Analysis of imagery motor effective networks based on dynamic partial directed coherence_Journal of Biomedical Engineering

Authors：

LI Yabing ^1,2 ,  XIE Songyun ¹ , YU Zhenning ³ , XIE Xinzhou ¹ , DUAN Xu ¹ , LIU Chang ¹

1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, P.R.China;
2. School of Computer Science and Technology, Xi’an University of Posts & Telecommunications, Xi’an 710121, P.R.China;
3. Beijing Institute of Computer Application Technology, Beijing 100089, P.R.China;

Corresponding author：

XIE Songyun, Email: syxie@nwpu.edu.cn

Keywords：

effective networks; motor imagery; parameter attributes; small world property

DOI：

10.7507/1001-5515.201811013

Video：

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The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time–frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 (P = 0.007) and ROI3 (P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.

Citation： LI Yabing, XIE Songyun, YU Zhenning, XIE Xinzhou, DUAN Xu, LIU Chang. Analysis of imagery motor effective networks based on dynamic partial directed coherence. Journal of Biomedical Engineering, 2020, 37(1): 38-44. doi: 10.7507/1001-5515.201811013 Copy

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2.	Gonuguntla V, Wang Y, Veluvolu K C. Event-related functional network identification: application to EEG classification. IEEE J Sel Top Signal Process, 2016, 10(7): 1284-1294.
3.	Friston K. Functional and effective connectivity: A review. Brain Connectivity, 2011, 1(1): 13-36.
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6.	Adámek V, Valeš F. Graph theoretical analysis of organization of functional brain networks in ADHD. Clin EEG Neurosci, 2012, 43(1): 5.
7.	Power J D, Barnes K A, Snyder A Z, et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 2012, 59(3): 2142-2154.
8.	Laureys S, Goldman S, Phillips C, et al. Impaired effective cortical connectivity in vegetative state: preliminary investigation using PET. Neuroimage, 1999, 9(4): 377-382.
9.	Chavez M, Valencia M, Latora V, et al. Complex networks: new trends for the analysis of brain connectivity. Int J Bifurcat Chaos, 2010, 20(06): 1677-1686.
10.	Bernasconi C, König P. On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings. Biol Cybern, 1999, 81(3): 199-210.
11.	Omidvarnia A, Azemi G, Boashash B, et al. Measuring time-varying information flow in scalp EEG signals: orthogonalized partial directed coherence. IEEE Trans Biomed Eng, 2014, 61(3): 680-693.
12.	Dimitriadis S I, Laskaris N A, Del Rio-Portilla Y, et al. Characterizing dynamic functional connectivity across sleep stages from EEG. Brain Topogr, 2009, 22(2): 119-133.
13.	袁勤, 蒋涛. 基于格兰杰因果方法的注意脑电网络分析. 生物医学工程学杂志, 2016, 33(1): 56-60.
14.	Li Junhua, Wang Yijun, Zhang Liqing, et al. Decoding EEG in cognitive tasks with time-frequency and connectivity masks. IEEE Trans Cogn Devel Syst, 2016, 8(4): 298-308.
15.	Sauvage C, Jissendi P, Seignan S, et al. Brain areas involved in the control of speed during a motor sequence of the foot: real movement versus mental imagery. J Neuroradiol, 2013, 40(4): 267-280.
16.	徐佳琳. 运动想象电位的脑机接口及脑网络研究. 宁波: 中国科学院大学, 2017.
17.	Cai Xiang, Kallarackal A J, Kvarta M D, et al. Local potentiation of excitatory synapses by serotonin and its alteration in rodent models of depression. Nat Neurosci, 2013, 16(4): 464-472.
18.	Omidvarnia A, Mesbah M, O’Toole J M, et al. Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach// International Workshop on Systems, Signal Processing and their Applications, WOSSPA. Tipaza: IEEE, 2011: 179-182.
19.	Baccalá L A, Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern, 2001, 84(6): 463-474.
20.	He Yong, Chen Z J, Evans A C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex, 2007, 17(10): 2407-2419.
21.	Ferri R, Rundo F, Bruni O, et al. Small-world network organization of functional connectivity of EEG slow-wave activity during sleep. Clin Neurophysiol, 2007, 118(2): 449-456.
22.	Brunner C, Naeem M, Leeb R, et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using Independent components analysis. Pattern Recognit Lett, 2007, 28(8): 957-964.
23.	Charbonnier S, Roy R N, Bonnet S, et al. EEG index for control operators’ mental fatigue monitoring using interactions between brain regions. Expert Syst Appl, 2016, 52: 91-98.
24.	王伟. 基于 EEG 脑功能网络动态建模的脑-机接口技术. 西安: 西北工业大学, 2018.

