2020/10/02 by Narayan Puthanmadam Subramaniyam, Subramaniyam, Narayan Puthanmadam, Reik V. Donner +7
Biochemistry, Genetics and Molecular Biology · Economics, Econometrics and Finance · Mathematics · Neuroscience · Physics and Astronomy · #62Gxx #62Hxx #62Mxx #Complex Systems and Time Series Analysis #Data Analysis #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Methodology (stat.ME) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #Statistics and Probability (physics.data-an) #msc:62Gxx #msc:62Hxx #msc:62Mxx #physics.data-an #q-bio.NC #stat.ME
paper · pdf · doi:10.48550/arxiv.2010.00948
openalex publication_date 2020/10/02 · arxiv created 2021/06/01 · arxiv updated 2021/06/03 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
Identifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.