Transcranial brain atlas-based optimization for functional near-infrared spectroscopy optode arrangement: Theory, algorithm, and application.

The quality of optode arrangement is crucial for group imaging studies when using functional near-infrared spectroscopy (fNIRS). Previous studies have demonstrated the promising effectiveness of using transcranial brain atlases (TBAs), in a manual and intuition-based way, to guide optode arrangement when individual structural MRI data are unavailable. However, the theoretical basis of using TBA to optimize optode arrangement remains unclear, which leads to manual and subjective application. In this study, we first describe the theoretical basis of TBA-based optimization of optode arrangement using a mathematical framework. Second, based on the theoretical basis, an algorithm is proposed for automatically arranging optodes on a virtual scalp. The resultant montage is placed onto the head of each participant guided by a low-cost and portable navigation system. We compared our method with the widely used 10/20-system-assisted optode arrangement procedure, using finger-tapping and working memory tasks as examples of both low- and high-level cognitive systems. Performance, including optode montage designs, locations on each participant's scalp, brain activation, as well as ground truth indices derived from individual MRI data were evaluated. The results give convergent support for our method's ability to provide more accurate, consistent and efficient optode arrangements for fNIRS group imaging than the 10/20 method.

Dielectric properties of urine in relation to bladder cancer.

Many studies have investigated the dielectric properties of human and animal tissues, particularly to differentiate between normal cells and tumors. However, these studies are invasive as tissue samples have to be excised to measure the properties. This study aims to investigate the dielectric properties of urine in relation to bladder cancer, which is safe and non-invasive to patients. 30 healthy subjects and 30 bladder cancer patients were recruited. Their urine samples were subjected to urinalysis and cytology assessment. A vector network analyzer was used to measure the dielectric constant (Ɛ') and loss factor (Ɛ″) at microwave frequencies of between 0.2 and 50 GHz at 25 °C, 30 °C and 37 °C. Significant differences in Ɛ' and Ɛ″ were observed between healthy subjects and patients, especially at frequencies of between 25 and 40 GHz at 25 °C. Bladder cancer patients had significant lower Ɛ' and higher Ɛ″ compared with healthy subjects. The Ɛ' was negatively correlated with urinary exfoliated urothelial cell number, and Ɛ″ was positively correlated. The study achieved a receiver operating characteristic area under curve (ROC-AUC) score of 0.69099 and an optimum accuracy of 75% with a sensitivity of 80% and a specificity of 70%. The number of exfoliated urothelial cell had significant effect on the dielectric properties, especially in bladder cancer patients. Urinary dielectric properties could potentially be used as a tool to detect bladder cancer.

Quantitative comparison of resting-state functional connectivity derived from fNIRS and fMRI: a simultaneous recording study.

The feasibility of functional near-infrared spectroscopy (fNIRS) to assess resting-state functional connectivity (RSFC) has already been demonstrated. However the validity of fNIRS-based RSFC has rarely been studied. In the present study, fNIRS and fMRI data were simultaneously acquired from 21 subjects during the resting state. After the spatial correspondence was established between the two imaging modalities by transforming the fMRI data into fNIRS measurements space, the index of Between-Modality-Similarity (BMS) of RSFC was evaluated across multiple spatial scales. First, the RSFC between the bilateral primary motor ROI was quite similar between fNIRS and fMRI for all the subjects (BMS(ROI) = 0.95 ± 0.04 for HbO and BMS(ROI) = 0.86 ± 0.13 for HbR). Second, group-level sensorimotor RSFC maps (0.79 for HbO and 0.74 for HbR) showed higher between-modality similarity than individual-level RSFC maps (0.48 ± 0.16 for HbO and 0.41 ± 0.15 for HbR). Finally, for the first time, we combined fNIRS and graph theory to investigate topological properties of resting-state brain networks. The clustering coefficient (C(p)) and characteristic path length (L(p)) which are the most important network topological parameters, both showed high between-modality similarities (BMS(Cp) = 0.90 ± 0.03 for HbO and 0.90 ± 0.06 for HbR; BMS(Lp) = 0.92 ± 0.04 for HbO and 0.91 ± 0.05 for HbR). In summary, the converged results across all the spatial scales demonstrated that fNIRS is capable of providing comparable RSFC measures to fMRI, and thus provide direct evidence for the validity of the optical brain connectivity and the optical brain network approaches to functional brain integration during resting state.

