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Transcranial photobiomodulation (tPBM) is a novel and noninvasive intervention, which has shown promise for improving cognitive performances. Whether tPBM can modulate brain activity and thereby enhance working memory (WM) capacity in humans remains unclear. In this study, we delivered double-blind and sham-control tPBM with different wavelengths to the prefrontal cortex (PFC) in 90 healthy participants and conducted four electroencephalography (EEG) experiments to investigate whether individual visual working memory capacity and related neural response could be modulated. We found that 1064 nm tPBM applied to the right PFC has both a substantial impact on visual working memory capacity and occipitoparietal contralateral delay activity (CDA), no matter orientation or color feature of the memorized objects. Importantly, the CDA set-size effect during the retention mediated the effect between 1064 nm tPBM and subsequent WM performance. However, these behavioral benefits and the corresponding changes of CDA set-size effect were absent with tPBM at 852 nm wavelength or with the stimulation on the left PFC. Our findings provide converging evidence that 1064 nm tPBM applied on the right PFC can improve visual working memory capacity, as well as explain the individual’s electrophysiological changes about behavioral benefits.

Background: A growing awareness about non-pharmacological intervention for cognitively impaired individuals may represent an alternative therapeutic approach that is actively accepted by patients with very early stage of Alzheimer’s disease. Understanding the neural basis of non-pharmacological intervention is a crucial step toward wide use for patients with cognitive disorders. Objective: To investigate the underlying neural mechanism of shentai tea polyphenols in subjects with subjective cognitive decline (SCD) using functional near-infrared spectroscopy (fNIRS). Methods: A total number of 36 patients with SCD participated in the study and received supplementation with shentai tea polyphenols for three months. All participants underwent a series of tests on neuropsychological function and fNIRS assessment during n-back tasks at baseline and follow-up. Results: After intervention with shentai tea polyphenols in SCD, increased cerebral activity was observed in left dorsolateral prefrontal cortex (DLPFC), left premotor cortex (PMC), left primary somatosensory cortex (PSC), right inferior frontal gyrus (IFG), and premotor cortex (PMC). Moreover, shentai tea polyphenols intervention of three months significantly improved SCD subjects’ cognitive functions (memory, language, and subjective cognitive ability) and depression condition. We further found that the improvement of Hamilton Depression Rating Scale and Auditory Verbal Learning Test-recognition scores had positive correlations with increased brain activity in right IFG and left DLPFC, respectively. Conclusion: This study provides new evidence that the frontal cortex was found to be specifically activated after non-pharmacological intervention of shentai tea polyphenols in SCD, which may be associated with cognitive enhancement and mental wellbeing. These findings provide important implications for the selection of shentai tea polyphenols interventions for SCD.

Background: A minimum spanning tree (MST) is a unique efficient network comprising the necessary connections needed to connect all regions in a network while retaining the lowest possible cost of connection weight. Objective: This study aimed to utilize functional near-infrared spectroscopy (fNIRS) to analyze brain activity in different regions and then construct MST-based regions to characterize the brain topologies of participants with Alzheimer’s disease (AD), mild cognitive impairment (MCI), and normal controls (NC). Methods: A 46 channel fNIRS setup was used on all participants, with correlation being calculated for each channel pair. An MST was constructed from the resulting correlation matrix, from which graph theory measures were calculated. The average number of connections within a lobe in the left versus right hemisphere was calculated to identify which lobes displayed and abnormal amount of connectivity. Results: Compared to those in the MCI group, the AD group showed a less integrated network structure, with a higher characteristic path length, but lower leaf fraction, maximum degree, and degree divergence. The AD group also showed a higher number of connections in the frontal lobe within the left hemisphere and a lower number between hemispheric frontal lobes as compared to MCI. Conclusion: These results indicate a deviation in network structure and connectivity within patient groups that is consistent with the theory of dysconnectivity for AD. Additionally, the AD group showed strong correlations between the Hamilton depression rating scale and different graph metrics, suggesting a link between network organization and the recurrence of depression in AD.

