Use of fNIRS to assess resting state functional connectivity

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.

Language networks of normal-hearing infants exhibit topological differences between resting and steady states: An fNIRS functional connectivity study

Task-related studies have consistently reported that listening to speech sounds activate the temporal and prefrontal regions of the brain. However, it is not well understood how functional organization of auditory and language networks differ when processing speech sounds from its resting state form. The knowledge of language network organization in typically developing infants could serve as an important biomarker to understand network-level disruptions expected in infants with hearing impairment. We hypothesized that topological differences of language networks can be characterized using functional connectivity measures in two experimental conditions (1) complete silence (resting) and (2) in response to repetitive continuous speech sounds (steady). Thirty normal-hearing infants (14 males and 16 females, age: 7.8 ± 4.8 months) were recruited in this study. Brain activity was recorded from bilateral temporal and prefrontal regions associated with speech and language processing for two experimental conditions: resting and steady states. Topological differences of functional language networks were characterized using graph theoretical analysis. The normalized global efficiency and clustering coefficient were used as measures of functional integration and segregation, respectively. We found that overall, language networks of infants demonstrate the economic small-world organization in both resting and steady states. Moreover, language networks exhibited significantly higher functional integration and significantly lower functional segregation in resting state compared to steady state. A secondary analysis that investigated developmental effects of infants aged 6-months or below and above 6-months revealed that such topological differences in functional integration and segregation across resting and steady states can be reliably detected after the first 6-months of life. The higher functional integration observed in resting state suggests that language networks of infants facilitate more efficient parallel information processing across distributed language regions in the absence of speech stimuli. Moreover, higher functional segregation in steady state indicates that the speech information processing occurs within densely interconnected specialized regions in the language network.

Neurological activation during verbal fluency task and resting-state functional connectivity abnormalities in obsessive-compulsive disorder: a functional near-infrared spectroscopy study

Objective

This study aims to investigate the activation of frontotemporal functional brain areas in patients with Obsessive-Compulsive Disorder (OCD) during a Verbal Fluency Task (VFT), and to compare their brain functional connectivity in a resting state with that of healthy controls. The goal is to deepen our understanding of the neuropathological mechanisms underlying OCD.

Methods

32 patients with OCD and 32 controls matched for age, gender, handedness, and years of education participated in this study, they were divided into OCD group and healthy comtrol group. We conducted VFT task tests and 10-minute resting state tests on both groups by using functional Near-Infrared Spectroscopy (fNIRS). The VFT was utilized to assess the activation (beta values) and the integral and centroid values of the frontal and bilateral temporal lobes, including brain areas BA9 and 46 (dorsolateral prefrontal cortex), BA10 (frontal pole), BA45 (inferior frontal gyrus), BA21 (middle temporal gyrus), and BA22 (superior temporal gyrus). We evaluated the functional connectivity levels of these areas during the resting state. Differences in these measures between OCD patients and healthy controls were analyzed using two-sample independent t-tests and non-parametric Mann-Whitney U tests.

Results

During VFT, OCD had smaller integral values(z=5.371, p<0.001; t=4.720, p<0.001), and larger centroid values(t=-2.281, p=0.026; z=-2.182, p=0.029) compared to healthy controls, along with a reduced number of activated channels detected by fNIRS. Additionally, activation values (β) in various functional brain areas, including BA9, BA46, BA10, BA45, BA21, and BA22, were significantly lower in the OCD group (all p< 0.01). In the resting state, notable disparities in functional connectivity were observed between the inferior frontal gyrus (IFG) and dorsolateral prefrontal cortex (DLPFC) in the OCD group, in comparison to the control group. Specifically, there was a significant increase in connectivity between the left IFG and right DLPFC, suggesting the presence of altered connectivity patterns in these areas.

Conclusions

The study highlights significant disparities in neural activation and functional connectivity between OCD patients and healthy controls during VFT. Specifically, reduced activation was noted in the frontal and bilateral temporal lobes of OCD patients, alongside alterations in resting-state functional connectivity between the IFG and DLPFC. These findings contribute to our understanding of the neurobiological underpinnings of OCD and may guide future therapeutic strategies.

EEG-fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network

Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) can objectively reflect a person's emotional state and have been widely studied in emotion recognition. However, the effective feature fusion and discriminative feature learning from EEG-fNIRS data is challenging. In order to improve the accuracy of emotion recognition, a graph convolution and capsule attention network model (GCN-CA-CapsNet) is proposed. Firstly, EEG-fNIRS signals are collected from 50 subjects induced by emotional video clips. And then, the features of the EEG and fNIRS are extracted; the EEG-fNIRS features are fused to generate higher-quality primary capsules by graph convolution with the Pearson correlation adjacency matrix. Finally, the capsule attention module is introduced to assign different weights to the primary capsules, and higher-quality primary capsules are selected to generate better classification capsules in the dynamic routing mechanism. We validate the efficacy of the proposed method on our emotional EEG-fNIRS dataset with an ablation study. Extensive experiments demonstrate that the proposed GCN-CA-CapsNet method achieves a more satisfactory performance against the state-of-the-art methods, and the average accuracy can increase by 3-11%.

Investigation of hemispheric asymmetry in Alzheimer's disease patients during resting state revealed BY fNIRS

Alzheimer's disease (AD) is characterized by the gradual deterioration of brain structures and changes in hemispheric asymmetry. Meanwhile, healthy aging is associated with a decrease in functional hemispheric asymmetry. In this study, functional connectivity analysis was used to compare the functional hemispheric asymmetry in eyes-open resting-state fNIRS data of 16 healthy elderly controls (mean age: 60.4 years, MMSE (Mini-Mental State Examination): 27.3 ± 2.52) and 14 Alzheimer's patients (mean age: 73.8 years, MMSE: 22 ± 4.32). Increased interhemispheric functional connectivity was found in the premotor cortex, supplementary motor cortex, primary motor cortex, inferior parietal cortex, primary somatosensory cortex, and supramarginal gyrus in the control group compared to the AD group. The study revealed that the control group had stronger interhemispheric connectivity, leading to a more significant decrease in hemispheric asymmetry than the AD group. The results show that there is a difference in interhemispheric functional connections at rest between the Alzheimer's group and the control group, suggesting that functional hemispheric asymmetry continues in Alzheimer's patients.

