Functional near-infrared spectroscopy for speech protocols: characterization of motion artifacts and guidelines for improving data analysis

On the journey to measure cognitive expertise: What can functional imaging tell us?

BACKGROUND

Experience level correlates with motor cortex and supplementary motor area activation during laparoscopy. Whether brain activation patterns correlate with cognitive surgical task expertise is unknown. We compared the functional neuroimaging responses during simulated operative dictation-a cognitive surgical task-by experience level.

STUDY DESIGN

Junior (postgraduate years 1-3) and senior (postgraduate years 4-5) residents and attendings were recruited over 1 year. After a baseline rest period, participants were asked to dictate a simulated operative note for an open inguinal hernia repair with mesh. Functional near-infrared spectroscopy data were recorded from the prefrontal, sensorimotor, and occipital brain areas. The hemodynamic response based on changes in oxyhemoglobin and deoxyhemoglobin concentrations during the task relative to the pre-task baseline for each participant were calculated. Group-level differences in oxyhemoglobin were evaluated using a general linear model.

RESULTS

Thirty participants, 10 from each of the 3 experience levels, were recruited. In the left prefrontal cortex, senior activation (-182) was stronger than both junior (14) and attending (27) activation (P < .001). In the left premotor cortex, senior activation (-147) was stronger than both junior (-52) and attending (15) activation (P = .008). In the left parietal cortex, senior activation (-255) was stronger than both junior (-41) and attending (12) activation (P < .001).

CONCLUSION

Functional neuroimaging responses during the cognitive task of simulated operative dictation differ by skill level. This study represents the first brain imaging analysis of cognitive function connecting mental imagery, brain activation, and a cognitive surgical task linked to previously performed motor tasks. Functional neuroimaging may act as a nonbiased assessment tool of cognitive skill.

Developmental timing of adversity and neural network organization: An fNIRS study of the impact of refugee displacement

This study investigated the neurodevelopmental impacts of displacement on resettled Syrian refugee children in Canada, focusing on how the timing and duration of adversity experienced during displacement influence neural network organization. Using graph theoretical approaches within a network neuroscience framework, we examined how the developmental timing of displacement (age of displacement, duration of displacement) related to functional integration, segregation, and small-worldness. Syrian refugee children (n = 61, MAge=14 Range = 8-18), completed a resting state scan using functional Near Infrared Spectroscopy (fNIRS) neuroimaging. Data were analyzed to assess the link between neural network properties and developmental timing of adversity. Results indicate that prolonged displacement experienced earlier in life was significantly linked with neural network organization, impacting the balance between the brain's functional integration and segregation as quantified by the overall reduced small worldness in comparison to experiencing displacement at an older age. This study leverages the experiences of refugee children to advance our understanding of how the timing of adversity affects development, providing valuable insights into the broader impacts of early adversity on neurodevelopment.

Evaluation of Neural, Systemic and Extracerebral Activations During Active Walking Tasks in Older Adults Using fNIRS