The Application of Sulfonyl Hydrazides in Electrosynthesis: A Review of Recent Studies.

Sulfonyl hydrazides are viewed as alternatives to sulfinic acids and their salts or sulfonyl halides, which are broadly used in organic synthesis or work as active pharmaceutical substances. Generally, sulfonyl hydrazides are considered good building blocks and show powerful value in a diverse range of reactions to construct C-S bonds or C-C bonds, and even C-N bonds as sulfur, carbon, or nitrogen sources, respectively. As a profound synthetic tool, the electrosynthesis method was recently used to achieve efficient and green applications of sulfonyl hydrazides. Interestingly, many unique and novel electrochemical syntheses using sulfonyl hydrazides as radical precursors have been developed, including cascade reactions, functionalization of heterocycles, as well as a continuous flow method combining with electrochemical synthesis since 2017. Accordingly, it is necessary to specifically summarize the recent developments of electrosynthesis with only sulfonyl hydrazides as radical precursors to more deeply understand and better design novel electrochemical synthesis reactions. Herein, electrosynthesis research using sulfonyl hydrazides as radical precursors since 2017 is reviewed in detail based on the chemical structures of products and reaction mechanisms.

A parallel-group study of near-infrared spectroscopy-neurofeedback in children with attention deficit hyperactivity disorder.

The present study aimed to assess the efficacy of Near-infrared spectroscopy (NIRS) real-time neurofeedback (NF) vs. atomoxetine (AT) in children with attention deficit hyperactivity disorder (ADHD). A parallel-group study was conducted to enroll children with ADHD between 8 and 12 years of age. Participants were assigned into the NIRS group and AT group as their wish. Subjects in the NIRS group received 12 sessions of NF training within 6 weeks, and subjects in the AT group were given oral medication. Changes in Swanson, Nolan, and Pelham-V rating scales (SNAP-IV), and performance of Go/No-Go and N-back working memory tasks at week 3, 6 and 8 were evaluated. Forty-nine patients completed the study, including 18 ADHD in the NIRS group and 31 in the AT group. Total scores of SNAP-IV significantly decreased from baseline to week 3, week 6, and week 8 in both groups. Patients in the NIRS group showed significant lower scores on the inattention subscale of SNAP-IV at week 3 and week 6, compared to the AT group. NIRS group had a shorter reaction time during the Go/No-Go task at week 6 and fewer errors during 2-back than the AT group at week 3. The findings revealed that NIRS real-time NF is more efficacious relative to AT in improving behavioral performance, highlighting its potential role and advantages in treating patients with ADHD.

Test-retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy.

Recent studies of resting-state functional near-infrared spectroscopy (fNIRS) have emerged as a hot topic and revealed that resting-state functional connectivity (RSFC) is an inherent characteristic of the resting brain. However, it is currently unclear if fNIRS-based RSFC is test-retest reliable. In this study, we utilized independent component analysis (ICA) as an effective RSFC detection tool to address the reliability question. Sixteen subjects participated in two resting-state fNIRS recording sessions held 1week (6.88±1.09 days) apart. Then, RSFC in the sensorimotor regions was extracted using ICA. Test-retest reliability was assessed for intra- and inter-sessions, at both individual and group levels, and for different hemoglobin concentration signals. Our results clearly demonstrated that map-wise reliability was excellent at the group level (with Pearson's r coefficients up to 0.88) and generally fair at the individual level. Cluster-wise reliability was better at the group level (having reproducibility indices of up to 0.97 for the size and up to 0.80 for the location of the detected RSFC) and was weaker but still fair at the individual level (0.56 and 0.46 for intra- and inter-session reliabilities, respectively). Cluster-wise intra-class correlation coefficients (ICCs) also exhibited fair-to-good reliability (with single-measure ICC up to 0.56), while channel-wise single-measure ICCs indicated lower reliability. We conclude that fNIRS-based, ICA-derived RSFC is an essential and reliable biomarker at the individual and group levels if interpreted in map- and cluster-wise manners. Our results also suggested that channel-wise individual-level RSFC results should be interpreted with caution if no optode co-registration procedure had been conducted and indicated that "cluster" should be treated as a minimal analytical unit in further RSFC studies using fNIRS.

NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies.

Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise and extracting neuronal activity-related sources. The application of ICA remains challenging due to its complexity in usage, and an easy-to-use toolbox dedicated to ICA processing is still lacking in the fNIRS community. In this study, we propose NIRS-ICA, an open-source MATLAB toolbox to ease the difficulty of ICA application for fNIRS studies. NIRS-ICA incorporates commonly used ICA algorithms for source separation, user-friendly GUI, and quantitative evaluation metrics assisting source selection, which facilitate both removing noise and extracting neuronal activity-related sources. The options used in the processing can also be reported easily, which promotes using ICA in a more reproducible way. The proposed toolbox is validated and demonstrated based on both simulative and real fNIRS datasets. We expect the release of the toolbox will extent the application for ICA in the fNIRS community.

Dyad sex composition effect on inter-brain synchronization in face-to-face cooperation.

Human cooperation behavior based on reciprocal altruism has been a hallmark of ancient and modern societies. Prior studies have indicated that inter-brain synchronization (IBS) between partners could exist during cooperation. However, how the sex composition of dyads influences the neural synchronization is still poorly understood. Here, we adopted functional near-infrared spectroscopy (fNIRS) based hyperscanning and a task of building blocks to investigate the sex composition effect on IBS in face-to-face cooperation in a natural situation, by evaluating brain-to-brain interactions of forty-five same-sex and mixed-sex dyads. Results showed significantly stronger inter-brain synchronization in Brodmann area 10 (BA10) in cooperation. In addition, variance analysis indicated that only male-male dyads showed increased inter-brain synchronization in left inferior frontal region (i.e., BA10) specific to cooperation. More importantly, the inter-brain synchronization in male-male dyads was significantly greater than that in male-female and female-female dyads. These findings provide support for the impact of sex composition on social cooperation in a naturalistic interactive setting and extend our knowledge on the neural basis of face-to-face cooperation.

Subject order-independent group ICA (SOI-GICA) for functional MRI data analysis.

Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets.

Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements.

As a promising non-invasive imaging technique, functional near infrared spectroscopy (fNIRS) has recently earned increasing attention in resting-state functional connectivity (RSFC) studies. Preliminary fNIRS-based RSFC studies adopted a seed correlation approach and yielded interesting results. However, the seed correlation approach has several inherent problems, such as neglecting of interactions among multiple regions and a dependence on seed region selection. Moreover, ineffectively reduced noise and artifacts in fNIRS measurements also negatively affect RSFC results. In this study, independent component analysis (ICA) was introduced to meet these challenges in RSFC detection based on resting-state fNIRS measurements. The results of ICA on data from the sensorimotor and the visual systems both showed functional system-specific RSFC maps. Results from comparison between ICA and the conventional seed correlation approach demonstrated, both qualitatively and quantitatively, the superior performance of ICA with higher sensitivity and specificity, especially in the case of higher noise level. The capability of ICA to separate noise and artifacts from resting-state fNIRS data was also demonstrated, and the extracted noise and artifacts were illustrated. Finally, some practical issues on performing ICA on resting-state fNIRS data were discussed.

A review on the accuracy of bladder cancer detection methods.

