ABSTRACT
With the widespread use of electrical equipment, cognitive functions such as working memory (WM) could be severely affected when people are exposed to 50 Hz electromagnetic fields (EMF) for long term. However, the effects of EMF exposure on WM and its neural mechanism remain unclear. In the present paper, 15 rats were randomly assigned to three groups, and exposed to an EMF environment at 50 Hz and 2 mT for a different duration: 0 days (control group), 24 days (experimental group I), and 48 days (experimental group II). Then, their WM function was assessed by the T-maze task. Besides, their local field potential (LFP) in the media prefrontal cortex (mPFC) was recorded by the in vivo multichannel electrophysiological recording system to study the power spectral density (PSD) of θ and γ oscillations and the phase-amplitude coupling (PAC) intensity of θ-γ oscillations during the T-maze task. The results showed that the PSD of θ and γ oscillations decreased in experimental groups I and II, and the PAC intensity between θ and high-frequency γ (hγ) decreased significantly compared to the control group. The number of days needed to meet the task criterion was more in experimental groups I and II than that of control group. The results indicate that long-term exposure to EMF could impair WM function. The possible reason may be the impaired communication between different rhythmic oscillations caused by a decrease in θ-hγ PAC intensity. This paper demonstrates the negative effects of EMF on WM and reveals the potential neural mechanisms from the changes of PAC intensity, which provides important support for further investigation of the biological effects of EMF and its mechanisms.
Subject(s)
Humans , Rats , Animals , Memory, Short-Term/physiology , Electromagnetic Fields/adverse effects , Prefrontal Cortex , CognitionABSTRACT
In recent years, exploring the physiological and pathological mechanisms of brain functional integration from the neural network level has become one of the focuses of neuroscience research. Due to the non-stationary and nonlinear characteristics of neural signals, its linear characteristics are not sufficient to fully explain the potential neurophysiological activity mechanism in the implementation of complex brain functions. In order to overcome the limitation that the linear algorithm cannot effectively analyze the nonlinear characteristics of signals, researchers proposed the transfer entropy (TE) algorithm. In recent years, with the introduction of the concept of brain functional network, TE has been continuously optimized as a powerful tool for nonlinear time series multivariate analysis. This paper first introduces the principle of TE algorithm and the research progress of related improved algorithms, discusses and compares their respective characteristics, and then summarizes the application of TE algorithm in the field of electrophysiological signal analysis. Finally, combined with the research progress in recent years, the existing problems of TE are discussed, and the future development direction is prospected.
Subject(s)
Algorithms , Brain/physiology , Entropy , Neural Networks, Computer , Nonlinear DynamicsABSTRACT
AIM: To investigate the effect of telmisartan on intestinal flora and metabolite TMAO in atherosclerosis. METHODS: Seventeen ApoE