 29 healthy young adults (7 women) between the ages of 19 and 33 with fair to good cardiovascular fitness were submitted to two different exercise protocols and a recovery session. Cognitive function was assessed using a digital Trail-Making-Test (dTMT). Cortical activity in the prefrontal and the motor cortex using functional near-infrared spectroscopy (fNIRS) was measured before, after acute exercise, and during recovery. The statistical analysis of fNIRS data was performed by comparing the slope and mean of the hemodynamic response. High levels of hemodynamic responses were observed in the prefrontal and motor cortex on the brain during performing the dTMT while walking from pre- to post-exercise and decreased again in post-recovery, accompanied by improvement and maintenance of cognitive performance. Notably, a high hemodynamic response in the left motor area of the brain was maintained by HIIE in post-recovery compared with MCE. The high cortical activation in the left motor area from post-exercise to recovery for the HIIE group may be due to the additional availability of neural resources for fine motor and postural control by high-intensity exercise-induced fatigue. Additionally, the improved cognitive performance may have effectively utilized the available neural resources in the frontal lobe, depending on the condition (sitting and walking) and the two types of exercise protocol (HIIE and MCE).

LIONirs: flexible Matlab toolbox for fNIRS data analysis

BackgroundFunctional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses. New methodWe introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System and available on GitHub. ResultsWithin the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures. Comparison with existing methodsSeveral good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format. ConclusionsLIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples.

The Effect of Yoga Meditation Practice on Young Adults' Inhibitory Control: An fNIRS Study.

Objectives: The present study aimed to test the effect of yoga meditation (YoMed) practice on inhibitory control of young adults.Methods: A total of 50 participants (23 male, 21–28 years old) from a university in Jinan, Shandong Province were enrolled in this study. Participants were randomly assigned to a YoMed group or a Control group. Participants’ basic information, physical activity, and inhibitory control were measured. A multi-channel continuous-wave near-infrared spectrometer was used to monitor the brain’s hemodynamic responses.Results: After the intervention, we found significant differences in Flanker tasks between the YoMed group and Control group. The accuracy in the YoMed group was higher than those in the Control group (p < 0.05). Analysis of fNIRS data showed that oxyhemoglobin (oxy-Hb) levels in the prefrontal cortex (PFC) increased in the YoMed group during the Flanker tasks after the YoMed intervention.Conclusion: YoMed has a temporarily promoting effect on the brain activation of young adults. It is an effective and appropriate exercise to improve the inhibitory control of young adults.

NIRS-KIT: a MATLAB toolbox for both resting-state and task fNIRS data analysis.

.Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to probe human brain function during task state and resting state. However, the existing analysis toolboxes mainly focus on task activation analysis, few software packages can assist resting-state fNIRS studies.Aim: We aimed to provide a versatile and easy-to-use toolbox to perform analysis for both resting state and task fNIRS.Approach: We developed a MATLAB toolbox called NIRS-KIT that works for both resting-state analysis and task activation detection.Results: NIRS-KIT implements common and necessary processing steps for performing fNIRS data analysis, including data preparation, quality control, preprocessing, individual-level analysis, group-level statistics with several popular statistical models, and multiple comparison correction methods, and finally results visualization. For resting-state fNIRS analysis, functional connectivity analysis, graph theory-based network analysis, and amplitude of low-frequency fluctuations analysis are provided. Additionally, NIRS-KIT also supports activation analysis for task fNIRS.Conclusions: NIRS-KIT offers an open source tool for researchers to analyze resting-state and/or task fNIRS data in one suite. It contains several key features: (1) good compatibility, supporting multiple fNIRS recording systems, data formats of NIRS-SPM and Homer2, and the shared near-infrared spectroscopy format data format recommended by the fNIRS society; (2) flexibility, supporting customized preprocessing scripts; (3) ease-to-use, allowing processing fNIRS signals in batch manner with user-friendly graphical user interfaces; and (4) feature-packed data viewing and result visualization. We anticipate that this NIRS-KIT will facilitate the development of the fNIRS field.

Effective brain connectivity for fNIRS data analysis based on multi-delays symbolic phase transfer entropy

Objective. Recently, effective connectivity (EC) calculation methods for functional near-infrared spectroscopy (fNIRS) data mainly face two problems: the first problem is that noise can seriously affect the EC calculation and even lead to false connectivity; the second problem is that it ignores the various real neurotransmission delays between the brain region, and instead uses a fixed delay coefficient for calculation. Approach. To overcome these two issues, a delay symbolic phase transfer entropy (dSPTE) is proposed by developing traditional transfer entropy (TE) to estimate EC for fNIRS. Firstly, the phase time sequence was obtained from the original sequence by the Hilbert transform and state-space reconstruction was realized using a uniform embedding scheme. Then, a symbolization technique was applied based on a neural-gas algorithm to improve its noise robustness. Finally, the EC was calculated on multiple time delay scales to match different inter-region neurotransmission delays. Main results. A linear AR model, a nonlinear model and a multivariate hybrid model were introduced to simulate the performance of dSPTE, and the results showed that the accuracy of dSPTE was the highest, up to 74.27%, and specificity was 100% which means no false connectivity. The results confirmed that the dSPTE method realized better noise robustness, higher accuracy, and correct identification even if there was a long delay between series. Finally, we applied dSPTE to fNIRS dataset to analyse the EC during the finger-tapping task, the results showed that EC strength of task state significantly increased compared with the resting state. Significance. The proposed dSPTE method is a promising way to measure the EC for fNIRS. It incorporates the phase information TE with a symbolic process for fNIRS analysis for the first time. It has been confirmed to be noise robust and suitable for the complex network with different coupling delays.

Statistical Analysis of fNIRS Data: Consideration of Spatial Varying Coefficient Model of Prefrontal Cortex Activity Changes During Speech Motor Learning in Apraxia of Speech

One of the key cognitive functions for new learning is cognitive flexibility. In a healthy system, the role of the prefrontal cortex (PFC) during speech is of growing interest. For individuals who have an impairment in the programing and planning of speech (apraxia of speech) secondary to a stroke or traumatic brain injury, the once hardwired activity is now a goal directed rule-based act. The literature falls short in PFC involvement for rehabilitation of speech following a brain injury. This repeated measures single case-design study involves two individuals with chronic acquired apraxia of speech. Hemodynamic changes in PFC activity during speech motor learning are measured using functional near-infrared spectroscopy. Two models - a generalized linear model (GLM) and a spatial varying coefficient model - are used to distinguish PFC activity differences corresponding the task, stage, and time of intervention. The significant differences in the pattern of activity for the regions of interest in the PFC are associated with structure of practice, feedback schedule, and the time of intervention. This pilot study provides evidence for the PFC role in speech motor learning and that neural reorganization can continue several years post-stroke. The implications include considerations for statistical methods used for fNIRS analysis with the potential to build tailored interventions to facilitate neural network reorganization for speech motor learning following an acquired brain injury.

Measuring Changes In Attention Task And Hemodynamic Oxygenation In Post-Concussion Patients Using Functional Near-infrared Spectroscopy

Current concussion assessment protocols rely on clinical functioning and thus may not be sensitive to underlying neural deficits. PURPOSE: The purpose of this study was to measure hemodynamic response changes using functional near-infrared spectroscopy (fNIRS) in asymptomatic, post-concussion participants (CON) compared to healthy controls (CTL). METHODS: CON participants (n=9, age=18.44±1.51 years, sex=66% female) diagnosed with a concussion at a Midwestern emergency department were recruited from 2018-2019. CTL participants (n=22, age=23.63±4.55 years, sex=54% female) were recruited through electronic postings and classroom announcements. During the first study visit, participants completed a demographics questionnaire, pain and symptom severity scores, and an attention task. Participants were then fitted for a silicon headband with two fNIRS diode arrays consisting of eight emitters and ten detectors over each hemisphere’s temporal and frontal cortices, superior and middle temporal regions and the parietal cortex. The computerized behavioral attention task consisted of 144 trials spread over six 24-trial task blocks. Mean accuracy (%) and reaction times (s) were recorded, while the fNIRS device measured hemoglobin response. After the first visit, participants were monitored daily for symptom resolution, and completed a second lab visit once symptom count and severity scores reached normative baseline values. Behavioral and neuroimaging fNIRS data from the attention task were analyzed using independent t-tests, with alpha levels set to p<0.05. RESULTS: Once asymptomatic, attention task analyses yielded no significant differences between CON and CTL groups for both mean reaction time (0.003±0.040s, p=0.953) and accuracy (-0.50±0.40%, p=0.47). Analysis of fNIRS data indicated hyperactivity in the pre-frontal cortex, temporal lobe and frontotemporal region of the CON group’s left hemisphere compared to the CTL group (q<0.05, false discovery rate corrected). CONCLUSIONS: Our results suggest post-concussion participants may require additional cognitive resources during attentional tasks in order to maintain normative vigilance. Researchers should continue to evaluate hemodynamic changes, and how these effects may influence making a safe return to activity decision.

Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework.

Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.

Symbolic time series analysis of fNIRS signals in brain development assessment

Objective. Assessing an infant’s brain development remains a challenge for neuroscientists and pediatricians despite great technological advances. As a non-invasive neuroimaging tool, functional near-infrared spectroscopy (fNIRS) has great advantages in monitoring an infant’s brain activity. To explore the dynamic features of hemodynamic changes in infants, in-pattern exponent (IPE), anti-pattern exponent (APE), as well as permutation cross-mutual information (PCMI) based on symbolic dynamics are proposed to measure the phase differences and coupling strength in oxyhemoglobin (HbO) and deoxyhemoglobin (Hb) signals from fNIRS. Approach. First, simulated sinusoidal oscillation signals and four coupled nonlinear systems were employed for performance assessments. Hilbert transform based measurements of hemoglobin phase oxygenation and deoxygenation (hPod) and phase-locking index of hPod (hPodL) were calculated for comparison. Then, the IPE, APE and PCMI indices from resting state fNIRS data of preterm, term infants and adults were calculated to estimate the phase difference and coupling of HbO and Hb. All indices’ performance was assessed by the degree of monotonicity (DoM). The box plots and coefficients of variation (CV) were employed to assess the measurements and robustness in the results. Main results. In the simulation analysis, IPE and APE can distinguish the phase difference of two sinusoidal oscillation signals. Both hPodL and PCMI can track the strength of two coupled nonlinear systems. Compared to hPodL, the PCMI had higher DoM indices in measuring the coupling of two nonlinear systems. In the fNIRS data analysis, similar to hPod, the IPE and APE can distinguish preterm, term infants, and adults in 0.01–0.05 Hz, 0.05–0.1 Hz, and 0.01–0.1 Hz frequency bands, respectively. PCMI more effectively distinguished the term and preterm infants than hPodL in the 0.05–0.1 Hz frequency band. As symbolic time series measures, the IPE and APE were able to detect the brain developmental changes in subjects of different ages. PCMI can assess the resting-state HbO and Hb coupling changes across different developmental ages, which may reflect the metabolic and neurovascular development. Significance. The symbolic-based methodologies are promising measures for fNIRS in estimating the brain development, especially in assessing newborns’ brain developmental status.

The NIRS Brain AnalyzIR Toolbox.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique that uses low-levels of light (650-900 nm) to measure changes in cerebral blood volume and oxygenation. Over the last several decades, this technique has been utilized in a growing number of functional and resting-state brain studies. The lower operation cost, portability, and versatility of this method make it an alternative to methods such as functional magnetic resonance imaging for studies in pediatric and special populations and for studies without the confining limitations of a supine and motionless acquisition setup. However, the analysis of fNIRS data poses several challenges stemming from the unique physics of the technique, the unique statistical properties of data, and the growing diversity of non-traditional experimental designs being utilized in studies due to the flexibility of this technology. For these reasons, specific analysis methods for this technology must be developed. In this paper, we introduce the NIRS Brain AnalyzIR toolbox as an open-source Matlab-based analysis package for fNIRS data management, pre-processing, and first- and second-level (i.e., single subject and group-level) statistical analysis. Here, we describe the basic architectural format of this toolbox, which is based on the object-oriented programming paradigm. We also detail the algorithms for several of the major components of the toolbox including statistical analysis, probe registration, image reconstruction, and region-of-interest based statistics.

Consistency of fNIRS parameters: A parallel forms and test-retest reliability study

Consistency of fNIRS parameters: A parallel forms and test-retest reliability study