Automated Processing of fNIRS Data-A Visual Guide to the Pitfalls and Consequences

NiReject: toward automated bad channel detection in functional near-infrared spectroscopy

Significance

The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these "bad channels," little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored.

Aim

We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches.

Approach

We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simulated scenario space of diverse phenomena, we evaluated the NiReject models, six of the most established detection methods in fNIRS, and 11 prominent methods from other domains.

Results

Although the results indicated that a lack of proper detection can strongly bias findings, detection methods were reported in only 32% of the included studies. Semi-supervised models, specifically semi-supervised NiReject, outperformed both established thresholding-based and unsupervised detectors. Hybrid NiReject, utilizing a human feedback loop, addressed the practical challenges of semi-supervised methods while maintaining precise detection and low rating effort.

Conclusions

This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.

Lights on music cognition: A systematic and critical review of fNIRS applications and future perspectives

Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.

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 cohorts 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.

Intoxication due to Δ9-tetrahydrocannabinol is characterized by disrupted prefrontal cortex activity

Neural states of impairment from intoxicating substances, including cannabis, are poorly understood. Cannabinoid 1 receptors, the main target of Δ9-tetrahydrocannabinol (THC), the primary intoxicating cannabinoid in cannabis, are densely localized within prefrontal cortex; therefore, prefrontal brain regions are key locations to examine brain changes that characterize acute intoxication. We conducted a double-blind, randomized, cross-over study in adults, aged 18-55 years, who use cannabis regularly, to determine the effects of acute intoxication on prefrontal cortex resting-state measures, assessed with portable functional near-infrared spectroscopy. Participants received oral THC (10-80 mg, individually dosed to overcome tolerance and achieve acute intoxication) and identical placebo, randomized for order; 185 adults were randomized and 128 completed both study days and had usable data. THC was associated with expected increases in subjective intoxication ratings (ES = 35.30, p < 0.001) and heart rate (ES = 11.15, p = 0.001). THC was associated with decreased correlations and anticorrelations in static resting-state functional connectivity within the prefrontal cortex relative to placebo, with weakest correlations and anticorrelations among those who reported greater severity of intoxication (RSFC between medial PFC-ventromedial PFC and DEQ scores, r = 0.32, p < 0.001; RSFC between bilateral mPFC and DEQ scores, r = -0.28, p = 0.001). Relative to placebo, THC was associated with increased variability (or reduced stability) in dynamic resting-state functional connectivity of the prefrontal cortex at p = 0.001, consistent across a range of window sizes. Finally, using frequency power spectrum analyses, we observed that relative to placebo, THC was associated with widespread reduced spectral power within the prefrontal cortex across the 0.073-0.1 Hz frequency range at p < 0.039. These neural features suggest a disruptive influence of THC on the neural dynamics of the prefrontal cortex and may underlie cognitive impairing effects of THC that are detectable with portable imaging. This study is registered in Clinicaltrials.gov (NCT03655717).

Applications of Functional Near-Infrared Spectroscopy (fNIRS) Neuroimaging During Rehabilitation Following Stroke: A Review

Stroke is a cerebrovascular disease that impairs blood supply to localized brain tissue regions due to various causes. This leads to ischemic and hypoxic lesions, necrosis of the brain tissue, and a variety of functional disorders. Abnormal cortical activation and functional connectivity occur in the brain after a stroke, but the activation patterns and functional reorganization are not well understood. Rehabilitation interventions can enhance functional recovery in stroke patients. However, clinicians require objective measures to support their practice, as outcome measures for functional recovery are based on scale scores. Furthermore, the most effective rehabilitation measures for treating patients are yet to be investigated. Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging method that detects changes in cerebral hemodynamics during task performance. It is widely used in neurological research and clinical practice due to its safety, portability, high motion tolerance, and low cost. This paper briefly introduces the imaging principle and the advantages and disadvantages of fNIRS to summarize the application of fNIRS in post-stroke rehabilitation.

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.

Opening the dialogue: A preliminary exploration of hair color, hair cleanliness, light, and motion effects on fNIRS signal quality

INTRODUCTION

Functional near-infrared spectroscopy (fNIRS) is a promising tool for studying brain activity, offering advantages such as portability and affordability. However, challenges in data collection persist due to factors like participant physiology, environmental light, and gross-motor movements, with limited literature on their impact on fNIRS signal quality. This study addresses four potentially influential factors-hair color, hair cleanliness, environmental light, and gross-motor movements-on fNIRS signal quality. Our aim is to raise awareness and offer insights for future fNIRS research.

METHODS

Six participants (4 Females, 2 Males) took part in four different experiments investigating the effects of hair color, hair cleanliness, environmental light, and gross-motor movements on fNIRS signal quality. Participants in Experiment 1, categorized by hair color, completed a finger-tapping task in a between-subjects block design. Signal quality was compared between each hair color. Participants in Experiments 2 and 3 completed a finger-tapping task in a within-subjects block design, with signal quality being compared across hair cleanliness (i.e., five consecutive days without washing the hair) and environmental light (i.e., sunlight, artificial light, no light, etc.), respectively. Experiment 4 assessed three gross-motor movements (i.e., walking, turning and nodding the head) in a within-subjects block design. Motor movements were then compared to resting blocks. Signal quality was evaluated using Scalp Coupling Index (SCI) measurements.

RESULTS

Lighter hair produced better signals than dark hair, while the impact of environmental light remains uncertain. Hair cleanliness showed no significant effects, but gross motor movements notably reduced signal quality.

CONCLUSION

Our results suggest that hair color, environmental light, and gross-motor movements affect fNIRS signal quality while hair cleanliness does not. Nevertheless, future studies with larger sample sizes are warranted to fully understand these effects. To advance future research, comprehensive documentation of participant demographics and lab conditions, along with signal quality analyses, is essential.

fNIRS is capable of distinguishing laterality of lower body contractions

The use of functional near-infrared spectroscopy (fNIRS) for brain imaging during human movement continues to increase. This technology measures brain activity non-invasively using near-infrared light, is highly portable, and robust to motion artifact. However, the spatial resolution of fNIRS is lower than that of other imaging modalities. It is unclear whether fNIRS has sufficient spatial resolution to differentiate nearby areas of the cortex, such as the leg areas of the motor cortex. Therefore, the purpose of this study was to determine fNIRS' ability to discern laterality of lower body contractions. Activity in the primary motor cortex was recorded in forty participants (mean = 23.4 years, SD = 4.5, female = 23, male = 17) while performing unilateral lower body contractions. Contractions were performed at 30% of maximal force against a handheld dynamometer. These contractions included knee extension, knee flexion, dorsiflexion, and plantar flexion of the left and right legs. fNIRS signals were recorded and stored for offline processing and analysis. Channels of fNIRS data were grouped into regions of interest, with five tolerance conditions ranging from strict to lenient. Four of five tolerance conditions resulted in significant differences in cortical activation between hemispheres. During right leg contractions, the left hemisphere was more active than the right hemisphere. Similarly, during left leg contractions, the right hemisphere was more active than the left hemisphere. These results suggest that fNIRS has sufficient spatial resolution to distinguish laterality of lower body contractions. This makes fNIRS an attractive technology in research and clinical applications in which laterality of brain activity is required during lower body activity.

Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann-Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.

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.

On decoding of rapid motor imagery in a diverse population using a high-density NIRS device

Introduction

Functional near-infrared spectroscopy (fNIRS) aims to infer cognitive states such as the type of movement imagined by a study participant in a given trial using an optical method that can differentiate between oxygenation states of blood in the brain and thereby indirectly between neuronal activity levels. We present findings from an fNIRS study that aimed to test the applicability of a high-density (>3000 channels) NIRS device for use in short-duration (2 s) left/right hand motor imagery decoding in a diverse, but not explicitly balanced, subject population. A side aim was to assess relationships between data quality, self-reported demographic characteristics, and brain-computer interface (BCI) performance, with no subjects rejected from recruitment or analysis.

Methods

BCI performance was quantified using several published methods, including subject-specific and subject-independent approaches, along with a high-density fNIRS decoder previously validated in a separate study.

Results

We found that decoding of motor imagery on this population proved extremely challenging across all tested methods. Overall accuracy of the best-performing method (the high-density decoder) was 59.1 +/- 6.7% after excluding subjects where almost no optode-scalp contact was made over motor cortex and 54.7 +/- 7.6% when all recorded sessions were included. Deeper investigation revealed that signal quality, hemodynamic responses, and BCI performance were all strongly impacted by the hair phenotypical and demographic factors under investigation, with over half of variance in signal quality explained by demographic factors alone.

Discussion

Our results contribute to the literature reporting on challenges in using current-generation NIRS devices on subjects with long, dense, dark, and less pliable hair types along with the resulting potential for bias. Our findings confirm the need for increased focus on these populations, accurate reporting of data rejection choices across subject intake, curation, and final analysis in general, and signal a need for NIRS optode designs better optimized for the general population to facilitate more robust and inclusive research outcomes.

Atypical brain lateralization for speech processing at the sublexical level in autistic children revealed by fNIRS

Autistic children often exhibit atypical brain lateralization of language processing, but it is unclear what aspects of language contribute to this phenomenon. This study employed functional near-infrared spectroscopy to measure hemispheric lateralization by estimating hemodynamic responses associated with processing linguistic and non-linguistic auditory stimuli. The study involved a group of autistic children (N = 20, mean age = 5.8 years) and a comparison group of nonautistic peers (N = 20, mean age = 6.5 years). The children were presented with stimuli with systematically decreasing linguistic relevance: naturalistic native speech, meaningless native speech with scrambled word order, nonnative speech, and music. The results revealed that both groups showed left lateralization in the temporal lobe when listening to naturalistic native speech. However, the distinction emerged between autism and nonautistic in terms of processing the linguistic hierarchy. Specifically, the nonautistic comparison group demonstrated a systematic reduction in left lateralization as linguistic relevance decreased. In contrast, the autism group displayed no such pattern and showed no lateralization when listening to scrambled native speech accompanied by enhanced response in the right hemisphere. These results provide evidence of atypical neural specialization for spoken language in preschool- and school-age autistic children and shed new light on the underlying linguistic correlates contributing to such atypicality at the sublexical level.

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons

Significance

Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities.

Aim

In this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter.

Approach

Oxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i) a typical homogeneous reconstruction and (ii) a theoretical layered reconstruction.

Results

Results suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude).

Conclusions

We have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.

A new perspective for evaluating the efficacy of tACS and tDCS in improving executive functions: A combined tES and fNIRS study

BACKGROUND

Executive function enhancement is considered necessary for improving the quality of life of patients with neurological or psychiatric disorders, such as attention-deficit/hyperactivity disorder, obsessive-compulsive disorder and Alzheimer's disease. Transcranial electrical stimulation (tES) has been shown to have some beneficial effects on executive functioning, but the quantification of these improvements remains controversial. We aimed to explore the potential beneficial effects on executive functioning induced by the use of transcranial alternating current stimulation (tACS)/transcranial direct current stimulation (tDCS) on the right inferior frontal gyrus (IFG) and the accompanying brain function variations in the resting state.

METHODS

We recruited 229 healthy adults to participate in Experiments 1 (105 participants) and 2 (124 participants). The participants in each experiment were randomly divided into tACS, tDCS, and sham groups. The participants completed cognitive tasks to assess behavior related to three core components of executive functions. Functional near-infrared spectroscopy (fNIRS) was used to monitor the hemodynamic changes in crucial cortical regions in the resting state.

RESULTS

Inhibition and cognitive flexibility (excluding working memory) were significantly increased after tACS/tDCS, but there were no significant behavioral differences between the tACS and tDCS groups. fNIRS revealed that tDCS induced decreases in the functional connectivity (increased neural efficiency) of the relevant cortices.

CONCLUSIONS

Enhancement of executive function was observed after tES, and the beneficial effects of tACS/tDCS may need to be precisely evaluated via brain imaging indicators at rest. tDCS revealed better neural benefits than tACS during the stimulation phase. These findings might provide new insights for selecting intervention methods in future studies and for evaluating the clinical efficacy of tES.

Prefrontal cortex hemodynamic activity during a test of lower extremity functional muscle strength in children with cerebral palsy: A functional near-infrared spectroscopy study

Children with cerebral palsy (CP) exhibit impaired motor control and significant muscle weakness due to a brain lesion. However, studies that assess the relationship between brain activity and performance on dynamic functional muscle strength assessments in CP are needed. The aim of this study was to determine the effect of a progressive lateral step-up test on prefrontal cortex (PFC) hemodynamic activity in children with CP. Fourteen ambulatory children with spastic CP (Gross Motor Function Classification System level I; 5-11 y) and 14 age- and sex-matched typically developing control children completed a progressive lateral step-up test at incremental step heights (0, 10, 15 and 20 cm) using their non-dominant lower limb. Hemodynamic activity in the PFC was assessed using non-invasive, portable functional neuroimaging (functional near-infrared spectroscopy). Children with CP completed fewer repetitions at each step height and exhibited lower PFC hemodynamic activity across step heights compared to controls. Lower PFC activation in CP was maintained after statistically controlling for the number of repetitions completed at each step height. PFC hemodynamic activity was not associated with LSUT task performance in children with CP, but a positive relationship was observed in controls at the most challenging 20 cm step height. The results suggest there is an altered PFC recruitment pattern in children with CP during a highly dynamic test of functional strength. Further studies are needed to explore the mechanisms underlying the suppressed PFC activation observed in children with CP compared to typically developing children.

Mechanisms underlying fNIRS-neurofeedback over the prefrontal cortex for participants with binge-eating disorder

OBJECTIVE

Despite the increasing popularity of neurofeedback (NF), aiming at voluntary modulation of dysfunctional prefrontal cortex (PFC) signals in the treatment of binge-eating disorder (BED) and/or overweight, mechanisms remain poorly understood.

METHODS

Based on a randomized-controlled trial offering 12 food-specific real-time functional near-infrared spectroscopy (rtfNIRS)-NF sessions to participants with BED (n = 22), this preregistered study examined (1) online regulation success as predictor for offline regulation success, defined by PFC signals during regulation versus watch, and subjective regulation success, and (2) changes in loss of control (LOC) eating after vs. before and across 12 rtfNIRS-NF-sessions.

RESULTS

Higher online regulation success expectedly predicted better subjective, but worse offline regulation success. LOC eating decreased after vs. before, but not over rtfNIRS-NF-sessions, and was not associated with subjective or offline regulation success.

CONCLUSIONS

The association between online and subjective regulation success confirmed the presumed mechanism of operant conditioning underlying rtfNIRS-NF-learning. The contrary association between online and offline regulation indicated differential PFC involvement upon subtraction of automatic food-specific responses from regulation signals for offline success. Decreased LOC eating after food-specific rtfNIRS-NF-sessions suggested the potential of NF in BED treatment.

SIGNIFICANCE

Results may guide the optimization of future NF studies in larger samples with BED.

Premotor and Posterior Parietal Cortex Activity is Increased for Slow, as well as Fast Walking Poststroke: An fNIRS Study

Methods

Twenty individuals in the chronic stage of stroke walked: (1) at their normal pace, (2) slower than normal, and (3) as fast as possible. Functional near-infrared spectroscopy was used to assess bilateral prefrontal, premotor, sensorimotor, and posterior parietal cortices during walking.

Results

No significant differences in laterality were observed between walking speeds. The ipsilesional prefrontal cortex was overall more active than the contralesional prefrontal cortex. Premotor and posterior parietal cortex activity were larger during slow and fast walking compared to normal-paced walking with no differences between slow and fast walking. Greater increases in brain activation in the ipsilesional prefrontal cortex during fast compared to normal-paced walking related to greater gait speed modulation.

Conclusions

Brain activation is not linearly related to gait speed. Ipsilesional prefrontal cortex, bilateral premotor, and bilateral posterior parietal cortices are important areas for gait speed modulation and could be an area of interest for neurostimulation.

Neural correlates of valence-dependent belief and value updating during uncertainty reduction: An fNIRS study

Selective use of new information is crucial for adaptive decision-making. Combining a gamble bidding task with assessing cortical responses using functional near-infrared spectroscopy (fNIRS), we investigated potential effects of information valence on behavioral and neural processes of belief and value updating during uncertainty reduction in young adults. By modeling changes in the participants' expressed subjective values using a Bayesian model, we dissociated processes of (i) updating beliefs about statistical properties of the gamble, (ii) updating values of a gamble based on new information about its winning probabilities, as well as (iii) expectancy violation. The results showed that participants used new information to update their beliefs and values about the gambles in a quasi-optimal manner, as reflected in the selective updating only in situations with reducible uncertainty. Furthermore, their updating was valence-dependent: information indicating an increase in winning probability was underweighted, whereas information about a decrease in winning probability was updated in good agreement with predictions of the Bayesian decision theory. Results of model-based and moderation analyses showed that this valence-dependent asymmetry was associated with a distinct contribution of expectancy violation, besides belief updating, to value updating after experiencing new positive information regarding winning probabilities. In line with the behavioral results, we replicated previous findings showing involvements of frontoparietal brain regions in the different components of updating. Furthermore, this study provided novel results suggesting a valence-dependent recruitment of brain regions. Individuals with stronger oxyhemoglobin responses during value updating was more in line with predictions of the Bayesian model while integrating new information that indicates an increase in winning probability. Taken together, this study provides first results showing expectancy violation as a contributing factor to sub-optimal valence-dependent updating during uncertainty reduction and suggests limitations of normative Bayesian decision theory.

A promising tool to explore functional impairment in neurodegeneration: A systematic review of near-infrared spectroscopy in dementia

This systematic review aimed to evaluate previous studies which used near-infrared spectroscopy (NIRS) in dementia given its suitability as a diagnostic and investigative tool in this population. From 800 identified records which used NIRS in dementia and prodromal stages, 88 studies were evaluated which employed a range of tasks testing memory (29), word retrieval (24), motor (8) and visuo-spatial function (4), and which explored the resting state (32). Across these domains, dementia exhibited blunted haemodynamic responses, often localised to frontal regions of interest, and a lack of task-appropriate frontal lateralisation. Prodromal stages, such as mild cognitive impairment, revealed mixed results. Reduced cognitive performance accompanied by either diminished functional responses or hyperactivity was identified, the latter suggesting a compensatory response not present at the dementia stage. Despite clear evidence of alterations in brain oxygenation in dementia and prodromal stages, a consensus as to the nature of these changes is difficult to reach. This is likely partially due to the lack of standardisation in optical techniques and processing methods for the application of NIRS to dementia. Further studies are required exploring more naturalistic settings and a wider range of dementia subtypes.

Test-retest reliability of fNIRS in resting-state cortical activity and brain network assessment in stroke patients

Resting-state functional near infrared spectroscopy (fNIRS) scanning has attracted considerable attention in stroke rehabilitation research in recent years. The aim of this study was to quantify the reliability of fNIRS in cortical activity intensity and brain network metrics among resting-state stroke patients, and to comprehensively evaluate the effects of frequency selection, scanning duration, analysis and preprocessing strategies on test-retest reliability. Nineteen patients with stroke underwent two resting fNIRS scanning sessions with an interval of 24 hours. The haemoglobin signals were preprocessed by principal component analysis, common average reference and haemodynamic modality separation (HMS) algorithm respectively. The cortical activity, functional connectivity level, local network metrics (degree, betweenness and local efficiency) and global network metrics were calculated at 25 frequency scales × 16 time windows. The test-retest reliability of each fNIRS metric was quantified by the intraclass correlation coefficient. The results show that (1) the high-frequency band has higher ICC values than the low-frequency band, and the fNIRS metric is more reliable than at the individual channel level when averaged within the brain region channel, (2) the ICC values of the low-frequency band above the 4-minute scan time are generally higher than 0.5, the local efficiency and global network metrics reach high and excellent reliability levels after 4 min (0.5 < ICC < 0.9), with moderate or even poor reliability for degree and betweenness (ICC < 0.5), (3) HMS algorithm performs best in improving the low-frequency band ICC values. The results indicate that a scanning duration of more than 4 minutes can lead to high reliability of most fNIRS metrics when assessing low-frequency resting brain function in stroke patients. It is recommended to use the global correction method of HMS, and the reporting of degree, betweenness and single channel level should be performed with caution. This paper provides the first comprehensive reference for resting-state experimental design and analysis strategies for fNIRS in stroke rehabilitation.

Transcranial direct current stimulation over the right dorsolateral prefrontal cortex increases oxyhemoglobin concentration and cognitive performance dependent on cognitive load

Transcranial direct current stimulation (tDCS) has been explored as a potential method for cognitive enhancement. tDCS may induce a cascade of neurophysiological changes including alterations in cerebral oxygenation. However, the effects of tDCS on the cognitive-cerebral oxygenation interaction remains unclear. Further, oxygenation variability across individuals remains minimally controlled for. The purpose of this sham-controlled study was to test the effects of anodal tDCS over the right dorsolateral prefrontal cortex (DLPFC) on the interaction between working memory and cerebral oxygenation while controlling for individual oxygenation variability. Thirty-three adults received resting-state functional near-infrared spectroscopy (fNIRS) recordings over bilateral prefrontal cortices. Following this, working memory was tested using a Toulouse n-back task concurrently paired with fNIRS, with measurements taken before and after 20 min of anodal or sham tDCS at 1.5 mA. With individual oxygenation controlled for, anodal tDCS was found to increase the oxyhemoglobin concentration over the right DLPFC during the 2-back (q = .015) and 3-back (q = .008) conditions. Additionally, anodal tDCS was found to improve accuracy during the 3-back task by 13.4 % (p = .028) and decrease latency by 250 ms (p = .013). The increase in oxyhemoglobin was strongly correlated with increases in accuracy (p = .041) and decreases in latency during the 3-back span (p = .017). Taken together, anodal tDCS over the right DLPFC was found to regionally increase oxyhemoglobin concentrations and improve working memory performance in higher cognitive load conditions.