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.

Cerebral hemodynamics underlying ankle force sense modulated by high-definition transcranial direct current stimulation

This study aimed to investigate the effects of high-definition transcranial direct current stimulation on ankle force sense and underlying cerebral hemodynamics. Sixteen healthy adults (8 males and 8 females) were recruited in the study. Each participant received either real or sham high-definition transcranial direct current stimulation interventions in a randomly assigned order on 2 visits. An isokinetic dynamometer was used to assess the force sense of the dominant ankle; while the functional near-infrared spectroscopy was employed to monitor the hemodynamics of the sensorimotor cortex. Two-way analyses of variance with repeated measures and Pearson correlation analyses were performed. The results showed that the absolute error and root mean square error of ankle force sense dropped more after real stimulation than after sham stimulation (dropped by 23.4% vs. 14.9% for absolute error, and 20.0% vs. 10.2% for root mean square error). The supplementary motor area activation significantly increased after real high-definition transcranial direct current stimulation. The decrease in interhemispheric functional connectivity within the Brodmann's areas 6 was significantly correlated with ankle force sense improvement after real high-definition transcranial direct current stimulation. In conclusion, high-definition transcranial direct current stimulation can be used as a potential intervention for improving ankle force sense. Changes in cerebral hemodynamics could be one of the explanations for the energetic effect of high-definition transcranial direct current stimulation.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

The efficacy and methodology of using near-infrared spectroscopy to determine resting-state brain networks

Functional connectivity is a critical aspect of brain function and is essential for understanding, diagnosing, and treating neurological and psychiatric disorders. It refers to the synchronous activity between different regions of the brain, which gives rise to communication and information processing. Resting-state functional connectivity is a subarea of study that allows researchers to examine brain activity in the absence of a task or stimulus. This can provide insight into the brain's intrinsic functional architecture and help identify neural networks that are active during rest. Thus, determining functional connectivity topography is valuable both clinically and in research. Traditional methods using functional magnetic resonance imaging have proven to be effective, however, they have their limitations. In this review, we investigate the feasibility of using functional near-infrared spectroscopy (fNIRS) as a low-cost, portable alternative for measuring functional connectivity. We first establish fNIRS' ability to detect localized brain activity during task-based experiments. Next, we verify its use in resting-state studies with results showing a high degree of correspondence with resting-state functional magnetic resonance imaging (rs-fMRI). Also discussed are various data-processing methods and the validity of filtering the global signal, which is the current standard for analysis. We consider the possible origins of the global signal, if it contains pertinent neuronal information that could be of importance in better understanding neuronal networks, and what we believe is the best method of approaching signal analysis and regression.

Wearable functional near-infrared spectroscopy for measuring dissociable activation dynamics of prefrontal cortex subregions during working memory

The prefrontal cortex (PFC) has been extensively studied in relation to various cognitive abilities, including executive function, attention, and memory. Nevertheless, there is a gap in our scientific knowledge regarding the functionally dissociable neural dynamics across the PFC during a cognitive task and their individual differences in performance. Here, we explored this possibility using a delayed match-to-sample (DMTS) working memory (WM) task using NIRSIT, a high-density, wireless, wearable functional near-infrared spectroscopy (fNIRS) system. First, upon presentation of the sample stimulus, we observed an immediate signal increase in the ventral (orbitofrontal) region of the anterior PFC, followed by activity in the dorsolateral PFC. After the DMTS test stimulus appeared, the orbitofrontal cortex activated once again, while the rest of the PFC showed overall disengagement. Individuals with higher accuracy showed earlier and sustained activation of the PFC across the trial. Furthermore, higher network efficiency and functional connectivity in the PFC were correlated with individual WM performance. Our study sheds new light on the dynamics of PFC subregional activity during a cognitive task and its potential applicability in explaining individual differences in experimental, educational, or clinical populations. PRACTITIONER POINTS: Wearable functional near-infrared spectroscopy (fNIRS) captured dissociable temporal dynamics across prefrontal subregions during a delayed match-to-sample task. Anterior regions of the orbitofrontal cortex (OFC) activated first during the delay period, followed by the dorsolateral prefrontal cortex (PFC). PFC disengaged overall after the delay, but the OFC reactivated to the test stimulus. Earlier and sustained activation of PFC was associated with better accuracy. Functional connectivity and network efficiency also varied with task performance.

Towards the neurobiology of insomnia: A systematic review of neuroimaging studies

Insomnia disorder signifies a major public health concern. The development of neuroimaging techniques has permitted to investigate brain mechanisms at a structural and functional level. The present systematic review aims at shedding light on functional, structural, and metabolic substrates of insomnia disorder by integrating the available published neuroimaging data. The databases PubMed, PsycARTICLES, PsycINFO, CINAHL and Web of Science were searched for case-control studies comparing neuroimaging data from insomnia patients and healthy controls. 85 articles were judged as eligible. For every observed finding of each study, the effect size was calculated from standardised mean differences, statistic parameters and figures, showing a marked heterogeneity that precluded a comprehensive quantitative analysis. From a qualitative point of view, considering the findings of significant group differences in the reported regions across the articles, this review highlights the major involvement of the anterior cingulate cortex, thalamus, insula, precuneus and middle frontal gyrus, thus supporting some central themes in the debate on the neurobiology of and offering interesting insights into the psychophysiology of sleep in this disorder.

Interpersonal neural synchronization underlies mnemonic similarity during collaborative remembering

Although collaborative remembering is a ubiquitous feature of human beings, its underlying neurocognitive process is not well understood. Here we hypothesized that interpersonal neural synchronization (INS) might underlie collaborative remembering, while real collaboration as opposed to other modes of offline collaboration should enhance INS and facilitate mnemonic similarity. To test these hypotheses, brain activity was measured simultaneously from two individuals who performed a group-based selective retrieval practice task either in a real collaboration or in a pseudo-collaboration, i.e., an individual performed the task together with a pre-recorded audio. The results showed that the memory of two individuals converged to a greater level than the chance level in real collaboration but not in control condition. Moreover, collaborative remembering was associated with significant INS increase in the prefrontal cortex (PFC) relative to the baseline in the real collaboration only. Additionally, INS increase was significantly greater in the real collaboration than in control condition. Finally, the PFC's INS increase was positively correlated with and could accurately predict the level of mnemonic similarity in real collaboration. These findings support the hypothesis that the enhanced INS underlies the cognitive process of collaborative remembering.

