The impact of latent confounders in directed network analysis in neuroscience

Rebecca Ramb (Corresponding Author), Michael Eichler, Alex Ing, Marco Thiel, Cornelius Weiller, Celso Grebogi, Christian Schwarzbauer, Jens Timmer, Bjoern Schelter

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.
Original languageEnglish
Article number20110612
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
Issue number1997
Early online date15 Jul 2013
Publication statusPublished - 28 Aug 2013


  • Granger causality
  • renormalized partial directed coherence
  • vector autoregressive modelling
  • latent confounders


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