title: Multivariate analysis of dynamical processes with applications to the neurosciences.
abstract: Nowadays, data are recorded with increasing spatial and temporal resolution. Commonly these data are analyzed using univariate or bivariate approaches. Most of the analysis techniques assume stationarity of the underlying dynamical processes. Here, we present an approach that is capable of analyzing multivariate data, the so-called renormalized partial directed coherence. It utilizes the concept of Granger causality and is applicable to non-stationary data. We discuss its abilities and limitations, and demonstrate its usefulness in an application to murine electroencephalography (EEG) data during sleep transitions.
authors.affiliation: Faculty of Mathematics and Physics, University of Freiburg, Freiburg, Germany. schelter@fdm.uni-freiburg.de
authors.forename: Björn
authors.initials: B
authors.lastname: Schelter
authors.forename: Linda
authors.initials: L
authors.lastname: Sommerlade
authors.forename: Bettina
authors.initials: B
authors.lastname: Platt
authors.forename: Andrea
authors.initials: A
authors.lastname: Plano
authors.forename: Marco
authors.initials: M
authors.lastname: Thiel
authors.forename: Jens
authors.initials: J
authors.lastname: Timmer
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D051379:Mice; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012890:Sleep / Q000502:physiology*