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Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross-frequency coupling (CFC) of Electroencephalogram (EEG) signals. The PSI matrixes for the multi-channel EEG signals are calculated from interictal to ictal period in each sliding time window. The results show that CFC changes obviously once seizure occurs between the different bands, and such alteration is earlier than the appearance of clinical symptoms in seizure. Considering the similar role of the within-frequency coupling (WFC), we further reconstruct multi-layered brain networks, including CFC networks and WFC networks. The graph metrics are applied to investigate the variation of network structure of the epileptic brain. Significant decreases/increases of the local/global efficiency are found in δ-β, δ-α, θ-α and δ-θ bands from the CFC network, while WFC network shows a significant decline in the local efficiency in θ and α bands. These findings suggest that CFC may provide a new perspective to observe the alteration of brain structure when seizure occurs, and the investigation of functional connectivity across the full frequency spectrum can give us a deeper understanding of epileptic brains.

Changes in functional brain connectivity have been associated with the progression of Alzheimer's disease (AD). However, the investigation of functional connectivity by electroencephalographic (EEG) is usually limited to within certain frequency bands. In this study, we reconstruct the functional brain network of AD patients with both within and between band connectivity using resting-state EEG. Through the cross-frequency phase synchronization technique, we find that the cross-frequency connectivity is different between groups and among brain regions. Especially, the connectivity strength is significantly declined between delta and other bands for patients. In addition to these differences, a negative correlation can be found between within and cross frequency synchronization in AD. We further calculate the graph metrics of functional brain networks to investigate the alteration of network dynamics in AD. Compared to the controls, the patients exhibit elevated small world properties in the cross-frequency networks, indicating an impaired balance between within and cross frequency interaction. These findings suggest that they may provide new insights into the underlying brain dynamics in AD.

Background Research on the degradation of silk fibroin (SF) scaffolds in vivo lacks uniform and effective standards and experimental evaluation methods. This study aims to evaluate the application of ultrasound in assessing the degradation of SF scaffolds. Methods Two groups of three-dimensional regenerated SF scaffolds (3D RSFs) were implanted subcutaneously into the backs of Sprague-Dawley rats. B-mode ultrasound and hematoxylin and eosin (HE) staining were performed on days 3, 7, 14, 28, 56, 84, 112, 140, and 196. The cross-sectional areas for two groups of 3D RSFs that were obtained using these methods were semi-quantitatively analyzed and compared to evaluate the biodegradation of the implanted RSFs. Results The 3D RSFs in the SF-A group were wholly degraded at the 28th week after implantation. In contrast, the 3D RSFs in the SF-B group were completely degraded at the 16th week. Ultrasonic examination showed that the echoes of 3D RSFs in both groups gradually decreased with the increase of the implantation time. In the early stages of degradation, the echoes of the samples were higher than the echo of the muscle. In the middle of degeneration, the echoes were equal to the echo of the muscle. In the later stage, the echoes of the samples were lower than that of the muscle. The above changes in the SF-B group were earlier than those in the SF-A group. Semi-quantitative analysis of the cross-sectional areas detected using B-mode ultrasound revealed that the degradations of the two 3D RSF groups were significantly different. The degradation rate of the SF-B group was found to be higher than that of the SF-A group. This was consistent with the semi-quantitative detection results for HE staining. Regression analysis showed that the results of the B-mode ultrasound and HE staining were correlated in both groups, indicating that B-mode ultrasound is a reliable method to evaluate the SF scaffold degradation in vivo. Conclusions This study suggests that B-mode ultrasound can clearly display the implanted SF scaffolds non-invasively and monitor the degradation of the different SF scaffolds after implantation in living organisms in real-time.

