Electroencephalogram Signal Correlations between Default Mode Network and Attentional Functioning

Attentional issues may affect acquiring new information, task performance, and learning. Cortical network activities change during different functional brain states, including the default mode network (DMN) and attention network. We investigated the neural mechanisms underlying attentional functions and correlations between DMN connectivity and attentional function using the Trail-Making Test (TMT)-A and -B. Electroencephalography recordings were performed by placing 19 scalp electrodes per the 10 - 20 system. The mean power level was calculated for each rest and task condition. Non-parametric Spearman’s rank correlation was used to examine the correlation in power levels between the rest and TMT conditions. The most significant correlations during TMT-A were observed in the high gamma wave, followed by theta and beta waves, indicating that most correlations were in the parietal lobe, followed by the frontal, central, and temporal lobes. The most significant correlations during TMT-B were observed in the beta wave, followed by the high and low gamma waves, indicating that most correlations were in the temporal lobe, followed by the parietal, frontal, and central lobes. Frontoparietal beta and gamma waves in the DMN may represent attentional functions.

Application of virtual reality technology improves the functionality of brain networks in individuals experiencing pain

Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.

Resting-state connectivity in the default mode network and insula during experimental low back pain

Functional magnetic resonance imaging studies have shown that the insular cortex has a signif- icant role in pain identification and information integration, while the default mode network is associated with cognitive and memory-related aspects of pain perception. However, changes in the functional connectivity between the defauk mode network and insula during pain remain unclear. This study used 3.0 T functional magnetic resonance imaging scans in 12 healthy sub- jects aged 24.8 ± 3.3 years to compare the differences in the functional activity and connectivity of the insula and default mode network between the baseline and pain condition induced by intramuscular injection of hypertonic saline. Compared with the baseline, the insula was more functionally connected with the medial prefrontal and lateral temporal cortices, whereas there was lower connectivity with the posterior cingulate cortex, precuneus and inferior parietal lobule in the pain condition. In addition, compared with baseline, the anterior cingulate cortex exhibited greater connectivity with the posterior insula, but lower connectivity with the anterior insula, during the pain condition. These data indicate that experimental low back pain led to dysfunction in the connectivity between the insula and default mode network resulting from an impairment of the regions of the brain related to cognition and emotion, suggesting the impor- tance of the interaction between these regions in pain processing.

Changes in the default mode network in the prefrontal lobe, posterior cingulated cortex and hippocampus of heroin users

The default mode network is associated with senior cognitive functions in humans. In this study, we performed independent component analysis of blood oxygenation signals from 14 heroin users and 13 matched normal controls in the resting state through functional MRI scans. Results showed that the default mode network was significantly activated in the prefrontal lobe, posterior cingulated cortex and hippocampus of heroin users, and an enhanced activation signal was observed in the right inferior parietal Iobule （P 〈 0.05, corrected for false discovery rate）. Experimental findings indicate that the default mode network is altered in heroin users.

Altered amplitude of low-frequency fluctuations and default mode network connectivity in high myopia:a resting-state fMRI study

AIM:To analyze changes in amplitude of low-frequency fluctuations(ALFFs)and default mode network(DMN)connectivity in the brain,using resting-state functional magnetic resonance imaging(rs-fMRI),in high myopia(HM)patients.METHODS:Eleven patients with HM(HM group)and 15 age-and sex-matched non-HM controls(non-HM group)were recruited.ALFFs were calculated and compared between HM group and non-HM group.Independent component analysis(ICA)was conducted to identify DMN,and comparisons between DMNs of two groups were performed.Region-of-interest(ROI)-based analysis was performed to explore functional connectivity(FC)between DMN regions.RESULTS:Significantly increased ALFFs in left inferior temporal gyrus(ITG),bilateral rectus gyrus(REC),bilateral middle temporal gyrus(MTG),left superior temporal gyrus(STG),and left angular gyrus(ANG)were detected in HM group compared with non-HM group(all P<0.01).HM group showed increased FC in the posterior cingulate gyrus(PCC)/precuneus(preCUN)and decreased FC in the left medial prefrontal cortex(mPFG)within DMN compared with nonHM group(all P<0.01).Compared with non-HM group,HM group showed higher FC between mPFG and bilateral middle frontal gyrus(MFG),ANG,and MTG(all P<0.01).In addition,HM patients showed higher FC between PCC/(preCUN)and the right cerebellum,superior frontal gyrus(SFG),left pre CUN,superior frontal gyrus(SFG),and medial orbital of the superior frontal gyrus(ORB supmed;all P<0.01).CONCLUSION:HM patients show different ALFFs and DMNs compared with non-HM subjects,which may imply the cognitive alterations related to HM.