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.

Interpersonal Competition in Elderly Couples: A Functional Near-Infrared Spectroscopy Hyperscanning Study

Elderly people tend not to compete with others, and if they do, the mechanism behind the competition is not clear. In this study, groups of elderly couples and matched cross-sex controls were recruited to perform a competitive button-pressing task, while their brain signals were simultaneously collected using functional near-infrared spectroscopy (fNIRS) hyperscanning. Several fundamental observations were made. First, controls showed attenuated interpersonal competition across task processes, but couples held the competition with each other. Second, couples demonstrated increased inter-brain synchronization (IBS) between the middle temporal cortex and the temporoparietal junction across task processes. Third, Granger causality analysis in couples revealed significant differences between the directions (i.e., from men to women, and from women to men) in the first half of the competitive task, whereas there was no significant difference in the second half. Finally, the groups of couples and controls could be successfully discriminated against based on IBS by using a machine-learning approach. In sum, these findings indicate that elderly couples can maintain interpersonal competition, and such maintenance might be associated with changes in the IBS of the mentalizing system. It suggests the possible positive impact of long-term spouse relationships on interpersonal interactions, both behaviorally and neurally, in terms of competition.

Optical neuroimaging and neurostimulation in surgical training and assessment: A state-of-the-art review

Introduction

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique used to assess surgeons' brain function. The aim of this narrative review is to outline the effect of expertise, stress, surgical technology, and neurostimulation on surgeons' neural activation patterns, and highlight key progress areas required in surgical neuroergonomics to modulate training and performance.

Methods

A literature search of PubMed and Embase was conducted to identify neuroimaging studies using fNIRS and neurostimulation in surgeons performing simulated tasks.

Results

Novice surgeons exhibit greater haemodynamic responses across the pre-frontal cortex than experts during simple surgical tasks, whilst expert surgical performance is characterized by relative prefrontal attenuation and upregulation of activation foci across other regions such as the supplementary motor area. The association between PFC activation and mental workload follows an inverted-U shaped curve, activation increasing then attenuating past a critical inflection point at which demands outstrip cognitive capacity Neuroimages are sensitive to the impact of laparoscopic and robotic tools on cognitive workload, helping inform the development of training programs which target neural learning curves. FNIRS differs in comparison to current tools to assess proficiency by depicting a cognitive state during surgery, enabling the development of cognitive benchmarks of expertise. Finally, neurostimulation using transcranial direct-current-stimulation may accelerate skill acquisition and enhance technical performance.

Conclusion

FNIRS can inform the development of surgical training programs which modulate stress responses, cognitive learning curves, and motor skill performance. Improved data processing with machine learning offers the possibility of live feedback regarding surgeons' cognitive states during operative procedures.

Functional Near-Infrared Spectroscopy as a Personalized Digital Healthcare Tool for Brain Monitoring

The sustained growth of digital healthcare in the field of neurology relies on portable and cost-effective brain monitoring tools that can accurately monitor brain function in real time. Functional near-infrared spectroscopy (fNIRS) is one such tool that has become popular among researchers and clinicians as a practical alternative to functional magnetic resonance imaging, and as a complementary tool to modalities such as electroencephalography. This review covers the contribution of fNIRS to the personalized goals of digital healthcare in neurology by identifying two major trends that drive current fNIRS research. The first major trend is multimodal monitoring using fNIRS, which allows clinicians to access more data that will help them to understand the interconnection between the cerebral hemodynamics and other physiological phenomena in patients. This allows clinicians to make an overall assessment of physical health to obtain a more-detailed and individualized diagnosis. The second major trend is that fNIRS research is being conducted with naturalistic experimental paradigms that involve multisensory stimulation in familiar settings. Cerebral monitoring of multisensory stimulation during dynamic activities or within virtual reality helps to understand the complex brain activities that occur in everyday life. Finally, the scope of future fNIRS studies is discussed to facilitate more-accurate assessments of brain activation and the wider clinical acceptance of fNIRS as a medical device for digital healthcare.

Functional near-infrared spectroscopy measures of neural activity in children with and without developmental language disorder during a working memory task

INTRODUCTION

Children with developmental language disorder (DLD) exhibit cognitive deficits that interfere with their ability to learn language. Little is known about the functional neuroanatomical differences between children developing typically (TD) and children with DLD.

METHODS

Using functional near-infrared spectroscopy, we recorded oxygenated hemoglobin (O2 hb) concentration values associated with neural activity in children with and without DLD during an auditory N-back task that included 0-back, 1-back, and 2-back conditions. Analyses focused on the left dorsolateral prefrontal cortex (DLPFC) and left inferior parietal lobule (IPL). Multilevel models were constructed with accuracy, response time, and O2 hb as outcome measures, with 0-back outcomes as fixed effects to control for sustained attention.

RESULTS

Children with DLD were significantly less accurate than their TD peers at both the 1-back and 2-back tasks, and they demonstrated slower response times during 2-back. In addition, children in the TD group demonstrated significantly greater sensitivity to increased task difficulty, showing increased O2 hb to the IPL during 1-back and to the DLPFC during the 2-back, whereas the DLD group did not. A secondary analysis revealed that higher O2 hb in the DLPFC predicted better task accuracy across groups.

CONCLUSION

When task difficulty increased, children with DLD failed to recruit the DLPFC for monitoring information and the IPL for processing information. Reduced memory capacity and reduced engagement likely contribute to the language learning difficulties of children with DLD.

Cerebral Hemodynamics and Microvasculature Changes in Relation to White Matter Microstructure After Pediatric Mild Traumatic Brain Injury: An A-CAP Pilot Study

Advanced neuroimaging techniques show promise as a biomarker for mild traumatic brain injury (mTBI). However, little research has evaluated cerebral hemodynamics or its relation to white matter microstructure post-mTBI in children. This novel pilot study examined differences in cerebral hemodynamics, as measured using functional near-infrared spectroscopy (fNIRS), and its association with diffusion tensor imaging (DTI) metrics in children with mTBI or mild orthopedic injury (OI) to address these gaps. Children 8.00-16.99 years of age with mTBI (n = 9) or OI (n = 6) were recruited in a pediatric emergency department, where acute injury characteristics were assessed. Participants completed DTI twice, post-acutely (2-33 days) and chronically (3 or 6 months), and fNIRS ∼1 month post-injury. Automated deterministic tractography was used to compute DTI metrics. There was reduced absolute phase globally and coherence in the dorsolateral pre-frontal cortex (DLPFC) after mTBI compared to the OI group. Coherence in the DLPFC and absolute phase globally showed distinct associations with fractional anisotropy in interhemispheric white matter pathways. Two fNIRS metrics (coherence and absolute phase) differentiated mTBI from OI in children. Variability in cerebral hemodynamics related to white matter microstructure. The results provide initial evidence that fNIRS may have utility as a clinical biomarker of pediatric mTBI.

Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory

Although theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this relationship. The present study aimed to fill this gap in the literature. Using a between-subject design, 17 programming novices and 18 advanced students performed an n-back working-memory task. During the experiment, their prefrontal hemodynamic responses were measured using a 48-channel functional near-infrared spectroscopy (fNIRS) device. The results indicated that the advanced students had a higher working-memory capacity than the novice students, validating the relationship between programming ability and working memory. The analysis results also showed that the hemodynamic responses in the prefrontal cortex can be used to discriminate between novices and advanced students. Additionally, we utilized an attention-based convolutional neural network to analyze the spatial domains of the fNIRS signals and demonstrated that the left prefrontal cortex was more important than other brain regions for programming ability prediction. This result was consistent with the results of statistical analysis, which in turn improved the interpretability of neural networks.

Whole-Head Functional Near-Infrared Spectroscopy as an Ecological Monitoring Tool for Assessing Cortical Activity in Parkinson's Disease Patients at Different Stages

Functional near-infrared spectroscopy (fNIRS) is increasingly employed as an ecological neuroimaging technique in assessing age-related chronic neurological disorders, such as Parkinson's disease (PD), mainly providing a cross-sectional characterization of clinical phenotypes in ecological settings. Current fNIRS studies in PD have investigated the effects of motor and non-motor impairment on cortical activity during gait and postural stability tasks, but no study has employed fNIRS as an ecological neuroimaging tool to assess PD at different stages. Therefore, in this work, we sought to investigate the cortical activity of PD patients during a motor grasping task and its relationship with both the staging of the pathology and its clinical variables. This study considered 39 PD patients (age 69.0 ± 7.64, 38 right-handed), subdivided into two groups at different stages by the Hoehn and Yahr (HY) scale: early PD (ePD; N = 13, HY = [1; 1.5]) and moderate PD (mPD; N = 26, HY = [2; 2.5; 3]). We employed a whole-head fNIRS system with 102 measurement channels to monitor brain activity. Group-level activation maps and region of interest (ROI) analysis were computed for ePD, mPD, and ePD vs. mPD contrasts. A ROI-based correlation analysis was also performed with respect to contrasted subject-level fNIRS data, focusing on age, a Cognitive Reserve Index questionnaire (CRIQ), disease duration, the Unified Parkinson's Disease Rating Scale (UPDRS), and performances in the Stroop Color and Word (SCW) test. We observed group differences in age, disease duration, and the UPDRS, while no significant differences were found for CRIQ or SCW scores. Group-level activation maps revealed that the ePD group presented higher activation in motor and occipital areas than the mPD group, while the inverse trend was found in frontal areas. Significant correlations with CRIQ, disease duration, the UPDRS, and the SCW were mostly found in non-motor areas. The results are in line with current fNIRS and functional and anatomical MRI scientific literature suggesting that non-motor areas-primarily the prefrontal cortex area-provide a compensation mechanism for PD motor impairment. fNIRS may serve as a viable support for the longitudinal assessment of therapeutic and rehabilitation procedures, and define new prodromal, low-cost, and ecological biomarkers of disease progression.

A functional near-infrared spectroscopy study of the effects of video game-based bilateral upper limb training on brain cortical activation and functional connectivity

Video game-based therapies are widely used in rehabilitation. Compared with conventional bilateral upper limb training (CBULT), the effects of video game-based bilateral upper limb training (VGBULT) on brain cortical activation and functional connectivity, still not fully clear. We have developed a VGBULT system, and measured the brain activity of 20 elderly subjects (10 male, mean age = 62.4 ± 5.8) while performing CBULT and VGBULT tasks by using functional near infrared spectroscopy (fNIRS). The results showed that the cerebral cortex of the two groups both showed significant activation (p < 0.05), compared with the baseline; In the VGBLUT group, the activation of motor cortex (MC) and prefrontal cortex (PFC) was stronger, and the functional connectivity between PFC and MC was also enhanced. This study showed that VGBULT is potentially more beneficial for the elderly neural activities and cognitive control, and provides a theoretical basis for future research and development of such rehabilitation products. Moreover, fNIRS is a reliable tool for tracking brain activation in the evaluation of retraining regimens.

Systematic review of fNIRS studies reveals inconsistent chromophore data reporting practices

SIGNIFICANCE

Functional near-infrared spectroscopy (fNIRS) is unique among neuroimaging techniques in its ability to estimate changes in both oxyhemoglobin (HbO) and deoxyhemoglobin (HbR). However, fNIRS research has applied various data reporting practices based on these chromophores as measures of neural activation.

AIM

To quantify the variability of fNIRS chromophore data reporting practices and to explore recent data reporting trends in the literature.

APPROACH

We reviewed 660 fNIRS papers from 2015, 2018, and 2021 to extract information on fNIRS chromophore data reporting practices.

RESULTS

Our review revealed five general practices for reporting fNIRS chromophores: (1) HbO only, (2) HbR only, (3) HbO and HbR, (4) correlation-based signal improvement, and (5) either the total (HbT) or difference (HbDiff) in concentration between chromophores. The field was primarily divided between reporting HbO only and reporting HbO and HbR. However, reporting one chromophore (HbO) was consistently observed as the most popular data reporting practice for each year reviewed.

CONCLUSIONS

Our results highlight the high heterogeneity of chromophore data reporting in fNIRS research. We discuss its potential implications for study comparison efforts and interpretation of results. Most importantly, our review demonstrates the need for a standard chromophore reporting practice to improve scientific transparency and, ultimately, to better understand how neural events relate to cognitive phenomena.

A Machine Learning Perspective on fNIRS Signal Quality Control Approaches

Despite a rise in the use of functional Near Infra-Red Spectroscopy (fNIRS) to study neural systems, fNIRS signal processing is not standardized and is highly affected by empirical and manual procedures. At the beginning of any signal processing procedure, Signal Quality Control (SQC) is critical to prevent errors and unreliable results. In fNIRS analysis, SQC currently relies on applying empirical thresholds to handcrafted Signal Quality Indicators (SQIs). In this study, we use a dataset of fNIRS signals (N = 1,340) recorded from 67 subjects, and manually label the signal quality of a subset of segments (N = 548) to investigate the pitfalls of current practices while exploring the opportunities provided by Deep Learning approaches. We show that SQIs statistically discriminate signals with bad quality, but the identification by means of empirical thresholds lacks sensitivity. Alternatively to manual thresholding, conventional machine learning models based on the SQIs have been proven more accurate, with end-to-end approaches, based on Convolutional Neural Networks, capable of further improving the performance. The proposed approach, based on machine learning, represents a more objective SQC for fNIRS and moves towards the use of fully automated and standardized procedures.

