Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike’s Information Criterion_Journal of Biomedical Engineering

Authors：

 YANG Chunfeng ^1,2,4 , XIANG Wentao ^3,4 , WU Jiasong ^1,2,4 , KONG Youyong ^1,4 , JIANG Longyu ^1,4 , LE BOUQUIN JÈANNES Régine ^3,4 , SHU Huazhong ^1,2,4

1. Key Laboratory of Computer Network and Information Integration of Ministry of Education, School of Computer Science and Engineering, Southeast University, Nanjing 210096, P.R.China;
2. International Joint Research Laboratory of Information Display and Visualization, Southeast University, Nanjing 210096, P.R.China;
3. INSERM U1099, LTSI, Université de Rennes 1, Rennes 35000, France;
4. Centre de Recherche en Information Biomédicale Sino-français, Nanjing 210096 & Rennes 35000, P.R.China & France;

Corresponding author：

YANG Chunfeng, Email: chunfeng.yang@seu.edu.cn

Keywords：

causality index; Akaike's information criterion; physiology-based model; epilepsy; brain connectivity

DOI：

10.7507/1001-5515.201709032

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The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike’s information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.

Citation： YANG Chunfeng, XIANG Wentao, WU Jiasong, KONG Youyong, JIANG Longyu, LE BOUQUIN JÈANNES Régine, SHU Huazhong. Research on effective connectivity of intracerebral electroencephalogram based on Wiener-Granger Causality Index modified by generalized Akaike’s Information Criterion. Journal of Biomedical Engineering, 2018, 35(5): 665-671. doi: 10.7507/1001-5515.201709032 Copy

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2.	Ansari-Asl K, Senhadji L, Bellanger J J, et al. Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals. Phys Rev E Stat Nonlin Soft Matter Phys, 2006, 74(3 Pt 1): 031916.
3.	Wiener N. The theory of prediction. Modern Mathematics for Engineers, 1956: 125-139.
4.	Granger C W. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969, 37(3): 424-438.
5.	Kayser A S, Sun F T, D'Esposito M. A comparison of Granger causality and coherency in fMRI-based analysis of the motor system. Hum Brain Mapp, 2009, 30(11): 3475-3494.
6.	Cadotte A J, DeMarse T B, Mareci T H, et al. Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy. J Neurosci Methods, 2010, 189(1): 121-129.
7.	Bressler S L, Seth A K. Wiener-Granger causality: a well established methodology. Neuroimage, 2011, 58(2): 323-329.
8.	Akaike H. Information theory and an extension of the maximum likelihood principle//Petrov B N, Caski F. Proceedings of the Second International Symposium on Information Theory. Budapest: Akademiai Kiado, 1973: 267-281.
9.	Yang Chunfeng, Le Bouquin Jeannes R, Bellanger J J, et al. A new strategy for model order identification and its application to transfer entropy for EEG signals analysis. IEEE Trans Biomed Eng, 2013, 60(5): 1318-1327.
10.	Kariya T, Kurata H. Generalized least squares. Chichester: John Wiley & Sons, 2004.
11.	Wendling F, Bellanger J J, Bartolomei F, et al. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol Cybern, 2000, 83(4): 367-378.
12.	Wendling F, Hernandez A, Bellanger J J, et al. Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol, 2005, 22(5): 343-356.

1. Engel J Jr. Outcome with respect to epileptic seizures. Surgical Treatment of the Epilepsies, 1993: 609-621.
2. Ansari-Asl K, Senhadji L, Bellanger J J, et al. Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals. Phys Rev E Stat Nonlin Soft Matter Phys, 2006, 74(3 Pt 1): 031916.
3. Wiener N. The theory of prediction. Modern Mathematics for Engineers, 1956: 125-139.
4. Granger C W. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969, 37(3): 424-438.
5. Kayser A S, Sun F T, D'Esposito M. A comparison of Granger causality and coherency in fMRI-based analysis of the motor system. Hum Brain Mapp, 2009, 30(11): 3475-3494.
6. Cadotte A J, DeMarse T B, Mareci T H, et al. Granger causality relationships between local field potentials in an animal model of temporal lobe epilepsy. J Neurosci Methods, 2010, 189(1): 121-129.
7. Bressler S L, Seth A K. Wiener-Granger causality: a well established methodology. Neuroimage, 2011, 58(2): 323-329.
8. Akaike H. Information theory and an extension of the maximum likelihood principle//Petrov B N, Caski F. Proceedings of the Second International Symposium on Information Theory. Budapest: Akademiai Kiado, 1973: 267-281.
9. Yang Chunfeng, Le Bouquin Jeannes R, Bellanger J J, et al. A new strategy for model order identification and its application to transfer entropy for EEG signals analysis. IEEE Trans Biomed Eng, 2013, 60(5): 1318-1327.
10. Kariya T, Kurata H. Generalized least squares. Chichester: John Wiley & Sons, 2004.
11. Wendling F, Bellanger J J, Bartolomei F, et al. Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals. Biol Cybern, 2000, 83(4): 367-378.
12. Wendling F, Hernandez A, Bellanger J J, et al. Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J Clin Neurophysiol, 2005, 22(5): 343-356.

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