Interhemispheric functional connectivity for Alzheimer's disease and amnestic mild cognitive impairment based on the triple network model

The purpose of this study was to explore the differences in interhemispheric functional connectivity in patients with Alzheimer’s disease(AD) and amnestic mild cognitive impairment(aMCI) based on a triple network model consisting of the default mode network(DMN), salience network(SN), and executive control network(ECN). The technique of voxel-mirrored homotopic connectivity(VMHC) analysis was applied to explore the aberrant connectivity of all patients. The results showed that:(1) the statistically significant connections of interhemispheric brain regions included DMN-related brain regions(i.e. precuneus, calcarine, fusiform, cuneus, lingual gyrus, temporal inferior gyrus, and hippocampus), SN-related brain regions(i.e. frontoinsular cortex), and ECN-related brain regions(i.e. frontal middle gyrus and frontal inferior);(2) the precuneus and frontal middle gyrus in the AD group exhibited lower VMHC values than those in the aMCI and healthy control(HC) groups, but no significant difference was observed between the a MCI and HC groups; and(3) significant correlations were found between peak VMHC results from the precuneus and Mini Mental State Examination(MMSE) and Montreal Cognitive Scale(MOCA) scores and their factor scores in the AD, a MCI, and AD plus aMCI groups, and between the results from the frontal middle gyrus and MOCA factor scores in the a MCI group. These findings indicated that impaired interhemispheric functional connectivity was observed in AD and could be a sensitive neuroimaging biomarker for AD. More specifically, the DMN was inhibited, while the SN and ECN were excited. VMHC results were correlated with MMSE and MOCA scores, highlighting that VMHC could be a sensitive neuroimaging biomarker for AD and the progression from aMCI to AD.

Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control （SMC）. Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.

Wave Azimuth Cutoff Compensation Method and the Multisatellite Networking Mode of SAR

Synthetic aperture radars(SARs)encounter the azimuth cutoff problem when observing sea waves.Consequently,SARs can only capture the waves with wavelengths larger than the cutoff wavelength and lose the information of waves with smaller wavelengths.To increase the accuracy of SAR wave observations,this paper investigates an azimuth cutoff compensation method based on the simulated multiview SAR wave synchronization data obtained by the collaborative observation via networked satellites.Based on the simulated data and the equivalent multiview measured data from Sentinel-1 virtual networking,the method is verified and the cutoff wavelengths decrease by 16.40%and 14.00%.The biases of the inversion significant wave height with true values decrease by 0.04 m and 0.14 m,and the biases of the mean wave period decrease by 0.17 s and 0.22 s,respectively.These results demonstrate the effectiveness of the azimuth cutoff compensation method.Based on the azimuth cutoff compensation method,the multisatellite SAR networking mode for wave observations are discussed.The highest compensation effect is obtained when the combination of azimuth angle is(95°,115°,135°),the orbital intersection angle is(50°,50°),and three or four satellites are used.The study of the multisatellite networking mode in this paper can provide valuable references for the compensation of azimuth cutoff and the observation of waves by a multisatellite network.

Study on Decision Method of Neutral Point Grounding Mode for Medium-Voltage Distribution Network

The neutral grounding mode of medium-voltage distribution network decides the reliability, overvoltage, relay protection and electrical safety. Therefore, a comprehensive consideration of the reliability, safety and economy is particularly important for the decision of neutral grounding mode. This paper proposes a new decision method of neutral point grounding mode for mediumvoltage distribution network. The objective function is constructed for the decision according the life cycle cost. The reliability of the neutral point grounding mode is taken into account through treating the outage cost as an operating cost. The safety condition of the neutral point grounding mode is preserved as the constraint condition of decision models, so the decision method can generate the most economical and reliable scheme of neutral point grounding mode within a safe limit. The example is used to verify the feasibility and effectiveness of the decision method.

Exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and mode-dependent probabilistic time-varying delays

In this paper, the problem of exponential synchronization of complex dynamical networks with Markovian jumping parameters using sampled-data and Mode-dependent probabilistic time-varying coupling delays is investigated. The sam- pling period is assumed to be time-varying and bounded. The information of probability distribution of the time-varying delay is considered and transformed into parameter matrices of the transferred complex dynamical network model. Based on the condition, the design method of the desired sampled data controller is proposed. By constructing a new Lyapunov functional with triple integral terms, delay-distribution-dependent exponential synchronization criteria are derived in the form of linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

Next Generation Network Service Provision Mode and Its Developing Trend

The provision mode of the telecommunication service has experienced an evolving process, and showing the developing trend from distributed to centralized, from integrated to separated, and from closed to open. To suit this trend, there will be three provision modes as Session Initiation Protocol (SIP) server, Open Service Access (OSA) application server and intelligent network(IN) in Next Generation Network (NGN), provides all kinds of services and applications to the subscribers. With the popularity of broadband access and Internet, the NGN will provide single telecommunication service and act as the important national infrastructure to offer various information services to the subscribers. The service provision mode will be more open, diversified, and individualized.

Finite-time robust control of uncertain fractional-order Hopfield neural networks via sliding mode control

The finite-time control of uncertain fractional-order Hopfield neural networks is investigated in this paper. A switched terminal sliding surface is proposed for a class of uncertain fractional-order Hopfield neural networks. Then a robust control law is designed to ensure the occurrence of the sliding motion for stabilization of the fractional-order Hopfield neural networks. Besides, for the unknown parameters of the fractional-order Hopfield neural networks, some estimations are made. Based on the fractional-order Lyapunov theory, the finite-time stability of the sliding surface to origin is proved well. Finally, a typical example of three-dimensional uncertain fractional-order Hopfield neural networks is employed to demonstrate the validity of the proposed method.

Construction of Network Legal Supervision Mode With Chinese Characteristics

Localizing network legal supervision based on national conditions, legal traditions and the needs of economic and political development is the basis to manage and administrate the network by law. Adjusted to media convergence and the needs of network cultural industry development, China has made the lawmaking of media transform from practical service oriented management to functional oriented management of media. This strategy aims to prevent network medium risk effectively according to network communication regularity.

The default mode network is affected in the early stage of simian immunodeficiency virus infection:a longitudinal study

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode network.Most recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral therapy.Specifically,when brain imaging data at only one time point are analyzed,determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency syndrome.However,because infection times and other factors are often uncertain,longitudinal studies cannot be conducted directly in the clinic.Therefore,in this study,we performed a longitudinal study on the dynamic changes in the default mode network over time in a rhesus monkey model of simian immunodeficiency virus infection.We found marked changes in default mode network connectivity in 11 pairs of regions of interest at baseline and 10 days and 4 weeks after virus inoculation.Significant interactions between treatment and time were observed in the default mode network connectivity of regions of interest pairs area 31/V6.R and area 8/frontal eye field(FEF).L,area 8/FEF.L and caudal temporal parietal occipital area(TPOC).R,and area 31/V6.R and TPOC.L.ART administered 4 weeks after infection not only interrupted the progress of simian immunodeficiency virus infection but also preserved brain function to a large extent.These findings suggest that the default mode network is affected in the early stage of simian immunodeficiency virus infection and that it may serve as a potential biomarker for early changes in brain function and an objective indicator for making early clinical intervention decisions.

Spatiotemporal chaos synchronization of an uncertain network based on sliding mode control

The sliding mode control method is used to study spatiotemporal chaos synchronization of an uncertain network.The method is extended from synchronization between two chaotic systems to the synchronization of complex network composed of N spatiotemporal chaotic systems.The sliding surface of the network and the control input are designed.Furthermore,the effectiveness of the method is analysed based on the stability theory.The Burgers equation with spatiotemporal chaos behavior is taken as an example to simulate the experiment.It is found that the synchronization performance of the network is very stable.