Phase-dependent Brain Activation of the Frontal and Parietal Regions During Walking After Stroke - An fNIRS Study

Background

Recovery of walking post-stroke is highly variable. Accurately measuring and documenting functional brain activation characteristics during walking can help guide rehabilitation. Previous work in this area has been limited to investigations of frontal brain regions and have not utilized recent technological and analytical advances for more accurate measurements. There were three aims for this study: to characterize the hemodynamic profile during walking post-stroke, to investigate regional changes in brain activation during different phases of walking, and to related brain changes to clinical measures.

Methods

Functional near-infrared spectroscopy (fNIRS) along the pre-frontal, premotor, sensorimotor, and posterior parietal cortices was used on twenty individuals greater than six months post-stroke. Individual fNIRS optodes were digitized and used to estimate channel locations on each participant and short separation channels were used to control for extracerebral hemodynamic changes. Participants walked at their comfortable pace several times along a hallway while brain activation was recorded. Exploratory cluster analysis was conducted to determine if there was a link between brain activation and clinical measures.

Results

Sustained activation was observed in the pre-frontal cortex with the ipsilesional hemisphere showing greater activation compared to the contralesional side. Sensorimotor cortex was active during the early, acceleration stage of walking only. Posterior parietal cortex showed changes in activation during the later, steady-state stage of walking. Faster gait speeds also related to increased activation in contralesional sensorimotor and posterior parietal cortices. Exploratory analysis clustered participants into two distinct groups based on their brain activation profiles and generally showed that individuals with greater activation tended to have better physical outcomes.

Conclusions

These findings can guide future research for obtaining adequate power and determining factors that can be used as effect modifiers to reduce inter-subject variability. Overall, this is the first study to report specific oxygenated and deoxygenated hemoglobin changes in frontal to parietal regions during walking in the stroke population. Our results shed light on the importance of measuring brain activation across the cortex and show the importance of pre-frontal, sensorimotor, and posterior parietal cortices in walking after a stroke.

Head hemodynamics and systemic responses during auditory stimulation

The present study aims to analyze the systemic response to auditory stimulation by means of hemodynamic (cephalic and peripheral) and autonomic responses in a broad range of auditory intensities (70.9, 77.9, 84.5, 89.5, 94.5 dBA). This approach could help to understand the possible influence of the autonomic nervous system on the cephalic blood flow. Twenty-five subjects were exposed to auditory stimulation while electrodermal activity (EDA), photoplethysmography (PPG), electrocardiogram, and functional near-infrared spectroscopy signals were recorded. Seven trials with 20 individual tones, each for the five intensities, were presented. The results showed a differentiated response to the higher intensity (94.5 dBA) with a decrease in some peripheral signals such as the heart rate (HR), the pulse signal, the pulse transit time (PTT), an increase of the LFnu power in PPG, and at the head level a decrease in oxygenated and total hemoglobin concentration. After the regression of the visual channel activity from the auditory channels, a decrease in deoxyhemoglobin in the auditory cortex was obtained, indicating a likely active response at the highest intensity. Nevertheless, other measures, such as EDA (Phasic and Tonic), and heart rate variability (Frequency and time domain) showed no significant differences between intensities. Altogether, these results suggest a systemic and complex response to high-intensity auditory stimuli. The results obtained in the decrease of the PTT and the increase in LFnu power of PPG suggest a possible vasoconstriction reflex by a sympathetic control of vascular tone, which could be related to the decrease in blood oxygenation at the head level.

Frontal, Sensorimotor, and Posterior Parietal Regions Are Involved in Dual-Task Walking After Stroke

Background

Walking within the community requires the ability to walk while simultaneously completing other tasks. After a stroke, completing an additional task while walking is significantly impaired, and it is unclear how the functional activity of the brain may impact this.

Methods

Twenty individual in the chronic stage post-stroke participated in this study. Functional near-infrared spectroscopy (fNIRS) was used to measure prefrontal, pre-motor, sensorimotor, and posterior parietal cortices during walking and walking while completing secondary verbal tasks of varying difficulty. Changes in brain activity during these tasks were measured and relationships were accessed between brain activation changes and cognitive or motor abilities.

Results

Significantly larger activations were found for prefrontal, pre-motor, and posterior parietal cortices during dual-task walking. Increasing dual-task walking challenge did not result in an increase in brain activation in these regions. Higher general cognition related to lower increases in activation during the easier dual-task. With the harder dual-task, a trend was also found for higher activation and less motor impairment.

Conclusions

This is the first study to show that executive function, motor preparation/planning, and sensorimotor integration areas are all important for dual-task walking post-stroke. A lack of further brain activation increase with increasing challenge suggests a point at which a trade-off between brain activation and performance occurs. Further research is needed to determine if training would result in further increases in brain activity or improved performance.

Functional Reorganization of the Central Auditory System in Children with Single-Sided Deafness: A Protocol Using fNIRS

In children, single-sided deafness (SSD) affects the development of linguistic and social skills and can impede educational progress. These difficulties may relate to cortical changes that occur following SSD, such as reduced inter-hemispheric functional asymmetry and maladaptive brain plasticity. To investigate these neuronal changes and their evolution in children, a non-invasive technique is required that is little affected by motion artifacts. Here, we present a research protocol that uses functional near-infrared spectroscopy (fNIRS) to evaluate the reorganization of cortical auditory asymmetry in children with SSD; it also examines how the cortical changes relate to auditory and language skills. The protocol is designed for children whose SSD has not been treated, because hearing restoration can alter both brain reorganization and behavioral performance. We propose a single-center, cross-sectional study that includes 30 children with SSD (congenital or acquired moderate-to-profound deafness) and 30 children with normal hearing (NH), all aged 5–16 years. The children undergo fNIRS during monaural and binaural stimulation, and the pattern of cortical activity is analyzed using measures of the peak amplitude and area under the curve for both oxy- and deoxyhemoglobin. These cortical measures can be compared between the two groups of children, and analyses can be run to determine whether they relate to binaural hearing (speech-in-noise and sound localization), speech perception and production, and quality of life (QoL). The results could be of relevance for developing individualized rehabilitation programs for SSD, which could reduce patients’ difficulties and prevent long-term neurofunctional and clinical consequences.

Detecting Fear of Heights Response to a Virtual Reality Environment Using Functional Near-Infrared Spectroscopy

To enable virtual reality exposure therapy (VRET) that treats anxiety disorders by gradually exposing the patient to fear using virtual reality (VR), it is important to monitor the patient's fear levels during the exposure. Despite the evidence of a fear circuit in the brain as reflected by functional near-infrared spectroscopy (fNIRS), the measurement of fear response in highly immersive VR using fNIRS is limited, especially in combination with a head-mounted display (HMD). In particular, it is unclear to what extent fNIRS can differentiate users with and without anxiety disorders and detect fear response in a highly ecological setting using an HMD. In this study, we investigated fNIRS signals captured from participants with and without a fear of height response. To examine the extent to which fNIRS signals of both groups differ, we conducted an experiment during which participants with moderate fear of heights and participants without it were exposed to VR scenarios involving heights and no heights. The between-group statistical analysis shows that the fNIRS data of the control group and the experimental group are significantly different only in the channel located close to right frontotemporal lobe, where the grand average oxygenated hemoglobin Δ[HbO] contrast signal of the experimental group exceeds that of the control group. The within-group statistical analysis shows significant differences between the grand average Δ[HbO] contrast values during fear responses and those during no-fear responses, where the Δ[HbO] contrast values of the fear responses were significantly higher than those of the no-fear responses in the channels located towards the frontal part of the prefrontal cortex. Also, the channel located close to frontocentral lobe was found to show significant difference for the grand average deoxygenated hemoglobin contrast signals. Support vector machine-based classifier could detect fear responses at an accuracy up to 70% and 74% in subject-dependent and subject-independent classifications, respectively. The results demonstrate that cortical hemodynamic responses of a control group and an experimental group are different to a considerable extent, exhibiting the feasibility and ecological validity of the combination of VR-HMD and fNIRS to elicit and detect fear responses. This research thus paves a way toward the a brain-computer interface to effectively manipulate and control VRET.