Non-Invasive Continuous Optical Monitoring of Cerebral Blood Flow after Traumatic Brain Injury in Mice Using Fiber Camera-Based Speckle Contrast Optical Spectroscopy

Neurocritical care focuses on monitoring cerebral blood flow (CBF) to prevent secondary brain injuries before damage becomes irreversible. Thus, there is a critical unmet need for continuous neuromonitoring methods to quantify CBF within the vulnerable cortex continuously and non-invasively. Animal models and imaging biomarkers can provide valuable insights into the mechanisms and kinetics of head injury, as well as insights for potential treatment strategies. For this purpose, we implemented an optical technique for continuous monitoring of blood flow changes after a closed head injury in a mouse model, which is based on laser speckle contrast imaging and a fiber camera-based approach. Our results indicate a significant decrease (~10%, p-value < 0.05) in blood flow within 30 min of a closed head injury. Furthermore, the low-frequency oscillation analysis also indicated much lower power in the trauma group compared to the control group. Overall, blood flow has the potential to be a biomarker for head injuries in the early phase of a trauma, and the system is useful for continuous monitoring with the potential for clinical translation.

Why people engage in corrupt collaboration: an observation at the multi-brain level

Recent studies suggest that corrupt collaboration (i.e. acquiring private benefits with joint immoral acts) represents a dilemma between the honesty and reciprocity norms. In this study, we asked pairs of participants (labeled as A and B) to individually toss a coin and report their outcomes; their collective benefit could be maximized by dishonestly reporting (a corrupt behavior). As expected, the likelihood of corrupt behavior was high; this probability was negatively correlated with player A's moral judgment ability but positively correlated with player B's empathic concern (EC). Functional near-infrared spectroscopy data revealed that the brain-to-brain synchronization in the right dorsolateral prefrontal cortex was associated with fewer corrupt behaviors, and that it mediated the relationship between player A's moral judgment ability and corrupt collaboration. Meanwhile, the right temporal-parietal junction synchronization was associated with more corrupt behaviors, and that it mediated the relationship between player B's EC and corrupt collaboration. The roles of these 2 regions are interpreted according to the influence of the honesty and reciprocity norms on corrupt collaboration. In our opinion, these findings provide insight into the underlying mechanisms and modulating factors of corrupt collaboration.

Time-of-day influences resting-state functional cortical connectivity

Time-of-day is rarely considered during experimental protocols investigating motor behavior and neural activity. The goal of this work was to investigate differences in functional cortical connectivity at rest linked to the time of the day using functional Near-Infrared Spectroscopy (fNIRS). Since resting-state brain is shown to be a succession of cognitive, emotional, perceptual, and motor processes that can be both conscious and nonconscious, we studied self-generated thought with the goal to help in understanding brain dynamics. We used the New-York Cognition Questionnaire (NYC-Q) for retrospective introspection to explore a possible relationship between the ongoing experience and the brain at resting-state to gather information about the overall ongoing experience of subjects. We found differences in resting-state functional connectivity in the inter-hemispheric parietal cortices, which was significantly greater in the morning than in the afternoon, whilst the intra-hemispheric fronto-parietal functional connectivity was significantly greater in the afternoon than in the morning. When we administered the NYC-Q we found that the score of the question 27 ("during RS acquisition my thoughts were like a television program or film") was significantly greater in the afternoon with respect to the morning. High scores in question 27 point to a form of thought based on imagery. It is conceivable to think that the unique relationship found between NYC-Q question 27 and the fronto-parietal functional connectivity might be related to a mental imagery process during resting-state in the afternoon.

Divergent interpersonal neural synchronization patterns in the first, second language and interlingual communication

An accumulating number of studies have highlighted the importance of interpersonal neural synchronization (INS) between interlocutors in successful verbal communications. The opportunities for communication across different language contexts are rapidly expanding, thanks to the frequent interactions among people all over the world. However, whether the INS changes in different language contexts and how language choice affects the INS remain scarcely explored. The study recruited twenty pairs of participants to communicate in the first language (L1), second language (L2) and interlingual contexts. Using functional near-infrared spectroscopy (fNIRS), we examined the neural activities of interlocutors and analyzed their wavelet transform coherence to assess the INS of dyads. Results showed that as compared to the resting state, stronger INS was observed at the left inferior temporal gyrus, middle temporal gyrus, pre-motor and supplementary motor cortex, dorsolateral prefrontal cortex, and inferior frontal gyrus in L1; at the left middle temporal gyrus, superior temporal gyrus, and inferior frontal gyrus in L2; at the left inferior temporal gyrus and inferior frontal gyrus in interlingual context. Additionally, INS at the left inferior frontal gyrus was significantly stronger in L2 than in L1. These findings reveal the differences of the INS in different language contexts and confirm the importance of language choice for the INS changes.

High-Speed Time-Domain Diffuse Optical Tomography with a Sensitivity Equation-based Neural Network

Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve 'nontrivial' empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.

The analysis of brain functional connectivity of post-stroke cognitive impairment patients: an fNIRS study

Background

Post-stroke cognitive impairment (PSCI) is a considerable risk factor for developing dementia and reoccurrence of stroke. Understanding the neural mechanisms of cognitive impairment after stroke can facilitate early identification and intervention.

Objectives

Using functional near-infrared spectroscopy (fNRIS), the present study aimed to examine whether resting-state functional connectivity (FC) of brain networks differs in patients with PSCI, patients with Non-PSCI (NPSCI), and healthy controls (HCs), and whether these features could be used for clinical diagnosis of PSCI.

Methods

The present study recruited 16 HCs and 32 post-stroke patients. Based on the diagnostic criteria of PSCI, post-stroke patients were divided to the PSCI or NPSCI group. All participants underwent a 6-min resting-state fNRIS test to measure the hemodynamic responses from regions of interests (ROIs) that were primarily distributed in the prefrontal, somatosensory, and motor cortices.

