fNIRS Experiments Research Articles

Investigating differences in brain activity between physical and digital prototyping in open and constrained design tasks

AbstractThis article presents an fNIRS experiment investigating cognitive differences between physical and digital prototyping methods in designers (N=25) engaged in open and constrained design tasks. Initial results suggest that physical prototyping yields increased hemodynamic response (i.e., brain activity) compared to digital design, and that constrained design yields increased hemodynamic response compared to open design, in the prefrontal cortex. Further work will seek to triangulate results by investigating potential correlations to design processes and design outputs.

Effects of individual research practices on fNIRS signal quality and latent characteristics.

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular tool for cross-cultural neuroimaging studies. However, the reproducibility and comparability of fNIRS studies is still an open issue in the scientific community. The paucity of experimental practices and the lack of clear guidelines regarding fNIRS use contribute to undermining the reproducibility of results. For this reason, much effort is now directed at assessing the impact of heterogeneous experimental practices in creating divergent fNIRS results. The current work aims to assess differences in fNIRS signal quality in data collected by two different labs in two different cohorts: Singapore (N=74) and Italy (N=84). Random segments of 20s were extracted from each channel in each participant's NIRScap and 1280 deep features were obtained using a deep learning model trained to classify the quality of fNIRS data. Two datasets were generated: ALL dataset (segments with bad and good data quality) and GOOD dataset (segments with good quality). Each dataset was divided into train and test partitions, which were used to train and evaluate the performance of a Support Vector Machine (SVM) model in classifying the co-horts from signal quality features. Results showed that the SG cohort had significantly higher occurrences of bad signal quality in the majority of the fNIRS channels. Moreover, the SVM correctly classified the cohorts when using the ALL dataset. However, the performance dropped almost completely (except for five channels) when the SVM had to classify the cohorts using data from the GOOD dataset. These results suggest that fNIRS raw data obtained by different labs might possess different levels of quality as well as different latent characteristics beyond quality per se. The current study highlights the importance of defining clear guidelines in the conduction of fNIRS experiments in the reporting of data quality in fNIRS manuscripts.

Identifying autism spectrum disorder in resting-state fNIRS signals based on multiscale entropy and a two-branch deep learning network

BackgroundThe demand for early and precise identification of autism spectrum disorder (ASD) presented a challenge to the prediction of ASD with a non-invasive neuroimaging method. New methodA deep learning model was proposed to identify children with ASD using the resting-state functional near-infrared spectroscopy (fNIRS) signals. In this model, the input was the pattern of brain complexity represented by multiscale entropy of fNIRS time-series signals, with the purpose to solve the problem of deep learning analysis when the raw signals were limited by length and the number of subjects. The model consisted of a two-branch deep learning network, where one branch was a convolution neural network and the other was a long short-term memory neural network based on an attention mechanism. ResultsOur model could achieve an identification accuracy of 94%. Further analysis used the SHapley Additive exPlanations (SHAP) method to balance the accuracy and the number of optical channels, thus reducing the complexity of fNIRS experiment. Comparison with previously used method(s)in identification accuracy, our model was about 14% higher than previously used deep learning models with the same input and 4% higher than the same model but directly using fNIRS signals as input. We could obtain a discriminative accuracy of 90% with nearly half of the measurement channels by the SHAP method. ConclusionsUsing the pattern of brain complexity as input was effective in the deep learning model when the fNIRS signals were insufficient. With the SHAP method, it was possible to reduce the number of optical channels, while maintaining high accuracy in ASD identification.

