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

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

Wearables and the brain

The brain is the last frontier for wearable sensing. Commercially available wearables can monitor your vital signs and physical activity, but few have the ability to monitor what goes on inside your head. With the advent of new wearable and portable neuroimaging technologies, this situation might be about to change, with profound implications for neuroscience and for wearables

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology

Developing customized NIRS-EEG for infant sleep research: methodological considerations

Significance: Studies using simultaneous functional near-infrared spectroscopy (fNIRS)-electroencephalography (EEG) during natural sleep in infancy are rare. Developments for combined fNIRS-EEG for sleep research that ensure optimal comfort as well as good coupling and data quality are needed. // Aim: We describe the steps toward developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5 to 9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG. // Approach: N = 49 5- to 9-month-old infants participated. In phase 1, N = 26 (10 = slept) participated using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, N = 23 infants (21 = slept) participated with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel wearable NIRS. We used QT-NIRS to assess the NIRS data quality based on the good time window percentage, included channels, nap duration, and valid EEG percentage. // Results: The infant nap rate during phase 1 was ∼40 % (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61%) than phase 2 (50%), though this difference was not statistically significant. // Conclusions: We demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep with both non-wearable and wearable NIRS systems. The wearable NIRS-EEG headgear represents a good compromise between data quality, opportunities of applications (home visits and toddlers), and experiment success (infants’ comfort, longer sleep duration, and opportunities for caregiver–child interaction)

Brain mechanisms of social signalling in live social interactions with autistic and neurotypical adults

The simple act of watching another person can change a person's behaviour in subtle but important ways; the individual being watched is now capable of signalling to the watcher, and may use this opportunity to communicate to the watcher. Recent data shows that people will spontaneously imitate more when being watched. Here, we examine the neural and cognitive mechanisms of being watched during spontaneous social imitation in autistic and neurotypical adults using fNIRS brain imaging. Participants (n = 44) took part in a block-moving task where they were instructed only to copy the block sequence which people normally do using a straight low action trajectory. Here, the demonstrator sometimes used an atypical 'high' action trajectory, giving participants the opportunity to spontaneously copy the high trajectory even if this slowed their performance. The confederate who demonstrated each block sequence could watch the participant's actions or close her eyes, giving a factorial design with factors of trajectory (high/low) and watched (watched/unwatched). Throughout the task, brain signals were captured from bilateral temporal/parietal/occipital cortex using fNIRS. We found that all participants performed higher actions when being watched by the confederate than when not being watched, with no differences between autistic and neurotypical participants. The unwatched conditions were associated with higher activity of the right inferior parietal lobule in all participants and also engagement of left STS only in autistic participants. These findings are consistent with the claim that people engage different neural mechanisms when watched and unwatched and that participants with autism may engage additional brain mechanisms to match neurotypical behaviour and compensate for social difficulties. However, further studies will be needed to replicate these results in a larger sample of participants

Investigation of functional near-infrared spectroscopy signal quality and development of the hemodynamic phase correlation signal

SIGNIFICANCE: There is a longstanding recommendation within the field of fNIRS to use oxygenated ( HbO 2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on HbO 2 only. Previous work has shown that HbO 2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both HbO 2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of HbO 2 and HHb has been recommended as a method to utilize both signals in analysis. AIM: We present the development of the hemodynamic phase correlation (HPC) signal to combine HbO 2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. APPROACH: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [ HbO 2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. RESULTS: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on HbO 2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. CONCLUSION: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using HbO 2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives

Biological age estimation using an eHealth system based on wearable sensors

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An analysis framework for the integration of broadband NIRS and EEG to assess neurovascular and neurometabolic coupling