Results

The results showed that, when compared to the HC group, the PSCI group exhibited significantly decreased interhemispheric FC and intra-right hemispheric FC. ROI analyses showed significantly decreased FC among the regions of somatosensory cortex, dorsolateral prefrontal cortex, and medial prefrontal cortex for the PSCI group than for the HC group. However, no significant difference was found in the FC between the PSCI and the NPSCI groups.

Conclusion

Our findings provide evidence for compromised interhemispheric and intra-right hemispheric functional connectivity in patients with PSCI, suggesting that fNIRS is a promising approach to investigate the effects of stroke on functional connectivity of brain networks.

The single- and dual-brain mechanisms underlying the adviser's confidence expression strategy switching during influence management

Effective influence management during advice-giving requires individuals to express confidence in the advice properly and switch timely between the 'competitive' strategy and the 'defensive' strategy. However, how advisers switch between these two strategies, and whether and why there exist individual differences during this process remain elusive. We used an advice-giving game that manipulated incentive contexts (Incentivized/Non-Incentivized) to induce the adviser's confidence expression strategy switching and measured the brain activities of adviser and advisee concurrently using functional near-infrared spectroscopy (fNIRS). Behaviorally, we observed individual differences in strategy switching. Some advisers applied the 'defensive' strategy when incentivized and the 'competitive' strategy when not incentivized, while others applied the 'competitive' strategy when incentivized and the 'defensive' strategy when not incentivized. This effect was mediated by the adviser's perceived stress in each condition and was reflected by the frequencies of advice-taking in the advisees. Neurally, brain activation in the dorsolateral prefrontal cortex (DLPFC) supported strategy switching, as well as interpersonal neural synchronization (INS) in the temporoparietal junction (TPJ) that supported influence management. This two-in-one process, i.e., confidence expression strategy switching and the corresponding influence management, was linked and modulated by the strength of DLPFC-TPJ functional connectivity in the adviser. We further developed a descriptive model that contributed to understanding the adviser's strategy switching during influence management.

Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS

Objective.Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy.Approach.Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively.Main results.A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R²= 0.216,p= 0.0169) and HbR (R²= 0.206,p= 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R²= 0.237,p= 0.0117) but not for HbR (R²= 0.111,p= 0.0956).Significance.The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.

Letting leaders spontaneously emerge yields better creative outcomes and higher leader-follower interbrain synchrony during creative group communication

This study aimed to investigate how the ways leaders arise (appointed vs. emergent) affect the leader-follower interaction during creative group communication. Hyperscanning technique was adopted to reveal the underlying interpersonal neural correlates using functional near-infrared spectroscopy. Participants were assigned into 3-person groups to complete a creative problem-solving task. These groups were randomly split into conditions of appointed (condition A) and emergent (condition E) leaders. Creative group outcomes were better in condition E, accompanied by more frequent perspective-taking behaviors between leaders and followers. The interpersonal brain synchronization (IBS) increment for leader-follower pairs was significantly higher at the right angular gyrus (rAG), between the rAG and the right supramarginal gyrus (rSMG), and between the right middle temporal gyrus and the right motor cortex in condition E and positively correlated with perspective-taking behaviors between leaders and followers. The graph-based analysis showed higher nodal betweenness of the rAG and the rSMG in condition E. These results indicated the neural coupling of brain regions involved in mentalizing, semantic processing and motor imagery may underlie the dynamic information transmission between leaders and followers during creative group communication.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

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

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

Confounding effects of heart rate, breathing rate, and frontal fNIRS on interoception

Recent studies have established that cardiac and respiratory phases can modulate perception and related neural dynamics. While heart rate and respiratory sinus arrhythmia possibly affect interoception biomarkers, such as heartbeat-evoked potentials, the relative changes in heart rate and cardiorespiratory dynamics in interoceptive processes have not yet been investigated. In this study, we investigated the variation in heart and breathing rates, as well as higher functional dynamics including cardiorespiratory correlation and frontal hemodynamics measured with fNIRS, during a heartbeat counting task. To further investigate the functional physiology linked to changes in vagal activity caused by specific breathing rates, we performed the heartbeat counting task together with a controlled breathing rate task. The results demonstrate that focusing on heartbeats decreases breathing and heart rates in comparison, which may be part of the physiological mechanisms related to "listening" to the heart, the focus of attention, and self-awareness. Focusing on heartbeats was also observed to increase frontal connectivity, supporting the role of frontal structures in the neural monitoring of visceral inputs. However, cardiorespiratory correlation is affected by both heartbeats counting and controlled breathing tasks. Based on these results, we concluded that variations in heart and breathing rates are confounding factors in the assessment of interoceptive abilities and relative fluctuations in breathing and heart rates should be considered to be a mode of covariate measurement of interoceptive processes.

Reliability and similarity of resting state functional connectivity networks imaged using wearable, high-density diffuse optical tomography in the home setting

BACKGROUND

When characterizing the brain's resting state functional connectivity (RSFC) networks, demonstrating networks' similarity across sessions and reliability across different scan durations is essential for validating results and possibly minimizing the scanning time needed to obtain stable measures of RSFC. Recent advances in optical functional neuroimaging technologies have resulted in fully wearable devices that may serve as a complimentary tool to functional magnetic resonance imaging (fMRI) and allow for investigations of RSFC networks repeatedly and easily in non-traditional scanning environments.

METHODS

Resting-state cortical hemodynamic activity was repeatedly measured in a single individual in the home environment during COVID-19 lockdown conditions using the first ever application of a 24-module (72 sources, 96 detectors) wearable high-density diffuse optical tomography (HD-DOT) system. Twelve-minute recordings of resting-state data were acquired over the pre-frontal and occipital regions in fourteen experimental sessions over three weeks. As an initial validation of the data, spatial independent component analysis was used to identify RSFC networks. Reliability and similarity scores were computed using metrics adapted from the fMRI literature.

RESULTS

We observed RSFC networks over visual regions (visual peripheral, visual central networks) and higher-order association regions (control, salience and default mode network), consistent with previous fMRI literature. High similarity was observed across testing sessions and across chromophores (oxygenated and deoxygenated haemoglobin, HbO and HbR) for all functional networks, and for each network considered separately. Stable reliability values (described here as a <10% change between time windows) were obtained for HbO and HbR with differences in required scanning time observed on a network-by-network basis.

