title: Analysing connectivity with Granger causality and dynamic causal modelling.
abstract: This review considers state-of-the-art analyses of functional integration in neuronal macrocircuits. We focus on detecting and estimating directed connectivity in neuronal networks using Granger causality (GC) and dynamic causal modelling (DCM). These approaches are considered in the context of functional segregation and integration and--within functional integration--the distinction between functional and effective connectivity. We review recent developments that have enjoyed a rapid uptake in the discovery and quantification of functional brain architectures. GC and DCM have distinct and complementary ambitions that are usefully considered in relation to the detection of functional connectivity and the identification of models of effective connectivity. We highlight the basic ideas upon which they are grounded, provide a comparative evaluation and point to some outstanding issues.
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK. k.friston@ucl.ac.uk
authors.forename: Karl
authors.initials: K
authors.lastname: Friston
authors.forename: Rosalyn
authors.initials: R
authors.lastname: Moran
authors.forename: Anil K
authors.initials: AK
authors.lastname: Seth
mesh_terms: D001921:Brain / Q000166:cytology* / Q000502:physiology*; D001931:Brain Mapping; D015984:Causality; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics