title: Akaike causality in state space. Instantaneous causality between visual cortex in fMRI time series.
abstract: We present a new approach of explaining instantaneous causality in multivariate fMRI time series by a state space model. A given single time series can be divided into two noise-driven processes, a common process shared among multivariate time series and a specific process refining the common process. By assuming that noises are independent, a causality map is drawn using Akaike noise contribution ratio theory. The method is illustrated by an application to fMRI data recorded under visual stimulation.
authors.affiliation: Institute of Statistical Mathematics, Minami Azabu 4-6-7, Tokyo 106-8569, Japan. wong@ism.ac.jp
authors.forename: Kin Foon Kevin
authors.initials: KF
authors.lastname: Wong
authors.forename: Tohru
authors.initials: T
authors.lastname: Ozaki
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001931:Brain Mapping / Q000379:methods; D015984:Causality; D003198:Computer Simulation; D005074:Evoked Potentials, Visual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008432:Mathematical Computing; D008959:Models, Neurological; D015999:Multivariate Analysis; D010775:Photic Stimulation; D025341:Principal Component Analysis; D013997:Time Factors; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*