Brain Efficient Connectivity Analysis of Attention Based on the Granger Causality Method_Journal of Biomedical Engineering

Authors：

 YUANQin , JIANGTao

School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China;

Corresponding author：

YUANQin, Email: yuanqin@uestc.edu.cn

Keywords：

electroencephalogram; event-related potential; visual attention; Granger causal connectivity; independent component analysis

DOI：

10.7507/1001-5515.201600011

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The study of brain information flow is of great significance to understand brain function in the field of neuroscience. The Granger causality is widely used functional connectivity analysis using multivariate autoregressive model based on the predicted mechanism. High resolution electroencephalogram (EEG) signals of ten healthy subjects were collected with a visual selective attention task. Firstly, independent component analysis was used to extract three spatially independent components of the occipital, parietal, and frontal cortices. Secondly, the Granger causal connectivity was computed between these three regions based on the Granger causality method and then independent sample t-test and bootstrap were used to test the significance of connections. The results showed that Granger causal connectivity existed from frontal to occipital and from parietal to occipital in attentional condition, while causal connectivity from frontal to occipital disappeared in unattentional condition.

Citation： YUANQin, JIANGTao. Brain Efficient Connectivity Analysis of Attention Based on the Granger Causality Method. Journal of Biomedical Engineering, 2016, 33(1): 56-60. doi: 10.7507/1001-5515.201600011 Copy

1.	FRISTON K J, FRITH C D, LIDDLE P F, et al. Functional connectivity: the principal-component analysis of large (PET) data sets[J]. Journal of Cerebral Blood Flow and Metabolism, 1993, 13(1): 5-14.
2.	FRISTON K J, FRITH C D, FRACKOWIAK R S J. Time-dependent changes in effective connectivity measured with PET[J]. Human Brain Mapping, 1993, 1(1): 69-79.
3.	VAN DEN HEUVEL M P, HULSHOFF POL H E. Exploring the brain network: A review on resting-state fMRI functional connectivity[J]. European Neuropsychopharmacology, 2010, 20(8): 519-534.
4.	SIEGEL M, DONNER T H, ENGEL A K. Spectral fingerprints of large-scale neuronal interactions[J]. Nature Reviews Neuroscience, 2012, 13(2): 121-134.
5.	ZOU C L, LADROUE C, GUO S X, et al. Identifying interactions in the time and frequency domains in local and global networks--A Granger Causality Approach[J]. BMC Bioinformatics, 2010, 11: 337.
6.	PENNY W D, STEPHAN K E, MECHELLI A, et al. Modelling functional integration: a comparison of structural equation and dynamic causal models[J]. Neuroimage, 2004, 23(Suppl 1): S264-S274.
7.	PARK B, KIM D S, PARK H J. Graph independent component analysis reveals repertoires of intrinsic network components in the human brain[J]. PLoS One, 2014, 9(1): e82873.
8.	BUGLI C, LAMBERT P. Comparison between principal component analysis and independent component analysis in electroencephalograms modelling[J]. Biom J, 2007, 49(2): 312-327.
9.	GONALVES M S, HALL D A. Connectivity analysis with structural equation modelling: an example of the effects of voxel selection[J]. Neuroimage, 2003, 20(3): 1455-1467.
10.	RAMNANI N, BEHRENS T E, PENNY W, et al. New approaches for exploring anatomical and functional connectivity in the human brain[J]. Biological psychiatry, 2004, 56(9): 613-619.
11.	FRISTON K J, HARRISON L, PENNY W. Dynamic causal modeling[J]. Neuroimage, 2003, 19(4): 1273-1302.
12.	KIEBEL S J, GARRIDO M I, MORAN R J. et al., Dynamic causal modelling for EEG and MEG[J]. Cogn Neurodyn, 2008, 2(2): 121-136.
13.	PENNY W D, STEPHAN K, MECHELLI A, et al. Comparing dynamic causal models[J]. Neuroimage, 2004, 22(3): 1157-1172.
14.	MECHELLI A, PRICE C J, NOPPENEY U, et al. A dynamic causal modeling study on category effects: bottom-up or top-down mediation?[J]. Journal of Cognitive Neuroscience, 2003, 15(7): 925-934.
15.	HAYNES J D, TREGELLAS J, REES G. Attentional integration between anatomically distinct stimulus representations in early visual cortex[J]. Proc Natl Acad Sci U S A, 2005, 102(41): 14925-14930.
16.	GAO W, TIAN Z. Learning Granger causality graphs for multivariate nonlinear time series[J]. Journal of Systems Science and Systems Engineering, 2009, 18(1): 38-52.
17.	KAMISKI M, DING M Z, TRUCCOLO W A, et al. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance[J]. Biol Cybern, 2001, 85(2): 145-157.
18.	KANNAN R, TANGIRALA A K. Correntropy-based partial directed coherence for testing multivariate Granger causality in nonlinear processes[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 89(6): 062144.
19.	MCMAINS S, KASTNER S. Interactions of top-down and bottom-up mechanisms in human visual cortex[J]. J Neurosci, 2011, 31(2): 587-597.

