No. 1018

Title:

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

Author(s):

Wong, Kin Foon Kevin (The Institute of Statistical mathematics);
Ozaki, Tohru (The Institute of Statistical mathematics)

Key words:

Akaike causality; noise contribution ratio; state space model; common source; partial causality; functional MRI; primary visual cortex; middle temporal cortex; posterior parietal cortex.

Abstract:

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.