title: The impact of latent confounders in directed network analysis in neuroscience.
abstract: In the analysis of neuroscience data, the identification of task-related causal relationships between various areas of the brain gives insights about the network of physiological pathways that are active during the task. One increasingly used approach to identify causal connectivity uses the concept of Granger causality that exploits predictability of activity in one region by past activity in other regions of the brain. Owing to the complexity of the data, selecting components for the analysis of causality as a preprocessing step has to be performed. This includes predetermined-and often arbitrary-exclusion of information. Therefore, the system is confounded by latent sources. In this paper, the effect of latent confounders is demonstrated, and paths of influence among three components are studied. While methods for analysing Granger causality are commonly based on linear vector autoregressive models, the effects of latent confounders are expected to be present also in nonlinear systems. Therefore, all analyses are also performed for a simulated nonlinear system and discussed with regard to applications in neuroscience.
authors.affiliation: FDM, Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstrasse 1, 79104 Freiburg, Germany.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Ramb
authors.forename: Michael
authors.initials: M
authors.lastname: Eichler
authors.forename: Alex
authors.initials: A
authors.lastname: Ing
authors.forename: Marco
authors.initials: M
authors.lastname: Thiel
authors.forename: Cornelius
authors.initials: C
authors.lastname: Weiller
authors.forename: Celso
authors.initials: C
authors.lastname: Grebogi
authors.forename: Christian
authors.initials: C
authors.lastname: Schwarzbauer
authors.forename: Jens
authors.initials: J
authors.lastname: Timmer
authors.forename: Björn
authors.initials: B
authors.lastname: Schelter
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005163:Factor Analysis, Statistical; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D009415:Nerve Net / Q000502:physiology*; D009488:Neurosciences / Q000379:methods; D012044:Regression Analysis; D009435:Synaptic Transmission / Q000502:physiology*
keywords: Granger causality; latent confounders; renormalized partial directed coherence; vector autoregressive modelling