1. 梁夏, 王金辉, 贺永. 人脑连接组研究: 脑结构网络和脑功能网络. 科学通报, 2010(16): 1565-1583.
2. Gonuguntla V, Wang Y, Veluvolu K C. Event-related functional network identification: application to EEG classification. IEEE J Sel Top Signal Process, 2016, 10(7): 1284-1294.
3. Friston K. Functional and effective connectivity: A review. Brain Connectivity, 2011, 1(1): 13-36.
4. Brunner C, Scherer R, Graimann B, et al. Online control of a brain-computer interface using phase synchronization. IEEE Trans Biomed Eng, 2006, 53(12): 2501-2506.
5. Rissman J, Gazzaley A, D’Esposito M. Measuring functional connectivity during distinct stages of a cognitive task. Neuroimage, 2004, 23(2): 752-763.
6. Adámek V, Valeš F. Graph theoretical analysis of organization of functional brain networks in ADHD. Clin EEG Neurosci, 2012, 43(1): 5.
7. Power J D, Barnes K A, Snyder A Z, et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 2012, 59(3): 2142-2154.
8. Laureys S, Goldman S, Phillips C, et al. Impaired effective cortical connectivity in vegetative state: preliminary investigation using PET. Neuroimage, 1999, 9(4): 377-382.
9. Chavez M, Valencia M, Latora V, et al. Complex networks: new trends for the analysis of brain connectivity. Int J Bifurcat Chaos, 2010, 20(06): 1677-1686.
10. Bernasconi C, König P. On the directionality of cortical interactions studied by structural analysis of electrophysiological recordings. Biol Cybern, 1999, 81(3): 199-210.
11. Omidvarnia A, Azemi G, Boashash B, et al. Measuring time-varying information flow in scalp EEG signals: orthogonalized partial directed coherence. IEEE Trans Biomed Eng, 2014, 61(3): 680-693.
12. Dimitriadis S I, Laskaris N A, Del Rio-Portilla Y, et al. Characterizing dynamic functional connectivity across sleep stages from EEG. Brain Topogr, 2009, 22(2): 119-133.
13. 袁勤, 蒋涛. 基于格兰杰因果方法的注意脑电网络分析. 生物医学工程学杂志, 2016, 33(1): 56-60.
14. Li Junhua, Wang Yijun, Zhang Liqing, et al. Decoding EEG in cognitive tasks with time-frequency and connectivity masks. IEEE Trans Cogn Devel Syst, 2016, 8(4): 298-308.
15. Sauvage C, Jissendi P, Seignan S, et al. Brain areas involved in the control of speed during a motor sequence of the foot: real movement versus mental imagery. J Neuroradiol, 2013, 40(4): 267-280.
16. 徐佳琳. 运动想象电位的脑机接口及脑网络研究. 宁波: 中国科学院大学, 2017.
17. Cai Xiang, Kallarackal A J, Kvarta M D, et al. Local potentiation of excitatory synapses by serotonin and its alteration in rodent models of depression. Nat Neurosci, 2013, 16(4): 464-472.
18. Omidvarnia A, Mesbah M, O’Toole J M, et al. Analysis of the time-varying cortical neural connectivity in the newborn EEG: A time-frequency approach// International Workshop on Systems, Signal Processing and their Applications, WOSSPA. Tipaza: IEEE, 2011: 179-182.
19. Baccalá L A, Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern, 2001, 84(6): 463-474.
20. He Yong, Chen Z J, Evans A C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex, 2007, 17(10): 2407-2419.
21. Ferri R, Rundo F, Bruni O, et al. Small-world network organization of functional connectivity of EEG slow-wave activity during sleep. Clin Neurophysiol, 2007, 118(2): 449-456.
22. Brunner C, Naeem M, Leeb R, et al. Spatial filtering and selection of optimized components in four class motor imagery EEG data using Independent components analysis. Pattern Recognit Lett, 2007, 28(8): 957-964.
23. Charbonnier S, Roy R N, Bonnet S, et al. EEG index for control operators’ mental fatigue monitoring using interactions between brain regions. Expert Syst Appl, 2016, 52: 91-98.
24. 王伟. 基于 EEG 脑功能网络动态建模的脑-机接口技术. 西安: 西北工业大学, 2018.

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