Pathological neural activity in subthalamic nucleus (STN) is closely related to the symptoms of Parkinson's disease. Local field potentials (LFPs) recordings from subthalamic nucleus show that power spectral peaks exist at tremor, double tremor and tripble tremor frequencies, respectively. The interaction between these components in the multi-frequency tremor may be related to the generation of tremor. To study the linear and nonlinear relationship between those components, we analyzed STN LFPs from 9 Parkinson's disease patients using time frequency, cross correlation, Granger casuality and bi-spectral analysis. Results of the time-frequency analysis and cross-frequency correlation analysis demonstrated that the power density of those components significantly decreased as the alleviation of tremor and cross-correlation (0.18~0.50) exists during tremor period. Granger causality of the time-variant amplitude showed stronger contribution from tremor to double tremor components, and contributions from both tremor and double tremor components to triple tremor component. Quadratic phase couplings among these three components were detected by the bispectral approaches. The linear and nonlinear relationships existed among the multi-components and certainly confirmed that the dependence cross those frequencies and neurological mechanism of tremor involved complicate neural processes.
The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike’s information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.
The motor nervous system transmits motion control information through nervous oscillations, which causes the synchronous oscillatory activity of the corresponding muscle to reflect the motion response information and give the cerebral cortex feedback, so that it can sense the state of the limbs. This synchronous oscillatory activity can reflect connectivity information of electroencephalography-electromyography (EEG-EMG) functional coupling. The strength of the coupling is determined by various factors including the strength of muscle contraction, attention, motion intention etc. It is very significant to study motor functional evaluation and control methods to analyze the changes of EEG-EMG synchronous coupling caused by different factors. This article mainly introduces and compares coherence and Granger causality of linear methods, the mutual information and transfer entropy of nonlinear methods in EEG-EMG synchronous coupling, and summarizes the application of each method, so that researchers in related fields can understand the current research progress on analysis methods of EEG-EMG synchronous systematically.
Objective To investigate the association between neuroticism and gastroesophageal reflux disease (GERD) using Mendelian randomization (MR). Methods Exposure and outcome data were downloaded from the IEU database (https://gwas.mrcieu.ac.uk/), containing summary statistics from genome-wide association studies (GWAS) for neuroticism (n=374 323) and gastroesophageal reflux disease (n=602 604). Using the weighted median (WM), MR-Egger, inverse variance weighted (IVW), weighted mode and simple mode methods for Mendelian randomization analysis. Odds ratio (OR) values were used to assess the causal relationship, while sensitivity analysis was used to ensure the accuracy of the results. ResultsNeuroticism (OR=1.229, 95%CI 1.186-1.274, P<0.001) was associated with an increased risk of GERD. Meanwhile, gastroesophageal reflux disease (OR=1.786, 95%CI 1.623-1.965, P<0.001) was also associated with increased risk of neuroticism. Conclusion The study finds a bidirectional causal relationship between neuroticism and gastroesophageal reflux disease.