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.
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.
ObjectiveTo systematically evaluate the causal relationship between blood metabolites and pancreatic cancer (PC) risk using Mendelian randomization (MR). MethodsWe conducted a two-sample MR analysis using genetic instruments for 8 299 blood metabolites derived from a European genome-wide association study (GWAS) and PC outcome data from the GWAS Catalog. The primary analysis employed inverse-variance weighted (IVW) regression, with sensitivity analyses including MR-Egger, weighted median, weighted mode, and simple mode methods. Heterogeneity was assessed using Cochran’s Q test, pleiotropy was evaluated via MR-Egger intercept tests, and outliers were identified using MR-PRESSO. Robustness was confirmed through leave-one-out analyses. For metabolites showing significant associations (P<0.05), we performed independent replication using the same European PC GWAS cohort, followed by meta-analysis of all results. Reverse causation was excluded using Steiger directionality tests and bidirectional MR, while genetic confounding was assessed via linkage disequilibrium score regression (LDSC). ResultsAfter multi-stage screening, 26 blood metabolites demonstrated statistically significant associations with PC risk (P<0.05), comprising 18 annotated metabolites (12 lipids, 3 amino acids, 2 coenzymes/vitamins, 1 nucleotide, and 1 peptide), 3 metabolite ratios, and 5 unannotated metabolites. Replication analysis confirmed 6 of these 26 metabolites in the independent European PC GWAS cohort. Crucially, meta-analysis of all 26 metabolites yielded consistent significant associations (P<0.05 for all), with 1-palmitoleoylglycerol (16:1) exhibiting the strongest protective effect [OR=0.76, 95%CI (0.68, 0.85), P<1.0×10-5]. LDSC analysis revealed no significant genetic confounding for 25/26 metabolites (P>0.05), except for myristate (14:0), Rg=1.534, Se=0.571, P=0.007), where genetic correlation potentially influenced MR estimates. Pathway analysis further implicated dysregulation of lipid metabolism as a key mechanism, with 1-palmitoleoylglycerol emerging as a high-priority biomarker candidate for PC risk stratification. ConclusionsThis study provides causal evidence within the European population that some blood metabolites are associated with PC risk, identifying 1-palmitoleoylglycerol as a novel protective biomarker and highlighting targeting lipid metabolic pathways as a promising therapeutic strategy for pancreatic cancer.
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 causal relationships between various circulating micronutrients and aneurysms at different sites using Mendelian randomization (MR) analysis. Methods Summary-level genetic data for 15 common blood micronutrients, including vitamin D, calcium, iron, copper, selenium, zinc, folate, carotene, vitamin C, vitamin B12, vitamin E, magnesium, vitamin B6, omega-3 fatty acids, and homocysteine, were obtained from the IEU Open GWAS database. Genetic associations with aneurysms, including intracranial aneurysm and thoracic aortic aneurysm, were retrieved from the GWAS Catalog and the FinnGen consortium. Bidirectional MR analyses were performed using seven MR approaches, with the inverse-variance weighted (IVW) method as the primary analysis. Multiple sensitivity analyses and visualization tools were used to assess pleiotropy and heterogeneity. Furthermore, multivariable MR was applied to explore the interactions and independent effects of multiple micronutrients on aneurysm risk, and meta-analysis was employed to integrate results from different data sources and minimize bias. Results Through multiple MR and sensitivity analyses, combined with multivariate MR and meta-analysis, the results confirmed that elevated blood levels of vitamin D could significantly increase the risk of intracranial aneurysm [odds ratio (OR)=1.65, 95% confidence interval (CI) (1.20, 2.29), P=0.002], while omega-3 fatty acids [OR=0.82, 95%CI (0.73, 0.92), P=0.001] could significantly reduce the risk. For thoracic aortic aneurysm, selenium [OR=1.08, 95%CI (1.00, 1.15), P=0.042] and folate [OR=1.45, 95%CI (1.13, 1.87), P=0.004] were identified as potential risk factors. No heterogeneity or horizontal pleiotropy was detected, and no reverse causality was found between micronutrients and aneurysm development. Conclusions Variations in circulating micronutrient levels can influence the risk of aneurysm development. These findings provide new insights into the potential roles of micronutrients in aneurysm prevention and treatment and offer a scientific basis for developing targeted clinical intervention strategies.