DISCUSSION

Using RSFC data from a highly sampled individual, the present work demonstrates that wearable HD-DOT can be used to obtain RSFC measurements with high similarity across imaging sessions and reliability across recording durations in the home environment. Wearable HD-DOT may serve as a complimentary tool to fMRI for studying RSFC networks outside of the traditional scanning environment and in vulnerable populations for whom fMRI is not feasible.

Brain Functional Connectivity in Middle-Aged Hong Chuan Tai Chi Players in Resting State

Tai Chi is an effective strategy for slowing cognitive decline, although the underlying mechanism remains unclear. We designed a cross-sectional study to examine brain functional connectivity in middle-aged Hong Chuan Tai Chi practitioners. Eighteen middle-aged Hong Chuan Tai Chi practitioners and 22 age-matched Tai Chi-naïve controls completed functional near-infrared spectroscopy (fNIRS) tests to evaluate oxyhemoglobin changes in the prefrontal cortex (PFC), motor cortex (MC), and occipital cortex (OC) in five frequency intervals (I, 0.6-2 Hz; II, 0.145-0.6 Hz; III, 0.052-0.145 Hz; IV, 0.021-0.052 Hz; V, 0.0095-0.021 Hz). Wavelet phase coherence was used to analyze the match between the instantaneous phases of the two signals to accurately measure brain functional connectivity. Global cognition was measured using the Montreal Cognitive Assessment scale. Compared with the control group, Hong Chuan Tai Chi practitioners had better global cognition (p < 0.01) and showed higher functional connectivity of the PFC, MC, and OC in intervals I, III, VI, and V in the resting state within the same brain hemispheres or between the left and right hemispheres. Our findings revealed that middle-aged Hong Chuan Tai Chi practitioners had higher functional connectivity of the PFC, MC, and OC across both brain hemispheres in cardiac activity, myogenic activity, sympathetic nervous system, and endothelial cell metabolic activities which may contribute to higher global cognition.

Increased cortical activation and enhanced functional connectivity in the prefrontal cortex ensure dynamic postural balance during dual-task obstacle negotiation in the older adults: A fNIRS study

OBJECTIVE

By analyzing the cortical activation and functional connectivity of the prefrontal cortex (PFC) during dual-task obstacle negotiation in the older adults, cognitive resources allocation and neural regulatory mechanisms of aging brain were shed light on in complex walking conditions.

METHODS

Twenty-eight healthy right-handed subjects participated in the study, including 15 men and 13 women (age: 68.6 ± 4.1 years, height: 162.96 ± 6.05 cm, weight: 63.63 ± 9.64 kg). There were four tasks: Normal Walk (NW), Obstacle Negotiation during Normal Walk (NW + ON), Walk while performing Cognitive Task (WCT), and Obstacle Negotiation during Walk while performing Cognitive Task (WCT + ON). Participants wore functional near-infrared spectroscopy (fNIRS) to collect hemodynamic signals from various regions of interest (ROIs) in the PFC, while the three-dimensional motion capture system was used to test the gait velocity. Cognitive task data was recorded.

RESULTS

In WCT + ON, the HbO₂ concentration change value (△HbO₂) of the left dorsolateral prefrontal cortex was significantly greater than that in the other three tasks (p < 0.05), and the△HbO₂ of the right dorsolateral prefrontal cortex was significantly greater than that in NW + ON (p < 0.05). The gait velocities in the four tasks were significantly different (p < 0.05) (NW > WCT > NW + ON > WCT + ON). There was no significant difference in cognitive performance between in the WCT and WCT + ON (p > 0.05). In WCT + ON, the left and right dorsolateral prefrontal areas had strong functional connectivity and the left frontal pole was most widely connected to the other ROIs. Compared to that in NW, the functional connectivity of the left prefrontal lobe was significantly enhanced in WCT + ON (p < 0.05).

CONCLUSIONS

As walking difficulty increased, the PFC activation in the older adults changed from right-sided to bilateral activation, indicating that the left PFC cognitive resources compensated for the right PFC in dual-task obstacle negotiation. The cognitive resources recruitment in dual-task obstacle negotiation might be achieved by synchronization and coordination of associated brain areas in the PFC, primarily to maintain dynamic postural balance when walking.

Bimodal EEG-fNIRS in Neuroergonomics. Current Evidence and Prospects for Future Research

Neuroergonomics focuses on the brain signatures and associated mental states underlying behavior to design human-machine interfaces enhancing performance in the cognitive and physical domains. Brain imaging techniques such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have been considered key methods for achieving this goal. Recent research stresses the value of combining EEG and fNIRS in improving these interface systems' mental state decoding abilities, but little is known about whether these improvements generalize over different paradigms and methodologies, nor about the potentialities for using these systems in the real world. We review 33 studies comparing mental state decoding accuracy between bimodal EEG-fNIRS and unimodal EEG and fNIRS in several subdomains of neuroergonomics. In light of these studies, we also consider the challenges of exploiting wearable versions of these systems in real-world contexts. Overall the studies reviewed suggest that bimodal EEG-fNIRS outperforms unimodal EEG or fNIRS despite major differences in their conceptual and methodological aspects. Much work however remains to be done to reach practical applications of bimodal EEG-fNIRS in naturalistic conditions. We consider these points to identify aspects of bimodal EEG-fNIRS research in which progress is expected or desired.

Reorganization of prefrontal network in stroke patients with dyskinesias: evidence from resting-state functional near-infrared spectroscopy

Stroke usually causes multiple functional disability. To develop novel rehabilitation strategies, it is quite necessary to improve the understanding of post-stroke brain plasticity. Here, we use functional near-infrared spectroscopy to investigate the prefrontal cortex (PFC) network reorganization in stroke patients with dyskinesias. The PFC hemodynamic signals in the resting state from 16 stroke patients and 10 healthy subjects are collected and analyzed with the graph theory. The PFC networks for both groups show small-world attributes. The stroke patients have larger clustering coefficient and transitivity and smaller global efficiency and small-worldness than healthy subjects. Based on the selected network features, the established support vector machine model classifies the two groups of subjects with an accuracy rate of 88.5%. Besides, the clustering coefficient and local efficiency negatively correlate with patients' motor function. This study suggests that the PFC of stroke patients with dyskinesias undergoes specific network reorganization.

