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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.

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.

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.

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.

Early childhood (7–8 years old) and early adolescence (11–12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development. Keywords: Brain development, Connectome, Brain networks, fNIRS, Resting state

Understanding the properties of attentional control, along with the neural mechanisms subserving them, has long invited intense scrutiny in research groups. However, it has not been demonstrated how the top-down anticipatory hemodynamic activation influences the subsequent attentional processing of targets and distractors. Here, with concurrent fNIRS–ERP recording, we explored the potential contribution of anticipatory oxygenated hemoglobin (HbO) based brain activity to attentional control by examining how HbO influences the subsequent ERP N2pc components assumed to reflect attentional selection. We found that expecting a target led to a larger increase of preparatory HbO response over the visual cortex contralateral to the upcoming target, which was positively correlated with the subsequent target-evoked N2pc amplitude. Further, anticipation concerning the presence of a competing distractor resulted in large and prolonged preparatory HbO signals in the visual cortex contralateral to the distractor, indicating that the salient distractor might be actively suppressed by preparatory top-down attentional control. However, the pre-suppressed distractor still captured part of the attention in the subsequent visual search as revealed by a decrease in the N2pc amplitude, and such a distraction effect on N2pc was negatively correlated with preparatory HbO enhancement contralateral to the anticipated distractor. Overall, each individuals attentional shift to the target and resistance to the distractor measured by ERP is predictable in advance via anticipatory hemodynamic activity in the visual cortex measured by fNIRS.

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.

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.

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. Hum Brain Mapp 36:3777–3792, 2015. © 2015 Wiley Periodicals, Inc.

Resting-state functional connectivity (RSFC) has been widely used to investigate spontaneous brain activity that exhibits correlated fluctuations. RSFC has been found to be changed along the developmental course and after learning. Here, we investigated whether and how visual learning modified the resting oxygenated hemoglobin (HbO) functional brain connectivity by using functional near-infrared spectroscopy (fNIRS). We demonstrate that after five days of training on an orientation discrimination task constrained to the right visual field, resting HbO functional connectivity and directed mutual interaction between high-level visual cortex and frontal/central areas involved in the top-down control were significantly modified. Moreover, these changes, which correlated with the degree of perceptual learning, were not limited to the trained left visual cortex. We conclude that the resting oxygenated hemoglobin functional connectivity could be used as a predictor of visual learning, supporting the involvement of high-level visual cortex and the involvement of frontal/central cortex during visual perceptual learning.

Recent research has demonstrated the feasibility of combining functional near-infrared spectroscopy (fNIRS) and graph theory approaches to explore the topological attributes of human brain networks. However, the test-retest (TRT) reliability of the application of graph metrics to these networks remains to be elucidated. Here, we used resting-state fNIRS and a graph-theoretical approach to systematically address TRT reliability as it applies to various features of human brain networks, including functional connectivity, global network metrics and regional nodal centrality metrics. Eighteen subjects participated in two resting-state fNIRS scan sessions held ∼20 min apart. Functional brain networks were constructed for each subject by computing temporal correlations on three types of hemoglobin concentration information (HbO, HbR, and HbT). This was followed by a graph-theoretical analysis, and then an intraclass correlation coefficient (ICC) was further applied to quantify the TRT reliability of each network metric. We observed that a large proportion of resting-state functional connections (∼90%) exhibited good reliability (0.6< ICC

BackgroundThe human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics.ResultsWe utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques.ConclusionsOur results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.

Functional near-infrared spectroscopy (fNIRS) is recently utilized as a new approach to assess resting-state functional connectivity (RSFC) in the human brain. For any new technique or new methodology, it is necessary to be able to replicate similar experiments using different instruments in order to establish its liability and reproducibility. We apply two different diffuse optical tomographic (DOT) systems (i.e., DYNOT and CW5), with various probe arrangements to evaluate RSFC in the sensorimotor cortex by utilizing a previously published experimental protocol and seed-based correlation analysis. Our results exhibit similar spatial patterns and strengths in RSFC between the bilateral motor cortexes. The consistent observations are obtained from both DYNOT and CW5 systems, and are also in good agreement with the previous fNIRS study. Overall, we demonstrate that the fNIRS-based RSFC is reproducible by various DOT imaging systems among different research groups, enhancing the confidence of neuroscience researchers and clinicians to utilize fNIRS for future applications.

Diffuse optical tomography endures poor depth localization, since its sensitivity decreases severely with increased depth. In this study, we demonstrate a depth compensation algorithm (DCA), which optimally counterbalances the decay nature of light propagation in tissue so as to accurately localize absorbers in deep tissue. The novelty of DCA is to directly modify the sensitivity matrix, rather than the penalty term of regularization. DCA is based on maximum singular values (MSVs) of layered measurement sensitivities; these MSVs are inversely utilized to create a balancing weight matrix for compensating the measurement sensitivity in increased depth. Both computer simulations and laboratory experiments were performed to validate DCA. These results demonstrate that one (or two) 3-cm-deep absorber(s) can be accurately located in both lateral plane and depth within the laboratorial position errors.

Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data.