Cerebrovascular reactivity (CVR) is routinely measured as a predictor of stroke in people with a high risk of ischemic attack. Neuroimaging techniques such as emission tomography, magnetic resonance imaging, and transcranial doppler are frequently used to measure CVR even though each technique has its limitations. Functional near-infrared spectroscopy (fNIRS), also based on the principle of neurovascular coupling, is relatively inexpensive, portable, and allows for the quantification of oxy- and deoxy-hemoglobin concentration changes at a high temporal resolution. This study examines the relationship between age and CVR using fNIRS in 45 young healthy adult participants aged 18–41 years (6 females, 26.64 ± 5.49 years) performing a simple breath holding task. Eighteen of the 45 participants were scanned again after a week to evaluate the feasibility of fNIRS in reliably measuring CVR. Results indicate (a) a negative relationship between age and hemodynamic measures of breath holding task in the sensorimotor cortex of 45 individuals and (b) widespread positive coactivation within medial sensorimotor regions and between medial sensorimotor regions with supplementary motor area and prefrontal cortex during breath holding with increasing age. The intraclass correlation coefficient (ICC) indicated only a low to fair/good reliability of the breath hold hemodynamic measures from sensorimotor and prefrontal cortices. However, the average hemodynamic response to breath holding from the two sessions were found to be temporally and spatially in correspondence. Future improvements in the sensitivity and reliability of fNIRS metrics could facilitate fNIRS-based assessment of cerebrovascular function as a potential clinical tool.

Decline in cognitive ability is a significant issue associated with healthy aging. Transcranial photobiomodulation (tPBM) is an emerging non-invasive neuromodulation technique and has shown promise to overcome this challenge.This study aimed to investigate the effects of seven-day repeated tPBM, compared to those of single tPBM and baseline, on improvingIn a sham-controlled and within-subject design, 61 healthy older adults were recruited to participate in a longitudinal study involving an experimental baseline, seven days of tPBM treatment (12 min daily, 1064-nm laser,Repeated and single tPBM significantly improved accuracy rate in 1- and 3-back tasks and decreased response time in 3-back compared to the baseline. Moreover, the repeated tPBM resulted in a significantly higher improvement in accuracy rate than the single tPBM. These improvements in accuracy rate and response time lasted at least three weeks following repeated tPBM. In contrast, the control group showed no significant improvement in behavioral performance.This study demonstrated that seven-day repeated tPBM improved the working memory of healthy older adults more efficiently, with the beneficial effect lasting at least three weeks. These findings provide fundamental evidence that repeated tPBM may be a potential intervention for older individuals with memory decline.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral features that appear early in life. Although studies have shown that atypical brain functional and structural connectivity are associated with these behavioral traits, the occurrence and initial alterations of brain networks have not been fully investigated. The current study aimed to map early brain network efficiency and information transferring in infants at elevated likelihood (EL) compared to infants at typical likelihood (TL) for ASD in the first year of life. This study used a resting-state functional near-infrared spectroscopy (fNIRS) approach to obtain the length and strength of functional connections in the frontal and temporal areas in 45 5-month-old and 38 10-month-old infants. Modular organization and small-world properties were detected in both EL and TL infants at 5 and 10 months. In 5-month-old EL infants, local and nodal efficiency were significantly greater than age-matched TL infants, indicating overgrown local connections. Furthermore, we used a support vector machine (SVM) model to classify infants with or without EL based on the obtained global properties of the network, achieving an accuracy of 77.6%. These results suggest that infants with EL for ASD exhibit inefficiencies in the organization of brain networks during the first year of life. Lay Summary The brain networks of 5- and 10-month-old infants are able to support efficient communication of information in the brain. Neural networks of 5-month-old with an elevated likelihood (EL) for autism spectrum disorder (ASD) show an overgrowth in local functional connection, which may not support efficient communication between distant brain regions, while we found no such differences in 10-month-old infants with or without EL for ASD.

Background: Resting-state fNIRS (R-fNIRS) imaging data has proven to be a valuable technique to quantitatively characterize functional architectures of human brain network. However, whether the brain network metrics derived using weighted brain network model is test-retest (TRT) reliable remains largely unknown. Methods: Here, we firstly constructed weighted brain networks on a group of 18 participants, and then applied graph-theory approach to quantify topological parameters of each weighted network. The intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of network metrics. Results: We found that the reliability of the weighted network metrics is threshold-sensitive, and most of these network metrics showed fair to excellent reliability. Specifically, the global network metrics, e.g., clustering coefficient, path length, local efficiency and global efficiency were of excellent level reliability (ICC >0.75) on both HbO and HbR signals. The nodal network metrics, e.g., degree and efficiency, generally also showed excellent level reliability on both HbO and HbR signals, and the reliability of these two metrics was better than that of nodal betweenness. Conclusions: Overall, these findings demonstrated that most weighted network metrics derived from fNIRS are TRT reliable and can be used for brain network research.

