A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics

Property analysis and application in biomedicine of two kinds of copula related to FGM

FGM copula is widely used in correlation analysis because of its simple form and excellent properties, but its application in asymmetric correlation analysis is limited by its symmetrical function form. This paper mainly analyzes the properties of two kinds of asymmetric copulas related to FGM copula-generalized FGM copula and three-section copula. As two kinds of asymmetric copula related to FGM copula, they retain many excellent properties of FGM copula, such as closure under convolution operation and the relation between Spearman ρ and Kendall τ, etc. The closeness of convolution operation provides convenience for the calculation of Copula dependence coefficient. Finally, the generalized FGM is selected to analyze the asymmetric dependence between variables in biomedicine, which lays a foundation for its application in biomedicine.

Intermuscular coupling network analysis of upper limbs based on R-vine copula transfer entropy

In the field of neuroscience, it is very important to evaluate the causal coupling characteristics between bioelectrical signals accurately and effectively. Transfer entropy is commonly used to analyze complex data, especially the causal relationship between data with non-linear, multidimensional characteristics. However, traditional transfer entropy needs to estimate the probability density function of the variable, which is computationally complex and unstable. In this paper, a new and effective method for entropy transfer is proposed, by means of applying R-vine copula function estimation. The effectiveness of R-vine copula transfer entropy is first verified on several simulations, and then applied to intermuscular coupling analysis to explore the characteristics of the intermuscular coupling network of muscles in non-fatigue and fatigue conditions. The experiment results show that as the muscle group enters the fatigue state, the community structure can be adjusted and the muscle nodes participating in the exercise are fully activated, enabling the two-way interaction between different communities. Finally, it comes to the conclusion that the proposed method can make accurate inferences about complex causal coupling. Moreover, the characteristics of the intermuscular coupling network in both non-fatigue and fatigue states can provide a new theoretical perspective for the diagnosis of neuromuscular fatigue and sports rehabilitation, which has good application value.