Akaike Causality in State Space Part I - Instantaneous Causality Between Visual Cortex in fMRI Time Series

We present a new approach of explaining partial causality in multivariate fMRI time series by a state space model. A given single time series can be divided into two noise-driven processes, which comprising a homogeneous process shared among multivariate time series and a particular process refining the homogeneous process. Causality map is drawn using Akaike noise contribution ratio theory, by assuming that noises are independent. The method is illustrated by an application to fMRI data recorded under visual stimulus.