A0686
Title: Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components
Authors: Shanghong Xie - Southwestern University of Finance and Economics (China) [presenting]
Abstract: Time series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artefacts, structured noise, and other unobserved factors (e.g., genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modelling, do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. A novel method is proposed to identify latent temporal pathways using time series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. The algorithm is fast and can easily scale up. The identifiability and the asymptotic properties of the temporal and contemporaneous networks are derived. Superior performance of the method is demonstrated by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD).
