Search

Significance: Time-domain functional near-infrared spectroscopy (TD-fNIRS) has been considered as the gold standard of noninvasive optical brain imaging devices. However, due to the high cost, complexity, and large form factor, it has not been as widely adopted as continuous wave NIRS systems. Aim: Kernel Flow is a TD-fNIRS system that has been designed to break through these limitations by maintaining the performance of a research grade TD-fNIRS system while integrating all of the components into a small modular device. Approach: The Kernel Flow modules are built around miniaturized laser drivers, custom integrated circuits, and specialized detectors. The modules can be assembled into a system with dense channel coverage over the entire head. Results: We show performance similar to benchtop systems with our miniaturized device as characterized by standardized tissue and optical phantom protocols for TD-fNIRS and human neuroscience results. Conclusions: The miniaturized design of the Kernel Flow system allows for broader applications of TD-fNIRS.

Significance: Quantitative measurements of cerebral hemodynamic changes due to functional activation are widely accomplished with commercial continuous wave (CW-NIRS) instruments despite the availability of the more rigorous multi-distance frequency domain (FD-NIRS) approach. A direct comparison of the two approaches to functional near-infrared spectroscopy can help in the interpretation of optical data and guide implementations of diffuse optical instruments for measuring functional activation. Aim: We explore the differences between CW-NIRS and multi-distance FD-NIRS by comparing measurements of functional activation in the human auditory cortex. Approach: Functional activation of the human auditory cortex was measured using a commercial frequency domain near-infrared spectroscopy instrument for 70 dB sound pressure level broadband noise and pure tone (1000 Hz) stimuli. Changes in tissue oxygenation were calculated using the modified Beer–Lambert law (CW-NIRS approach) and the photon diffusion equation (FD-NIRS approach). Results: Changes in oxygenated hemoglobin measured with the multi-distance FD-NIRS approach were about twice as large as those measured with the CW-NIRS approach. A finite-element simulation of the functional activation problem was performed to demonstrate that tissue oxygenation changes measured with the CW-NIRS approach is more accurate than that with multi-distance FD-NIRS. Conclusions: Multi-distance FD-NIRS approaches tend to overestimate functional activation effects, in part due to partial volume effects.

Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks. Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths. Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity. Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups. Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.

Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain–computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

The first two years of human life are characterized by the most dynamic growth in brain structures1–5 and remarkable cognitive and behavioral changes.6,7 Functional near-infrared spectroscopy (fNIRS; a list of abbreviations is provided in Table S1 in the Supplementary Material for the convenience of the reader) is an irreplaceable neuroimaging tool for studying early brain functional development, providing unprecedented opportunities for recording the hemodynamic response in awake, behaving infants because of its balanced temporal-spatial resolution and resilience to movement.8–11 Notably, although the Centers for Disease Control and Prevention defines 0- to 1-year-olds (yo) and 2- to 3-yo children as infants and toddlers, respectively, we have described 0- to 2-yo children as infants in this study for readability. Despite the suitability of fNIRS in infant studies, a major limitation of this technique is the inability of fNIRS data to provide structural information of the head tissue. In fNIRS measurements, a source–detector (SD) pair positioned on the scalp surface measures local concentration changes in oxygenated and deoxygenated hemoglobin caused by neural activity.12–15 On the other hand, while neural activity tied to a specific human function originates in local brain regions. The absence of structural information in the fNIRS signal makes it impossible to correlate the signal response with the anatomical brain regions. Therefore, in fNIRS studies, the scalp location where the SD pair is attached to its underlying brain region where the fNIRS signal originates should be mapped. We call this mapping the scalp-cortex correlation (SCC). Several methods for obtaining SCC have been proposed for studies on adults; however, only a few studies have provided SCC for the infant population. Similar to adults, infant SCC is mostly based on a simple geometrical technique, i.e., correlating the location of the SD pair on the scalp, typically, the midpoint of the SD pair, with cortical regions in a simple point-to-point geometrical manner. For instance, researchers often referred to the international 10-20 or 10-10 system16 when attaching SD pairs on the scalp and then inferred the anatomical locations17–19 according to the geometrical SCC of the adult head20,21 or infant head.22–25 Notably, by linearly reducing the size of the adult heads, the virtual registration method26 has also been employed to estimate SCC in infant studies.27–29 As described above, the point-to-point geometrical SCC provides a tolerable estimation of the underlying brain regions for the absorption change acquired by the SD pair. Nevertheless, the geometrical SCC is based on the assumption that the absorption change occurs at the cortical projection point below the midpoint between the SD pair, and light scattering in the head tissue is not considered. Mounting evidence from light propagation analysis in the adult head revealed light scattering in the head tissue could have a considerable influence on the partial pathlength (PPL) in the brain and the spatial sensitivity profile (SSP).30–34 Notably, a few studies have already demonstrated that light propagation in the infant heads is distinct from that in adults owing to structural differences.35,36 Furthermore, very recent studies on adults have started considering light propagation in turbid media when calculating the SCC.37,38 However, to date, no light propagation analysis-based SCC data are available for 0- to 2-yo infants. In addition to age, SD distance must have a significant influence on optics-based SCC during early development. Only a few studies have examined the effect of SD distance on fNIRS sensitivity in infant brain tissue.35,39 For example, Fukui et al.35 found that fNIRS sensitivity to gray matter (GM) and white matter (WM) of neonates was modulated by the SD distance. These threads of evidence revealed that it remains largely unknown how age and SD distance affect optics-based SCC in 0- to 2-yo infants and how to choose an appropriate SD distance to ensure both the sensitivity to cerebral hemodynamics and the selectivity of signals from a specific brain region of interest. To address these issues, the current study aimed to create a precise optics-based SCC between SD pairs on the scalp fiducial point and brain regions defined by a macro-anatomical atlas by considering the light scattering in 0-, 1-, and 2-yo infant heads. In addition, we quantitatively characterized the changes in SCC with age and SD distance and evaluated the suitable SD distances for each age. The optics-based SCC was obtained for each SD pair by solving the diffusion equation.

Significance: By incorporating multiple overlapping functional near-infrared spectroscopy (fNIRS) measurements, high-density diffuse optical tomography (HD-DOT) images human brain function with fidelity comparable to functional magnetic resonance imaging (fMRI). Previous work has shown that frequency domain high-density diffuse optical tomography (FD-HD-DOT) may further improve image quality over more traditional continuous wave (CW) HD-DOT. Aim: The effects of modulation frequency on image quality as obtainable with FD-HD-DOT is investigated through simulations with a realistic noise model of functional activations in human head models, arising from 11 source modulation frequencies between CW and 1000 MHz. Approach: Simulations were performed using five representative head models with an HD regular grid of 158 light sources and 166 detectors and an empirically derived noise model. Functional reconstructions were quantitatively assessed with multiple image quality metrics including the localization error (LE), success rate, full width at half maximum, and full volume at half maximum (FVHM). All metrics were evaluated against CW-based models. Results: Compared to CW, localization accuracy is improved by >40% throughout brain depths of 13 to 25 mm below the surface with 300 to 500 MHz modulation frequencies. Additionally, the reliable field of view in brain tissue is enlarged by 35% to 48% within an optimal frequency of 300 MHz after considering realistic noise, depending on the dynamic range of the system. Conclusions: These results point to the tremendous opportunities in further development of high bandwidth FD-HD-DOT system hardware for applications in human brain mapping.

Significance: High-density diffuse optical tomography (HD-DOT) has been shown to approach the resolution and localization accuracy of blood oxygen level dependent-functional magnetic resonance imaging in the adult brain by exploiting densely spaced, overlapping samples of the probed tissue volume, but the technique has to date required large and cumbersome optical fiber arrays. Aim: To evaluate a wearable HD-DOT system that provides a comparable sampling density to large, fiber-based HD-DOT systems, but with vastly improved ergonomics. Approach: We investigated the performance of this system by replicating a series of classic visual stimulation paradigms, carried out in one highly sampled participant during 15 sessions to assess imaging performance and repeatability. Results: Hemodynamic response functions and cortical activation maps replicate the results obtained with larger fiber-based systems. Our results demonstrate focal activations in both oxyhemoglobin and deoxyhemoglobin with a high degree of repeatability observed across all sessions. A comparison with a simulated low-density array explicitly demonstrates the improvements in spatial localization, resolution, repeatability, and image contrast that can be obtained with this high-density technology. Conclusions: The system offers the possibility for minimally constrained, spatially resolved functional imaging of the human brain in almost any environment and holds particular promise in enabling neuroscience applications outside of the laboratory setting. It also opens up new opportunities to investigate populations unsuited to traditional imaging technologies.

Significance: Surgical simulators, both virtual and physical, are increasingly used as training tools for teaching and assessing surgical technical skills. However, the metrics used for assessment in these simulation environments are often subjective and inconsistent. Aim: We propose functional activation metrics, derived from brain imaging measurements, to objectively assess the correspondence between brain activation with surgical motor skills for subjects with varying degrees of surgical skill. Approach: Cortical activation based on changes in the oxygenated hemoglobin (HbO) of 36 subjects was measured using functional near-infrared spectroscopy at the prefrontal cortex (PFC), primary motor cortex, and supplementary motor area (SMA) due to their association with motor skill learning. Inter-regional functional connectivity metrics, namely, wavelet coherence (WCO) and wavelet phase coherence were derived from HbO changes to correlate brain activity to surgical motor skill levels objectively. Results: One-way multivariate analysis of variance found a statistically significant difference in the inter-regional WCO metrics for physical simulator based on Wilk’s Λ for expert versus novice, F ( 10,1 ) = 7495.5, p

Significance: With the increasing popularity of functional near-infrared spectroscopy (fNIRS), the need to determine localization of the source and nature of the signals has grown. Aim: We compare strategies for removal of non-neural signals for a finger-thumb tapping task, which shows responses in contralateral motor cortex and a visual checkerboard viewing task that produces activity within the occipital lobe. Approach: We compare temporal regression strategies using short-channel separation to a spatial principal component (PC) filter that removes global signals present in all channels. For short-channel temporal regression, we compare non-neural signal removal using first and combined first and second PCs from a broad distribution of short channels to limited distribution on the forehead. Results: Temporal regression of non-neural information from broadly distributed short channels did not differ from forehead-only distribution. Spatial PC filtering provides results similar to short-channel separation using the temporal domain. Utilizing both first and second PCs from short channels removes additional non-neural information. Conclusions: We conclude that short-channel information in the temporal domain and spatial domain regression filtering methods remove similar non-neural components represented in scalp hemodynamics from fNIRS signals and that either technique is sufficient to remove non-neural components.

Significance: Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are both commonly used methodologies for neuronal source reconstruction. While EEG has high temporal resolution (millisecond-scale), its spatial resolution is on the order of centimeters. On the other hand, in comparison to EEG, fNIRS, or diffuse optical tomography (DOT), when used for source reconstruction, can achieve relatively high spatial resolution (millimeter-scale), but its temporal resolution is poor because the hemodynamics that it measures evolve on the order of several seconds. This has important neuroscientific implications: e.g., if two spatially close neuronal sources are activated sequentially with only a small temporal separation, single-modal measurements using either EEG or DOT alone would fail to resolve them correctly. Aim: We attempt to address this issue by performing joint EEG and DOT neuronal source reconstruction. Approach: We propose an algorithm that utilizes DOT reconstruction as the spatial prior of EEG reconstruction, and demonstrate the improvements using simulations based on the ICBM152 brain atlas. Results: We show that neuronal sources can be reconstructed with higher spatiotemporal resolution using our algorithm than using either modality individually. Further, we study how the performance of the proposed algorithm can be affected by the locations of the neuronal sources, and how the performance can be enhanced by improving the placement of EEG electrodes and DOT optodes. Conclusions: We demonstrate using simulations that two sources separated by 2.3-3.3 cm and 50 ms can be recovered accurately using the proposed algorithm by suitably combining EEG and DOT, but not by either in isolation. We also show that the performance can be enhanced by optimizing the electrode and optode placement according to the locations of the neuronal sources.

Epub 2021 Jun 12. Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism- induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain’s organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort (N ¼ 99) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development. The authors would like to thank all the parents and infants who generously participate in our studies. The authors also would like to thank Elena Aguirrebengoa for her assistance on recruiting and testing participants and Enrico Amico for discussion regarding the connICA approach. This work was supported by the Basque Government (Nos. PRE_2018_2_0154, PIBA_2019_ 104, and BERC 2018-2021); the Spanish Ministry of Economy and Competitiveness (Nos. RYC-2017-21845, PID2019-105520GB-100, and PSI2014-5452-P); the Natural Sciences and Engineering Research Council of Canada (Nos. 506948 and 506993); and the Engineering and Physical Sciences Research Council (Nos. EP/N025946/1 and EP/509577/1).

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.

International audience; The application of functional near-infrared spectroscopy (fNIRS) in the neurosciences has been expanding over the last 40 years. Today, it is addressing a wide range of applications within different populations and utilizes a great variety of experimental paradigms. With the rapid growth and the diversification of research methods, some inconsistencies are appearing in the way in which methods are presented, which can make the interpretation and replication of studies unnecessarily challenging. The Society for Functional Near-Infrared Spectroscopy has thus been motivated to organize a representative (but not exhaustive) group of leaders in the field to build a consensus on the best practices for describing the methods utilized in fNIRS studies. Our paper has been designed to provide guidelines to help enhance the reliability, repeatability, and traceability of reported fNIRS studies and encourage best practices throughout the community. A checklist is provided to guide authors in the preparation of their manuscripts and to assist reviewers when evaluating fNIRS papers. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Significance: Solutions for group-level analysis of connectivity from fNIRS observations exist, but groupwise explorative analysis with classical solutions is often cumbersome. Manifold-based solutions excel at data exploration, but there are infinite surfaces crossing the observations cloud of points. Aim: We aim to provide a systematic choice of surface for a manifold-based analysis of connectivity at group level with small surface interpolation error. Approach: This research introduces interpolated functional manifold (IFM). IFM builds a manifold from reconstructed changes in concentrations of oxygenated ΔcHbO2 and reduced ΔcHbR hemoglobin species by means of radial basis functions (RBF). We evaluate the root mean square error (RMSE) associated to four families of RBF. We validated our model against psychophysiological interactions (PPI) analysis using the Jaccard index (JI). We demonstrate the usability in an experimental dataset of surgical neuroergonomics. Results: Lowest interpolation RMSE was 1.26e − 4 ± 1.32e − 8 for ΔcHbO2 [A.U.] and 4.30e − 7 ± 2.50e − 13 [A.U.] for ΔcHbR. Agreement with classical group analysis was JI = 0.89 ± 0.01 for ΔcHbO2. Agreement with PPI analysis was JI = 0.83 ± 0.07 for ΔcHbO2 and JI = 0.77 ± 0.06 for ΔcHbR. IFM successfully decoded group differences [ANOVA: ΔcHbO2: F ( 2,117 ) = 3.07; p

Significance: Neonates are a highly vulnerable population. The risk of brain injury is greater during the first days and weeks after birth than at any other time of life. Functional neuroimaging that can be performed longitudinally and at the cot-side has the potential to improve our understanding of the evolution of multiple forms of neurological injury over the perinatal period. However, existing technologies make it very difficult to perform repeated and/or long-duration functional neuroimaging experiments at the cot-side. Aim: We aimed to create a modular, high-density diffuse optical tomography (HD-DOT) technology specifically for neonatal applications that is ultra-lightweight, low profile and provides high mechanical flexibility. We then sought to validate this technology using an anatomically accurate dynamic phantom. Approach: An advanced 10-layer rigid-flexible printed circuit board technology was adopted as the basis for the DOT modules, which allows for a compact module design that also provides the flexibility needed to conform to the curved infant scalp. Two module layouts were implemented: dual-hexagon and triple-hexagon. Using in-built board-to-board connectors, the system can be configured to provide a vast range of possible layouts. Using epoxy resin, thermochromic dyes, and MRI-derived 3D-printed moulds, we constructed an electrically switchable, anatomically accurate dynamic phantom. This phantom was used to quantify the imaging performance of our flexible, modular HD-DOT system. Results: Using one particular module configuration designed to cover the infant sensorimotor system, the device provided 36 source and 48 detector positions, and over 700 viable DOT channels per wavelength, ranging from 10 to ∼45 mm over an area of approximately 60 cm2. The total weight of this system is only 70 g. The signal changes from the dynamic phantom, while slow, closely simulated real hemodynamic response functions. Using difference images obtained from the phantom, the measured 3D localization error provided by the system at the depth of the cortex was in the of range 3 to 6 mm, and the lateral image resolution at the depth of the neonatal cortex is estimated to be as good as 10 to 12 mm. Conclusions: The HD-DOT system described is ultra-low weight, low profile, can conform to the infant scalp, and provides excellent imaging performance. It is expected that this device will make functional neuroimaging of the neonatal brain at the cot-side significantly more practical and effective.

Significance: Dystonia is a dynamic and complex disorder. Real-time analysis of brain activity during motor tasks may increase our knowledge on its pathophysiology. Functional near-infrared spectroscopy (fNIRS) is a noninvasive method that enables the measurement of cortical hemodynamic activity in unconstrained environments. Aim: We aimed to explore the feasibility of using fNIRS for the study of task-related brain activity in dystonia. Task-related functional magnetic resonance imaging (fMRI) and resting-state functional connectivity were also analyzed. Approach: Patients with idiopathic right-upper limb dystonia and controls were assessed through nonsimultaneous fMRI and fNIRS during a finger-tapping task. Seed-based connectivity analysis of resting-state fMRI was performed in both groups. Results: The fMRI results suggest nonspecific activation of the cerebellum and occipital lobe in dystonia patients during the finger-tapping task with the affected hand. Moreover, fNIRS data show lower activation in terms of oxyhemoglobin and total hemoglobin in the frontal, ipsilateral cortex, and somatosensory areas during this task. In dystonia, both fMRI and fNIRS data resulted in hypoactivation of the frontal cortex during finger tapping with both hands simultaneously. Resting-state functional connectivity analysis suggests that the cerebellar somatomotor network in dystonia has an increased correlation with the medial prefrontal cortex and the paracingulate gyrus. Conclusions: These data suggest that unbalanced activation of the cerebellum, somatosensory, and frontal cortical areas are associated with dystonia. To our knowledge, this is the first study using fNIRS to explore the pathophysiology of dystonia. We show that fNIRS and fMRI are complementary methods and highlight the potential of fNIRS for the study of dystonia and other movement disorders as it can overcome movement restrictions, enabling experiments in more naturalistic conditions.

Significance: Functional near-infrared spectroscopy (fNIRS) is an emerging brain imaging technique due to its small size, low cost, minimum scanning sonic noise, and portability. Unfortunately, because this technique does not provide neuroanatomical information to accompany the functional data, its data interpretation remains a persistent challenge in fNIRS brain imaging applications. The two most popular approaches for fNIRS anatomical registration are magnetic resonance imaging (MRI) and three-dimensional (3-D) digitization. MRI scanning yields high-precision registration but reduces the cost-effectiveness and accessibility of fNIRS imaging. Alternatively, the low cost and portable 3-D digitizers are affected by magnetic properties of ambient metal objects, including participant clothing, testing equipment, medical implants, and so forth. Aim: To overcome these obstacles and provide accessible and reliable neuroanatomical registration for fNIRS imaging, we developed and explored a photogrammetry optode registration (POR) method. Approach: The POR method uses a consumer-grade camera to reconstruct a 3-D image of the fNIRS optode-set, including light emitters and detectors, on a participant’s head. This reconstruction process uses a linear-time incremental structure from motion (LTI-SfM) algorithm, based on 100 to 150 digital photos. The POR method then aligns the reconstructed image with an anatomical template of the brain. Results: To validate this method, we tested 22 adult and 19 child participants using the POR method and MRI imaging. The results comparisons suggest on average 55% and 46% overlap across all data channel measurements registered by the two methods in adult and children, respectively. Importantly, this overlap reached 65% and 60% in only the frontal channels. Conclusions: These results suggested that the mismatch in registration was partially due to higher variation in backward optode placement rather than the registration efficacy. Therefore, the photo-based registration method can offer an accessible and reliable approach to neuroanatomical registration of fNIRS as well as other surface-based neuroimaging and neuromodulation methods.

Significance: Functional near-infrared spectroscopy (fNIRS) uses surface-placed light sources and detectors to record underlying changes in the brain due to fluctuations in hemoglobin levels and oxygenation. Since these measurements are recorded from the surface of the scalp, the mapping from underlying regions-of-interest (ROIs) in the brain space to the fNIRS channel space measurements depends on the registration of the sensors, the anatomy of the head/brain, and the sensitivity of these diffuse measurements through the tissue. However, small displacements in the probe position can change the distribution of recorded brain activity across the fNIRS measurements. Aim: We propose an approach using either individual or atlas-based brain-space anatomical information to define ROI-based statistical hypotheses to test the null involvement of specific regions, which allows us to test the analogous ROI across subjects while adjusting for fNIRS probe placement and sensitivity differences due to head size variations without a localizer task. Approach: We use the optical forward model to project the underlying brain-space ROI into a tapered contrast vector, which defines the relative weighting of the fNIRS channels contributing to the ROI and allows us to test the null hypothesis of no brain activity in this region during a functional task. We demonstrate this method through simulation and compare the sensitivity-specificity of this approach to other conventional methods. Results: We examine the performance of this method in the scenario where head size and probe registration are both an accurately known parameters and where this is subject to unknown experimental errors. This method is compared with the performance of the conventional method using 364 different simulation parameter combinations. Conclusion: The proposed method is always recommended in ROI-based analysis, since it significantly improves the analysis performance without a localizer task, wherever the fNIRS probe registration is known or unknown.

Significance: Functional near-infrared spectroscopy (fNIRS) is a technique for detecting regional hemodynamic responses associated with neural activation in the cerebral cortex. The absorption changes due to hemodynamic changes in the scalp cause considerable signal contamination in the fNIRS measurement. A method for extracting hemodynamic changes in the cerebral tissue is required for reliable fNIRS measurement. Aim: To exclusively detect cerebral functional hemodynamic changes, we developed an fNIRS technique using reflectance modulation of the scalp surface. Approach: The theoretical feasibility of the proposed method was proven by a simulation calculation of light propagation. Its practical feasibility was evaluated by a phantom experiment and brain activation simulation mimicking human fNIRS experiments. Results: The simulation calculation revealed that the partial path length of the scalp was changed by reflectance modulation of the scalp surface. The influence of absorption change in the superficial layer was successfully reduced by the proposed method, using only measurement data, in the phantom experiment. The proposed method was applicable to human experiments of standard designs, achieving statistical significance within an acceptable experimental time-frame. Conclusions: Removal of the scalp hemodynamic effect by the proposed technique will increase the quality of fNIRS data, particularly in measurements in neonates and infants that typically would require a dense optode arrangement.

Significance: The reliability of functional near-infrared spectroscopy (fNIRS) measurements is reduced by systemic physiology. Short-channel regression algorithms aim at removing systemic “noise” by subtracting the signal measured at a short source–detector separation (mainly scalp hemodynamics) from the one of a long separation (brain and scalp hemodynamics). In literature, incongruent approaches on the selection of the optimal regressor signal are reported based on different assumptions on scalp hemodynamics properties. Aim: We investigated the spatial and temporal distribution of scalp hemodynamics over the sensorimotor cortex and evaluated its influence on the effectiveness of short-channel regressions. Approach: We performed hand-grasping and resting-state experiments with five subjects, measuring with 16 optodes over sensorimotor areas, including eight 8-mm channels. We performed detailed correlation analyses of scalp hemodynamics and evaluated 180 hand-grasping and 270 simulated (overlaid on resting-state measurements) trials. Five short-channel regressor combinations were implemented with general linear models. Three were chosen according to literature, and two were proposed based on additional physiological assumptions [considering multiple short channels and their Mayer wave (MW) oscillations]. Results: We found heterogeneous hemodynamics in the scalp, coming on top of a global close-to-homogeneous behavior (correlation 0.69 to 0.92). The results further demonstrate that short-channel regression always improves brain activity estimates but that better results are obtained when heterogeneity is assumed. In particular, we highlight that short-channel regression is more effective when combining multiple scalp regressors and when MWs are additionally included. Conclusion: We shed light on the selection of optimal regressor signals for improving the removal of systemic physiological artifacts in fNIRS. We conclude that short-channel regression is most effective when assuming heterogeneous hemodynamics, in particular when combining spatial- and frequency-specific information. A better understanding of scalp hemodynamics and more effective short-channel regression will promote more accurate assessments of functional brain activity in clinical and research settings., Neurophotonics, 7 (03)

Significance: Our study is the first comparison of brain activation patterns during motor tasks across unilateral cerebral palsy (UCP), bilateral cerebral palsy (BCP), and typical development (TD) to elucidate neural mechanisms and inform rehabilitation strategies. Aim: Cortical activation patterns were compared for distal upper and lower extremity tasks in UCP, BCP, and TD using functional near-infrared spectroscopy (fNIRS) and related to functional severity. Approach: Individuals with UCP (n=10, 18.8±6.8 years), BCP (n=14, 17.5±9.6 years), and TD (n=16, 17.3±9.1 years) participated in this cross-sectional cohort study. The fNIRS was used to noninvasively monitor the hemodynamic response to task-related cortical activation. The block design involved repetitive nondominant hand squeezing and ankle dorsiflexion. Results: Individuals with UCP demonstrated the highest levels of activation for the squeeze task (UCP>BCP q=0.049; BCP>TD qTD q=0.001) and more activity in the ipsilateral versus contralateral hemisphere. Individuals with BCP showed the highest levels of cortical activation in the dorsiflexion task (BCP>UCP qTD). Conclusions: Grouping by CP subtype and manual function or mobility level demonstrated significant differences from TD, even for individuals with the mildest forms of CP. Hemispheric activation patterns showed hypothesized but nonsignificant trends.

Significance: Knee osteoarthritis (OA) is a common joint disease causing chronic pain and functional alterations (stiffness and swelling) in the elderly population. OA is currently treated pharmacologically with analgesics, although neuromodulation via transcranial direct current stimulation (tDCS) has recently generated a growing interest as a safe side-effect free treatment alternative or a complement to medications for chronic pain conditions. Although a number of studies have shown that tDCS has a beneficial effect on behavioral measures of pain, the mechanistic action of neuromodulation on pain sensitivity and coping at the central nervous system is not well understood. Aim: We aimed at observing longitudinal changes of cortical hemodynamics in older adults with knee OA associated with a two-week-long tDCS self-treatment protocol. Approach: Hemodynamics was measured bilaterally in the motor and somatosensory cortices with functional near-infrared spectroscopy (fNIRS) in response to thermal pain induced ipsilaterally to the knee primarily affected by OA. Results: We found that both oxyhemoglobin- and deoxyhemoglobin-related functional activations significantly increased during the course of the tDCS treatment, supporting the notion that tDCS yields an increased cortical excitability. Concurrently, clinical measures of pain decreased with tDCS treatment, hinting at a potential spatial dissociation between cortically mediated pain perception and suppression and the prevalence of neuromodulatory effects over cortical pain processing. Conclusions: fNIRS is a valid method for objectively tracking pain in an ambulatory setting and it could potentially be used to inform strategies for optimized tDCS treatment and to develop innovative tDCS protocols.

Significance: The human brain is a highly complex system with nonlinear, dynamic behavior. A majority of brain imaging studies employing functional near-infrared spectroscopy (fNIRS), however, have considered only the spatial domain and have ignored the temporal properties of fNIRS recordings. Methods capable of revealing nonlinearities in fNIRS recordings can provide new insights about how the brain functions. Aim: The temporal characteristics of fNIRS signals are explored by comprehensively investigating their fractal properties. Approach: Fractality of fNIRS signals is analyzed using scaled windowed variance (SWV), as well as using visibility graph (VG), a method which converts a given time series into a graph. Additionally, the fractality of fNIRS signals obtained under resting-state and task-based conditions is compared, and the application of fractality in differentiating brain states is demonstrated for the first time via various classification approaches. Results: Results from SWV analysis show the existence of high fractality in fNIRS recordings. It is shown that differences in the temporal characteristics of fNIRS signals related to task-based and resting-state conditions can be revealed via the VGs constructed for each case. Conclusions: fNIRS recordings, regardless of the experimental conditions, exhibit high fractality. Furthermore, VG-based metrics can be employed to differentiate rest and task-execution brain states.

Significance: Combining transcranial direct-current stimulation (tDCS) with functional near-infrared spectroscopy (fNIRS) is a recent approach to exploring brain activation evoked by neurostimulation. Aim: To critically evaluate studies combining tDCS and fNIRS and provide a consolidated overview of cortical hemodynamic responses to neurostimulation. Approach: Key terms were searched in three databases (MEDLINE, EMBASE, and PsycINFO) with cross-referencing and works from Google Scholar also evaluated. All studies reporting on fNIRS-derived hemoglobin changes evoked by tDCS were included. Results: Literature searches revealed 474 articles, of which 28 were included for final review (22 in healthy individuals: 9 involving rest and 13 with tasks; 6 in the clinical setting). At rest, an overall increase in cortical activation was observed in fNIRS responses at the site of stimulation, with evidence suggesting nonstimulated brain regions are also similarly affected. Conversely, during tasks, reduced cortical activation was observed during online stimulation. Offline and poststimulation effects were less consistent, as is the impact on clinical populations and their symptom correlation. Conclusion: This review explores the methodological frameworks for fNIRS-tDCS evaluations and summarizes hemodynamic responses associated with tDCS in all populations. Our findings provide further evidence of the impact of tDCS on neuronal activation within functionally connected networks.

Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic “brain” responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.

Significance: Our study reveals that frontal cerebral oxygenation asymmetry (FCOA), i.e. a difference in the oxygenation between the right and left prefrontal cortex (PFC), is a real phenomenon in healthy human subjects at rest. Aim: To investigate FCOA, we performed a study with 134 healthy right-handed subjects with the systemic physiology augmented functional near infrared spectroscopy (SPA-fNIRS) approach. Approach: Subjects were measured 2 to 4 times on different days resulting in an unprecedented number of 518 single measurements of the absolute values of tissue oxygen saturation (StO2) and total hemoglobin concentration ([tHb]) of the right and left PFC. Measurements were performed with frequency-domain functional near-infrared spectroscopy. In addition, the cardiorespiratory parameters were measured simultaneously. Results: We found that (i) subjects showed an FCOA (higher StO2 on the right PFC), but not for tHb; (ii) intrasubject variability was excellent for both StO2 and tHb, and fair for FCOA; (iii) StO2 correlated significantly with blood CO2 concentration, [tHb] with heart rate, respiration rate (RR), and the pulse–respiration quotient (PRQ), and FCOA with RR and PRQ; (iv) FCOA and StO2 were dependent on season and time of day, respectively; (v) FCOA was negatively correlated with the room temperature; and (vi) StO2 and tHb were not correlated with the subjects mood but with their chronotype, whereas FCOA was not dependent on the chronotype. Conclusion: Our study demonstrates that FCOA is real, and it provides unique insights into this remarkable phenomenon.

Connectivity impairment has frequently been associated with the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). Although the connectivity of the resting state has mainly been studied, we expect the transition between baseline and task may also be impaired in ADHD children. Twenty-three typically developing (i.e., control) and 36 disordered (ADHD and autism-comorbid ADHD) children were subjected to connectivity analysis. Specifically, they performed an attention task, visual oddball, while their brains were measured by functional near-infrared spectroscopy. The results of the measurements revealed three key findings. First, the control group maintained attentive connectivity, even in the baseline interval. Meanwhile, the disordered group showed enhanced bilateral intra- and interhemispheric connectivities while performing the task. However, right intrahemispheric connectivity was found to be weaker than those for the control group. Second, connectivity and activation characteristics might not be positively correlated with each other. In our previous results, disordered children lacked activation in the right middle frontal gyrus. However, within region connectivity of the right middle frontal gyrus was relatively strong in the baseline interval and significantly increased in the task interval. Third, the connectivity-based biomarker performed better than the activation-based biomarker in terms of screening. Activation and connectivity features were independently optimized and cross validated to obtain the best performing threshold-based classifier. The effectiveness of connectivity features, which brought significantly higher training accuracy than the optimum activation features, was confirmed (88% versus 76%). The optimum screening features were characterized by two trends: (1) strong connectivities of right frontal, left frontal, and left parietal lobes and (2) weak connectivities of left frontal, left parietal, and right parietal lobes in the control group. We conclude that the attentive task-based connectivity effectively shows the difference between control and disordered children and may represent pathological characteristics to be feasibly implemented as a supporting tool for clinical screening.

Functional near-infrared spectroscopy (fNIRS) is a relatively new seizure-free technique and its value for intraoperative brain mapping is unknown. We examine the feasibility of fNIRS for intraoperative functional brain mapping. A 1×1 cm2 density fNIRS probe specially designed for intraoperative use was used to map brain function in adult patients undergoing awake brain surgery and performing motor and/or language tasks. The ability of fNIRS for functional mapping was compared with direct cortical stimulation (DCS) and regression was used to determine if mean blood pressure (MBP) and blood hemoglobin influenced fNIRS measurements. Eighteen patients underwent awake craniotomy and performed 19 language- and 17 motor-related tasks. fNIRS mapping was highly correlated with DCS for 10 language- and 7 motor-related tasks. fNIRS was able to detect functional language (p

Significance: Attention-deficit/hyperactivity disorder (ADHD) is the most common psychological disease in childhood. Currently, widely used neuroimaging techniques require complete body confinement and motionlessness and thus are extremely hard for brain scanning of ADHD children. Aim: We present resting-state functional near-infrared spectroscopy (fNIRS) as an imaging technique to record spontaneous brain activity in children with ADHD. Approach: The brain functional connectivity was calculated, and the graph theoretical analysis was further applied to investigate alterations in the global and regional properties of the brain network in the patients. In addition, the relationship between brain network features and core symptoms was examined. Results: ADHD patients exhibited significant decreases in both functional connectivity and global network efficiency. Meanwhile, the nodal efficiency in children with ADHD was also found to be altered, e.g., increase in the visual and dorsal attention networks and decrease in somatomotor and default mode networks, compared to the healthy controls. More importantly, the disrupted functional connectivity and nodal efficiency significantly correlated with dimensional ADHD scores. Conclusions: We clearly demonstrate the feasibility and potential of fNIRS-based connectome technique in ADHD or other neurological diseases in the future.

How neurovascular coupling develops in preterm-born neonates has been largely neglected in scientific research. We measured visually evoked (flicker light) hemodynamic responses (HRs) in preterm-born neonates ( n = 25 , gestational age: 31.71 ± 3.37 weeks, postnatal age: 25.48 ± 23.94 days) at the visual cortex (VC) and left frontotemporal lobe (FTL) using functional near-infrared spectroscopy (fNIRS) neuroimaging. We found that the HR characteristics show a large intersubject variability but could be classified into three groups according to the changes of oxyhemoglobin concentration at the VC [(A) increase, (B) decrease, or (C) inconclusive]. In groups A and B, the HRs at the left FTL were correlated with those at the VC, indicating the presence of a frontotemporal-occipital functional connectivity. Neonates in group A had a higher weight at measurement compared to those in group B, and had the lowest baseline total hemoglobin concentration and hematocrit compared to group C. To the best of our knowledge, this is the first fNIRS study showing (1) that the HRs of preterm-born neonates need to be classified into subgroups, (2) that the subgroups differed in terms of weight at measurement, and (3) that HRs can be observed also at the FTL during visual stimulation. These findings add insights into how neurovascular coupling develops in preterm-born neonates.

