Akaike causality in state space

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.

[1]  Katarzyna J. Blinowska,et al.  A new method of the description of the information flow in the brain structures , 1991, Biological Cybernetics.

[2]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[3]  王 健歡.  Multivariate time series analysis of heteroscedastic data with application to neuroscience , 2006 .

[4]  J. Geweke,et al.  Measurement of Linear Dependence and Feedback between Multiple Time Series , 1982 .

[5]  Luiz A. Baccalá,et al.  Partial directed coherence: a new concept in neural structure determination , 2001, Biological Cybernetics.

[6]  Tohru Ozaki,et al.  Evaluating frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time-series models , 2005, NeuroImage.

[7]  Juan C. Jiménez,et al.  Nonlinear EEG analysis based on a neural mass model , 1999, Biological Cybernetics.

[8]  R. Mehra.  Identification of stochastic linear dynamic systems , 1969 .

[9]  Karl J. Friston,et al.  Multivariate Autoregressive Modelling of fMRI time series , 2003 .

[10]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[11]  T. Nakagawa,et al.  Statistical approach to computer control of cement rotary kilns , 1972 .

[12]  H. W. Sorenson,et al.  Kalman filtering : theory and application , 1985 .

[13]  H. Akaike.  On the use of a linear model for the identification of feedback systems , 1968 .

[14]  Genshiro Kitagawa,et al.  State Space Modeling of Time Series , 1994 .

[15]  C. Büchel,et al.  Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. , 1997, Cerebral cortex.

[16]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter. , 1991 .

[17]  Karl Johan Åström,et al.  Application of System Identification Techniques to the Determination of Ship Dynamics , 1973 .

[18]  G. Kitagawa.  Smoothness priors analysis of time series , 1996 .

[19]  R. K. Mehra,et al.  Identification of stochastic linear dynamic systems using Kalman filter representation , 1971 .

[20]  Kohyu Fukunishi.  Diagnostic Analyses of a Nuclear Power Plant Using Multivariate Autoregressive Processes , 1977 .

[21]  R. Willison.  Recent Advances in EEG and EMG Data Processing , 1982 .