Functional near infrared spectroscopy (fNIRS) is being increasingly used to assess brain hemodynamic responses during active walking in older adults due to its wearability, and relative immunity to motion artifacts. Specifically, fNIRS allows for continuous monitoring of brain activations that vary in response to experimental manipulations of cognitive demands during active walking tasks. Studies using fNIRS highlighted increased involvement of the prefrontal cortex (PFC) in dual compared to single task walking, operationalized using oxygenated hemoglobin (HbO), due to increasing attention demands inherent in the former task condition in aging and clinical populations. However, current literature utilizing fNIRS in mobility research has not been uniform in terms of fNIRS instrumentation characteristics and the accompanying signal processing methods to separate various signal sources (i.e. neural activations, extracerebral signals, systemic responses) which can raise questions about prior research findings. In our previous studies, we have used a forehead fNIR sensor (fNIR Imager 1100 by fNIR Devices, LLC) with 2.5 cm source detector separation (SDS) at 2 Hz sampling rate which allowed us to reliably evaluate changes in brain activations in the PFC during active walking. However, there exists other fNIRS devices incorporating a number of different types of light sources and detectors allowing multiple channels of long (3 cm SDS) and short (0.8 cm SDS) distance measurements in complex configurations for the monitoring of cognitive activations on various head locations at different depths with higher sampling rates of ~5 Hz (i.e. NIRx sensor, NIR Sport2 by NIRx Medizintechnik GmbH). Such involved designs further allowed the implementation of advanced signal processing algorithms to separate and evaluate neural, systemic and extracerebral signal contributions on the overall measurements. In this study, we collected brain imaging data on a sample of healthy older adults (n =15, age ) under single (STW) and dual task walking (DTW) conditions; participants were evaluated twice during one study visit, once wearing fNIR sensor and a second time while wearing NIRx sensor. This study design allowed us to address critical gaps in the extant literature concerning fNIRS-derived brain activations during active walking. Specifically, we evaluated potential effects of penetration depth as defined by the SDS of the fNIRS device, extracerebral activations (i.e. skin blood flow) and systemic signals (i.e. heart rate) on the observed HbO increases from STW to DTW. Our findings suggested that PFC activation differences between STW and DTW conditions observed in older adults were consistent across fNIRS instrumentations and the observed differences in HbO between STW and DTW were not materially influenced by scalp activations or systemic changes. Nevertheless, efforts to optimize extraction of fNIRS-derived brain signal measurements should continue taking advantage of technological advancement.

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data

Significance

Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data.

Aim

We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination.

Approach

Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach.

Results

When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results.

Conclusions

This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.

Functional near-infrared spectroscopy: A novel tool for detecting consciousness after acute severe brain injury

Recent advancements in functional neuroimaging have demonstrated that some unresponsive patients in the intensive care unit retain a level of consciousness that is inconsistent with their behavioral diagnosis of awareness. Functional near-infrared spectroscopy (fNIRS) is a portable optical neuroimaging method that can be used to measure neural activity with good temporal and spatial resolution. However, the reliability of fNIRS for detecting the neural correlates of consciousness remains to be established. In a series of studies, we evaluated whether fNIRS can record sensory, perceptual, and command-driven neural processing in healthy participants and in behaviorally nonresponsive patients. At the individual healthy subject level, we demonstrate that fNIRS can detect commonly studied resting state networks, sensorimotor processing, speech-specific auditory processing, and volitional command-driven brain activity to a motor imagery task. We then tested fNIRS with three acutely brain injured patients and found that one could willfully modulate their brain activity when instructed to imagine playing a game of tennis-providing evidence of preserved consciousness despite no observable behavioral signs of awareness. The successful application of fNIRS for detecting preserved awareness among behaviorally nonresponsive patients highlights its potential as a valuable tool for uncovering hidden cognitive states in critical care settings.

Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications

The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.

Parkinson's disease patients show delayed hemodynamic changes in primary motor cortex in fine motor tasks and decreased resting-state interhemispheric functional connectivity: a functional near-infrared spectroscopy study

Significance

People with Parkinson's disease (PD) experience changes in fine motor skills, which is viewed as one of the hallmark signs of this disease. Due to its non-invasive nature and portability, functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing changes related to fine motor skills.

Aim

We aim to compare activation patterns in the primary motor cortex using fNIRS, comparing volunteers with PD and sex- and age-matched control participants during a fine motor task and walking. Moreover, inter and intrahemispheric functional connectivity (FC) was investigated during the resting state.

Approach

We used fNIRS to measure the hemodynamic changes in the primary motor cortex elicited by a finger-tapping task in 20 PD patients and 20 controls matched for age, sex, education, and body mass index. In addition, a two-minute walking task was carried out. Resting-state FC was also assessed.

Results

Patients with PD showed delayed hypoactivation in the motor cortex during the fine motor task with the dominant hand and delayed hyperactivation with the non-dominant hand. The findings also revealed significant correlations among various measures of hemodynamic activity in the motor cortex using fNIRS and different cognitive and clinical variables. There were no significant differences between patients with PD and controls during the walking task. However, there were significant differences in interhemispheric connectivity between PD patients and control participants, with a statistically significant decrease in PD patients compared with control participants.

Conclusions

Decreased interhemispheric FC and delayed activity in the primary motor cortex elicited by a fine motor task may one day serve as one of the many potential neuroimaging biomarkers for diagnosing PD.