An fNIRS Investigation of Discrete and Continuous Cognitive Demands During Dual-Task Walking in Young Adults

Introduction: Dual-task studies have demonstrated that walking is attention-demanding for younger adults. However, numerous studies have attributed this to task type rather than the amount of required to accomplish the task. This study examined four tasks: two discrete (i.e., short intervals of attention) and two continuous (i.e., sustained attention) to determine whether greater attentional demands result in greater dual-task costs due to an overloaded processing capacity.

Methods: Nineteen young adults (21.5 ± 3.6 years, 13 females) completed simple reaction time (SRT) and go/no-go (GNG) discrete cognitive tasks and n-back (NBK) and double number sequence (DNS) continuous cognitive tasks with or without self-paced walking. Prefrontal cerebral hemodynamics were measured using functional near-infrared spectroscopy (fNIRS) and performance was measured using response time, accuracy, and gait speed.

Results: Repeated measures ANOVAs revealed decreased accuracy with increasing cognitive demands (p = 0.001) and increased dual-task accuracy costs (p < 0.001). Response times were faster during the single compared to dual-tasks during the SRT (p = 0.005) and NBK (p = 0.004). DNS gait speed was also slower in the dual compared to single task (p < 0.001). Neural findings revealed marginally significant interactions between dual-task walking and walking alone in the DNS (p = 0.06) and dual -task walking compared to the NBK cognitive task alone (p = 0.05).

Conclusion: Neural findings suggest a trend towards increased PFC activation during continuous tasks. Cognitive and motor measures revealed worse performance during the discrete compared to continuous tasks. Future studies should consider examining different attentional demands of motor tasks.

Brain network effects related to physical and virtual surgical training revealed by Granger causality

this study investigates the difference in effective connectivity among novice medical students trained on physical and virtual simulators to perform the Fundamental laparoscopic surgery (FLS) pattern cutting task (PC). We propose using dynamic spectral Granger causality (GC) in the frequency band of [0.01-0.07]Hz to measure the effect of surgical training on effective brain connectivity. To obtain the dynamics relationship between the cortical regions, we propose to use the short-time Fourier transform (STFT) method. FLS pattern cutting is a complex bimanual task requiring fine motor skills and increased brain activity. With this in mind, we have used high resolution functional near-infrared spectroscopy to leverage its high temporal resolution for capturing the change in hemodynamics (HbO2) in 14 healthy subjects. Analysis of variance (ANOVA) found a statistically significant difference in "LPMC granger causes RPMC" (LPMC→ RPMC) in the subject trained on these two simulator in the first 40 sec of the task. We showed that the directed brain connectivity was affected by the type of surgical simulator used for training the medical students.

Conditional-GAN Based Data Augmentation for Deep Learning Task Classifier Improvement Using fNIRS Data

Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique used for mapping the functioning human cortex. fNIRS can be widely used in population studies due to the technology’s economic, non-invasive, and portable nature. fNIRS can be used for task classification, a crucial part of functioning with Brain-Computer Interfaces (BCIs). fNIRS data are multidimensional and complex, making them ideal for deep learning algorithms for classification. Deep Learning classifiers typically need a large amount of data to be appropriately trained without over-fitting. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the accuracy of the deep learning classifier when the sample size is insufficient. The proposed system uses a CGAN with a CNN classifier to enhance the accuracy through data augmentation. The system can determine whether the subject’s task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 96.67% for the CGAN-CNN combination.

Load-dependent relationships between frontal fNIRS activity and performance: A data-driven PLS approach

Neuroimaging research frequently demonstrates load-dependent activation in prefrontal and parietal cortex during working memory tasks such as the N-back. Most of this work has been conducted in fMRI, but functional near-infrared spectroscopy (fNIRS) is gaining traction as a less invasive and more flexible alternative to measuring cortical hemodynamics. Few fNIRS studies, however, have examined how working memory load-dependent changes in brain hemodynamics relate to performance. The current study employs a newly developed and robust statistical analysis of task-based fNIRS data in a large sample, and demonstrates the utility of data-driven, multivariate analyses to link brain activation and behavior in this modality. Seventy participants completed a standard N-back task with three N-back levels (N = 1, 2, 3) while fNIRS data were collected from frontal and parietal cortex. Overall, participants showed reliably greater fronto-parietal activation for the 2-back versus the 1-back task, suggesting fronto-parietal fNIRS measurements are sensitive to differences in cognitive load. The results for 3-back were much less consistent, potentially due to poor behavioral performance in the 3-back task. To address this, a multivariate analysis (behavioral partial least squares, PLS) was conducted to examine the interaction between fNIRS activation and performance at each N-back level. Results of the PLS analysis demonstrated differences in the relationship between accuracy and change in the deoxyhemoglobin fNIRS signal as a function of N-back level in eight mid-frontal channels. Specifically, greater reductions in deoxyhemoglobin (i.e., more activation) were positively related to performance on the 3-back task, unrelated to accuracy in the 2-back task, and negatively associated with accuracy in the 1-back task. This pattern of results suggests that the metabolic demands correlated with neural activity required for high levels of accuracy vary as a consequence of task difficulty/cognitive load, whereby more automaticity during the 1-back task (less mid-frontal activity) predicted superior performance on this relatively easy task, and successful engagement of this mid-frontal region was required for high accuracy on a more difficult and cognitively demanding 3-back task. In summary, we show that fNIRS activity can track working memory load and can uncover significant associations between brain activity and performance, thus opening the door for this modality to be used in more wide-spread applications.

Guiding functional near-infrared spectroscopy optode-layout design using individual (f)MRI data: effects on signal strength

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

Comparing different pre-processing routines for infant fNIRS data

Functional Near Infrared Spectroscopy (fNIRS) is an important neuroimaging technique in cognitive developmental neuroscience. Nevertheless, there is no general consensus yet about best pre-processing practices. This issue is highly relevant, especially since the development and variability of the infant hemodynamic response (HRF) is not fully known. Systematic comparisons between analysis methods are thus necessary. We investigated the performance of five different pipelines, selected on the basis of a systematic search of the infant NIRS literature, in two experiments. In Experiment 1, we used synthetic data to compare the recovered HRFs with the true HRF and to assess the robustness of each method against increasing levels of noise. In Experiment 2, we analyzed experimental data from a published study, which assessed the neural correlates of artificial grammar processing in newborns. We found that with motion artifact correction (as opposed to rejection) a larger number of trials were retained, but HRF amplitude was often strongly reduced. By contrast, artifact rejection resulted in a high exclusion rate but preserved adequately the characteristics of the HRF. We also found that the performance of all pipelines declined as the noise increased, but significantly less so than if no pre-processing was applied. Finally, we found no difference between running the pre-processing on optical density or concentration change data. These results suggest that pre-processing should thus be optimized as a function of the specific quality issues a give dataset exhibits.