Background and purpose: Bladder cancer is the most common malignant tumour in the urinary system, with a high incidence and recurrence rate. While the incidence of bladder cancer has been rising in recent years, the prevalence of bladder carcinoma is showing an increasing tendency in the younger age group. There are several methods to detect bladder cancer, but different methods have varying degrees of accuracy which intrinsically depends on the method's sensitivity and specificity. Our aim was to comprehensively summarize the current detection methods for bladder cancer based on the available literature, and at the same time, to find the best combination of different effective methods which can produce a high degree of accuracy in detecting the presence of cancerous cells in the bladder. Materials and Methods: We used key word retrieval method for searching related references in English that had been indexed in PubMed and Medline. Results and Discussion: This paper discussed the different detection methods and their sensitivities/specificities as well as the advantages and disadvantages. We summarized the best identified cancer cell detection methods with higher sensitivity/specificity. Conclusion: The results of this review can positively help to identify accurate methods for detecting bladder cancer and highlight areas to be further improved for future research work.

Corrigendum: Feasibility of Functional Near-Infrared Spectroscopy (fNIRS) to Investigate the Mirror Neuron System: An Experimental Study in a Real-Life Situation.

[This corrects the article DOI: 10.3389/fnhum.2018.00086.].

Correlations in spontaneous activity and gray matter density between left and right sensoritmotor areas of pianists.

Resting-state functional MRI and structural MRI were used to study correlations of spontaneous activity and gray matter density between the left and right primary sensorimotor areas in pianists and nonmusicians. Functional MRI analysis showed significant correlation of spontaneous activity between the left and right primary sensorimotor area in both groups; however, there was no between-group difference. Structural MRI analysis showed significant correlation in gray matter density between the left and right sensorimotor areas in nonmusicians (r=0.65, P=0.001), but not in pianists (r=0.07, P=0.78), with a significant between-group difference (P=0.035). The lack of correlation of gray matter density between the left and right sensorimotor areas might be the basis of bimanual coordination of the pianists.

An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF.

Most of the resting-state functional magnetic resonance imaging (fMRI) studies demonstrated the correlations between spatially distinct brain areas from the perspective of functional connectivity or functional integration. The functional connectivity approaches do not directly provide information of the amplitude of brain activity of each brain region within a network. Alternatively, an index named amplitude of low-frequency fluctuation (ALFF) of the resting-state fMRI signal has been suggested to reflect the intensity of regional spontaneous brain activity. However, it has been indicated that the ALFF is also sensitive to the physiological noise. The current study proposed a fractional ALFF (fALFF) approach, i.e., the ratio of power spectrum of low-frequency (0.01-0.08 Hz) to that of the entire frequency range and this approach was tested in two groups of resting-state fMRI data. The results showed that the brain areas within the default mode network including posterior cingulate cortex, precuneus, medial prefrontal cortex and bilateral inferior parietal lobule had significantly higher fALFF than the other brain areas. This pattern was consistent with previous neuroimaging results. The non-specific signal components in the cistern areas in resting-state fMRI were significantly suppressed, indicating that the fALFF approach improved the sensitivity and specificity in detecting spontaneous brain activities. Its mechanism and sensitivity to abnormal brain activity should be evaluated in the future studies.

Default mode network as revealed with multiple methods for resting-state functional MRI analysis.

Recently, human brain activity during a resting-state has attracted increasing attention. Several studies have found that there are two networks: the default mode network and its anti-correlation network. Some studies have subsequently showed that the functions of brain areas within the default mode network are crucial in human mental activity. To further discern the brain default mode network as well as its anti-correlation network during resting-state, we used three methods to analyze resting-state functional magnetic resonance imaging (fMRI) data; regional homogeneity analysis, linear correlation and independent component analysis, on four groups of dataset. Our results showed the existence of these two networks prominently and consistently during a resting- and conscious-state across the three methods. This consistency was exhibited in four independent groups of normal adults. Moreover, the current results provided evidences that the brain areas within the two anti-correlated networks are highly integrated at both the intra- and inter-regional level.

Feasibility of Functional Near-Infrared Spectroscopy (fNIRS) to Investigate the Mirror Neuron System: An Experimental Study in a Real-Life Situation.