Relationship between accelerometer-measured sleep duration and Stroop performance: a functional near-infrared spectroscopy study among young adults

OBJECTIVES

Short sleep is becoming more common in modern society. This study aimed to explore the relationship between accelerometer-measured sleep duration and cognitive performance among young adults as well as the underlying hemodynamic mechanisms.

METHODS

A total of 58 participants were included in this study. Participants were asked to wear an ActiGraph GT3X+ accelerometer to identify their sleep duration for 7 consecutive days. Cognitive function was assessed by the Stroop test. Two conditions, including the congruent and incongruent Stroop, were set. In addition, stratified analyses were used to examine sensitivity. 24-channel functional near-infrared spectroscopy (fNIRS) equipment was applied to measure hemodynamic changes of the prefrontal cortex (PFC) during cognitive tasks.

RESULTS

Results showed that sleep duration was positively associated with accuracy of the incongruent Stroop test (0.001 (0.000, 0.002), p = 0.042). Compared with the regular sleep (≥7 h) group, lower accuracy of the incongruent Stroop test (-0.012 (-0.023, -0.002), p = 0.024) was observed in the severe short sleep (<6 h). Moreover, a stratified analysis was conducted to examining gender, age, BMI, birthplace, and education's impact on sleep duration and the incongruent Stroop test accuracy, confirming a consistent correlation across all demographics. In the severe short sleep group, the activation of left middle frontal gyri and right dorsolateral superior frontal gyri were negatively associated with the cognitive performance.

CONCLUSIONS

This study emphasized the importance of maintaining enough sleep schedules in young college students from a fNIRS perspective. The findings of this study could potentially be used to guide sleep time in young adults and help them make sleep schemes.

Learning based motion artifacts processing in fNIRS: a mini review

This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area. We discuss the current landscape of learning-based MA correction methods and highlight research gaps. Noting the absence of standard evaluation metrics for quality assessment of MA correction, we suggest a novel framework, integrating signal and model quality considerations and employing metrics like ΔSignal-to-Noise Ratio (ΔSNR), confusion matrix, and Mean Squared Error. This work aims to facilitate the application of learning-based methodologies to fNIRS and improve the accuracy and reliability of neurovascular studies.

Using preregistration as a tool for transparent fNIRS study design

Significance

The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design and analytical decisions that researchers have to make. Several recent efforts have developed guidelines for preprocessing, analyzing, and reporting practices. For the planning stage of fNIRS studies, similar guidance is desirable. Study preregistration helps researchers to transparently document study protocols before conducting the study, including materials, methods, and analyses, and thus, others to verify, understand, and reproduce a study. Preregistration can thus serve as a useful tool for transparent, careful, and comprehensive fNIRS study design.

Aim

We aim to create a guide on the design and analysis steps involved in fNIRS studies and to provide a preregistration template specified for fNIRS studies.

Approach

The presented preregistration guide has a strong focus on fNIRS specific requirements, and the associated template provides examples based on continuous-wave (CW) fNIRS studies conducted in humans. These can, however, be extended to other types of fNIRS studies.

Results

On a step-by-step basis, we walk the fNIRS user through key methodological and analysis-related aspects central to a comprehensive fNIRS study design. These include items specific to the design of CW, task-based fNIRS studies, but also sections that are of general importance, including an in-depth elaboration on sample size planning.

Conclusions

Our guide introduces these open science tools to the fNIRS community, providing researchers with an overview of key design aspects and specification recommendations for comprehensive study planning. As such it can be used as a template to preregister fNIRS studies or merely as a tool for transparent fNIRS study design.

Controlling jaw-related motion artifacts in functional near-infrared spectroscopy

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) as a non-invasive optical neuroimaging technique has demonstrated great potential in monitoring cerebral activity. Due to its portability and compatibility with medical implants, fNIRS has seen increasing applications in studying the hearing, language and cognitive functions. However, fNIRS is susceptible to artifacts related to jaw movements, such as teeth clenching, swallowing and speaking, which affect recordings over the temporal, parietal and frontal/prefrontal cortices.

