In this paper, we investigate the abatement of spike-and-wave discharges in a thalamocortical model using a closed-loop brain stimulation method. We first explore the complex states and various transitions in the thalamocortical computational model of absence epilepsy by using bifurcation analysis. We demonstrate that the Hopf and double cycle bifurcations are the key dynamical mechanisms of the experimental observed bidirectional communications during absence seizures through top-down cortical excitation and thalamic feedforward inhibition. Then, we formulate the abatement of epileptic seizures to a closed-loop tracking control problem. Finally, we propose a neural network based sliding mode feedback control system to drive the dynamics of pathological cortical area to track the desired normal background activities. The control system is robust to uncertainties and disturbances, and its stability is guaranteed by Lyapunov stability theorem. Our results suggest that the seizure abatement can be modeled as a tracking control problem and solved by a robust closed-loop control method, which provides a promising brain stimulation strategy.
Fatigued driving is one of the major causes of traffic accidents. Frequent repetition of driving behavior for a long time may lead to driver fatigue, which is closely related to the central nervous system. In the present work, we designed a fatigue driving simulation experiment and collected the electroencephalogram (EEG) signals. Complex network theory was introduced to study the evolution of brain dynamics under different rhythms of EEG signals during several periods of the simulated driving. The results show that as the fatigue degree deepened, the functional connectivity and the clustering coefficients increased while the average shortest path length decreased for the delta rhythm. In addition, there was a significant increase of the degree centrality in partial channels on the right side of the brain for the delta rhythm. Therefore, it can be concluded that driving fatigue can cause brain complex network characteristics to change significantly for certain brain regions and certain rhythms. This exploration may provide a theoretical basis for further finding objective and effective indicators to evaluate the degree of driving fatigue and to help avoid fatigue driving.
Background B cell follicles are immune-privileged sites where intensive HIV-1 replication and latency occur, preventing a permanent cure. Recent study showed that CXCR5 + NK cells in B cell follicles can inhibit SIV replication in African green monkeys, but this has not been reported in HIV-1 infected patients.Methods Lymphocytes and tissue sections of lymph node were collected from 11 HIV-1 positive antiretroviral therapy (ART)-naive and 19 HIV-1 negative donors. We performed immunofluorescence and RNA-scope to detect the location of CXCR5 + NK cells and its relationship with HIV-1 RNA, and performed flow cytometry and RNA-seq to analyze the frequency, phenotypic and functional characteristics of CXCR5 + NK cells. The CXCL13 expression were detected by immunohistochemistry.Findings CXCR5 + NK cells, which accumulated in LNs from HIV-1 infected individuals, expressed high levels of activating receptors such as NKG2D and NKp44. CXCR5 + NK cells had upregulated expression of CD107a and b-chemokines, which were partially impaired in HIV-1 infection. Importantly, the frequency of CXCR5 + NK cells was inversely related to the HIV-1 viral burden in LNs. In addition, CXCL13-the ligand of CXCR5-was upregulated in HIV-1 infected individuals and positively correlated with the frequency of CXCR5 + NK cells.Interpretation During chronic HIV-1 infection, CXCR5 + NK cells accumulated in lymph node, exhibit altered immune characteristics and underlying anti-HIV-1 effect, which may be an effective target for a functional cure of HIV-1.
This paper investigates vibrational resonance in multi-layer feedforward network (FFN) based on FitzHugh-Nagumo (FHN) neuron model. High-frequency stimuli can improve the input-output linearity of firing rates, especially for the inputs with low firing rate. For FFN network, it is found that high-frequency disturbances play important roles in enhancing the propagation of weak signal through layers. Synfire-enhanced phenomenon of signal propagation is also observed in multi-layers network, when the signal transmission is affected by high-frequency disturbances. Network connections are found to be important for the propagation of weak signal. Besides that, the characteristics of high-frequency stimuli such as heterogeneity and frequency can also modulate the propagation of neural code through layers.
We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.
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