Cerebral asymmetry is a cardinal feature of functional organization in the human brain and an important biomarker of successful brain development. Studies have demonstrated that functional network asymmetries across hemispheres undergo significant development through childhood and adulthood. However, it remains unknown when such asymmetries of functional networks emerge and how they develop across the early months of infancy. To address this issue, we used multiple-channel functional near-infrared spectroscopy (fNIRS) imaging to record spontaneous brain activity in 66 healthy infants aged 3-9 months. We then adopted a graph-theory analysis approach to quantify the topological characteristics of hemispheric networks in each participant. Our results showed that infants aged 3 to 6 months old exhibited leftward asymmetries in local network efficiency, while infants aged 6 to 9 months old exhibited leftward asymmetries in global network efficiency. Importantly, the degree of leftward asymmetry in global network efficiency was increased over development from 3 to 9 months old, with a faster increase in the left hemisphere than in the right hemisphere. At the regional level, 3- to 6-month-old infants exhibited leftward asymmetries in functional connectivity strength (FCS) in the temporal cortex, whereas the FCS asymmetries were located in the temporal, frontal, and occipital cortexes for 6- to 9-month-old infants. Furthermore, the 6- to 9-month-old infants also exhibited leftward asymmetries in nodal efficiency around the frontal cortex. These combined findings demonstrate that functional asymmetric organization has emerged in early infancy, which could lay a critical foundation for the development of brain functions (e.g., language and social cognition functions) later in life.

The biological and neurological processes during the lifespan are dynamic with significant alterations associated with different stages of life. The phase and coupling of oxy-hemoglobin (Δ[HbO]) and deoxy-hemoglobin concentration changes (Δ[Hb]) measured by functional near-infrared spectroscopy (fNIRS) are shown to characterize the neurovascular and metabolic development of infants. However, the changes in phase and coupling across the human lifespan remain mostly unknown. Here, fNIRS measurements of Δ[HbO] and Δ[Hb] conducted at two sites on different age populations (from newborns to elderly) were combined. Firstly, we assessed the influence of random noise on the calculation of the phase difference and phase-locking index (PLI) in fNIRS measurement. The results showed that the phase difference is close to π as the noise intensity approaches -8 dB, and the coupling strength (i.e., PLI) presents a u-shape curve as the noise increase. Secondly, phase difference and PLI in the frequency range 0.01-0.10 Hz were calculated after denoising. It showed that the phase difference increases from newborns to 3-4-month-olds babies. This phase difference persists throughout adulthood until finally being disrupted in the old age. The children's PLI is the highest, followed by that of adults. These two groups’ PLI are significantly higher than those of infants and the elderly (p < 0.001). Lastly, a hemodynamic model was used to explain the observations and found close associations with cerebral autoregulation and speed of blood flow. These results demonstrate that the phase-related parameters measured by fNIRS can be used to study the brain and assess brain health throughout the lifespan.

Statistical values of group differences

Cerebrovascular reactivity (CVR) is routinely measured as a predictor of stroke in people with a high risk of ischemic attack. Neuroimaging techniques such as emission tomography, magnetic resonance imaging, and transcranial doppler are frequently used to measure CVR even though each technique has its limitations. Functional near-infrared spectroscopy (fNIRS), also based on the principle of neurovascular coupling, is relatively inexpensive, portable, and allows for the quantification of oxy- and deoxy-hemoglobin concentration changes at a high temporal resolution. This study examines the relationship between age and CVR using fNIRS in 45 young healthy adult participants aged 18-41 years (6 females, 26.64 ± 5.49 years) performing a simple breath holding task. Eighteen of the 45 participants were scanned again after a week to evaluate the feasibility of fNIRS in reliably measuring CVR. Results indicate (a) a negative relationship between age and hemodynamic measures of breath holding task in the sensorimotor cortex of 45 individuals and (b) widespread positive coactivation within medial sensorimotor regions and between medial sensorimotor regions with supplementary motor area and prefrontal cortex during breath holding with increasing age. The intraclass correlation coefficient (ICC) indicated only a low to fair/good reliability of the breath hold hemodynamic measures from sensorimotor and prefrontal cortices. However, the average hemodynamic response to breath holding from the two sessions were found to be temporally and spatially in correspondence. Future improvements in the sensitivity and reliability of fNIRS metrics could facilitate fNIRS-based assessment of cerebrovascular function as a potential clinical tool.