NEW METHOD

We investigated two new approaches to control the jaw-related motion artifacts, an individually customized bite bar apparatus and a denoising algorithm namely PCA-GLM based on multi-channel fNIRS recordings from long-separation and short-separation montage. We first recorded data while subjects performed a clenching task, then an auditory task and a resting-state task with and without the bite bar.

RESULTS

Our results have shown that jaw clenching can introduce spurious, task-evoked-like responses in fNIRS signals. A bite bar customized for each participant effectively suppressed the movement-related activities in fNIRS, at both task and resting-state conditions. Moreover, the bite bar and the PCA-GLM denoising method are shown to improve auditory responses, by significantly reducing the within-subject standard deviation, increasing the task-related contrast-to-noise ratio, and yielding stronger activations to the auditory stimuli.

COMPARISON WITH EXISTING METHOD(S)

The current study has demonstrated a novel method to control the jaw-related motion artifacts in fNIRS signals.

CONCLUSIONS

Our method will benefit the study of the hearing, language and cognitive functions in normal healthy subjects and patients.

Functional near-infrared spectroscopy is a useful tool for multi-perspective psychobiological study of neurophysiological correlates of parenting behaviour

The quality of the relationship between caregiver and child has long-term effects on the cognitive and socio-emotional development of children. A process involved in human parenting is the bio-behavioural synchrony that occurs between the partners in the relationship during interaction. Through interaction, bio-behavioural synchronicity allows the adaptation of the physiological systems of the parent to those of the child and promotes the positive development and modelling of the child's social brain. The role of bio-behavioural synchrony in building social bonds could be investigated using functional near-infrared spectroscopy (fNIRS). In this paper we have (a) highlighted the importance of the quality of the caregiver-child relationship for the child's cognitive and socio-emotional development, as well as the relevance of infantile stimuli in the activation of parenting behaviour; (b) discussed the tools used in the study of the neurophysiological substrates of the parental response; (c) proposed fNIRS as a particularly suitable tool for the study of parental responses; and (d) underlined the need for a multi-systemic psychobiological approach to understand the mechanisms that regulate caregiver-child interactions and their bio-behavioural synchrony. We propose to adopt a multi-system psychobiological approach to the study of parental behaviour and social interaction.

Shining a light on cultural neuroscience: Recommendations on the use of fNIRS to study how sociocultural contexts shape the brain

Functional near-infrared spectroscopy (fNIRS) is a portable neuroimaging technique that may serve as a methodological tool for studying how sociocultural contexts can shape the human brain and impact cognition and behavior. The use of fNIRS in community-based research may (a) advance theoretical knowledge in psychology and neuroscience, particularly regarding underrepresented ethnic-racial communities; (b) increase diversity in samples; and (c) provide neurobiological evidence of sociocultural factors supporting human development. The review aims to introduce the use of fNIRS, including its practicalities and limitations, to new adopters inquiring how sociocultural inputs affect the brain. The review begins with an introduction to cultural neuroscience, and a review on the use of fNIRS follows. Next, benefits and guidelines to the design of fNIRS research in naturalistic environments (in the community or in the field) using a cultural lens are discussed. Strengths-based and community-based approaches in cultural neuroscience are recommended throughout. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Revealing the spatiotemporal requirements for accurate subject identification with resting-state functional connectivity: a simultaneous fNIRS-fMRI study

SIGNIFICANCE

Brain fingerprinting refers to identifying participants based on their functional patterns. Despite its success with functional magnetic resonance imaging (fMRI), brain fingerprinting with functional near-infrared spectroscopy (fNIRS) still lacks adequate validation.

AIM

We investigated how fNIRS-specific acquisition features (limited spatial information and nonneural contributions) influence resting-state functional connectivity (rsFC) patterns at the intra-subject level and, therefore, brain fingerprinting.

APPROACH

We performed multiple simultaneous fNIRS and fMRI measurements in 29 healthy participants at rest. Data were preprocessed following the best practices, including the removal of motion artifacts and global physiology. The rsFC maps were extracted with the Pearson correlation coefficient. Brain fingerprinting was tested with pairwise metrics and a simple linear classifier.