With the rapid growth of optical-based neuroimaging to explore human brain functioning, our research group has been developing broadband Near Infrared Spectroscopy (bNIRS) instruments, a technological extension to functional Near Infrared Spectroscopy (fNIRS). bNIRS has the unique capacity of monitoring brain haemodynamics/oxygenation (measuring oxygenated and deoxygenated haemoglobin), and metabolism (measuring the changes in the redox state of cytochrome-c-oxidase). When combined with electroencephalography (EEG), bNIRS provides a unique neuromonitoring platform to explore neurovascular coupling mechanisms. In this paper, we present a novel pipeline for the integrated analysis of bNIRS and EEG signals, and demonstrate its use on multi-channel bNIRS data recorded with concurrent EEG on healthy adults during a visual stimulation task. We introduce the use of the Finite Impulse Response functions within the General Linear Model for bNIRS and show its feasibility to statistically localize the haemodynamic and metabolic activity in the occipital cortex. Moreover, our results suggest that the fusion of haemodynamic and metabolic measures unveils additional information on brain functioning over haemodynamic imaging alone. The cross-correlation-based analysis of interrelationships between electrical (EEG) and haemodynamic/metabolic (bNIRS) activity revealed that the bNIRS metabolic signal offers a unique marker of brain activity, being more closely coupled to the neuronal EEG response

Developing customized NIRS-EEG for infant sleep research: methodological considerations

Significance: Studies using simultaneous fNIRS-EEG during natural sleep in infancy are rare. New developments for combined fNIRS-EEG for sleep research are needed that ensure optimal comfort whilst ensuring good coupling and data quality. Aim: We describe the steps towards developing a comfortable, wearable NIRS-EEG headgear adapted specifically for sleeping infants ages 5-9 months and present the experimental procedures and data quality to conduct infant sleep research using combined fNIRS-EEG. Approach: N=49 5-to-9-months-old infants participated. In phase 1, N=26 (10=slept) using the non-wearable version of the NIRS-EEG headgear with 13-channel-wearable EEG and 39-channel fiber-based NIRS. In phase 2, N=23 infants (21=slept) with the wireless version of the headgear with 20-channel-wearable EEG and 47-channel-wearable-NIRS. We used QT-NIRS to assess NIRS data quality based on: good time window percentage, included channels, nap duration and valid EEG percentage. Results: Infant nap rate during phase 1 was ~40% (45% valid EEG data) and increased to 90% during phase 2 (100% valid EEG data). Infants slept significantly longer with the wearable system than the non-wearable system. However, there were more included good channels based on QT-NIRS in study phase 1 (61 %) than 2 (50 %), though this difference was not statistically significant. Conclusions: We demonstrated the usability of an integrated NIRS-EEG headgear during natural infant sleep both with a non-wearable and wearable NIRS system. The wearable EEG-NIRS headgear represents a good compromise between data quality, opportunities of applications (home visits, toddlers) and experiment success (infants’ comfort, longer sleep duration, opportunities for caregiver-child interaction)

Using multi-modal neuroimaging to characterise social brain specialisation in infants

The specialised regional functionality of the mature human cortex partly emerges through experience-dependent specialisation during early development. Our existing understanding of functional specialisation in the infant brain is based on evidence from unitary imaging modalities and has thus focused on isolated estimates of spatial or temporal selectivity of neural or haemodynamic activation, giving an incomplete picture. We speculate that functional specialisation will be underpinned by better coordinated haemodynamic and metabolic changes in a broadly orchestrated physiological response. To enable researchers to track this process through development, we develop new tools that allow the simultaneous measurement of coordinated neural activity (EEG), metabolic rate, and oxygenated blood supply (broadband near-infrared spectroscopy) in the awake infant. In 4- to 7-month-old infants, we use these new tools to show that social processing is accompanied by spatially and temporally specific increases in coupled activation in the temporal-parietal junction, a core hub region of the adult social brain. During non-social processing, coupled activation decreased in the same region, indicating specificity to social processing. Coupling was strongest with high-frequency brain activity (beta and gamma), consistent with the greater energetic requirements and more localised action of high-frequency brain activity. The development of simultaneous multimodal neural measures will enable future researchers to open new vistas in understanding functional specialisation of the brain