期刊
NEUROIMAGE
卷 50, 期 3, 页码 960-969出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.12.110
关键词
AR-modelling; Interacting patterns; Partial Granger causality index; Time-variant structure-function relationships; State-space-model; Simulation study; Laser-evoked potentials
资金
- German Research Council (DFG) [Wi 1166/9-1]
- Federal Ministry of Education and Research [01GQ0703]
- COST Action [BM0601 NEUROMATH]
In this methodological study we present a new version of a Kalman filter technique to estimate high-dimensional time-variant (tv) multivariate autoregressive (tvMVAR) models. It is based on an extension of the state-space model for a multivariate time series to a matrix-state-space model for multi-trial multivariate time series. The result is a general linear Kalman filter (GLKF). The GLKF enables a tvMVAR model estimation which was applied for interaction analysis of simulated data and high-dimensional multi-trial laser-evoked brain potentials (LEP). The tv partial Granger causality index (tvpGCI) Was used to investigate the interaction patterns between LEPs derived from an experiment with noxious laser stimulation. First, the new approach was compared with the multi-trial version of the recursive least squares (RLS) algorithm with forgetting factor (Moller et al., 2001) by using 24 distinct electrodes. The RLS failed for a channel number (dimension) higher than 24. Secondly, the analysis was repeated by using all 58 electrodes and the similarities and differences of the GCI-based interaction patterns are discussed. It can be demonstrated that the application of high-dimensional tvMVAR modelling will contribute to a better understanding of the relationship between structure and function. (C) 2010 Elsevier Inc. All rights reserved.
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