Significance: The expanding field of human social interaction is enabled by functional near-infrared spectroscopy (fNIRS) that acquires hemodynamic signals during live two-person interactions. These advances call for development of methods to quantify interactive processes. Aim: Wavelet coherence analysis has been applied to cross-brain neural coupling. However, fNIRS-specific computations have not been explored. This investigation determines the effects of global mean removal, wavelet equation, and choice of oxyhemoglobin versus deoxyhemoglobin signals. Approach: We compare signals with a known coherence with acquired signals to determine optimal computational approaches. The known coherence was calculated using three visual stimulation sequences of a contrast-reversing checkerboard convolved with the canonical hemodynamic response function. This standard was compared with acquired human fNIRS responses within visual cortex using the same sequences. Results: Observed coherence was consistent with known coherence with highest correlations within the wavelength range between 10 and 20 s. Removal of the global mean improved the correlation irrespective of the specific equation for wavelet coherence, and the oxyhemoglobin signal was associated with a marginal correlation advantage. Conclusions: These findings provide both methodological and computational guidance that enhances the validity and interpretability of wavelet coherence analysis for fNIRS signals acquired during live social interactions.

Face-specific neural processes in the human brain have been localized to multiple anatomical structures and associated with diverse and dynamic social functions. The question of how various face-related systems and functions may be bound together remains an active area of investigation. We hypothesize that face processing may be associated with specific frequency band oscillations that serve to integrate distributed face processing systems. Using a multimodal imaging approach, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), simultaneous signals were acquired during face and object picture viewing. As expected for face processing, hemodynamic activity in the right occipital face area (OFA) increased during face viewing compared to object viewing, and in a subset of participants, the expected N170 EEG response was observed for faces. Based on recently reported associations between the theta band and visual processing, we hypothesized that increased hemodynamic activity in a face processing area would also be associated with greater theta-band activity originating in the same area. Consistent with our hypothesis, theta-band oscillations were also localized to the right OFA for faces, whereas alpha- and beta-band oscillations were not. Together, these findings suggest that theta-band oscillations originating in the OFA may be part of the distributed face-specific processing mechanism.

Cognitive flexibility and reward processing critically rely on the orbitofrontal cortex (OFC). Dysregulations in these domains and orbitofrontal activation have been reported in major psychiatric disorders. Hemodynamic brain imaging-informed neurofeedback allows regional-specific control over brain activation and thus may represent an innovative intervention to regulate orbitofrontal dysfunctions. Against this background the present proof-of-concept study evaluates the feasibility and behavioral relevance of functional near-infrared spectroscopy (fNIRS)-assisted neurofeedback training of the lateral orbitofrontal cortex (lOFC). In a randomized sham-controlled between-subject design, 60 healthy participants have undergone four subsequent runs of training to enhance the lOFC activation. Training-induced changes in the lOFC, attentional set-shifting performance, and reward experience have served as primary outcomes. Feedback from the target channel significantly increases the regional-specific lOFC activation over the four training runs in comparison with sham neurofeedback. The real-time OFC neurofeedback group demonstrates a trend for faster responses during the set-shifting relative to the sham neurofeedback group. Within the real-time OFC neurofeedback group, stronger training-induced lOFC increases are associated with higher reward experience. The present results demonstrate that fNIRS-informed neurofeedback allows regional-specific regulation of lOFC activation and may have the potential to modulate the associated behavioral domains. As such fNIRS-informed neurofeedback may represent a promising strategy to regulate OFC dysfunctions in psychiatric disorders.

Significance: It has been reported that children with attention-deficit hyperactivity disorder (ADHD) have impairment in the recognition of angry but not of happy facial expressions, and they show atypical cortical activation patterns in response to facial expressions. However, little is known about neural mechanisms underlying the impaired recognition of facial expressions in school-aged children with ADHD and the effects of acute medication on their processing of facial expressions. Aim: We aimed to investigate the possibility that acute administration of methylphenidate (MPH) affects processing of facial expressions in ADHD children. Approach: We measured the hemodynamic changes in the bilateral temporo-occipital areas of ADHD children observing the happy and angry facial expressions before and 1.5 h after MPH or placebo administration in a randomized, double-blind, placebo-controlled, crossover design study. Results: We found that, regardless of medication, happy expressions induced increased oxyhemoglobin (oxy-Hb) responses in the right inferior occipital region but not in the superior temporal region. For angry expressions, oxy-Hb responses increased after MPH administration, but not after placebo administration, in the left inferior occipital area, whereas there was no significant activation before MPH administration. Conclusions: Our results suggest that (1) ADHD children consistently recruit the right inferior occipital regions to process happy expressions and (2) MPH administration to ADHD children enhances cortical activation in the left inferior occipital regions when they process angry expressions.

Significance: Functional near-infrared spectroscopy (fNIRS) has become an important research tool in studying human brains. Accurate quantification of brain activities via fNIRS relies upon solving computational models that simulate the transport of photons through complex anatomy. Aim: We aim to highlight the importance of accurate anatomical modeling in the context of fNIRS and propose a robust method for creating high-quality brain/full-head tetrahedral mesh models for neuroimaging analysis. Approach: We have developed a surface-based brain meshing pipeline that can produce significantly better brain mesh models, compared to conventional meshing techniques. It can convert segmented volumetric brain scans into multilayered surfaces and tetrahedral mesh models, with typical processing times of only a few minutes and broad utilities, such as in Monte Carlo or finite-element-based photon simulations for fNIRS studies. Results: A variety of high-quality brain mesh models have been successfully generated by processing publicly available brain atlases. In addition, we compare three brain anatomical models—the voxel-based brain segmentation, tetrahedral brain mesh, and layered-slab brain model—and demonstrate noticeable discrepancies in brain partial pathlengths when using approximated brain anatomies, ranging between −1.5% to 23% with the voxelated brain and 36% to 166% with the layered-slab brain. Conclusion: The generation and utility of high-quality brain meshes can lead to more accurate brain quantification in fNIRS studies. Our open-source meshing toolboxes “Brain2Mesh” and “Iso2Mesh” are freely available at http://mcx.space/brain2mesh.

Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.

The temporal evolution of cortical activation and connectivity patterns during a fatiguing handgrip task were studied by functional near-infrared spectroscopy (fNIRS). Twenty-three young adults (18 to 35 years old) were recruited to use a handheld force sensor to perform intermittent handgrip contractions with their dominant hand at their personal maximum voluntary contraction force level for 3.5 s followed by 6.5 s of rest for 120 blocks. Subjects were divided into self-reported physically active and inactive groups, and their hemodynamic activity over the prefrontal and sensory-motor cortices (111 channels) was mapped while they performed this task. Using this fNIRS setup, a more detailed time sequence of cortical activation and connectivity patterns was observed compared to prior studies. A temporal evolution sequence of hemodynamic activation patterns was noted, which was different between the active and the inactive groups. Physically active subjects demonstrated delayed fatigue onset and significantly longer-lasting and more spatially extended functional connectivity (FC) patterns, compared to inactive subjects. The observed differences in activation and FC suggested differences in cortical network adaptation patterns as fatigue set in, which were dependent on subjects’ physical activity. The findings of this study suggest that physical activity increases FC with regions involved in motor task control and correlates to extended fatigue onset and enhanced performance.

The position of each source and detector “optode” on the scalp, and their relative separations, determines the sensitivity of each functional near-infrared spectroscopy (fNIRS) channel to the underlying cortex. As a result, selecting appropriate scalp locations for the available sources and detectors is critical to every fNIRS experiment. At present, it is standard practice for the user to undertake this task manually; to select what they believe are the best locations on the scalp to place their optodes so as to sample a given cortical region-of-interest (ROI). This process is difficult, time-consuming, and highly subjective. Here, we propose a tool, Array Designer, that is able to automatically design optimized fNIRS arrays given a user-defined ROI and certain features of the available fNIRS device. Critically, the Array Designer methodology is generalizable and will be applicable to almost any subject population or fNIRS device. We describe and validate the algorithmic methodology that underpins Array Designer by running multiple simulations of array design problems in a realistic anatomical model. We believe that Array Designer has the potential to end the need for manual array design, and in doing so save researchers time, improve fNIRS data quality, and promote standardization across the field.

The use of functional near-infrared spectroscopy (fNIRS) in block designs provides measures of cortical activity in ecologically valid environments. However, in some cases, the use of block designs may be problematic when data are not corrected for performance in a time-restricted block. We sought to investigate the effects of task complexity and processing speed on hemodynamic responses in an fNIRS block design. To differentiate the effects of task complexity and processing speed, 20 subjects completed the trail making test (TMT) in two versions (TMT-A versus TMT-B) and three different speed levels (slow versus moderate versus fast). During TMT-A, subjects are asked to connect encircled numbers in numerically ascending order (1-2-3 ... ). In the more complex TMT-B, subjects are instructed to connect encircled numbers and letters in alternating ascending order (1-A-2-B ... ). To illustrate the obscuring effects of processing speed on task complexity, we perform two different analyses. First, we analyze the classical measures of oxygenated blood, and second, we analyze the measures corrected for the number of processed items. Our results show large effects for processing speed within the bilateral inferior frontal gyrus, left dorsolateral prefrontal cortex, and superior parietal lobule (SPL). The TMT contrast did not show significant effects with classical measures, although trends are observed for higher activation during TMT-B. When corrected for processed items, higher activity for TMT-B in comparison to TMT-A is found within the SPL. The results are discussed in light of recent research designs, and simple to use correction methods are suggested. (c) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Autism spectrum disorder (ASD) is characterized by impairment in social communication and the presence of restricted and repetitive behaviors and interests. Executive function impairment is reportedly partially responsible for these symptoms. Executive function includes planning, flexibility, and inhibitory control. Although planning and flexibility in ASD have been consistently reported as atypical, the atypicality of inhibitory control remains controversial. As most previous studies have used nonsocial stimuli to investigate inhibitory control in ASD, the effects of socially relevant information on the inhibitory control system in individuals with ASD remain unclear. Therefore, we developed a go/no-go task with gaze stimuli and measured hemodynamic responses in the right prefrontal cortex (PFC), involved in inhibitory processing in both typically developing (TD) children and children with ASD, using functional near-infrared spectroscopy. Direct gaze induced commission errors to similar extents in both groups. Contrary to the behavioral responses, neural activation in the right PFC was modulated by gaze direction only in the TD group. These findings suggest that the gaze-processing mechanisms in the prefrontal region may be affected by atypical gaze processing in other brain regions during an inhibitory control task with socially relevant information in ASD.

Significance: This study is a preliminary step toward the identification of a noninvasive and reliable tool for monitoring the presence and progress of gaiting dysfunctions. Aim: We present the results of a pilot study for monitoring the motor cortex hemodynamic response function (HRF) in freely walking subjects, with time-domain functional near-infrared spectroscopy (TD fNIRS). Approach: A compact and wearable single-channel TD fNIRS oximeter was employed. The lower limb motor cortex area of three healthy subjects was monitored while performing two different freely moving gaiting tasks: forward and backward walking. Results: The time course of oxygenated and deoxygenated hemoglobin was measured during the different walking tasks. Brain motor cortex hemodynamic activations have been analyzed throughout an adaptive HRF fitting procedure, showing a greater involvement of motor area in the backward walking task. By comparison with the HRF obtained in a finger-tapping task performed in a still condition, we excluded any effect of motion artifacts in the gaiting tasks. Conclusions: For the first time to our knowledge, the hemodynamic motor cortex response was measured by TD fNIRS during natural, freely walking exercises. The cortical response during forward and backward walking shows differences, possibly related to the diverse involvement of the motor cortex in the two types of gaiting.