Individual-specific characterization of event-related hemodynamic responses during an auditory task: An exploratory study

Functional near-infrared spectroscopy (fNIRS) has been established as an informative modality for understanding the hemodynamic-metabolic correlates of cortical auditory processing. To date, such knowledge has shown broad clinical applications in the diagnosis, treatment, and intervention procedures in disorders affecting auditory processing; however, exploration of the hemodynamic response to auditory tasks is yet incomplete. This holds particularly true in the context of auditory event-related fNIRS experiments, where preliminary work has shown the presence of valid responses while leaving the need for more comprehensive explorations of the hemodynamic correlates of event-related auditory processing. In this study, we apply an individual-specific approach to characterize fNIRS-based hemodynamic changes during an auditory task in healthy adults. Oxygenated hemoglobin (HbO2) concentration change time courses were acquired from eight participants. Independent component analysis (ICA) was then applied to isolate individual-specific class discriminative spatial filters, which were then applied to HbO2 time courses to extract auditory-related hemodynamic features. While six of eight participants produced significant class discriminative features before ICA-based spatial filtering, the proposed method identified significant auditory hemodynamic features in all participants. Furthermore, ICA-based filtering improved correlation between trial labels and extracted features in every participant. For the first time, this study demonstrates hemodynamic features important in experiments exploring auditory processing as well as the utility of individual-specific ICA-based spatial filtering in fNIRS-based feature extraction techniques in auditory experiments. These outcomes provide insights for future studies exploring auditory hemodynamic characteristics and may eventually provide a baseline framework for better understanding auditory response dysfunctions in clinical populations.

Infants' brain activity to cartoon face using functional near-infrared spectroscopy.

In this study, to investigate whether infants showed face-specific brain activity to a cartoon human face, we conducted a functional near-infrared spectroscopy (fNIRS) experiment and a behavioral experiment. In the fNIRS experiment, we measured the hemodynamic responses of 5- and 6-month-old infants to cartoon female and cartoon character faces using fNIRS. The results showed that the concentration of oxy-Hb increased for cartoon female faces but not for cartoon character faces. This indicates that face-specific brain activity occurred for cartoon female faces but not cartoon character faces, despite the fact that both are faces. In the behavioral experiment, we examined whether the 5- and 6-month-old infants preferred cartoon female faces to cartoon character faces in the upright and inverted conditions. The results showed a preference for cartoon female faces in the upright but not in the inverted condition. This indicates that 5- and 6-month-old infants can perceive cartoon female faces, but not cartoon character faces, as faces. The results of the two experiments indicated that face-specific brain activity occurred for cartoon female faces. This indicates that infants can perceive cartoon female faces as faces.

The newborn brain is sensitive to the communicative function of language

Recent studies demonstrated neural systems in bilateral fronto-temporal brain areas in newborns specialized to extract linguistic structure from speech. We hypothesized that these mechanisms show additional sensitivity when identically structured different pseudowords are used communicatively in a turn-taking exchange by two speakers. In an fNIRS experiment newborns heard pseudowords sharing ABB repetition structure in three conditions: two voices turn-takingly exchanged different pseudowords (Communicative); the different pseudowords were produced by a (Single Speaker); two voices turn-takingly repeated identical pseudowords (Echoing). Here we show that left fronto-temporal regions (including Broca’s area) responded more to the Communicative than the other conditions. The results demonstrate that newborns’ left hemisphere brain areas show additional activation when various pseudowords sharing identical structure are exchanged in turn-taking alternation by two speakers. This indicates that language processing brain areas at birth are not only sensitive to the structure but to the functional use of language: communicative information transmission. Newborns appear to be equipped not only with innate systems to identify the structural properties of language but to identify its use, communication itself, that is, information exchange between third party social agents—even outside of the mother–infant dyad.

Shining Light On The Brain to Understand How It Works

Have you ever thought that light could tell you something about your brain? Light is a powerful tool that helps brain researchers understand the brain. Our eyes can only see <1% of the total light around us. Some of the light is red, so-called near-infrared light. This type of light can travel through the head and the top layers of the brain, and thereby gives researchers important information about brain activity. The technique that uses near-infrared light has a long name: functional near-infrared spectroscopy (fNIRS). In this article, we will show you what a fNIRS machine looks like and what it is like to take part in a fNIRS experiment. We will explain how we can use near-infrared light to better understand the brain. Finally, we will give you some examples of what we use fNIRS for and how it might help children who face difficulties in their daily lives in the long run.

Effects of Acupuncture Treatment on Functional Brain Networks of Parkinson’s Disease Patients during Treadmill Walking: An fNIRS Study

Acupuncture is increasingly being used as an alternative treatment for patients with Parkinson’s disease (PD). We aimed to evaluate the effects of acupuncture on gait-related brain function in patients with PD using functional near-infrared spectroscopy (fNIRS). Twenty-four patients with PD were randomly assigned to intervention (acupuncture twice a week for 4 weeks) or control (non-treatment) groups. fNIRS experiments applying a block design were performed at baseline (0 weeks) and 4- and 8-week follow-up and cortical activation and connectivity were evaluated. After acupuncture treatment, oxy-hemoglobin (HbO) levels in the intervention group were significantly increased in the primary motor cortex (M1), supplementary motor area (SMA), and prefrontal cortex (PFC). Furthermore, following acupuncture treatment in the intervention group, the connectivities in the M1 and PFC regions increased. The results show that acupuncture may be a useful complementary treatment for gait disturbances in patients with PD, and fNIRS can be applicable to evaluate neural plasticity directly. The evaluation method in this paper can be used to assess the neural plasticity related to various rehabilitation techniques.

Using a Data-Driven Approach to Estimate Second-Language Proficiency From Brain Activation: A Functional Near-Infrared Spectroscopy Study

While non-invasive brain imaging has made substantial contributions to advance human brain science, estimation of individual state is becoming important to realize its applications in society. Brain activations were used to classify second-language proficiencies. Participants in functional near-infrared spectroscopy (fNIRS) experiment were 20/20 native Japanese speakers with high/low English abilities and 19/19 native English speakers with high/low Japanese abilities. Their cortical activities were measured by functional near-infrared spectroscopy while they were conducting Japanese/English listening comprehension tests. The data-driven method achieved classification accuracy of 77.5% in the case of Japanese speakers and 81.9% in the case of English speakers. The informative features predominantly originated from regions associated with language function. These results bring an insight of fNIRS neuroscience and its applications in society.

Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography.

Resting-state functional connectivity (RSFC) has been generally assessed with functional magnetic resonance imaging (fMRI) thanks to its high spatial resolution. However, fMRI has several disadvantages such as high cost and low portability. In addition, fMRI may not be appropriate for people with metal or electronic implants in their bodies, with claustrophobia and who are pregnant. Diffuse optical tomography (DOT), a method of neuroimaging using functional near-infrared spectroscopy (fNIRS) to reconstruct three-dimensional brain activity images, offers a non-invasive alternative, because fNIRS as well as fMRI measures changes in deoxygenated hemoglobin concentrations and, in addition, fNIRS is free of above disadvantages. We recently proposed a hierarchical Bayesian (HB) DOT algorithm and verified its performance in terms of task-related brain responses. In this study, we attempted to evaluate the HB DOT in terms of estimating RSFC. In 20 healthy males (21–38 years old), 10 min of resting-state data was acquired with 3T MRI scanner or high-density NIRS on different days. The NIRS channels consisted of 96 long (29-mm) source-detector (SD) channels and 56 short (13-mm) SD channels, which covered bilateral frontal and parietal areas. There were one and two resting-state runs in the fMRI and fNIRS experiments, respectively. The reconstruction performances of our algorithm and the two currently prevailing algorithms for DOT were evaluated using fMRI signals as a reference. Compared with the currently prevailing algorithms, our HB algorithm showed better performances in both the similarity to fMRI data and inter-run reproducibility, in terms of estimating the RSFC.

Improved physiological noise regression in fNIRS: A multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. −55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.

Investigation of the sensitivity-specificity of canonical- and deconvolution-based linear models in evoked functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique to measure evoked changes in cerebral blood oxygenation. In many evoked-task studies, the analysis of fNIRS experiments is based on a temporal linear regression model, which includes block-averaging, deconvolution, and canonical analysis models. The statistical parameters of this model are then spatially mapped across fNIRS measurement channels to infer brain activity. The trade-offs in sensitivity and specificity of using variations of canonical or deconvolution/block-average models are unclear. We quantitatively investigate how the choice of basis set for the regression linear model affects the sensitivity and specificity of fNIRS analysis in the presence of variability or systematic bias in underlying evoked response. For statistical parametric mapping of amplitude-based hypotheses, we conclude that these models are fairly insensitive to the parameters of the regression basis for task durations and we report the highest sensitivity-specificity results using a low degree-of-freedom canonical model under these conditions. For shorter duration task ( ), the signal-to-noise ratio of the data is also important in this decision and we find that deconvolution or block-averaging models outperform the canonical models at high signal-to-noise ratio but not at lower levels.