RESULTS

Our results show that average classification accuracy with fNIRS ranges from 75% to 98%, depending on the number of runs and brain regions used for classification. Under the right conditions, brain fingerprinting with fNIRS is close to the 99.9% accuracy found with fMRI. Overall, the classification accuracy is more impacted by the number of runs and the spatial coverage than the choice of the classification algorithm.

CONCLUSIONS

This work provides evidence that brain fingerprinting with fNIRS is robust and reliable for extracting unique individual features at the intra-subject level once relevant spatiotemporal constraints are correctly employed.

Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review

With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.

Deep learning-based motion artifact removal in functional near-infrared spectroscopy

Significance: Functional near-infrared spectroscopy (fNIRS), a well-established neuroimaging technique, enables monitoring cortical activation while subjects are unconstrained. However, motion artifact is a common type of noise that can hamper the interpretation of fNIRS data. Current methods that have been proposed to mitigate motion artifacts in fNIRS data are still dependent on expert-based knowledge and the post hoc tuning of parameters. Aim: Here, we report a deep learning method that aims at motion artifact removal from fNIRS data while being assumption free. To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE) architecture for motion artifact removal. Approach: To facilitate the training of this deep learning architecture, we (i) designed a specific loss function and (ii) generated data to mimic the properties of recorded fNIRS sequences. Results: The DAE model outperformed conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency. Conclusion: Overall, this work demonstrates the potential of deep learning models for accurate and fast motion artifact removal in fNIRS data.

Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

Clenching-Related Motion Artifacts in Functional Near-Infrared Spectroscopy in the Auditory Cortex

Functional near-infrared spectroscopy (fNIRS), a non-invasive optical neuroimaging technique, has demonstrated its great potential in monitoring cerebral activity as an alternative to functional magnetic resonance imaging (fMRI) in research and clinical usage. fNIRS has seen increasing applications in studying the auditory cortex in healthy subjects and cochlear implant users. However, fNIRS is susceptible to motion artifacts, especially those related to jaw movement, which can affect fNIRS signals in speech and auditory tasks. This study aimed to investigate the motion artifacts related to jaw movements including clenching, speaking, swallowing, and sniffing in a group of human subjects, and test whether our previously established denoising algorithm namely PCA-GLM can reduce the motion artifacts. Our results have shown that the jaw movements introduced artifacts that resemble task-evoked activations and that the PCA-GLM method effectively reduced the motion artifacts due to the clenching movements. The preliminary results of the present study underline the importance of the removal of the jaw-movement-related artifacts in fNIRS signals and suggest the efficacy of our PCA-GLM method in reducing the motion artifacts. Clinical Relevance- This work studies the motion artifacts due to jaw movements that frequently occur in speech perception and production tasks and validates the efficacy of an established denoising algorithm which benefits fNIRS studies on auditory and language functions.

Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy

Resting-state functional connectivity (rsFC) has gained popularity mainly due to its simplicity and potential for providing insights into various brain disorders. In this vein, functional near-infrared spectroscopy (fNIRS) is an attractive choice due to its portability, flexibility, and low cost, allowing for bedside imaging of brain function. While promising, fNIRS suffers from non-neural signal contaminations (i.e., systemic physiological noise), which can increase correlation across fNIRS channels, leading to spurious rsFC networks. In the present work, we hypothesized that additional measurements with short channels, heart rate, mean arterial pressure, and end-tidal CO2 could provide a better understanding of the effects of systemic physiology on fNIRS-based resting-state networks. To test our hypothesis, we acquired 12 min of resting-state data from 10 healthy participants. Unlike previous studies, we investigated the efficacy of different pre-processing approaches in extracting resting-state networks. Our results are in agreement with previous studies and reinforce the fact that systemic physiology can overestimate rsFC. We expanded on previous work by showing that removal of systemic physiology decreases intra- and inter-subject variability, increasing the ability to detect neural changes in rsFC across groups and over longitudinal studies. Our results show that by removing systemic physiology, fNIRS can reproduce resting-state networks often reported with functional magnetic resonance imaging (fMRI). Finally, the present work details the effects of systemic physiology and outlines how to remove (or at least ameliorate) their contributions to fNIRS signals acquired at rest.

A deep convolutional neural network for estimating hemodynamic response function with reduction of motion artifacts in fNIRS

OBJECTIVE

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique for monitoring hemoglobin concentration changes in a non-invasive manner. However, subject movements are often significant sources of artifacts. While several methods have been developed for suppressing this confounding noise, the conventional techniques have limitations on optimal selections of model parameters across participants or brain regions. To address this shortcoming, we aim to propose a method based on a deep convolutional neural network (CNN).

APPROACH

The U-net is employed as a CNN architecture. Specifically, large-scale training and testing data are generated by combining variants of hemodynamic response function (HRF) with experimental measurements of motion noises. The neural network is then trained to reconstruct hemodynamic response coupled to neuronal activity with a reduction of motion artifacts.

MAIN RESULTS

Using extensive analysis, we show that the proposed method estimates the task-related HRF more accurately than the existing methods of wavelet decomposition and autoregressive models. Specifically, the mean squared error and variance of HRF estimates, based on the CNN, are the smallest among all methods considered in this study. These results are more prominent when the semi-simulated data contains variants of shapes and amplitudes of HRF.

SIGNIFICANCE

The proposed CNN method allows for accurately estimating amplitude and shape of HRF with significant reduction of motion artifacts. This method may have a great potential for monitoring HRF changes in real-life settings that involve excessive motion artifacts.

Comparison of functional activation responses from the auditory cortex derived using multi-distance frequency domain and continuous wave near-infrared spectroscopy

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.

Speech planning and execution in children who stutter: Preliminary findings from a fNIRS investigation

Few studies have investigated the neural mechanisms underlying speech production in children who stutter (CWS), despite the critical importance of understanding these mechanisms closer to the time of stuttering onset. The relative contributions of speech planning and execution in CWS therefore are also unknown. Using functional near-infrared spectroscopy, the current study investigated neural mechanisms of planning and execution in a small sample of 9-12 year-old CWS and controls (N = 12) by implementing two tasks that manipulated speech planning and execution loads. Planning was associated with atypical activation in bilateral inferior frontal gyrus and right supramarginal gyrus. Execution was associated with atypical activation in bilateral precentral gyrus and inferior frontal gyrus, as well as right supramarginal gyrus and superior temporal gyrus. The CWS exhibited some activation patterns that were similar to the adults who stutter (AWS) as reported in our previous study: atypical planning in frontal areas including left inferior frontal gyrus and atypical execution in fronto-temporo-parietal regions including left precentral gyrus, and right inferior frontal, superior temporal, and supramarginal gyri. However, differences also emerged. Whereas CWS and AWS both appear to exhibit atypical activation in right inferior and supramarginal gyri during execution, only CWS appear to exhibit this same pattern during planning. In addition, the CWS appear to exhibit atypical activation in left inferior frontal and right precentral gyri related to execution, whereas AWS do not. These preliminary results are discussed in the context of possible impairments in sensorimotor integration and inhibitory control for CWS.

Correcting physiological noise in whole-head functional near-infrared spectroscopy

BACKGROUND

Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage.

NEW METHOD

We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated.

RESULTS

After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas.

COMPARISON WITH EXISTING METHODS

Analysis showed that our method improves upon the four established preprocessing methods in the literature.

CONCLUSIONS

An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.

Bi-Smoothed Functional Independent Component Analysis for EEG Artifact Removal

Motivated by mapping adverse artifactual events caused by body movements in electroencephalographic (EEG) signals, we present a functional independent component analysis based on the spectral decomposition of the kurtosis operator of a smoothed principal component expansion. A discrete roughness penalty is introduced in the orthonormality constraint of the covariance eigenfunctions in order to obtain the smoothed basis for the proposed independent component model. To select the tuning parameters, a cross-validation method that incorporates shrinkage is used to enhance the performance on functional representations with a large basis dimension. This method provides an estimation strategy to determine the penalty parameter and the optimal number of components. Our independent component approach is applied to real EEG data to estimate genuine brain potentials from a contaminated signal. As a result, it is possible to control high-frequency remnants of neural origin overlapping artifactual sources to optimize their removal from the signal. An R package implementing our methods is available at CRAN.

