Objectives The aim of this research was to find the acupoint combinations of manual and electro-acupuncture to treat chemotherapy-induced nausea and vomiting via the complex networks analysis. Methods We conducted searches using PubMed, ScienceDirect, MEDLINE, Ovid, spring, Wiley, EMBASE, the Chinese biomedicine database, VIP information network, and China National Knowledge Infrastructure from the establishment of the databases to the August, 2023. Information about titles, journals, interventions, and main acupoints was extracted using the self-established “acupoint for prevention CINV data base” powered by EpiData. According to the level of literature evidence and sample size, the clinical trials and weights of the outcome indicators including nausea/vomiting efficiency were combined. After identifying articles, literature processing and complex network analysis were conducted. The degree distribution of each node, the probability distribution of node degree, the node clustering coefficient, and the distance matrix are calculated by software. Results Of the 4001 screened publications, 489 were eligible after careful selection. Our result showed the acupoints ST36 and PC6 were the most common combination acupoints in both electro and manual acupuncture. In terms of efficiency, ST36, PC6, and CV12 are significantly effective acupoints for manual acupuncture, and the PC6 and ST36 are effective acupoint for electro-acupuncture. Conclusions We found that the near-far collocation method has been commonly used for different types of acupuncture treatment in CINV. Zhongwan, Shangwan, and Liangmen have been mainly used as local acupoints, while Neiguan, Hegu, Quchi, Zusanli, Gongsun, TaiChong, and Neiguan have been mainly used as distal acupoints. From the effect analysis, acupuncture treatment of nausea manual acupuncture effect is better; acupuncture treatment of vomiting or electro-acupuncture effect is better.

Joint attention deficit is one of the core disorders in children with autism, which seriously affects the development of multiple basic skills such as language and communication. Virtual reality scene intervention has great potential in improving joint attention skills in children with autism due to its good interactivity and immersion. This article reviewed the application of virtual reality based social and nonsocial scenarios in training joint attention skills for children with autism in recent years, summarized the problems and challenges of this intervention method, and proposed a new joint paradigm for social scenario assessment and nonsocial scenario training. Finally, it looked forward to the future development and application prospects of virtual reality technology in joint attention skill training for children with autism.

Neural oscillation is a rhythmic pattern of neural activity in the central nervous system, which has been found to be closely related to abnormal neural oscillations in psychoneurological disorders. Exogenous rhythmic stimulation can effectively modulate abnormal neural oscillations through entrainment and neuroplasticity, which has the potential to treat psychoneurological disorders. Currently, the main exogenous rhythmic brain stimulation techniques include transcranial alternating current stimulation (tACS), temporal interference (TI) stimulation and rhythmic sensory stimulation. We describe the effects of tACS and TI from the perspective of different frequency bands, and rhythmic sensory stimulation from the perspective of visual, acoustic, and synergistic stimulation patterns. This paper reviews the principles of exogenous rhythmic brain stimulation techniques, as well as the current intervention strategies and the progress of treatment effects of different techniques in the clinical treatment of neuropsychiatric disorders. The use of rhythmic stimulation for the treatment of clinical symptoms in patients with psychoneurological disorders is still in the exploratory stage and needs to be validated in larger samples for safety and long-term efficacy. Simultaneously, further studies that explore the exact mechanisms of such techniques are essential to optimize the design of the rhythmic modulation strategies so as to gain stronger positive effects. Additionally, clinical trials that analyze the effects of synergistic treatment with drugs and the design of portable home devices will be beneficial for patients.

Aiming at the problems on multi-robot cooperative patrol, such as large area of campus, large proportion of building area and ‘premature convergence’ of path planning, a multi-robot path planning method combining A*algorithm and genetic algorithm is proposed in this paper. In this algorithm, the multi-robot patrol path planning in campus environment is regarded as the multiple traveling saleman problem, and an adaptive genetic algorithm with hybrid fitness function is proposed, which can better solve the ‘premature convergence’ problem, improve the evolution speed and find the optimal solution. Based on ROS system, a multi-robot autonomous navigation simulation experiment is carried out. By constructing the model of robot and environment, and designing patrol scheme, the multi-robot autonomous navigation simulation under known path and environment map is realized,which verifies the feasibility and effectiveness of the algorithm.

Recent studies have shown that abnormal high-frequency brain activity represented by γ rhythms in schizophrenia severely affects patients′ low-level sensory processing and higher cognitive functions. The neurophysiological mechanisms of abnormal γ oscillations in schizophrenia patients, the progress of research on the EEG characteristics based on abnormal γ oscillations, the correlation between abnormal γ oscillations and symptoms, and the effects of different treatments on abnormal γ oscillations are comprehensively reviewed in this paper, and the current research status and problems are summarized to provide strong support for the clinical diagnosis and treatment of schizophrenia.

Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture's effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain's response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the "acupuncture mode ". In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli.

Manual acupuncture (MA) can effectively treat a variety of diseases, but its specific mechanism remains unclear. The "acupoint network" activated by MA participates in MA signal transduction, in which immune-related cells and cytokines play an important role. However, which cells and cytokines in the acupoint have changed after MA? What is the network relationship between them? Which cells and cytokines may play the most important role in MA effect? These problems are unclear. In this study, on the basis of affirming the analgesic, detumescence, and anti-inflammatory effect of MA, the concentration of 24 cytokines in ST36 acupoint in rats with inflammatory pain after MA treatment was detected by multiplex immunoassay technology. Then, using statistical and complex network and cell-cell communication (CCC) network diagram method to analyze the detected data depicts the network relationship between the cytokines and related cells objectively and establishes cytokine connection network and CCC network, respectively. The results showed that MA reinforced communication intensity between cells while reducing the overall correlation intensity. On this basis, the key cytokines and key cells at three MA time-points were screened out, cytokines IL-6, MCP-1, fibroblasts cell, and monocyte macrophage screened by the three methods at three MA time-points might be the key cytokines or key cells. After that, we detected the macrophages in ST36 acupoint by flow cytometry and immunofluorescence and found that the relative amount of macrophages increased significantly after MA, especially the macrophage of the dermis of skin. This study provided a basis for revealing the initiated mechanism of MA effect.

Multiple sensory signal, such as various visual stimuli, can be simultaneously transmitted and processed through different cortical areas. Using a biologically inspired model, we investigate the mechanism underlying such multiplexing in cortex. The network model is comprised of five feedforward-connected neural population of excitatory (E) and inhibitory (I) spiking neurons, each representing a cortical area. Numerical results indicate that multiple input signals are independently transmitted through feedforward neural networks via stochastic resonance (SR), and the transmission of information between adjacent cortical areas is altered by E-I relation of local cortical circuits. Due to different intrinsic properties of neurons, excitatory population can induce stochastic resonance to respond to low-frequency signal, and inhibitory neurons tend to respond to high-frequency signal through resonance. The E-I coupled neural ensemble shows selectivity for different input signal, which is gated by the gain between excitatory and inhibitory neurons. To be specific, neural network is inclined to gate-on signal with low-frequency when the excitation exceeds inhibition, whereas the high-frequency signal is selected. Moreover, neural signal can be transmitted from area to area only when the input frequency coincides with the inherent frequency of receivers in posterior areas, thus the independent conduction pathway is established through resonance. The transmission efficiency largely depends on the gain between excitatory and inhibitory input. Mean-field theory is further applied to validate the multiplexing in cortical networks and demonstrate the effective transmission of multiple information via SR in cortical networks.

The stochastic dynamics of neural network are studied and the phenomenon of stochastic resonance is found in inhibitory neurons, whose firing rate is close to the frequency of external stimulation. Time delay in the neural coupling process can induce multiple stochastic resonances, which appear intermittently at the integer multiples of the oscillation period of the input signal. It is found that the time delay can induce the periodic oscillation of neural firing rate, which may account for the occurrence of multiple stochastic resonances. In addition, the effect of synapses on firing rate oscillation and network resonance is investigated. As the strength of gap-junction and inhibitory-inhibitory chemical coupling is increased, the maximal resonant value increases, while the resonant frequency is unchanged. However, the resonant frequency and peak value increase with the coupling strength of excitatory–inhibitory chemical synapses. This difference may result from the interaction of excitation and inhibition within the cortical network. We further apply mean-field theory to time-delayed network model to validate the obtained numerical results. Both time delay and electrical–chemical synapses play an important role in firing rate oscillation and stochastic resonance within the cortical network, determining the ability to enhance the transmission of information in neural systems.

The feedforward neural network is a general structure of information transmission in the nervous system. It is widely used to simulate the transmission characteristics of neural information in the cortical neuronal networks. It is generally believed that cortical neurons conduct neural information through the firing rate. In this paper, a three-layered feedforward neural network is constructed based on the Izhikevich neuron model to explore the influence of inhibitory firing patterns on the information transmission of cortical networks.Numerical results show that, the number of inhibitory bursting neurons and inhibitory synaptic connection strength promote information transfer, but have different effects on the delivery of information between different layers.The results show that the most efficient transmission of information in the feedforward neural network can be achieved by adjusting the E/I balance,the number of inhibitiory bursting neurons and the input current.

We employed high-density microelectrode arrays to investigate spontaneous firing patterns of neurons in brain circuits of the primary somatosensory cortex (S1) in mice. We recorded from over 150 neurons for 10 min in each of eight different experiments, identified their location in Si, sorted their action potentials (spikes), and computed their power spectra and inter-spike interval (ISI) statistics. Of all persistently active neurons, 92% fired with a single dominant frequency - regularly firing neurons (RNs) - from 1 to 8Hz while 8% fired in burst with two dominant frequencies - bursting neurons (BNs) - corresponding to the inter-burst (2-6 Hz) and intra-burst intervals (20-160Hz). RNs were predominantly located in layers 2/3 and 5/6 while BNs localized to layers 4 and 5. Across neurons, the standard deviation of ISI was a power law of its mean, a property known as fluctuation scaling, with a power law exponent of 1 for RNs and 1.25 for BNs. The power law implies that firing and bursting patterns are scale invariant: the firing pattern of a given RN or BN resembles that of another RN or BN, respectively, after a time contraction or dilation. An explanation for this scale invariance is discussed in the context of previous computational studies as well as its potential role in information processing.

The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA) on the neuroendocrine-immune (NEI) network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund’s adjuvant (CFA) model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli) acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.

Stochasticity and oscillation play a vital role in neural signal processing. Time delay, which is inevitable in biological neural systems, has significant effect on the dynamics of neuronal networks. This paper provides an analysis of how time delay affects stochastic resonance and firing rate oscillation of cortical neuronal networks. A cortical network is established and mean-field theory is applied to analytically compute the dynamical response of networks. When the frequency of external stimulation is close to intrinsic frequency of neuronal networks, firing rate exhibits coherent oscillation and the phenomenon of stochastic resonance occurs in inhibitory neurons. Time delay can induce multiple stochastic resonances, which appear intermittently at integer multiples of the period of input signal, due to the transition of network dynamics induced by time delays. The fluctuation of membrane potential and instantaneous firing rate of cortical networks achieve maximal periodically with the variation of time delay. Furthermore, time delay and electrical coupling play complementary roles in determining network responses. Network oscillation can transit from unstable to stable when coupling strength exceeds a critical value. Transition threshold is lower for time delays close to integer multiples of input period where resonant response of cortical network enhances the formation of stable oscillation.

Neurons express diverse firing patterns in terms of their morphological, biochemical and electrophysiological properties. Different firing patterns can switch the information transmission in neuron network. However, it is not clear how the firing patterns of the neurons affect information transmission. Here we investigate the effect of different firing patterns of inhibitory neuron on network information transmission of triple-neuron feed-forward-loop motif. Results show that the stochastic response behavior can be optimized by certain noise intensity, which indicates stochastic resonance (SR) occurs in the neuronal network motifs. Changing the inhibitory neuron from burst spiking neurons to fast spiking neurons does not affect the optimal noise intensity. In the case of the same noise intensity and coupling coefficient, the bursting neurons produce higher transmission efficiency than the fast firing neurons. Different firing patterns of neuron in the output of the network motif achieved a greater impact on the transmission efficiency of the motif than the neuron located in the middle position. This simulation directly quantifies the influence of firing patterns on information transmission, which has great significance for improving signal transmission efficiency and detecting signals at different operating points.

A leading hypothesis holds that spiking activity propagates along neuronal sub-populations which are connected in a feed-forward manner, and the propagation efficiency would be affected by the dynamics of sub-populations. In this paper, how the interaction between local excitation and inhibition effects on synfire chain propagation in feed-forward network (FFN) is investigated. The simulation results show that there is an appropriate excitation–inhibition (EI) ratio maximizing the performance of synfire chain propagation. The optimal EI ratio can significantly enhance the selectivity of FFN to synchronous signals, which thereby increases the stability to background noise. Moreover, the effect of network topology on synfire chain propagation is also investigated. It is found that synfire chain propagation can be maximized by an optimal interlayer linking probability. We also find that external noise is detrimental to synchrony propagation by inducing spiking jitter. The results presented in this paper may provide insights into the effects of network dynamics on neuronal computations.

Channel noise, which is generated by the random transitions of ion channels between open and closed states, is distinguished from external sources of physiological variability such as spontaneous synaptic release and stimulus fluctuations. This inherent stochasticity in ion-channel current can lead to variability of the timing of spikes occurring both spontaneously and in response to stimuli. In this paper, we investigate how intrinsic channel noise affects the response of stochastic Hodgkin–Huxley (HH) neuron to external fluctuating inputs with different amplitudes and correlation time. It is found that there is an optimal correlation time of input fluctuations for the maximal spiking coherence, where the input current has a fluctuating rate approximately matching the inherent oscillation of stochastic HH model and plays a dominating role in the timing of spike firing. We also show that the reliability of spike timing in the model is very sensitive to the properties of the current input. An optimal time scale of input fluctuations exists to induce the most reliable firing. The channel-noise-induced unreliability can be mostly overridden by injecting a fluctuating current with an appropriate correlation time. The spiking coherence and reliability can also be regulated by the size of channel stochasticity. As the membrane area (or total channel number) of the neuron increases, the spiking coherence decreases but the spiking reliability increases.

