Objective:To investigate the effects and mechanism of cannabinoid (CBD) on the anxiety- and depression-like behaviors induced by lipopolysaccharide (LPS) in mice.Methods:C57BL/6J mice were intraperitoneally injected with LPS to establish the model of neuroinflammation. CBD was injected intraperitoneally 24 h after modeling. Behavioral tests were performed to evaluate the anxiety- and depression-like behaviors in mice. CBD-pretreated BV-2 microglia cells were stimulated with LPS in vitro. The levels of tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β) and CD86 in mouse cerebral cortex, hippocampus, prefrontal cortex and BV-2 cells were measured by qRT-PCR. The protein level of nuclear factor (NF-κB) in mouse brain and BV-2 cells was determined by Western blot. Results:CBD significantly increased the residence time and movement distance of LPS-treated mice in the central area in the open filed test (OFT), and reduced the immobility time in tail suspension test (TST) and force swimming test (FST). In addition, CBD alleviated the neuroinflammation and inhibited the activation of microglia in mouse brain. In vitro, CBD significantly inhibited the activation of BV-2 microglia cells. Both in vivo and in vitro experiments confirmed that CBD could inhibit NF-κB expression. Conclusions:LPS could induce the activation of BV-2 microglia cells and the expression of inflammatory factors in mouse brain accompanied with abnormal behaviors. CBD could inhibit the activation of microglia, alleviate the neuroinflammation in different regions of mouse brain and improve behavioral performance.

EEG recordings contain valuable physiological and pathological information in the process of seizure. The dynamic changes of brain electrical activity provide foundation and possibility for research and development of automatic detection system about epilepsy. In this paper, a nonlinear dynamic method is presented for analysis of the nonlinear dynamic characteristics of EEGs and delta, theta, alpha, and beta sub-bands of EEGs based on wavelet transform. The extracted feature is used as the input vector of a support vector machine (SVM) to construct classifiers. The results showed that the classification accuracy of SVM classifier based on nonlinear dynamic characteristics to classify the EEG into interictal EEGs and ictal EEGs reached 90% or higher. The support vector machine has good generalization in detecting the epilepsy EEG signals as a nonlinear classifier.
Algorithms ; Artifacts ; Electroencephalography ; methods ; Epilepsy ; diagnosis ; physiopathology ; Humans ; Nonlinear Dynamics ; Signal Processing, Computer-Assisted ; Support Vector Machine