The mirror neuron system (MNS), mainly including the premotor cortex (PMC), inferior frontal gyrus (IFG), superior parietal lobule (SPL), and rostral inferior parietal lobule (IPL), has attracted extensive attention as a possible neural mechanism of social interaction. Owing to high ecological validity, functional near-infrared spectroscopy (fNIRS) has become an ideal approach for exploring the MNS. Unfortunately, for the feasibility of fNIRS to detect the MNS, none of the four dominant regions were found in previous studies, implying a very limited capacity of fNIRS to investigate the MNS. Here, we adopted an experimental paradigm in a real-life situation to evaluate whether the MNS activity, including four dominant regions, can be detected by using fNIRS. Specifically, 30 right-handed subjects were asked to complete a table-setting task that included action execution and action observation. A double density probe configuration covered the four regions of the MNS in the left hemisphere. We used a traditional channel-based group analysis and also a ROI-based group analysis to find which regions are activated during both action execution and action observation. The results showed that the IFG, adjacent PMC, SPL, and IPL were involved in both conditions, indicating the feasibility of fNIRS to detect the MNS. Our findings provide a foundation for future research to explore the functional role of the MNS in social interaction and various disorders using fNIRS.

Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI.

In children with attention deficit hyperactivity disorder (ADHD), functional neuroimaging studies have revealed abnormalities in various brain regions, including prefrontal-striatal circuit, cerebellum, and brainstem. In the current study, we used a new marker of functional magnetic resonance imaging (fMRI), amplitude of low-frequency (0.01-0.08Hz) fluctuation (ALFF) to investigate the baseline brain function of this disorder. Thirteen boys with ADHD (13.0+/-1.4 years) were examined by resting-state fMRI and compared with age-matched controls. As a result, we found that patients with ADHD had decreased ALFF in the right inferior frontal cortex, [corrected] and bilateral cerebellum and the vermis as well as increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem. This resting-state fMRI study suggests that the changed spontaneous neuronal activity of these regions may be implicated in the underlying pathophysiology in children with ADHD.

A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation.

OBJECTIVE: Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). APPROACH: GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. MAIN RESULTS: Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus <54% in two-choice classification accuracy. SIGNIFICANCE: We believe GMMAC will be useful for clinical fNIRS-based brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.

Independent component analysis-based source-level hyperlink analysis for two-person neuroscience studies.

Two-person neuroscience, a perspective in understanding human social cognition and interaction, involves designing immersive social interaction experiments as well as simultaneously recording brain activity of two or more subjects, a process termed "hyperscanning." Using newly developed imaging techniques, the interbrain connectivity or hyperlink of various types of social interaction has been revealed. Functional near-infrared spectroscopy (fNIRS)-hyperscanning provides a more naturalistic environment for experimental paradigms of social interaction and has recently drawn much attention. However, most fNIRS-hyperscanning studies have computed hyperlinks using sensor data directly while ignoring the fact that the sensor-level signals contain confounding noises, which may lead to a loss of sensitivity and specificity in hyperlink analysis. In this study, on the basis of independent component analysis (ICA), a source-level analysis framework is proposed to investigate the hyperlinks in a fNIRS two-person neuroscience study. The performance of five widely used ICA algorithms in extracting sources of interaction was compared in simulative datasets, and increased sensitivity and specificity of hyperlink analysis by our proposed method were demonstrated in both simulative and real two-person experiments.

Semi-automatic 10/20 Identification Method for MRI-Free Probe Placement in Transcranial Brain Mapping Techniques.

The International 10/20 system is an important head-surface-based positioning system for transcranial brain mapping techniques, e.g., fNIRS and TMS. As guidance for probe placement, the 10/20 system permits both proper ROI coverage and spatial consistency among multiple subjects and experiments in a MRI-free context. However, the traditional manual approach to the identification of 10/20 landmarks faces problems in reliability and time cost. In this study, we propose a semi-automatic method to address these problems. First, a novel head surface reconstruction algorithm reconstructs head geometry from a set of points uniformly and sparsely sampled on the subject's head. Second, virtual 10/20 landmarks are determined on the reconstructed head surface in computational space. Finally, a visually-guided real-time navigation system guides the experimenter to each of the identified 10/20 landmarks on the physical head of the subject. Compared with the traditional manual approach, our proposed method provides a significant improvement both in reliability and time cost and thus could contribute to improving both the effectiveness and efficiency of 10/20-guided MRI-free probe placement.