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, STDEHbO (STDE based on Δ[HbO] oscillations) performs best. The STDE value increases with age throughout childhood (p

Early brain development from infancy through childhood is closely related to the development of cognition and behavior in later life. Human brain connectome is a novel framework for describing topological organization of the developing brain. Resting-state functional near-infrared spectroscopy (fNIRS), with a natural scanning environment, low cost, and high portability, is considered as an emerging imaging technique and has shown valuable potential in exploring brain network architecture and its changes during the development. Here, we review the recent advances involving typical and atypical development of the brain connectome from neonates to children using resting-state fNIRS imaging. This review highlights that the combination of brain connectome and resting-state fNIRS imaging offers a promising framework for understanding human brain development.

Significance: Attention-deficit/hyperactivity disorder (ADHD) is the most common psychological disease in childhood. Currently, widely used neuroimaging techniques require complete body confinement and motionlessness and thus are extremely hard for brain scanning of ADHD children. Aim: We present resting-state functional near-infrared spectroscopy (fNIRS) as an imaging technique to record spontaneous brain activity in children with ADHD. Approach: The brain functional connectivity was calculated, and the graph theoretical analysis was further applied to investigate alterations in the global and regional properties of the brain network in the patients. In addition, the relationship between brain network features and core symptoms was examined. Results: ADHD patients exhibited significant decreases in both functional connectivity and global network efficiency. Meanwhile, the nodal efficiency in children with ADHD was also found to be altered, e.g., increase in the visual and dorsal attention networks and decrease in somatomotor and default mode networks, compared to the healthy controls. More importantly, the disrupted functional connectivity and nodal efficiency significantly correlated with dimensional ADHD scores. Conclusions: We clearly demonstrate the feasibility and potential of fNIRS-based connectome technique in ADHD or other neurological diseases in the future.

Brain signal variability (BSV) has shown to be powerful in characterizing human brain development and neuropsychiatric disorders. Multiscale entropy (MSE) is a novel method for quantifying the variability of brain signal, and helps elucidate complex dynamic pathological mechanisms in children with attention-deficit/hyperactivity disorder (ADHD). Here, multiple-channel resting-state functional near-infrared spectroscopy (fNIRS) imaging data were acquired from 42 children with ADHD and 41 healthy controls (HCs) and then BSV was calculated for each participant based on the MSE analysis. Compared with HCs, ADHD group exhibited reduced BSV in both high-order and primary brain functional networks, e.g., the default mode, frontoparietal, attention and visual networks. Intriguingly, the BSV aberrations negatively correlated with ADHD symptoms in the frontoparietal network and negatively correlated with reaction time variability in the frontoparietal, default mode, somatomotor and attention networks. This study demonstrates a wide alternation in the moment-to-moment variability of spontaneous brain signal in children with ADHD, and highlights the potential for using MSE metric as a disease biomarker.

Communication within the brain is highly dynamic. Alzheimer’s disease (AD) exhibits dynamic progression corresponding to a decline in memory and cognition. However, little is known of whether brain dynamics are disrupted in AD and its prodromal stage, mild cognitive impairment (MCI). For our study, we acquired high sampling rate functional near-infrared spectroscopy imaging data at rest from the entire cortex of 23 patients with AD dementia, 25 patients with amnestic mild cognitive impairment (aMCI), and 30 age-matched healthy controls (HCs). Sliding-window correlation and k-means clustering analyses were used to construct dynamic functional connectivity (FC) maps for each participant. We discovered that the brain’s dynamic FC variability strength ([Formula: see text]) significantly increased in both aMCI and AD group as compared to HCs. Using the [Formula: see text] value as a measurement, the classification performance exhibited a good power in differentiating aMCI [area under the curve ([Formula: see text])] or AD ([Formula: see text]) from HCs. Furthermore, we identified two abnormal brain FC states in the AD group, of which the occurrence frequency ([Formula: see text]) exhibited a significant decrease for the first-level FC state (state 1) and a significant increase for the second-level FC state (state 2). We also found that the abnormal [Formula: see text] in these two states significantly correlated with the cognitive impairment in patients. These findings provide the first evidence to demonstrate the disruptions of dynamic brain connectivity in aMCI and AD and extend the traditional static (i.e., time-averaged) FC findings in the disease (i.e., disconnection syndrome) and thus provide insights into understanding the pathophysiological mechanisms occurring in aMCI and AD.