Array Designer: automated optimized array design for functional near-infrared spectroscopy.

.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.

Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic head model.

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that elicits growing interest for research and clinical applications. In the last decade, efforts have been made to develop a mathematical framework in order to image the effective sources of hemoglobin variations in brain tissues. Different approaches can be used to impose additional information or constraints when reconstructing the cerebral images of an ill-posed problem. The goal of this study is to compare the performance and limitations of several source localization techniques in the context of fNIRS tomography using individual anatomical magnetic resonance imaging (MRI) to model light propagation. The forward problem is solved using a Monte Carlo simulation of light propagation in the tissues. The inverse problem has been linearized using the Rytov approximation. Then, Tikhonov regularization applied to least squares, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography and Bayesian model averaging techniques are compared using a receiver operating characteristic analysis, blurring and localization error measures. Using realistic simulations (n = 450) and data acquired from a human participant, this study depicts how these source localization techniques behave in a human head fNIRS tomography. When compared to other methods, Bayesian model averaging is proposed as a promising method in DOT and shows great potential to improve specificity, accuracy, as well as to reduce blurring and localization error even in presence of noise and deep sources. Classical reconstruction methods, such as regularized least squares, offer better sensitivity but higher blurring; while more novel L1-based method provides sparse solutions with small blurring and high specificity but lower sensitivity. The application of these methods is also demonstrated experimentally using visual fNIRS experiment with adult participant.

FNIRS Optodes\u2019 Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest

The employment of functional near-infrared spectroscopy (fNIRS) as a method of brain imaging has increased over the last few years due to its portability, low-cost and robustness to subject movement. Experiments with fNIRS are designed in the face of a limited number of sources and detectors (optodes) to be positioned on selected portion(s) of the scalp. The optodes locations represent an expectation of assessing cortical regions relevant to the experiment’s hypothesis. However, this translation process remains a challenge for fNIRS experimental design. In the present study, we propose an approach that automatically decides the location of fNIRS optodes from a set of predefined positions with the aim of maximizing the anatomical specificity to brain regions-of-interest. The implemented method is based on photon transport simulations on two head atlases. The results are compiled into the publicly available “fNIRS Optodes’ Location Decider” (fOLD). This toolbox is a first-order approach to bring the achieved advancements of parcellation methods and meta-analyses from functional magnetic resonance imaging to more precisely guide the selection of optode positions for fNIRS experiments.

Brain activity underlying the recovery of meaning from degraded speech: A functional near-infrared spectroscopy (fNIRS) study

The purpose of this study was to establish whether functional near-infrared spectroscopy (fNIRS), an emerging brain-imaging technique based on optical principles, is suitable for studying the brain activity that underlies effortful listening. In an event-related fNIRS experiment, normally-hearing adults listened to sentences that were either clear or degraded (noise vocoded). These sentences were presented simultaneously with a non-speech distractor, and on each trial participants were instructed to attend either to the speech or to the distractor. The primary region of interest for the fNIRS measurements was the left inferior frontal gyrus (LIFG), a cortical region involved in higher-order language processing. The fNIRS results confirmed findings previously reported in the functional magnetic resonance imaging (fMRI) literature. Firstly, the LIFG exhibited an elevated response to degraded versus clear speech, but only when attention was directed towards the speech. This attention-dependent increase in frontal brain activation may be a neural marker for effortful listening. Secondly, during attentive listening to degraded speech, the haemodynamic response peaked significantly later in the LIFG than in superior temporal cortex, possibly reflecting the engagement of working memory to help reconstruct the meaning of degraded sentences. The homologous region in the right hemisphere may play an equivalent role to the LIFG in some left-handed individuals. In conclusion, fNIRS holds promise as a flexible tool to examine the neural signature of effortful listening.