A novel analytical framework combined fuzzy learning and complex network approaches is proposed for the identification of Alzheimer's disease (AD) with multichannel scalp-recorded electroencephalograph (EEG) signals. Weighted visibility graph (WVG) algorithm is first applied to transform each channel EEG into network and its topological parameters were further extracted. Statistical analysis indicates that AD and normal subjects show significant difference in the structure of WVG network and thus can be used to identify Alzheimer's disease. Taking network parameters as input features, a Takagi-Sugeno-Kang (TSK) fuzzy model is established to identify AD's EEG signal. Three feature sets-single parameter from multi-networks, multi-parameters from single network, and multi-parameters from multi-networks-are considered as input vectors. The number and order of input features in each set is optimized with various feature selection methods. Classification results demonstrate the ability of network-based TSK fuzzy classifiers and the feasibility of three input feature sets. The highest accuracy that can be achieved is 95.28% for single parameter from four networks, 93.41% for three parameters from single network. In particular, multi-parameters from the multi-networks set obtained the best result. The highest accuracy, 97.12%, is achieved with five features selected from four networks. The combination of network and fuzzy learning can highly improve the efficiency of AD's EEG identification.

A critical question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity characterized by perception, action, and adaptive behavior. The topology of network in modularity problem has been widely studied and can reflect many features of human brain. However, there are few researches on spatial structure and dynamic structure. In our research, we collect resting-state electroencephalogram (EEG) from 24 healthy controls, 45 patients with unresponsive wakefulness syndrome (UWS) and 25 patients with minimally conscious state (MCS). After dividing the EEG signal analysis into five bands, function network is constructed based on phase synchronization of the network. Then, we perform statistical analysis between groups of different frequency bands. The results show that there are statistically significant differences in the within-and between –module connectivity, community average spatial distance, community spatial diameter and network flexibility. In addition, most of the parameters are positively correlated with the consciousness level of the subjects. Our results suggest that the analysis of the modularized structure of complex networks including topology structure, spatial structure and dynamic structure may help to clarify the process of consciousness recovery and its pathophysiology in patients with disorder of consciousness (DOC), and may contribute to the clinical treatment and evaluation of patients with DOC.

Objective Growing evidence have linked disorders of consciousness (DOC) with the changes in frequency-specific functional networks. However, the alteration of inter-frequency dynamics in brain networks remain largely unknown. In this study, we investigated the network integration and segregation across frequency bands in a multiplex network framework. Approach Resting-state EEG data were recorded and analysed from 19 patients in minimally conscious state, 35 patients in unresponsive wakefulness syndrome (UWS) and 23 healthy controls. Frequency-based multiplex (cross-frequency) networks were reconstructed by integrating the five frequency-specific networks. Multiplex graph metrics, named multiplex participation coefficient and multiplex clustering coefficient, were employed to assess the network topology of subjects with different levels of consciousness. Main results Results revealed DOC networks, compared to those of healthy controls, may work at a less optimal point (closer to complete disorder) with increased integration and decreased segregation considering inter-frequency dynamics. Both metrics show increased spatial and temporal variability with the consciousness levels. Moreover, significant correlation can be found between the alteration of cross-frequency networks in DOC patients and their behavioural performance at both local and global scales. Significance These findings may contribute to the development of EEG network study and benefit our understanding of the processes of consciousness and their pathophysiology for DOC.

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

Investigating acupuncture effects contributes to improving clinical application and understanding neuronal dynamics under external stimulation. In this report, we recorded electroencephalography (EEG) signals evoked by acupuncture at ST36 acupoint with three stimulus frequencies of 50, 100 and 200 times per minutes, and selected non-acupuncture EEGs as the control group. Multiscale analyses were introduced to investigate the possible acupuncture effects on complexity and power in multiscale level. Using multiscale weighted-permutation entropy, we found the significant effects on increased complexity degree in EEG signals induced by acupuncture. The comparison of three stimulation manipulations showed that 100 times/min generated most obvious effects, and affected most cortical regions. By estimating average power spectral density, we found decreased power induced by acupuncture. The joint distribution of entropy and power indicated an inverse correlation, and this relationship was weakened by acupuncture effects, especially under the manipulation of 100 times/min frequency. Above findings are more evident and stable in large scales than small scales, which suggests that multiscale analysis allows evaluating significant effects in specific scale and enables to probe the inherent characteristics underlying physiological signals.

