title: Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application.
abstract: In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provides the possibility to investigate nonlinear causal interactions between time series by using nonlinear state space reconstruction. Aim of this study is to investigate the general applicability, and to show potentials and limitation of CCM. Influence of estimation parameters could be demonstrated by means of simulated data, whereas interval-based application of CCM on real data could be adapted for the investigation of interactions between heart rate and specific EEG components of children with temporal lobe epilepsy.
authors.forename: Karin
authors.initials: K
authors.lastname: Schiecke
authors.forename: Britta
authors.initials: B
authors.lastname: Pester
authors.forename: Martha
authors.initials: M
authors.lastname: Feucht
authors.forename: Lutz
authors.initials: L
authors.lastname: Leistritz
authors.forename: Herbert
authors.initials: H
authors.lastname: Witte
mesh_terms: D002648:Child; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004833:Epilepsy, Temporal Lobe / Q000175:diagnosis* / Q000503:physiopathology; D006339:Heart Rate; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics