Akaike causality in state space. Instantaneous causality between visual cortex in fMRI time series.
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.
Wong
Kin Foon Kevin
KF
Institute of Statistical Mathematics, Minami Azabu 4-6-7, Tokyo 106-8569, Japan. wong@ism.ac.jp
Ozaki
Tohru
T
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*