Epilepsy is commonly associated with abnormally synchronous activity of neurons located in epileptogenic zones. In this study, we investigated the synchronization characteristic of right temporal lobe epilepsy (RTLE). Multichannel electroencephalography (EEG) data were recorded from the RTLE patients during interictal period and normal controls. Power spectral density was first used to analyze the EEG power for two groups of subjects. It was found that the power of epileptics is increased in the whole brain compared with that of the control. We calculated phase lag index ( P L I ) to measure the phase synchronization between each pair of EEG signals. A higher degree of synchronization was observed in the epileptics especially between distant channels. In particular, the regional synchronization degree was negatively correlated with power spectral density and the correlation was weaker for epileptics. Moreover, the synchronization degree decayed with the increase of relative distance of channels for both the epilepsy and control, but the dependence was weakened in the former. The obtained results may provide new insights into the generation mechanism of epilepsy.

Alzheimer’s disease (AD) is a degenerative disorder of neural system that affects mainly the older population. Recently, many researches show that the EEG of AD patients can be characterized by EEG slowing, enhanced complexity of the EEG signals, and EEG synchrony. In order to examine the neural synchrony at multi scales, and to find a biomarker that help detecting AD in diagnosis, detrended cross-correlation analysis (DCCA) of EEG signals is applied in this paper. Several parameters, namely DCCA coefficients in the whole brain, DCCA coefficients at a specific scale, maximum DCCA coefficient over the span of all time scales and the corresponding scale of such coefficients, were extracted to examine the synchronization, respectively. The results show that DCCA coefficients have a trend of increase as scale increases, and decreases as electrode distance increases. Comparing DCCA coefficients in AD patients with healthy controls, a decrease of synchronization in the whole brain, and a bigger scale corresponding to maximum correlation is discovered in AD patients. The change of max-correlation scale may relate to the slowing of oscillatory activities. Linear combination of max DCCA coefficient and max-correlation scale reaches a classification accuracy of 90%. From the above results, it is reasonable to conclude that DCCA coefficient reveals the change of both oscillation and synchrony in AD, and thus is a powerful tool to differentiate AD patients from healthy elderly individuals.

Introduction The abnormal amyloid β (Aβ) accumulation and Aβ-related neural network dysfunction are considered central to the pathogenesis of Alzheimer's disease (AD) at the early stage. Deep-brain reachable low field magnetic stimulation (DMS), a novel noninvasive approach that was designed to intervene the network activity in brains, has been found to alleviate stress-related cognitive impairments. Methods Amyloid precursor protein/presenilin-1 transgenic mice (5XFAD) were treated with DMS, and cognitive behavior and AD-like pathologic changes in the neurochemical and electrophysiological properties in 5XFAD mice were assessed. Results We demonstrate that DMS treatment enhances cognitive performances, attenuates Aβ load, upregulates postsynaptic density protein 95 level, and promotes hippocampal long-term potentiation in 5XFAD mouse brain. Intriguingly, the gamma burst magnetic stimulation reverses the aberrant gamma oscillations in the transgenic hippocampal network. Discussion This work establishes a solid foundation for the effectiveness of DMS in treating AD and proposes a future study of gamma rhythm stimulation on reorganizing rhythmic neural activity in AD brain.