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Brain–Computer Interface-Robot Training Enhances Upper Extremity Performance and Changes the Cortical Activation in Stroke Patients: A Functional Near-Infrared Spectroscopy Study

Introduction

We evaluated the efficacy of brain–computer interface (BCI) training to explore the hypothesized beneficial effects of physiotherapy alone in chronic stroke patients with moderate or severe paresis. We also focused on the neuroplastic changes in the primary motor cortex (M1) after BCI training.

Methods

In this study, 18 hospitalized chronic stroke patients with moderate or severe motor deficits participated. Patients were operated on for 20 sessions and followed up after 1 month. Functional assessments were performed at five points, namely, pre1-, pre2-, mid-, post-training, and 1-month follow-up. Wolf Motor Function Test (WMFT) was used as the primary outcome measure, while Fugl-Meyer Assessment (FMA), its wrist and hand (FMA-WH) sub-score and its shoulder and elbow (FMA-SE) sub-score served as secondary outcome measures. Neuroplastic changes were measured by functional near-infrared spectroscopy (fNIRS) at baseline and after 20 sessions of BCI training. Pearson correlation analysis was used to evaluate functional connectivity (FC) across time points.

Results

Compared to the baseline, better functional outcome was observed after BCI training and 1-month follow-up, including a significantly higher probability of achieving a clinically relevant increase in the WMFT full score (ΔWMFT score = 12.39 points, F = 30.28, and P &lt; 0.001), WMFT completion time (ΔWMFT time = 248.39 s, F = 16.83, and P &lt; 0.001), and FMA full score (ΔFMA-UE = 12.72 points, F = 106.07, and P &lt; 0.001), FMA-WH sub-score (ΔFMA-WH = 5.6 points, F = 35.53, and P &lt; 0.001), and FMA-SE sub-score (ΔFMA-SE = 8.06 points, F = 22.38, and P &lt; 0.001). Compared to the baseline, after BCI training the FC between the ipsilateral M1 and the contralateral M1 was increased (P &lt; 0.05), which was the same as the FC between the ipsilateral M1 and the ipsilateral frontal lobe, and the FC between the contralateral M1 and the contralateral frontal lobe was also increased (P &lt; 0.05).

Conclusion

The findings demonstrate that BCI-based rehabilitation could be an effective intervention for the motor performance of patients after stroke with moderate or severe upper limb paresis and represents a potential strategy in stroke neurorehabilitation. Our results suggest that FC between ipsilesional M1 and frontal cortex might be enhanced after BCI training.

Clinical Trial Registration

www.chictr.org.cn, identifier: ChiCTR2100046301.

Oscillator decomposition of infant fNIRS data

The functional near-infrared spectroscopy (fNIRS) can detect hemodynamic responses in the brain and the data consist of bivariate time series of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) on each channel. In this study, we investigate oscillatory changes in infant fNIRS signals by using the oscillator decompisition method (OSC-DECOMP), which is a statistical method for extracting oscillators from time series data based on Gaussian linear state space models. OSC-DECOMP provides a natural decomposition of fNIRS data into oscillation components in a data-driven manner and does not require the arbitrary selection of band-pass filters. We analyzed 18-ch fNIRS data (3 minutes) acquired from 21 sleeping 3-month-old infants. Five to seven oscillators were extracted on most channels, and their frequency distribution had three peaks in the vicinity of 0.01-0.1 Hz, 1.6-2.4 Hz and 3.6-4.4 Hz. The first peak was considered to reflect hemodynamic changes in response to the brain activity, and the phase difference between oxy-Hb and deoxy-Hb for the associated oscillators was at approximately 230 degrees. The second peak was attributed to cardiac pulse waves and mirroring noise. Although these oscillators have close frequencies, OSC-DECOMP can separate them through estimating their different projection patterns on oxy-Hb and deoxy-Hb. The third peak was regarded as the harmonic of the second peak. By comparing the Akaike Information Criterion (AIC) of two state space models, we determined that the time series of oxy-Hb and deoxy-Hb on each channel originate from common oscillatory activity. We also utilized the result of OSC-DECOMP to investigate the frequency-specific functional connectivity. Whereas the brain oscillator exhibited functional connectivity, the pulse waves and mirroring noise oscillators showed spatially homogeneous and independent changes. OSC-DECOMP is a promising tool for data-driven extraction of oscillation components from biological time series data.

Decoding the Brain's Surface to Track Deeper Activity

Neural activity can be readily and non-invasively recorded from the scalp using electromagnetic and optical signals, but unfortunately all scalp-based techniques have depth-dependent sensitivities. We hypothesize, though, that the cortex's connectivity with the rest of the brain could serve to construct proxy signals of deeper brain activity. For example, functional magnetic resonance imaging (fMRI)-derived models that link surface connectivity to deeper regions could subsequently extend the depth capabilities of other modalities. Thus, as a first step toward this goal, this study examines whether or not surface-limited support vector regression of resting-state fMRI can indeed track deeper regions and distributed networks in independent data. Our results demonstrate that depth-limited fMRI signals can in fact be calibrated to report ongoing activity of deeper brain structures. Although much future work remains to be done, the present study suggests that scalp recordings have the potential to ultimately overcome their intrinsic physical limitations by utilizing the multivariate information exchanged between the surface and the rest of the brain.

