title: Uniform approach to linear and nonlinear interrelation patterns in multivariate time series.
abstract: Currently, a variety of linear and nonlinear measures is in use to investigate spatiotemporal interrelation patterns of multivariate time series. Whereas the former are by definition insensitive to nonlinear effects, the latter detect both nonlinear and linear interrelation. In the present contribution we employ a uniform surrogate-based approach, which is capable of disentangling interrelations that significantly exceed random effects and interrelations that significantly exceed linear correlation. The bivariate version of the proposed framework is explored using a simple model allowing for separate tuning of coupling and nonlinearity of interrelation. To demonstrate applicability of the approach to multivariate real-world time series we investigate resting state functional magnetic resonance imaging (rsfMRI) data of two healthy subjects as well as intracranial electroencephalograms (iEEG) of two epilepsy patients with focal onset seizures. The main findings are that for our rsfMRI data interrelations can be described by linear cross-correlation. Rejection of the null hypothesis of linear iEEG interrelation occurs predominantly for epileptogenic tissue as well as during epileptic seizures.
authors.affiliation: Support Center for Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland. crummel@web.de
authors.forename: Christian
authors.initials: C
authors.lastname: Rummel
authors.forename: Eugenio
authors.initials: E
authors.lastname: Abela
authors.forename: Markus
authors.initials: M
authors.lastname: Müller
authors.forename: Martinus
authors.initials: M
authors.lastname: Hauf
authors.forename: Olivier
authors.initials: O
authors.lastname: Scheidegger
authors.forename: Roland
authors.initials: R
authors.lastname: Wiest
authors.forename: Kaspar
authors.initials: K
authors.lastname: Schindler