Brain-wide functional diffuse optical tomography of resting state networks

Objective.Diffuse optical tomography (DOT) has the potential in reconstructing resting state networks (RSNs) in human brains with high spatio-temporal resolutions and multiple contrasts. While several RSNs have been reported and successfully reconstructed using DOT, its full potential in recovering a collective set of distributed brain-wide networks with the number of RSNs close to those reported using functional magnetic resonance imaging (fMRI) has not been demonstrated.Approach.The present study developed a novel brain-wide DOT (BW-DOT) framework that integrates a cap-based whole-head optode placement system with multiple computational approaches, i.e. finite-element modeling, inverse source reconstruction, data-driven pattern recognition, and statistical correlation tomography, to reconstruct RSNs in dual contrasts of oxygenated (HbO) and deoxygenated hemoglobins (HbR).Main results.Our results from the proposed framework revealed a comprehensive set of RSNs and their subnetworks, which collectively cover almost the entire neocortical surface of the human brain, both at the group level and individual participants. The spatial patterns of these DOT RSNs suggest statistically significant similarities to fMRI RSN templates. Our results also reported the networks involving the medial prefrontal cortex and precuneus that had been missed in previous DOT studies. Furthermore, RSNs obtained from HbO and HbR suggest similarity in terms of both the number of RSN types reconstructed and their corresponding spatial patterns, while HbR RSNs show statistically more similarity to fMRI RSN templates and HbO RSNs indicate more bilateral patterns over two hemispheres. In addition, the BW-DOT framework allowed consistent reconstructions of RSNs across individuals and across recording sessions, indicating its high robustness and reproducibility, respectively.Significance.Our present results suggest the feasibility of using the BW-DOT, as a neuroimaging tool, in simultaneously mapping multiple RSNs and its potential values in studying RSNs, particularly in patient populations under diverse conditions and needs, due to its advantages in accessibility over fMRI.

Hearing brain evaluated using near-infrared spectroscopy in congenital toxoplasmosis

Congenital toxoplasmosis (CT) is a known cause of hearing loss directly caused by Toxoplasma gondii. Hearing loss might result from sensory, neural, or sensorineural lesions. Early treated infants rarely develop hearing loss, but retinochoroidal lesions, intracranial calcifications and hydrocephalus are common. In this study, we aimed to evaluate the brain evoked hemodynamic responses of CT and healthy infants during four auditory stimuli: mother infant directed speech, researcher infant directed speech, mother reading and researcher recorded. Children underwent Transitionally Evoked Otoacoustic Emission Auditory Testing and Automated Brainstem Auditory Response tests with normal auditory results, but with a tendency for greater latencies in the CT group compared to the control group. We assessed brain hemodynamics with functional near-infrared spectroscopy (fNIRS) measurements from 61 infants, and we present fNIRS results as frequency maps of activation and deactivation for each stimulus. By evaluating infants in the three first months of life, we observed an individual heterogeneous brain activation pattern in response to all auditory stimuli for both groups. Each channel was activated or deactivated in less than 30% of children for all stimuli. There is a need of prospective studies to evaluate if the neurologic or auditory changes course with compromise of children outcomes.

Accurate Image-guided (Re)Placement of NIRS Probes

BACKGROUND AND OBJECTIVE

Functional near-infrared spectroscopy (fNIRS) has become an attractive choice to neuroscience because of its high temporal resolution, ease of use, non-invasiveness, and affordability. With the advent of wearable fNIRS technology, on-the-spot studies of brain function have become viable. However, the lack of within-subject reproducibility is one of the barriers to the full acceptability of fNIRS. To support the validation of the claim that within-subject reproducibility of fNIRS could benefit from accurate anatomical information, we present in this paper a method to develop an image-based system that improves the placement of the sensors on the scalp at interactive rates.

METHODS

The proposed solution consists of an electromagnetic digitizer and an interactive visualization system that allows monitoring the movements of the digitizer on a real head with respect to the underlying cerebral cortical structures. GPU-based volume raycasting rendering is applied to unveil these structures from the corresponding magnetic resonance imaging volume. Scalp and cortical surface are estimated from the scanned volume to improve depth perception. An alignment algorithm between the real and scanned heads is devised to visually feedback the position of the stylus of the digitizer. Off-screen rendering of the depthmaps of the visible surfaces makes spatial positioning of a 2D interaction pointer possible.