Electroencephalogram (EEG) signal evoked by acupuncture stimulation at ”Zusanli” acupoint is analyzed to investigate the modulatory effect of manual acupuncture on the functional brain activity. Power spectral density of EEG signal is first calculated based on the autoregressive Burg method. It is shown that the EEG power is significantly increased during and after acupuncture in delta and theta bands, but decreased in alpha band. Furthermore, synchronization likelihood is used to estimate the nonlinear correlation between each pairwise EEG signals. By applying a threshold to resulting synchronization matrices, functional networks for each band are reconstructed and further quantitatively analyzed to study the impact of acupuncture on network structure. Graph theoretical analysis demonstrates that the functional connectivity of the brain undergoes obvious change under different conditions: pre-acupuncture, acupuncture, and post-acupuncture. The minimum path length is largely decreased and the clustering coefficient keeps increasing during and after acupuncture in delta and theta bands. It is indicated that acupuncture can significantly modulate the functional activity of the brain, and facilitate the information transmission within different brain areas. The obtained results may facilitate our understanding of the long-lasting effect of acupuncture on the brain function.

In order to enhance the oxidation resistance of C/SiC composites, mullite/yttrium silicate coatings were fabricated on C/SiC composites through dip-coating route. Al2O3-SiO2 sol with high solid content was selected as the raw material for mullite and “silicone resin + Y2O3 powder” slurry was used to synthesize yttrium silicate. The microstructure and phase composition of coatings were characterized, and the investigation on oxidation resistance and anti-oxidation mechanism was emphasized. The as-fabricated coatings consisting of SiO2-rich mullite phase and Y2Si2O7 phase show high density and favorable bonding to C/SiC composites. After oxidized at 1 400 °C and 1 500 °C for 30min in static air, the coating-containing C/SiC composites possess 91.9% and 102.4% of the original flexural strength, respectively. The desirable thermal stability of coatings and the further densification of coatings due to viscous flow of rich SiO2 and Y-Si-Al-O glass are responsible for the excellent oxidation resistance. In addition, the coating-containing composites retain 99.0% of the original flexural strength and the coatings exhibit no cracking and desquamation after 12 times of thermal shock from 1 400 °C to room temperature, which are ascribed to the combination of anti-oxidation mechanism and preferable physical and chemical compatibility among C/SiC composites, mullite and Y2Si2O7. The carbothermal reaction at 1 600 °C between free carbon in C/SiC substrate and rich SiO2 in mullite results in severe frothing and desquamation of coatings and obvious degradation in oxidation resistance.

Since rhythmic oscillations are a general phenomenon of neuronal activity, the use of EEG-based spectral analysis is one of the most important methods for studying how the human brain processes information. Usually, during the functional integration and isolation of information in the brain, it is often accompanied by simultaneous rhythmic oscillations of different frequencies. Therefore, studying the phase-synchronous coupling between different frequencies of neurons may have important physiological significance. In this paper, we apply high order bispectral (BIS) analysis to investigate non-linear phase coupling of different oscillators of patients with chronic disorders of consciousness (DOC). We found that non-linear phase coupling of different oscillators of patients in minimally conscious state (MCS) showed significantly different from those with vegetative state (VS). In order to quantify the group differences, three relevant parameters of bispectrum are introduced. Moreover, Bispectral entropy (BE) and the weighted center frequencies fl of the bispectrum significantly correlated with the level of behavioral responsiveness measured using the JFK Coma Recovery Scale-Revised (CRS-R). And classifier based on support vector machine (SVM) is trained to predict states of consciousness of patients with DOC. What's more, sharpening the current knowledge of BIS by using EEG-based approaches may shed light on the processes of consciousness and their pathophysiology, and may contribute to clinical treatment and assessment of patents with DOC.

Objective Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels. Approach By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain changes its community structure and traverses across defined network states (integrated and segregated states) in subjects with different consciousness levels. Main results Network switching in the human brain is decreased with increasing time scale opposite to that in random systems. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in the alpha band) at short time scales. At long time scales, the opposite trend is found. Compared to healthy controls, patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales. Significance These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorders like DOC.