Modelling confounding effects from extracerebral contamination and systemic factors on functional near-infrared spectroscopy

Haemodynamics-based neuroimaging is widely used to study brain function. Regional blood flow changes characteristic of neurovascular coupling provide an important marker of neuronal activation. However, changes in systemic physiological parameters such as blood pressure and concentration of CO2 can also affect regional blood flow and may confound haemodynamics-based neuroimaging. Measurements with functional near-infrared spectroscopy (fNIRS) may additionally be confounded by blood flow and oxygenation changes in extracerebral tissue layers. Here we investigate these confounds using an extended version of an existing computational model of cerebral physiology, ‘BrainSignals’. Our results show that confounding from systemic physiological factors is able to produce misleading haemodynamic responses in both positive and negative directions. By applying the model to data from previous fNIRS studies, we demonstrate that such potentially deceptive responses can indeed occur in at least some experimental scenarios. It is therefore important to record the major potential confounders in the course of fNIRS experiments. Our model may then allow the observed behaviour to be attributed among the potential causes and hence reduce identification errors.

FMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson’s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

FMRI Validation of fNIRS Measurements During a Naturalistic Task

We present a method to compare brain activity recorded with near-infrared spectroscopy (fNIRS) in a dance video game task to that recorded in a reduced version of the task using fMRI (functional magnetic resonance imaging). Recently, it has been shown that fNIRS can accurately record functional brain activities equivalent to those concurrently recorded with functional magnetic resonance imaging for classic psychophysical tasks and simple finger tapping paradigms. However, an often quoted benefit of fNIRS is that the technique allows for studying neural mechanisms of complex, naturalistic behaviors that are not possible using the constrained environment of fMRI. Our goal was to extend the findings of previous studies that have shown high correlation between concurrently recorded fNIRS and fMRI signals to compare neural recordings obtained in fMRI procedures to those separately obtained in naturalistic fNIRS experiments. Specifically, we developed a modified version of the dance video game Dance Dance Revolution (DDR) to be compatible with both fMRI and fNIRS imaging procedures. In this methodology we explain the modifications to the software and hardware for compatibility with each technique as well as the scanning and calibration procedures used to obtain representative results. The results of the study show a task-related increase in oxyhemoglobin in both modalities and demonstrate that it is possible to replicate the findings of fMRI using fNIRS in a naturalistic task. This technique represents a methodology to compare fMRI imaging paradigms which utilize a reduced-world environment to fNIRS in closer approximation to naturalistic, full-body activities and behaviors. Further development of this technique may apply to neurodegenerative diseases, such as Parkinson’s disease, late states of dementia, or those with magnetic susceptibility which are contraindicated for fMRI scanning.

Functional data analysis view of functional near infrared spectroscopy data

Functional near infrared spectroscopy (fNIRS) is a powerful tool for the study of oxygenation and hemodynamics of living tissues. Despite the continuous nature of the processes generating the data, analysis of fNIRS data has been limited to discrete-time methods. We propose a technique, namely functional data analysis (fDA), that converts discrete samples to continuous curves. We used fNIRS data collected on forehead during a cold pressor test (CPT) from 20 healthy subjects. Using functional principal component analysis, oxyhemoglobin (HbO2) and deoxyhemoglobin (Hb) curves were decomposed into several components based on variability across the subjects. Each component corresponded to an experimental condition and provided qualitative and quantitative information of the shape and weight of that component. Furthermore, we applied functional canonical correlation analysis to investigate the interaction between Hb and HbO2 curves. We showed that the variation of Hb and HbO2 was positively correlated during the CPT, with a "far" channel on right forehead showing a smaller and faster HbO2 variation than Hb. This research suggests the fDA platform for the analysis of fNIRS data, which solves problem of high dimensionality, enables study of response dynamics, enhances characterization of the evoked response, and may improve design of future fNIRS experiments.