Estimation and Visualization of Brain Networks Using MEG Source Imaging
(0) Estimating a causal relationship among cortical activities using the MEG source space analysis has gained a great interest. Such causality analysis generally requires to compute some types of causality measures using the estimated time series of target source activities. Here, popular measures are Granger-causality-based measures, which rely on the accurate modeling of the multivariate vector auto-regressive process of the source time series. This paper examines the effectiveness of sparse Bayesian algorithm to estimate multivariate autoregressive coefficients when a large amount of background interference exists. This paper employs computer experiments to compare two methods in the source-space causality analysis: the conventional least-squares method and a sparse Bayesian method. Results of our computer experiments show that the interference affects the least-squares method in a very severe manner. It produces large false-positive results, unless the signal-to-interference ratio is very high. On the other hand, the sparse Bayesian method is relatively insensitive to the existence of interference. However, this robustness of the sparse Bayesian method is attained on the scarifies of the detectability of true causal relationship. Our experiments also show that the surrogate data bootstrapping method tends to give a statistical threshold that are too low for the sparse method. The permutation-test-based method gives a higher (more conservative) threshold and it should be used with the sparse Bayesian method whenever the control period is available.
Kensuke Sekihara Hagai Attias Julia Owen Srikantan S. Nagarajan
Tokyo Metropolitan University, Tokyo, Japan Golden Metallic Inc., San Francisco, USA University of California, San Francisco, San Francisco, USA
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-6
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）