Cerebral asymmetry is considered an important marker of the successful development of the human brain. Recent studies have demonstrated topological asymmetries between structurally hemispheric networks in the human brain. However, it remains largely unknown whether and how the functionally topological asymmetries evolve from childhood to adulthood, a critical period that constitutes the primary peak of human brain and cognitive development. Here, we adopted resting-state functional near-infrared spectroscopy imaging data to construct hemispheric functional networks and then applied graph theory analysis to quantify the topological characteristics of the hemispheric networks. We found that the adult group exhibited consistent leftward hemispheric asymmetries in both global and local network efficiency, and the degree of leftward asymmetry in local network efficiency was significantly increased with development from childhood to adulthood. At the nodal level, the degree of leftward asymmetry in nodal efficiency, mainly involving the frontal, parietal-occipital junction, and occipital regions, increased with development. These developmental patterns of topological asymmetries suggest that the protracted maturation of functional segregation in the left hemisphere could underlie language development from childhood to adulthood and provide insight into the development of human brain functional networks.

Social interaction plays an essential role in acquiring knowledge and developing our own personalities in our daily life. Meanwhile, functional magnetic resonance imaging (fMRI)-, electroencephalograph (EEG)-, and functional near inferred spectroscopy (fNIRS)-hyperscanning, enables us to concurrently map brain activation from two or more participants who are engaged in social interaction simultaneously. In this review, we first highlight the recent technologies advances and the most significant findings towards social interaction by using the hyperscanning method. In addition, we also illustrate several well-designed hyperscanning tasks that have been extensively adopted for the study of social interaction. Basically, hyperscanning contains six categories of experimental paradigms that can track the interactive neural process of interest. Furthermore, it contains two main elucidated neural systems which are involved in social interaction, including the mirror neuron system (MNS) and mentalizing system (MS). Finally, future research directions and clinical implications that are associated with hyperscanning are also highlighted and discussed.

Lifespan is a dynamic process with remarkable changes in brain structure and function. Previous neuroimaging studies have indicated age-related microstructural changes in specific white matter tracts during development and aging. However, the age-related alterations in the topological architecture of the white matter structural connectome across the human lifespan remain largely unknown. Here, a cohort of 113 healthy individuals (ages 9-85) with both diffusion and structural MRI acquisitions were examined. For each participant, the high-resolution white matter structural networks were constructed by deterministic fiber tractography among 1024 parcellation units and were quantified with graph theoretical analyses. The global network properties, including network strength, cost, topological efficiency, and robustness, followed an inverted U-shaped trajectory with a peak age around the third decade. The brain areas with the most significantly nonlinear changes were located in the prefrontal and temporal cortices. Different brain regions exhibited heterogeneous trajectories: the posterior cingulate and lateral temporal cortices displayed prolonged maturation/degeneration compared with the prefrontal cortices. Rich-club organization was evident across the lifespan, whereas hub integration decreased linearly with age, especially accompanied by the loss of frontal hubs and their connections. Additionally, age-related changes in structural connections were predominantly located within and between the prefrontal and temporal modules. Finally, based on the graph metrics of structural connectome, accurate predictions of individual age were obtained (r=0.77). Together, the data indicated a dynamic topological organization of the brain structural connectome across human lifespan, which may provide possible structural substrates underlying functional and cognitive changes with age. (C) 2015 Wiley Periodicals, Inc.

To investigate the associations between SIRT1 polymorphisms and their expression in patients with alcoholic fatty liver disease (AFLD). A total of 268 heavy drinkers were divided into the AFLD group (n = 176) and alcoholic control (n = 92) and 237 light-/non-drinkers into the NAFLD (non-AFLD) group (n = 117) and healthy control (n = 120). The genotyping of SIRT1 (rs33957861, rs11599176, rs12413112 and rs35689145) was detected by the Sequenom MassARRAY iPLEX test. The mRNA and protein expressions of SIRT1 were determined by quantitative real-time polymerase chain reaction (qRT-PCR) and enzyme-linked immunosorbent assays (ELISA), respectively. SIRT1 gene rs33957861 and rs11599176 polymorphisms significantly reduce the risk of NAFLD and AFLD, while rs35689145 remarkably increases the risk. Haplotypes of AAAA (rs33957861–rs11599176–rs12413112–rs35689145), AAAA, CAGA and CGAA can appreciably lower the presence of AFLD, but CAAG had an elevated AFLD risk. Besides, in the NAFLD and AFLD groups, a decreased BMI was found in the mutant genotype carriers of rs33957861, rs11599176 and rs12413112, but an increased BMI was observed in the rs35689145 mutant genotype carriers when compared to those with the wild-type homozygous genotype ones. Furthermore, rs33957861 C>T, rs11599176 A>G and BMI were independent risk factors of AFLD. There was no difference among four SNPs of SIRT1 and its mRNA and protein expressions in all groups. SIRT1 polymorphisms and their expression were associated with the presence of AFLD, and there was a close relationship among four SNPs and BMI in AFLD patients, but no SNP was related to its expression.

