基于自回归模型和相位斜率指数的动态脑连接分析

doi:10.3969/j.issn.0258-8021. 2016.01.001

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Abstract Functional and effective connectivity are important branches in brain network research. Electroencephalogram (EEG) has sufficient temporal resolution to catch the fast brain dynamic changes, so it is suitable for effective connectivity analysis. We proposed a new method to estimate the effective connectivity base on EEG, namely autoregressive phase slope index (AR-PSI). Spectral estimation based on MVAR has high frequency resolution even on short time data. PSI is insensitive to linear mixture of noninteracting sources. AR-PSI combined advantages of the two methods. Compared with conventional Granger causality model, AR-PSI could eliminate the interference caused by volume conduction. Compared with conventional PSI, AR-PSI could get more accurate estimation of effective connectivity with short time data. Experimental data indicated that AR-PSI could exactly detect the effective connectivity between two signals and exclude the false detection. AR-PSI was also applied to dynamic brain connectivity analysis based on EEG recorded in Stroop paradigm. We found that brain connectivity density had significant difference under two conditions in 250～500 ms and 550～800 ms. The results indicated that the incongruent stimulus could make the density of brain network increase more quickly and expand the span of connectivity.

Key words： effective connectivity MVAR model phase slope index( PSI) dynamic analysis

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	Huang Liang Wu Chaohua Gao Xiaorong^#*

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Huang Liang Wu Chaohua Gao Xiaorong^#*. Dynamic Brain Connectivity Analysis Based on Autoregressive-Model and Phase Slope Index[J]. journal1, 2016, 35(1): 1-9.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2016.01.001 OR http://cjbme.csbme.org/EN/Y2016/V35/I1/1

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