Spatial complexity method for tracking brain development and degeneration using functional near-infrared spectroscopy

Brain complexity analysis using functional near-infrared spectroscopy (fNIRS) has attracted attention as a biomarker for evaluating brain development and degeneration processes. However, most methods have focused on the temporal scale without capturing the spatial complexity. In this study, we propose a spatial time-delay entropy (STDE) method as the spatial complexity measure based on the time-delay measure between two oxy-hemoglobin (Δ[HbO]) or two deoxy-hemoglobin (Δ[Hb]) oscillations within the 0.01-0.1 Hz frequency band. To do this, we analyze fNIRS signals recorded from infants in their sleeping state, children, adults, and healthy seniors in their resting states. We also evaluate the effects of various noise to STDE calculations and STDE's performance in distinguishing various developmental age groups. Lastly, we compare the results with the normalized global spatial complexity (NGSC) and sample entropy (SampEn) measures. Among these measures, STDE_HbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p < 0.001), and then decreases in adults and healthy seniors in the 0.01-0.1 Hz frequency band. This trajectory correlates with cerebrovascular development and degeneration. These findings demonstrate that STDE can be used as a new tool for tracking cerebrovascular development and degeneration across a lifespan based on the fNIRS resting-state measurements.

Middle Occipital Area Differentially Associates with Malevolent versus Benevolent Creativity: An fNIRS investigation

This study aimed to explore the neural correlates underlying idea generation during malevolent creativity (MC) using functional near-infrared spectroscopy (fNIRS). Participants were asked to solve problems during three types of creativity tasks: malevolent creativity task (MCT), benevolent creativity task (BCT), and alternative uses task (AUT). fNIRS was used to record individual cerebral activity during the tasks. The results revealed that participants demonstrated weaker neural activation in the right middle occipital area (rMO) and lower neural coupling (NC) between the right frontopolar cortex (rFPC) and rMO during MCT than during BCT and AUT. These suggest that r-MO activity and NC between the rFPC and rMO may distinguish between malevolent and benevolent forms of creative ideation.

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study

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

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.

Cooperation with partners of differing social experience: An fNIRS-based hyperscanning study

Previous studies have shown the brain synchronization of all team members while completing a collaborative task. Moreover, this effect is influenced by a team's compositional elements, such as gender (opposite or same) or relationships (i.e., friends, lovers, or strangers) among team members. However, whether interpersonal brain synchronization (IBS) is affected by team members' experience, as well as the temporal dynamics of such brain synchronization, remains to be investigated. In the current study, we combined behavioral methods and functional near-infrared spectroscopy-based hyperscanning to examine the effect of member experience on team cooperation by an adopted continuous joint drawing task with 21 student-student dyads (S-S dyads) and 22 teacher-student dyads (T-S dyads). The results revealed that team members with differing experiences (T-S dyads) perform better than those with similar ones (S-S dyads). Moreover, we observed IBS in the left frontopolar region (channel 11). However, we did not observe significant changes of the task-related IBS across time. Besides, IBS was negatively correlated with the participants' behavioral performance. Our findings demonstrate the importance of social experience in teamwork in the real world and suggest a possible mechanism for cooperation from a temporal and spatial perspective.

Antidepressant Monotherapy and Combination Therapy with Acupuncture in Depressed Patients: A Resting-State Functional Near-Infrared Spectroscopy (fNIRS) Study

Depression is a common psychiatric illness affecting over 300 million people globally. Acupuncture has been reported to be a safe complementary treatment for depression. This study is aimed to investigate the efficacy and mechanism of combining acupuncture with antidepressants in treating depression compared to the sole use of antidepressants. Seventy depression patients were randomly assigned to the treatment group (n = 50) and control group (n = 20). The treatment group received acupuncture combined antidepressants treatment for 3 weeks, while the control group took antidepressants monotherapy for 3 weeks. Among the 70 patients, 40 participants (20 control; 20 treatment) were randomized for studying functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) measured by the functional near-infrared spectroscopy. The primary outcome was HAMD-17 and secondary outcomes were PHQ-9, and the relationships of resting-state FC (rsFC) with the depression severity. PHQ-9 and HAMD-17 scores in the treatment group were significantly lower than those in the control group at Week 3 (p = 0.01) with effect sizes of -0.4 and -0.61 respectively. The rsFC in F1, F3, AF3, AF7, FC3, FC5 (left DLPFC, 10-20 system), AF8, and F6 (right DLPFC) in the treatment group had significant temporal correlation (p < 0.05, FDR corrected) in DLPFC compared to the channels in the control group. No significant correlation was found between the changes of rsFC and depression severity. In conclusion, depressed patients receiving acupuncture combined with antidepressants have improvement of depressive symptoms and the stronger rsFC in the DLPFC compared to those using antidepressants alone.

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

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

Neural and biomechanical tradeoffs associated with human-exoskeleton interactions

Industrial passive low-back exoskeletons have gained recent attention as ergonomic interventions to manual handling tasks. This research utilized a two-armed experimental approach (single vs dual-task paradigms) to quantify neural and biomechanical tradeoffs associated with short-term human-exoskeleton interaction (HEI) during asymmetrical lifting in twelve healthy adults balanced by gender. A dynamic, electromyography-assisted spine model was employed that indicated statistical, but marginal, biomechanical benefits of the tested exoskeleton, which diminished with the introduction of the cognitive dual-task. Using Near Infrared Spectroscopy (fNIRS)-based brain connectivity analyses, we found that the tested exoskeleton imposed greater neurocognitive and motor adaptation efforts by engaging action monitoring and error processing brain networks. Collectively, these findings indicate that a wearer's biomechanical response to increased cognitive demands in the workplace may offset the mechanical advantages of exoskeletons. We also demonstrate the utility of ambulatory fNIRS to capture the neural cost of HEI without the need for elaborate dual-task manipulations.

A guide for the use of fNIRS in microcephaly associated to congenital Zika virus infection

Congenital Zika Syndrome (CZS) is characterized by changes in cranial morphology associated with heterogeneous neurological manifestations and cognitive and behavioral impairments. In this syndrome, longitudinal neuroimaging could help clinicians to predict developmental trajectories of children and tailor treatment plans accordingly. However, regularly acquiring magnetic resonance imaging (MRI) has several shortcomings besides cost, particularly those associated with childrens' clinical presentation as sensitivity to environmental stimuli. The indirect monitoring of local neural activity by non-invasive functional near-infrared spectroscopy (fNIRS) technique can be a useful alternative for longitudinally accessing the brain function in children with CZS. In order to provide a common framework for advancing longitudinal neuroimaging assessment, we propose a principled guideline for fNIRS acquisition and analyses in children with neurodevelopmental disorders. Based on our experience on collecting fNIRS data in children with CZS we emphasize the methodological challenges, such as clinical characteristics of the sample, desensitization, movement artifacts and environment control, as well as suggestions for tackling such challenges. Finally, metrics based on fNIRS can be associated with established clinical metrics, thereby opening possibilities for exploring this tool as a long-term predictor when assessing the effectiveness of treatments aimed at children with severe neurodevelopmental disorders.