1. FRISTON K J, FRITH C D, LIDDLE P F, et al. Functional connectivity: the principal-component analysis of large (PET) data sets[J]. Journal of Cerebral Blood Flow and Metabolism, 1993, 13(1): 5-14.
2. FRISTON K J, FRITH C D, FRACKOWIAK R S J. Time-dependent changes in effective connectivity measured with PET[J]. Human Brain Mapping, 1993, 1(1): 69-79.
3. VAN DEN HEUVEL M P, HULSHOFF POL H E. Exploring the brain network: A review on resting-state fMRI functional connectivity[J]. European Neuropsychopharmacology, 2010, 20(8): 519-534.
4. SIEGEL M, DONNER T H, ENGEL A K. Spectral fingerprints of large-scale neuronal interactions[J]. Nature Reviews Neuroscience, 2012, 13(2): 121-134.
5. ZOU C L, LADROUE C, GUO S X, et al. Identifying interactions in the time and frequency domains in local and global networks--A Granger Causality Approach[J]. BMC Bioinformatics, 2010, 11: 337.
6. PENNY W D, STEPHAN K E, MECHELLI A, et al. Modelling functional integration: a comparison of structural equation and dynamic causal models[J]. Neuroimage, 2004, 23(Suppl 1): S264-S274.
7. PARK B, KIM D S, PARK H J. Graph independent component analysis reveals repertoires of intrinsic network components in the human brain[J]. PLoS One, 2014, 9(1): e82873.
8. BUGLI C, LAMBERT P. Comparison between principal component analysis and independent component analysis in electroencephalograms modelling[J]. Biom J, 2007, 49(2): 312-327.
9. GONALVES M S, HALL D A. Connectivity analysis with structural equation modelling: an example of the effects of voxel selection[J]. Neuroimage, 2003, 20(3): 1455-1467.
10. RAMNANI N, BEHRENS T E, PENNY W, et al. New approaches for exploring anatomical and functional connectivity in the human brain[J]. Biological psychiatry, 2004, 56(9): 613-619.
11. FRISTON K J, HARRISON L, PENNY W. Dynamic causal modeling[J]. Neuroimage, 2003, 19(4): 1273-1302.
12. KIEBEL S J, GARRIDO M I, MORAN R J. et al., Dynamic causal modelling for EEG and MEG[J]. Cogn Neurodyn, 2008, 2(2): 121-136.
13. PENNY W D, STEPHAN K, MECHELLI A, et al. Comparing dynamic causal models[J]. Neuroimage, 2004, 22(3): 1157-1172.
14. MECHELLI A, PRICE C J, NOPPENEY U, et al. A dynamic causal modeling study on category effects: bottom-up or top-down mediation?[J]. Journal of Cognitive Neuroscience, 2003, 15(7): 925-934.
15. HAYNES J D, TREGELLAS J, REES G. Attentional integration between anatomically distinct stimulus representations in early visual cortex[J]. Proc Natl Acad Sci U S A, 2005, 102(41): 14925-14930.
16. GAO W, TIAN Z. Learning Granger causality graphs for multivariate nonlinear time series[J]. Journal of Systems Science and Systems Engineering, 2009, 18(1): 38-52.
17. KAMISKI M, DING M Z, TRUCCOLO W A, et al. Evaluating causal relations in neural systems: granger causality, directed transfer function and statistical assessment of significance[J]. Biol Cybern, 2001, 85(2): 145-157.
18. KANNAN R, TANGIRALA A K. Correntropy-based partial directed coherence for testing multivariate Granger causality in nonlinear processes[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2014, 89(6): 062144.
19. MCMAINS S, KASTNER S. Interactions of top-down and bottom-up mechanisms in human visual cortex[J]. J Neurosci, 2011, 31(2): 587-597.

Journal of Biomedical Engineering

Brain Efficient Connectivity Analysis of Attention Based on the Granger Causality Method

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content