Resting-state functional MRI (R-fMRI) has emerged as a promising neuroimaging technique used to identify global hubs of the human brain functional connectome. However, most R-fMRI studies on functional hubs mainly utilize traditional R-fMRI data with relatively low sampling rates (e.g., repetition time [TR]=2 s). R-fMRI data scanned with higher sampling rates are important for the characterization of reliable functional connectomes because they can provide temporally complementary information about functional integration among brain regions and simultaneously reduce the effects of high frequency physiological noise. Here, we employed a publicly available multiband R-fMRI dataset with a sub-second sampling rate (TR=645 ms) to identify global hubs in the human voxel-wise functional networks, and further examined their test-retest (TRT) reliability over scanning time. We showed that the functional hubs of human brain networks were mainly located at the default-mode regions (e.g., medial prefrontal and parietal cortex as well as the lateral parietal and temporal cortex) and the sensorimotor and visual cortex. These hub regions were highly anatomically distance-dependent, where short-range and long-range hubs were primarily located at the primary cortex and the multimodal association cortex, respectively. We found that most functional hubs exhibited fair to good TRT reliability using intraclass correlation coefficients. Interestingly, our analysis suggested that a 6-minute scan duration was able to reliably detect these functional hubs. Further comparison analysis revealed that these results were approximately consistent with those obtained using traditional R-fMRI scans of the same subjects with TR=2500 ms, but several regions (e.g., lateral frontal cortex, paracentral lobule and anterior temporal lobe) exhibited different TRT reliability. Finally, we showed that several regions (including the medial/lateral prefrontal cortex and lateral temporal cortex) were identified as brain hubs in a high frequency band (0.2-0.3 Hz), which is beyond the frequency scope of traditional R-fMRI scans. Our results demonstrated the validity of multiband R-fMRI data to reliably detect functional hubs in the voxel-wise whole-brain networks, which motivated the acquisition of high temporal resolution R-fMRI data for the studies of human brain functional connectomes in healthy and diseased conditions.

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain’s low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Resting-state functional near-infrared spectroscopy (fNIRS) is a potential technique for the study of brain functional connectivity (FC) and networks in children. However, the necessary fNIRS scanning duration required to map accurate and stable functional brain connectivity and graph theory metrics in the resting-state brain activity remains largely unknown. Here, we acquired resting-state fNIRS imaging data from 53 healthy children to provide the first empirical evidence for the minimum imaging time required to obtain accurate and stable FC and graph theory metrics of brain network activity (e.g., nodal efficiency and network global and local efficiency). Our results showed that FC was accurately and stably achieved after 7.0-min fNIRS imaging duration, whereas the necessary scanning time for accurate and stable network measures was a minimum of 2.5 min at low network thresholds. These quantitative results provide direct evidence for the choice of the resting-state fNIRS imaging time in children in brain FC and network topology study. The current study also demonstrates that these methods are feasible and cost-effective in the application of time-constrained infants and critically ill children.