How Mother-Child Interactions are Associated with a Child's Compliance

While social interaction between a mother and her child has been found to play an important role in the child's committed compliance, the underlying neurocognitive process remains unclear. To investigate this process, we simultaneously recorded and assessed brain activity in 7-year-old children and in children's mothers or strangers during a free-play task using functional near-infrared spectroscopy-based hyperscanning. The results showed that a child's committed compliance was positively associated with the child's responsiveness but was negatively associated with mutual responsiveness and was not associated with the mother's responsiveness during mother-child interactions. Moreover, interpersonal neural synchronization (INS) at the temporoparietal junction mediated the relationship between the child's responsiveness and the child's committed compliance during mother-child interactions when the child's brain activity lagged behind that of the mother. However, these effects were not found during stranger-child interactions, nor were there significant effects in the mother-child pair when no real interactions occurred. Finally, we found a transfer effect of a child's committed compliance from mother-child interactions to stranger-child interactions via the mediation of mother-child INS, but the opposite did not occur. Together, these findings suggest that a child's responsiveness during mother-child interactions can significantly facilitate her or his committed compliance by increasing mother-child INS.

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

BACKGROUND

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

NEW METHOD

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

RESULTS

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

COMPARISON WITH EXISTING METHODS

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

CONCLUSIONS

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

Gender Differences in Transnational Brand Purchase Decision Toward Mixed Culture and Original Culture Advertisements: An fNIRS Study

Culture strategy is very important for transnational brand marketing. Functional near-infrared spectroscopy (fNIRS) is a promising brain imaging modality for neuromarketing research. In the present study, we used fNIRS to explore the neural correlates of consumers' purchase decision on different cross-culture marketing strategies. Forty Chinese participants watched transnational brands and products advertised with photographs of the brands' original culture (the original culture advertisements) and advertised with photographs of Chinese culture (the mixed culture advertisements), respectively. The behavioral results showed that the female participants showed significantly higher purchase rate when watching the original culture advertisements than the mixed culture advertisements, whereas the male participants did not show significant preference between these two types. The fNIRS results further revealed that for the female participants, watching mixed culture advertisements evoked significant positive activation in the left dorsolateral prefrontal cortex and negative activation in the medial prefrontal cortex, which was not found in the male participants. These findings suggest possible cognitive and emotional differences between men and women in purchase decision making toward different cross-culture marketing strategy. The present study also demonstrates the great potential of fNIRS in neuromarketing research.

Narrowband Resting-State fNIRS Functional Connectivity in Autism Spectrum Disorder

Hemispheric asymmetry in the power spectrum of low-frequency spontaneous hemodynamic fluctuations has been previously observed in autism spectrum disorder (ASD). This observation may imply a specific narrow-frequency band in which individuals with ASD could show more significant alteration in resting-state functional connectivity (RSFC). To test this assumption, we evaluated narrowband RSFC at several frequencies for functional near-infrared spectroscopy signals recorded from the bilateral temporal lobes on 25 children with ASD and 22 typically developing (TD) children. In several narrow-frequency bands, we observed altered interhemispheric RSFC in ASD. However, in the band of 0.01–0.02 Hz, more mirrored channel pairs (or cortical sites) showed significantly weaker RSFC in the ASD group. Receiver operating characteristic analysis further demonstrated that RSFC in the narrowband of 0.01–0.02 Hz might have better differentiation ability between the ASD and TD groups. This may indicate that the narrowband RSFC could serve as a characteristic for the prediction of ASD.

Variability in Infants' Functional Brain Network Connectivity Is Associated With Differences in Affect and Behavior

Variability in functional brain network connectivity has been linked to individual differences in cognitive, affective, and behavioral traits in adults. However, little is known about the developmental origins of such brain-behavior correlations. The current study examined functional brain network connectivity and its link to behavioral temperament in typically developing newborn and 1-month-old infants (M [age] = 25 days; N = 75) using functional near-infrared spectroscopy (fNIRS). Specifically, we measured long-range connectivity between cortical regions approximating fronto-parietal, default mode, and homologous-interhemispheric networks. Our results show that connectivity in these functional brain networks varies across infants and maps onto individual differences in behavioral temperament. Specifically, connectivity in the fronto-parietal network was positively associated with regulation and orienting behaviors, whereas connectivity in the default mode network showed the opposite effect on these behaviors. Our analysis also revealed a significant positive association between the homologous-interhemispheric network and infants' negative affect. The current results suggest that variability in long-range intra-hemispheric and cross-hemispheric functional connectivity between frontal, parietal, and temporal cortex is associated with individual differences in affect and behavior. These findings shed new light on the brain origins of individual differences in early-emerging behavioral traits and thus represent a viable novel approach for investigating developmental trajectories in typical and atypical neurodevelopment.

Affiliative bonding between teachers and students through interpersonal synchronisation in brain activity

Human beings organise socially. Theories have posited that interpersonal neural synchronisation might underlie the creation of affiliative bonds. Previous studies tested this hypothesis mainly during a social interaction, making it difficult to determine whether the identified synchronisation is associated with affiliative bonding or with social interaction. This study addressed this issue by focusing on the teacher–student relationship in the resting state both before and after a teaching period. Brain activity was simultaneously measured in both individuals using functional near-infrared spectroscopy. The results showed a significant increase in brain synchronisation at the right sensorimotor cortex between the teacher and student in the resting state after, but not before, the teaching period. Moreover, the synchronisation increased only after a turn-taking mode of teaching but not after a lecturing or video mode of teaching. A chain mediation analysis showed that brain synchronisation during teaching partially mediated the relationship between the brain synchronisation increase in the resting state and strength of the affiliative bond. Finally, both role assignment and social interaction were found to be required for affiliative bonding. Together, these results support the hypothesis that interpersonal synchronisation in brain activity underlies affiliative bonding and that social interaction mechanically mediates the bonding process.