Brain hemodynamic response in Examiner-Examinee dyads during spatial short-term memory task: an fNIRS study

The Corsi Block-Tapping test (CBT) is a measure of spatial working memory (WM) in clinical practice, requiring an examinee to reproduce sequences of cubes tapped by an examiner. CBT implies complementary behaviors in the examiners and the examinees, as they have to attend a precise turn taking. Previous studies demonstrated that the Prefrontal Cortex (PFC) is activated during CBT, but scarce evidence is available on the neural correlates of CBT in the real setting. We assessed PFC activity in dyads of examiner–examinee participants while completing the real version of CBT, during conditions of increasing and exceeding workload. This procedure allowed to investigate whether brain activity in the dyads is coordinated. Results in the examinees showed that PFC activity was higher when the workload approached or reached participants’ spatial WM span, and lower during workload conditions that were largely below or above their span. Interestingly, findings in the examiners paralleled the ones in the examinees, as examiners’ brain activity increased and decreased in a similar way as the examinees’ one. In the examiners, higher left-hemisphere activity was observed suggesting the likely activation of non-spatial WM processes. Data support a bell-shaped relationship between cognitive load and brain activity, and provide original insights on the cognitive processes activated in the examiner during CBT.

Wavelength censoring for spectroscopy in optical functional neuroimaging

Optical neuromonitoring provides insight into neurovascular physiology and brain structure and function. These methods rely on spectroscopy to relate light absorption changes to variation of concentrations of physiologic chromophores such as oxy- and deoxyhemoglobin. In clinical or preclinical practice, data quality can vary significantly across wavelengths. In such situations, standard spectroscopic methods may perform poorly, resulting in data loss and limiting field-of-view. To address this issue, and thereby improve the robustness of optical neuromonitoring, we develop, in this manuscript, novel methods to perform spectroscopy even when data quality exhibits wavelength-dependent spatial variation. We sought to understand the impact of spatial, wavelength-based censoring on the physiologic accuracy and utility of hemoglobin spectroscopy. The principles of our analysis are quite general, but to make the methodology tangible we focused on optical intrinsic signal imaging of resting-state functional connectivity in mice. Starting with spectroscopy using four sources, all possible subset spectroscopy matrices were assessed theoretically, using simulated data, and using experimental data. These results were compared against the use of the full spectroscopy matrix to determine which subsets yielded robust results. Our results demonstrated that accurate calculation of changes in hemoglobin concentrations and the resulting functional connectivity network maps was possible even with censoring of some wavelengths. Additionally, we found that the use of changes in total hemoglobin (rather than oxy- or deoxyhemoglobin) yielded results more robust to experimental noise and allowed for the preservation of more data. This new and rigorous image processing method should improve the fidelity of clinical and preclinical functional neuroimaging studies.

Task-Related Hemodynamic Response Alterations During Slacklining: An fNIRS Study in Advanced Slackliners

The ability to maintain balance is based on various processes of motor control in complex neural networks of subcortical and cortical brain structures. However, knowledge on brain processing during the execution of whole-body balance tasks is still limited. In the present study, we investigated brain activity during slacklining, a task with a high demand on balance capabilities, which is frequently used as supplementary training in various sports disciplines as well as for lower extremity prevention and rehabilitation purposes in clinical settings. We assessed hemodynamic response alterations in sensorimotor brain areas using functional near-infrared spectroscopy (fNIRS) during standing (ST) and walking (WA) on a slackline in 16 advanced slackliners. We expected to observe task-related differences between both conditions as well as associations between cortical activity and slacklining experience. While our results revealed hemodynamic response alterations in sensorimotor brain regions such as primary motor cortex (M1), premotor cortex (PMC), and supplementary motor cortex (SMA) during both conditions, we did not observe differential effects between ST and WA nor associations between cortical activity and slacklining experience. In summary, these findings provide novel insights into brain processing during a whole-body balance task and its relation to balance expertise. As maintaining balance is considered an important prerequisite in daily life and crucial in the context of prevention and rehabilitation, future studies should extend these findings by quantifying brain processing during task execution on a whole-brain level.

Effects of Motor Tempo on Frontal Brain Activity: An fNIRS Study

People are able to modify the spontaneous pace of their actions to interact with their environment and others. This ability is underpinned by high-level cognitive functions but little is known in regard to the brain areas that underlie such temporal control. A salient practical issue is that current neuroimaging techniques (e.g., EEG, fMRI) are extremely sensitive to movement, which renders challenging any investigation of brain activity in the realm of whole-body motor paradigms. Within the last decade, the noninvasive imaging method of functional near-infrared spectroscopy (fNIRS) has become the reference tool for experimental motor paradigms due to its tolerance to motion artefacts. In the present study, we used a continuous-wave fNIRS system to record the prefrontal and motor hemodynamic responses of 16 participants, while they performed a spatial-tapping task varying in motor complexity and externally-paced tempi (i.e., 300 ms, 500 ms, 1200 ms). To discriminate between physiological noise and cerebral meaningful signals, the physiological data (i.e., heart and respiratory rates) were recorded so that frequency bands of such signals could be regressed from the fNIRS data. Particular attention was taken to control the precise position of the optodes in reference to the cranio-cerebral correlates of the NIR channels throughout the experimental session. Results indicated that fast pacing relied on greater activity of the motor areas whereas moving at close-to-spontaneous pace placed a heavier load on posterior prefrontal processes. These results provide new insight concerning the role of frontal cognitive control in modulating the pacing of voluntary motor behaviors.

Effects of three different rehabilitation games' interaction on brain activation using functional near-infrared spectroscopy

OBJECTIVE

This study reveals the changes in brain activation due to different game interaction states based on functional near-infrared spectroscopy signals and discusses their significance for stroke rehabilitation.

APPROACH

The oxygenated hemoglobin concentration (Delta [HbO₂]) signals and the deoxygenated hemoglobin (Delta [HbR]) signals were recorded from the prefrontal cortex (PFC), the motor cortex (MC), the occipital lobe (OL) and the temporal lobe of 21 subjects (mean age: 24.6 ± 1.9 years old) in three game interaction states: physical, motion-sensing, and button-push training. The subjects were also asked to complete user-satisfaction survey scales after the experiment.

MAIN RESULTS

Compared with the button-training state, several channels in the PFC and MC region of the physical-training state were significantly altered as were several channels in the RMC region of the motion-sensing training state (P < 0.05 after adjustment). The motion-sensing state of the PFC had a significant correlation with that of the MC and the OL. The subjective scale results show that the acceptability of the physical and motion-sensing states was greater than the acceptability of the button-push training state.

SIGNIFICANCE

The results show that the brain regions responded more strongly when activated by the physical and motion-sensing states compared with the button-push training state, and the physical and motion-sensing states are more conducive to the rehabilitation of the nervous system. The design of rehabilitation products for stroke patients is discussed and valuable insights are offered to support the selection of better interactive training methods.