Highlights • We have found that PD can be characterized by unique spatial microstate different from healthy controls, which may be related to the brain dysfunction in PD. • The drug-free patients with PD show abnormal brain dynamics revealed by the regular changes of temporal microstate features in early PD and such temporal dynamics in microstates are correlated with motor function and cognition of the subjects. • The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD., The clinical diagnosis of Parkinson's disease (PD) is very difficult, especially in the early stage of the disease, because there is no physiological indicator that can be referenced. Drug-free patients with early PD are characterized by clinical symptoms such as impaired motor function and cognitive decline, which was caused by the dysfunction of brain's dynamic activities. The indicators of brain dysfunction in patients with PD at an early unmedicated condition may provide a valuable basis for the diagnosis of early PD and later treatment. In order to find the spatiotemporal characteristic markers of brain dysfunction in PD, the resting-state EEG microstate analysis is used to explore the transient state of the whole brain of 23 drug-free patients with PD on the sub-second timescale compared to 23 healthy controls. EEG microstates reflect a transiently stable brain topological structure with spatiotemporal characteristics, and the spatial characteristic microstate classes and temporal parameters provide insight into the brain's functional activities in PD patients. The further exploration was to explore the relation between temporal microstate parameters and significant clinical symptoms to determine whether these parameters could be used as a basis for clinically assisted diagnosis. Therefore, we used a general linear model (GLM) to explore the relevance of microstate parameters to clinical scales and multiple patient attributes, and the Wilcoxon rank sum test was used to quantify the linear relation between influencing factors and microstate parameters. Results of microstate analysis revealed that there was an unique spatial microstate different from healthy controls in PD, and several other typical microstates had significant differences compared with the normal control group, and these differences were reflected in the microstate parameters, such as longer durations and more occurrences of one class of microstates in PD compared with healthy controls. Furthermore, correlation analysis showed that there was a significant correlation between multiple microstate classes’ parameters and significant clinical symptoms, including impaired motor function and cognitive decline. These results indicate that we have found multiple quantifiable feature tags that reflect brain dysfunction in the early stage of PD. Importantly, such temporal dynamics in microstates are correlated with clinical scales which represent the motor function and recognize level. The obtained results may deepen our understanding of the brain dysfunction caused by PD, and obtain some quantifiable signatures to provide an auxiliary reference for the early diagnosis of PD.

Neurons communicate with each other through action potentials, which is an energy cost process. And spiking pattern of different neurons may be varying because of their intrinsic ionic dynamics. However, the relationship between neuronal ionic character and energy consumption is still an unclear problem. This paper study why and how two different kinds of neuron consume different energy for generating an action potential based on regular spiking neuron (RS) and fast spiking neuron (FS) in the view of ionic level. It is noted that the difference in ionic dynamic between them results in the higher stimulation threshold of RS neuron for spiking compared with that of FS neuron. But the firing frequency of FS neuron is larger than RS neuron, together leading to different energy cost rate. Moreover, the energy consumption of FS neuron is higher than that of RS neurons for generation an action potential whereas the energy efficiency of FS neuron is lower than that of RS neural due to the overlap of ionic movement. These findings are beneficial for us to understand diverse spiking pattern energy consumption decided by their intrinsic dynamics and neural information transmission function.

This study was aimed at characterizing spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) using a novel approach named weighted visibility graph (WVG). More than 10 minutes of spontaneous EEG were recorded from 15 AD patients and 15 age-matched normal controls. Two graph metrics, clustering coefficient and average weighted degree, are extracted in different frequency bands for each EEG channel based on the WVG methodology. Furthermore, statistical analysis was performed in different bands and channels for both groups. It is demonstrated that AD patients are characterized with a significant increase of clustering coefficient and degree in theta band, which can be observed in most brain regions. Our results suggest that the WVG method can be are effective to distinguish different brain states (AD and normal) and may provide further insights into the underlying brain dynamics in AD.