The synergistic effect of hybrid electrical-chemical synapses and information transmission delay on the stochastic response behavior in small-world neuronal networks is investigated. Numerical results show that, the stochastic response behavior can be regulated by moderate noise intensity to track the rhythm of subthreshold pacemaker, indicating the occurrence of stochastic resonance (SR) in the considered neural system. Inheriting the characteristics of two types of synapses-electrical and chemical ones, neural networks with hybrid electrical-chemical synapses are of great improvement in neuron communication. Particularly, chemical synapses are conducive to increase the network detectability by lowering the resonance noise intensity, while the information is better transmitted through the networks via electrical coupling. Moreover, time delay is able to enhance or destroy the periodic stochastic response behavior intermittently. In the time-delayed small-world neuronal networks, the introduction of electrical synapses can significantly improve the signal detection capability by widening the range of optimal noise intensity for the subthreshold signal, and the efficiency of SR is largely amplified in the case of pure chemical couplings. In addition, the stochastic response behavior is also profoundly influenced by the network topology. Increasing the rewiring probability in pure chemically coupled networks can always enhance the effect of SR, which is slightly influenced by information transmission delay. On the other hand, the capacity of information communication is robust to the network topology within the time-delayed neuronal systems including electrical couplings. (C) 2016 Elsevier B.V. All rights reserved.

The effect of inhibitory firing patterns on coherence resonance (CR) in random neuronal network is systematically studied. Spiking and bursting are two main types of firing pattern considered in this work. Numerical results show that, irrespective of the inhibitory firing patterns, the regularity of network is maximized by an optimal intensity of external noise, indicating the occurrence of coherence resonance. Moreover, the firing pattern of inhibitory neuron indeed has a significant influence on coherence resonance, but the efficacy is determined by network property. In the network with strong coupling strength but weak inhibition, bursting neurons largely increase the amplitude of resonance, while they can decrease the noise intensity that induced coherence resonance within the neural system of strong inhibition. Different temporal windows of inhibition induced by different inhibitory neurons may account for the above observations. The network structure also plays a constructive role in the coherence resonance. There exists an optimal network topology to maximize the regularity of the neural systems.

Reconstruction of effective connectivity between neurons is essential for neural systems with function-related significance, characterizing directionally causal influences among neurons. In this work, causal interactions between neurons in spinal dorsal root ganglion, activated by manual acupuncture at Zusanli acupoint of experimental rats, are estimated using Granger causality (GC) method. Different patterns of effective connectivity are obtained for different frequencies and types of acupuncture. Combined with synchrony analysis between neurons, we show a dependence of effective connection on the synchronization dynamics. Based on the experimental findings, a neuronal circuit model with synaptic connections is constructed. The variation of neuronal effective connectivity with respect to its structural connectivity and synchronization dynamics is further explored. Simulation results show that reciprocally causal interactions with statistically significant are formed between well-synchronized neurons. The effective connectivity may be not necessarily equivalent to synaptic connections, but rather depend on the synchrony relationship. Furthermore, transitions of effective interaction between neurons are observed following the synchronization transitions induced by conduction delay and synaptic conductance. These findings are helpful to further investigate the dynamical mechanisms underlying the reconstruction of effective connectivity of neuronal population.

The phenomenon of vibrational resonance is investigated in adaptive Newman–Watts small-world neuronal networks, where the strength of synaptic connections between neurons is modulated based on spike-timing-dependent plasticity. Numerical results demonstrate that there exists appropriate amplitude of high-frequency driving which is able to optimize the neural ensemble response to the weak low-frequency periodic signal. The effect of networked vibrational resonance can be significantly affected by spike-timing-dependent plasticity. It is shown that spike-timing-dependent plasticity with dominant depression can always improve the efficiency of vibrational resonance, and a small adjusting rate can promote the transmission of weak external signal in small-world neuronal networks. In addition, the network topology plays an important role in the vibrational resonance in spike-timing-dependent plasticity-induced neural systems, where the system response to the subthreshold signal is maximized by an optimal network structure. Furthermore, it is demonstrated that the introduction of inhibitory synapses can considerably weaken the phenomenon of vibrational resonance in the hybrid small-world neuronal networks with spike-timing-dependent plasticity.