Uniformed model of networked control systems with long time delay

Feedback control systems wherein the control loops are closed through a real-time network are called networked control systems （NCS）. The defining feature of an NCS is that information is exchanged using a network among control system components. Two new concepts including long time delay and short time delay are proposed. The sensor is almost always clock driven. The controller or the actuator is either clock driven or event driven. Four possible driving modes of networked control systems are presented. The open loop mathematic models of networked control systems with long time delay are developed when the system is driven by anyone of the four different modes. The uniformed modeling method of networked control systems with long time delay is proposed. The simulation results are given in the end.

An Adaptive Sliding Mode Tracking Controller Using BP Neural Networks for a Class of Large-scale Nonlinear Systems

A new type controller, BP neural-networks-based sliding mode controller is developed for a class of large-scale nonlinear systems with unknown bounds of high-order interconnections in this paper. It is shown that decentralized BP neural networks are used to adaptively learn the uncertainty bounds of interconnected subsystems in the Lyapunov sense, and the outputs of the decentralized BP neural networks are then used as the parameters of the sliding mode controller to compensate for the effects of subsystems uncertainties. Using this scheme, not only strong robustness with respect to uncertainty dynamics and nonlinearities can be obtained, but also the output tracking error between the actual output of each subsystem and the corresponding desired reference output can asymptotically converge to zero. A simulation example is presented to support the validity of the proposed BP neural-networks-based sliding mode controller.

ON THE STABILITY OF CELLULAR NEURAL NETWORKS WITH FEEDBACK MODE

Cellular Neural Networks (CNN) with feedback mode and M×N cells are equivalent to a network which possesses 2M×N cells, a neighborhood with mirror-like structure, space-variant templates and without feedback as well as without input templates. The stability of the CNN with feedback mode and transformations with the neighborhood of mirror-like structure are discussed.

Disrupted functional connectivity of default mode network and executive control network in patients with vascular cognitive impairment, no dementia

Objective： To investigate functional connectivity within default mode network （DMN） and ex-ecutive control network （ECN） in vascular cognitive im-pairment, no dementia （VCIND）. Methods： Twenty-eight VCIND patients and sixteen healthy controls were recruit-ed. A seed-based connectivity analysis was performed us-ing data from resting-state functional magnetic resonance imaging （fMRI）. Based on previous fndings, posteriorcingulate cortex （PCC） and dorsolateral prefrontal cortex （DLPFC） were chosen as regions of interest to study these networks.One-sample t-test and two-sample t-test were used for statistical analysis. Results： Compared with thecontrols, the VCIND group exhibited increased functional activity in such DMN regions as the left inferior temporal gyrus, parahippocampal gyrus, and medial frontal gyrus. The VCIND group had decreased functional connectivity of DMN at right superior frontal gyrus, left mid-cingu-late area, the medial part of left superior frontal gyrus, and bilateral medial frontal gyrus. The VCIND group also showed decreased functional connectivity of ECN pri-marilyat left inferior parietal gyrus, right angular gyrus, right middle occipital gyrus, and right middle frontal cor-tex. Conclusions： Increased functional connectivity with-in DMN and decreased functional connectivity within ECN suggested dysfunction of these two networks, which mightbe associated with the cognitive defcitsin patients with VCIND. These fndingsmay help usunderstandthe pathogenesis and clinical characteristics of VCIND.

SYNCHRONIZATION OF MASTER-SLAVE MARKOVIAN SWITCHING COMPLEX DYNAMICAL NETWORKS WITH TIME-VARYING DELAYS IN NONLINEAR FUNCTION VIA SLIDING MODE CONTROL

In this article, a synchronization problem for master-slave Markovian switching complex dynamical networks with time-varying delays in nonlinear function via sliding mode control is investigated. On the basis of the appropriate Lyapunov-Krasovskii functional, introducing some free weighting matrices, new synchronization criteria are derived in terms of linear matrix inequalities （LMIs）. Then, an integral sliding surface is designed to guarantee synchronization of master-slave Markovian switching complex dynamical networks, and the suitable controller is synthesized to ensure that the trajectory of the closed-loop error system can be driven onto the prescribed sliding mode surface. By using Dynkin＇s formula, we established the stochastic stablity of master-slave system. Finally, numerical example is provided to demonstrate the effectiveness of the obtained theoretical results.