Epilepsy is a common neurological disorder of the brain and can be assessed by electroencephalogram (EEG). By combining discrete wavelet transformation (DWT) and weighted visibility graph (WVG), we employed a new approach for detecting epileptic seizure from EEG. First, DWT is applied to decompose EEG into different frequency bands, then WVG is constructed from the sub-band signals. Average weighted degree and clustering coefficient are extracted to characterize the topological structure of the networks and distinguish different brain states (healthy, inter-ictal and ictal). Four different test cases are designed to evaluate the performance of our DWT-WVG method. The obtained results demonstrate that both features are effective to discriminate the ictal EEG from interictal EEG or EEG recorded from healthy subjects.

The rapid increase in the number of people with Alzheimer's disease (AD) represents one of the major challenges to the health and social care systems. Early detection of AD makes it possible for patients to access appropriate services and to benefit from new therapies and treatments. The objective of the study reported in this paper is to develop the method of complexity characterization of EEG in AD patients in its early stages. In this paper, we propose the fractional permutation entropy (FPE) to analyze 16-channels electroencephalograph (EEG) signals from 15 AD groups and 15 age-matched control groups. FPE is a modified method based on the permutation entropy (PE), which can be used as a measurement to analyze the complexity of the EEG signals. Firstly, the simulation analysis of FPE was performed and the results show that FPE could effectively evaluate the complexity of time series. Then we calculated the FPE of real EEG series to detect the complexity abnormalities in AD. It is demonstrated that the FPE of AD patients is significantly decreased in alpha band at most EEG channels. Thus, it suggests that FPE may become a probably useful tool to analyze the complexity abnormalities of AD and some other neurologic disorders.

Entropy is a widely-used feature and a basic framework for electroencephalogram (EEG) analyses. Their corresponding multiscale methods were recently developed, adopting multiple time scales for entropy estimation. In this paper, towards Alzheimer's disease (AD) EEG recordings, we employed weighted-permutation entropy combining multiscale analysis, so that multiscale complexity information could be exposed across different scale factors. Our experiment and off-line analyses results confirmed that multiscale weighted-permutation entropy (MWPE) method could distinguish AD pattern and control pattern in terms of the evolution trend of MWPE profiles. The consistency of 16 channels were broken in AD brain. The entropy topographic distribution in AD patients distinctly differed from healthy subjects at large-scale.

Acupuncture, as an external stimulation, can produce clinical effects via the central nervous system. In order to investigate the modulatory efficacy of acupuncture on brain activity, multichannel EEG signals evoked by acupuncture at “Zusanli” acupoint were recorded from healthy humans in three states: pre-acupuncture, acupuncture, and post-acupuncture. Power spectral density is first used to analyze the EEG power change during acupuncture process. It is found that EEG power significantly increased in the delta and alpha bands under acupuncture and high power level remained in alpha band after acupuncture. Then, we calculated phase lag index to quantify the phase synchronization of pair-wise channels. In acupuncture state, delta and alpha bands exhibit significantly higher synchronization degree than pre-acupuncture state. Additionally, post-effect of acupuncture can be observed in alpha band as high synchronization degree remains in post-acupuncture state. Moreover, functional brain networks converted from synchronization matrix in each band are reconstructed. Acupuncture increases long-range connections between left and right hemispheres and changes the position of main nodes. Graph theory metrics are extracted to explore the change of functional connectivity in different states. The result shows the functional networks in delta and alpha bands are small world networks (SWN) and acupuncture improves the SWN efficiency of functional network.