RESULTS

We evaluated the alignment accuracy using four to eight anatomical landmarks and found seven to be a good compromise between precision and efficiency. Next, we evaluated reproducibility in positioning five arbitrarily chosen points on three volunteers by four operators over five sessions. In every session, seven anatomical landmarks were applied in the alignment of the real and the scanned head. For the same volunteer, one-way analysis of variance (ANOVA) revealed no significant differences within the five points digitized by the same operator over five sessions (α = 0.05). In addition, preliminary study of motor cortex activation by right-hand finger tapping showed the potential of our approach to increase functional fNIRS reproducibility.

CONCLUSIONS

Results of experiments suggest that the enhancement of the visualization of the location of the probes on the scalp, relative to the underlying cortical structures, improves reproducibility of fNIRS measurements. As further work, we plan to study the fNIRS reproducibility in other cortical regions and in clinical settings using the proposed system.

Best practices for fNIRS publications

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.

Functional Near-Infrared Spectroscopy in the Study of Speech and Language Impairment Across the Life Span: A Systematic Review

Purpose Functional brain imaging is playing an increasingly important role in the diagnosis and treatment of communication disorders, yet many populations and settings are incompatible with functional magnetic resonance imaging and other commonly used techniques. We conducted a systematic review of neuroimaging studies using functional near-infrared spectroscopy (fNIRS) with individuals with speech or language impairment across the life span. We aimed to answer the following question: To what extent has fNIRS been used to investigate the neural correlates of speech-language impairment? Method This systematic review was preregistered with PROSPERO, the international prospective register of systematic reviews (CRD42019136464). We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol for preferred reporting items for systematic reviews. The database searches were conducted between February and March of 2019 with the following search terms: (a) fNIRS or functional near-infrared spectroscopy or NIRS or near-infrared spectroscopy, (b) speech or language, and (c) disorder or impairment or delay. Results We found 34 fNIRS studies that involved individuals with speech or language impairment across nine categories: (a) autism spectrum disorders; (b) developmental speech and language disorders; (c) cochlear implantation and deafness; (d) dementia, dementia of the Alzheimer's type, and mild cognitive impairment; (e) locked-in syndrome; (f) neurologic speech disorders/dysarthria; (g) stroke/aphasia; (h) stuttering; and (i) traumatic brain injury. Conclusions Though it is not without inherent challenges, fNIRS may have advantages over other neuroimaging techniques in the areas of speech and language impairment. fNIRS has clinical applications that may lead to improved early and differential diagnosis, increase our understanding of response to treatment, improve neuroprosthetic functioning, and advance neurofeedback.

Integration of Spatial Information Increases Reproducibility in Functional Near-Infrared Spectroscopy

As functional near-infrared spectroscopy (fNIRS) is developed as a neuroimaging technique and becomes an option to study a variety of populations and tasks, the reproducibility of the fNIRS signal is still subject of debate. By performing test-retest protocols over different functional tasks, several studies agree that the fNIRS signal is reproducible over group analysis, but the inter-subject and within-subject reproducibility is poor. The high variability at the first statistical level is often attributed to global systemic physiology. In the present work, we revisited the reproducibility of the fNIRS signal during a finger-tapping task across multiple sessions on the same and different days. We expanded on previous studies by hypothesizing that the lack of spatial information of the optodes contributes to the low reproducibility in fNIRS, and we incorporated a real-time neuronavigation protocol to provide accurate cortical localization of the optodes. Our proposed approach was validated in 10 healthy volunteers, and our results suggest that the addition of neuronavigation can increase the within-subject reproducibility of the fNIRS data, particularly in the region of interest. Unlike traditional approaches to positioning the optodes, in which low intra-subject reproducibility has been found, we were able to obtain consistent and robust activation of the contralateral primary motor cortex at the intra-subject level using a neuronavigation protocol. Overall, our findings support the hypothesis that at least part of the variability in fNIRS cannot be only attributed to global systemic physiology. The use of neuronavigation to guide probe positioning, as proposed in this work, has impacts to longitudinal protocols performed with fNIRS.