title: Detecting causal interdependence in simulated neural signals based on pairwise and multivariate analysis.
abstract: Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can "drive" some other structures. This paper focuses on a linear Granger causality based measure to detect causal relation of interdependence in multivariate signals generated by a physiology-based model of coupled neuronal populations. When coupling between signals exists, statistical analysis supports the relevance of this index for characterizing the information flow and its direction among neuronal populations.
authors.affiliation: INSERM, U642, Rennes, F-35000, France.
authors.forename: C
authors.initials: C
authors.lastname: Yang
authors.forename: R
authors.initials: R
authors.lastname: Le Bouquin Jeannes
authors.forename: G
authors.initials: G
authors.lastname: Faucon
authors.forename: F
authors.initials: F
authors.lastname: Wendling
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D008959:Models, Neurological; D015999:Multivariate Analysis; D012815:Signal Processing, Computer-Assisted; D018709:Statistics, Nonparametric