Glucocorticoids are considered the main treatment option for chronic rhinosinusitis with nasal polyps (CRSwNP), but their effect rate ranges from 60.9% to 80%. Novel therapeutic means should be studied. The purpose of this study was to investigate the expression of Orai1 in nasal polyps (NPs) and the influence of intervention of Orai1 on NPs after in vitro treatment of 2-aminoethoxydiphenyl borate (2-APB).Prospective cross-sectional study.University hospital.Nasal biopsy samples were obtained from normal subjects or subjects with CRSwNP. We studied the localization of Orai1 protein in NPs by using immunohistochemistry. Then these tissues in cultures were maintained in the absence or presence of dexamethasone (DEX) or 2-APB. Orai1 was examined by Western blot, enzyme-linked immunosorbent assay (ELISA), and real-time reverse transcription-polymerase chain reaction (RT-PCR). Inflammatory mediators including interleukin (IL)-1β, IL-5, eosinophil cation protein (ECP), leukotriene (LT)C4, interferon (IFN)-γ, and dermatophagoides pteronyssinus (DP)-specific immunoglobulin E (sIgE) as well as mucins (MUCs) including MUC5B and MUC7 in cultures were analyzed with ELISA and real-time RT-PCR.The expression of Orai1 was localized to cytoplasmic membrane of inflammatory cells and submucosal glandular cells and was upregulated in NPs compared with normal nasal mucosa. Orai1 was decreased in NPs after in vitro treatment of 2-APB but not after DEX intervention. The levels of inflammatory mediators and mucins were reduced more after 2-APB treatment when compared with those after DEX treatment.Orai1 may play crucial roles in NP formation, and the intervention of Orai1 may inhibit NP development.

Real-time prediction of dynamical characteristics of Dopamine (DA) neurons, including properties in ion channels and membrane potentials, is meaningful and critical for the investigation of the dynamical mechanisms of DA cells and the related psychiatric disorders. However, obtaining the unobserved states of DA neurons is significantly challenging. In this paper, we present a real-time prediction system for DA unobserved states on a reconfigurable field-programmable gate array (FPGA). In the presented system, the unscented Kalman filter (UKF) is implemented into a DA neuron model for dynamics prediction. We present a modular structure to implement the prediction algorithm and a digital topology to compute the roots of matrices in the UKF implementation. Implementation results show that the proposed system provides the real-time computational ability to predict the DA unobserved states with high precision. Although the presented system is aimed at the state prediction of DA cells, it can also be applied into the dynamic-clamping technique in the electrophysiological experiments, the brain-machine interfaces and the neural control engineering works.

In this paper limited penetrable visibility graph (LPVG) approach is introduced to analyze the background electroencephalograph (EEG) activity in Alzheimer's disease (AD) patients. LPVG provides a measure of transforming the time series, i.e., EEG, into complex networks, which has become a research hotspot in the field of cognitive neurodynamics. So in this work, LPVG is chosen as the center method to transform every EEG signal of AD patients and normal controls into a complex network. Then distinction could be made between the two groups through the comparison of the GIC value and the topological features of the networks by statistical analysis. It is demonstrated that the complex network extracted from EEG signals of AD brain has been altered significantly. In particular, the small-world properties are weakened in AD group compared with the control group. These preliminary results may contribute to providing a new imaging marker in view of complex network for the clinical diagnosis of AD.

Weighted-permutation entropy (WPE) is modified from permutation entropy (PE), which recently has been proposed as a measurement for nonlinear time series. To explore the efficiency of this method and the features of seizure electroencephalogram (EEG) segments, we investigate the application of WPE in the complexity analysis for epileptic seizure detection based on EEG. It is found that the calculated values of WPE are decreased during seizure segments in the contrast with seizure-free. Moreover, WPE provides a highly distinguishable feature for classifier to obtain more accurate results of automatic classification compared with PE by support vector machine (SVM).

The complexity change of brain activity in Alzheimer’s disease (AD) is an interesting topic for clinical purpose. To investigate the dynamical complexity of brain activity in AD, a multivariate multi-scale weighted permutation entropy (MMSWPE) method is proposed to measure the complexity of electroencephalograph (EEG) obtained in AD patients. MMSWPE combines the weighted permutation entropy and the multivariate multi-scale method. It is able to quantify not only the characteristics of different brain regions and multiple time scales but also the amplitude information contained in the multichannel EEG signals simultaneously. The effectiveness of the proposed method is verified by both the simulated chaotic signals and EEG recordings of AD patients. The simulation results from the Lorenz system indicate that MMSWPE has the ability to distinguish the multivariate signals with different complexity. In addition, the EEG analysis results show that in contrast with the normal group, the significantly decreased complexity of AD patients is distributed in the temporal and occipitoparietal regions for the theta and the alpha bands, and also distributed from the right frontal to the left occipitoparietal region for the theta, the alpha and the beta bands at each time scale, which may be attributed to the brain dysfunction. Therefore, it suggests that the MMSWPE method may be a promising method to reveal dynamic changes in AD.

As a pattern of cross-frequency coupling (CFC), phase–amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB–MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6[Formula: see text]s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

Background: Vestibular schwannoma (VS) is a benign tumor with malignant biological consequence because of its special location, and its occurrence is highly related to merlin. Cell culture studies have demonstrated that merlin acts as a molecular linker between the cytoskeleton and specific membrane proteins and is linked to cell cycle control. Therefore, we sought to detect the expression of endogenous merlin and its subcellular distribution using cyclin D1 as a cell cycle marker in VS. Methods: To confirm endogenous merlin protein expression in abiogenous VS cells, in situ hybridization, immunohistochemistry, and Western blot was performed in tumor tissues surgically resected from patients. Merlin's subcellular distribution linked cell cycle in VS was analyzed and compared with cyclin D1 expression evaluated with confocal microscopy. Results: We found that merlin appeared in 98% of the VS tissue samples, with a mean cellular positivity of 46.66 ± 5.75%. Merlin is inversely correlated with cyclin D1 in regard to subcellular localization. Merlin locates in the cytoplasm during G0/G1 phase, moves to the nucleus at S phase, and accumulates in the cytoplasm at S phase after phosphorylation on serine 518 (S518). Conclusion: In line with the pathologic characteristic of VS as a benign tumor, merlin compensates for the malignant effect of S518 phosphorylation by limiting the loss of contact inhibition of growth and controlling proliferation. S518 phosphorylation can trigger re-entry into the cell cycle, failing to control transcription because of mislocalization and an inability to interact with the cytoskeleton.

Background: Chronic rhinosinusitis with nasal polyps (CRSwNP) is associated with Th2-dominant inflammation. However, effective treatments for CRSwNP have not yet been found. This study aimed to investigate the expression of Orai1 in nasal polyps (NP) and the influence on them of the intervention of Ca2+ release-activated Ca2+ (CRAC) channels. Materials and Methods: Nasal samples were obtained from normal subjects or subjects with CRSwNP. We studied the distribution of Orai1 protein in NP and normal mucosa (normal group) using immunohistochemistry. These tissues in cultures were then maintained in the absence (control group) or presence of 2-aminoethoxydiphenyl borate (2-APB) for 24 h. Orai1 was examined by means of enzyme-linked immunosorbent assay (ELISA) and real-time reverse transcription-polymerase chain reaction (RT-PCR). The Ca2+ mean fluorescence intensity (MFI) was evaluated by flow cytometry, and the inflammatory mediators, including interleukin (IL)-4, IL-5, IL-13, Dermatophagoides pteronyssinus-specific IgE, leukotriene C4 and eosinophil cation protein in cultures, were analyzed with ELISA and real-time RT-PCR. Results: The expression of Orai1 was localized to the cytoplasmic membrane of inflammatory cells, and upregulated in NP compared to that in the normal group. However, Orai1 protein was decreased in polyp tissues after the 2-APB treatment. The levels of Ca2+ MFI and above inflammatory mediators were also elevated in NP, and reduced after the 2-APB administration compared to those in the control group. Conclusions: Orai1 and CRAC channels may play a crucial role in NP formation and development, and the 2-APB intervention of Orai1 protein may alleviate inflammatory responses in NP.