Gastric intestinal metaplasia (IM) is regarded as a premalignant lesion, conferring risks for gastric cancer development. An intestinal transcription factor, CDX2, plays a vital role in establishing and maintaining IM. SOX2, an HMG-box transcription factor, is expressed in normal gastric mucosa and downregulated in IM. Therefore, it is important to elucidate the mutual interaction of SOX2 and CDX2 in gastric IM. This study aims to evaluate the negative correlation between SOX2 and CDX2 in mRNA expression and promoter methylation and to illuminate the effect of SOX2 on the promoter methylation of CDX2. Immunohistochemistry, real-time PCR and methylation-specific polymerase chain reaction assays were performed to evaluate the expression and promoter methylation of SOX2 and CDX2 in IM tissues from patients. SOX2 knockdown and CDX2 overexpression were performed in GES-1 cells to further clarify the relationship between SOX2 and CDX2. A negative correlation between SOX2 and CDX2 was found in 120 gastric IM specimens. Additionally, significant DNA demethylation of CDX2 promoter in clinical IM specimens was observed concomitantly with partial methylation of the SOX2 promoter. Furthermore, SOX2 knockdown in GES-1 cells triggered promoter demethylation of CDX2. Finally, the phenotype shift of gastric intestinal metaplasia in GES-1 cells, marked by MUC2 expression, was effectively induced by the combination of SOX2 RNAi and CDX2 overexpression. Aberrant DNA methylation of SOX2 and CDX2 genes contributes to the development of IM. Notably, SOX2 may play a role in establishing and maintaining the methylation status of the CDX2 gene in gastric tissues and cells.

Reflectance diffuse optical tomography (rDOT) of brain function is limited by its high sensitivity to the superficial tissues (i.e., the scalp and skull) and by its severe decrease in measurement sensitivity with increased depth. Significant interference in rDOT results from spontaneous fluctuations that are embedded in both the superficial tissues and brain, such as arterial pulsation and vasomotion. In this study, first we investigate coherence and phase shift of the spontaneous fluctuations in the resting state, within the superficial tissues and at various depths of the brain, respectively. We demonstrate that the spontaneous fluctuations originating from arterial pulsations ( ∼ 1 Hz) are spatially global and temporally coherent, while the fluctuations originating from vasomotion ( ∼ 0.1 Hz) tend to have less coherence with increased depth. Second, adaptive cancellation of spontaneous fluctuations with a frequency-specific strategy is utilized and validated in both resting and activation (evoked by a finger-tapping task) states. Third, improved depth localization of motor activation in reconstructed rDOT images is achieved by combining adaptive cancellation with a depth compensation algorithm that we recently reported.

Multiscale entropy (MSE) analysis is a novel entropy-based analysis method for quantifying the complexity of dynamic neural signals and physiological systems across multiple temporal scales. This approach may assist in elucidating the pathophysiologic mechanisms of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Using resting-state fNIRS imaging, we recorded spontaneous brain activity from 31 healthy controls (HC), 27 patients with aMCI, and 24 patients with AD. The quantitative analysis of MSE revealed that reduced brain signal complexity in AD patients in several networks, namely, the default, frontoparietal, ventral and dorsal attention networks. For the default and ventral attention networks, the MSE values also showed significant positive correlations with cognitive performances. These findings demonstrated that the MSE-based analysis method could serve as a novel tool for fNIRS study in characterizing and understanding the complexity of abnormal cortical signals in AD cohorts.

The brain is a complex network with time-varying functional connectivity (FC) and network organization. However, it remains largely unknown whether resting-state fNIRS measurements can be used to characterize dynamic characteristics of intrinsic brain organization. In this study, for the first time, we used the whole-cortical fNIRS time series and a sliding-window correlation approach to demonstrate that fNIRS measurement can be ultimately used to quantify the dynamic characteristics of resting-state brain connectivity. Our results reveal that the fNIRS-derived FC is time-varying, and the variability strength (Q) is correlated negatively with the time-averaged, static FC. Furthermore, the Q values also show significant differences in connectivity between different spatial locations (e.g., intrahemispheric and homotopic connections). The findings are reproducible across both sliding-window lengths and different brain scanning sessions, suggesting that the dynamic characteristics in fNIRS-derived cerebral functional correlation results from true cerebral fluctuation.

Functional near-infrared spectroscopy (fNIRS), a promising noninvasive imaging technique, has recently become an increasingly popular tool in resting-state brain functional connectivity (FC) studies. However, the corresponding software packages for FC analysis are still lacking. To facilitate fNIRS-based human functional connectome studies, we developed a MATLAB software package called “functional connectivity analysis tool for near-infrared spectroscopy data” (FC-NIRS). This package includes the main functions of fNIRS data preprocessing, quality control, FC calculation, and network analysis. Because this software has a friendly graphical user interface (GUI), FC-NIRS allows researchers to perform data analysis in an easy, flexible, and quick way. Furthermore, FC-NIRS can accomplish batch processing during data processing and analysis, thereby greatly reducing the time cost of addressing a large number of datasets. Extensive experimental results using real human brain imaging confirm the viability of the toolbox. This novel toolbox is expected to substantially facilitate fNIRS-data-based human functional connectome studies.