Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection_Journal of Biomedical Engineering

Authors：

HUANG Baoqiang ,  LI Chunsheng

Department of Biomedical Engineering, School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, P. R. China;

Corresponding author：

LI Chunsheng, Email: lichunsheng@sut.edu.cn

Keywords：

Epilepsy network; Epileptogenic zone localization; Neural computational model; Virtual resection

DOI：

10.7507/1001-5515.202205048

Video：

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Drug-refractory epilepsy (DRE) may be treated by surgical intervention. Intracranial EEG has been widely used to localize the epileptogenic zone (EZ). Most studies of epileptic network focus on the features of EZ nodes, such as centrality and degrees. It is difficult to apply those features to the treatment of individual patients. In this study, we proposed a spatial neighbor expansion approach for EZ localization based on a neural computational model and epileptic network reconstruction. The virtual resection method was also used to validate the effectiveness of our approach. The electrocorticography (ECoG) data from 11 patients with DRE were analyzed in this study. Both interictal data and surgical resection regions were used. The results showed that the rate of consistency between the localized regions and the surgical resections in patients with good outcomes was higher than that in patients with poor outcomes. The average deviation distance of the localized region for patients with good outcomes and poor outcomes were 15 mm and 36 mm, respectively. Outcome prediction showed that the patients with poor outcomes could be improved when the brain regions localized by the proposed approach were treated. This study provides a quantitative analysis tool for patient-specific measures for potential surgical treatment of epilepsy.

Citation： HUANG Baoqiang, LI Chunsheng. Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection. Journal of Biomedical Engineering, 2022, 39(6): 1165-1172. doi: 10.7507/1001-5515.202205048 Copy

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4.	梁赛兰, 王多琎. 动态因果模型在脑网络研究中的应用进展. 中国生物医学工程学报, 2022, 41(1): 86-99.
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7.	薛开庆, 罗程, 田银, 等. 基于弥散张量成像的儿童失神癫痫认知控制网络的结构连接研究. 中国生物医学工程学报, 2013, 32(4): 426-432.
8.	李素姣, 刘苏, 蓝贺, 等. 脑肌电信号同步耦合分析方法研究进展. 生物医学工程学杂志, 2019, 36(2): 334-337, 342.
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11.	Baccala L A, Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern, 2001, 84(6): 463-474.
12.	Pascual-Marqui R D, Biscay R J, Bosch-Bayard J, et al. Assessing direct paths of intracortical causal information flow of oscillatory activity with the isolated effective coherence (iCoh). Front Hum Neurosci, 2014, 8: 448.
13.	倪召兵, 李颖, 赵营鸽, 等. 基于脑功能网络的混合情绪因素对错误记忆影响的研究. 生物医学工程学杂志, 2021, 38(5): 828-837.
14.	Sporns O, Zwi J D. The small world of the cerebral cortex. Neuroinformatics, 2004, 2(2): 145-162.
15.	左锐, 张旭, 魏婧, 等. 基于脑网络信息传递特性的癫痫病灶区定位. 北京生物医学工程, 2018, 37(4): 331-336.
16.	Hao S, Subramanian S, Jordan A, et al. Computing network-based features from intracranial EEG time series data: application to seizure focus localization// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 5812-5815.
17.	李尊钰, 袁冠前, 黄平, 等. 基于立体定向脑电图的颞叶致痫网络独立有效相干分析. 生物医学工程学杂志, 2019, 36(4): 541-547.
18.	Li C S, Sohrabpour A, Jiang H T, et al. High-frequency hubs of the ictal cross-frequency coupling network predict surgical outcome in epilepsy patients. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1290-1299.
19.	吴雪瑞, 侯锦, 徐婷, 等. 癫痫脑电网络应用及研究进展. 现代医学, 2020, 48(11): 1470-1474.
20.	赵国光. 癫痫脑网络外科精准治疗的探索. 中华医学杂志, 2021, 101(41): 3361-3364.
21.	Li C S, Jacobs D, Hilton T, et al. Epileptogenic source imaging using cross-frequency coupled signals from scalp EEG. IEEE Trans Biomed Eng, 2016, 63(12): 2607-2618.
22.	Friston K J, Frith C D, Frackowiak R S J. Time-dependent changes in effective connectivity measured with PET. Hum Brain Mapp, 1993, 1: 69-79.
23.	Wilke C, van Drongelen W, Kohrman M, et al. Neocortical seizure foci localization by means of a directed transfer function method. Epilepsia, 2010, 51(4): 564-572.
24.	Wilke C, Worrell G A, He Bin. Analysis of epileptogenic network properties during ictal activity//2009 31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis: IEEE, 2009: 2220-2223.
25.	Ren Y, Cong F Y, Ristaniemi T, et al. Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy. J Neurol, 2019, 266(4): 844-859.
26.	闫铮, 高小榕, 应俊. 基于认知功能连接的信息流增益计算方法及应用. 电子与信息学报, 2014, 36(11): 2756-2761.
27.	Benjamin O, Fitzgerald T H, Ashwin P, et al. A phenomenological model of seizure initiation suggests network structure may explain seizure frequency in idiopathic generalised epilepsy. J Math Neurosci, 2012, 2(1): 1.
28.	Sinha N, Dauwels J, Kaiser M, et al. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain, 2017, 140(2): 319-332.
29.	Olmi S, Petkoski S, Guye M, et al. Controlling seizure propagation in large-scale brain networks. PLoS Comput Biol, 2019, 15(2): e1006805.
30.	Zweiphenning W J E M, Keijzer H M, van Diessen E, et al. Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach. Epilepsia, 2019, 60(9): 1908-1920.
31.	闫秀鹏, 王海祥, 周文静, 等. 基于高频颅内脑电的致痫区定位和网络分析. 北京生物医学工程, 2017, 36(3): 229-236.
32.	Nissen I A, Millan A P, Stam C J, et al. Optimization of epilepsy surgery through virtual resections on individual structural brain networks. Sci Rep, 2021, 11(1): 19025.
33.	潘多涛, 史洪岩, 黄明忠, 等. 流行病防治过程SIR模型的稳定性分析. 生物医学工程学杂志, 2015, 32(5): 1013-1018.

1. Kwan P, Brodie M J. Early identification of refractory epilepsy. N Engl J Med, 2000, 342(5): 314-319.
2. 江录伟, 钱若兵, 傅先明, 等. 难治性癫痫患者脑网络介数及其异常脑区间功能连接变化研究. 国际神经病学神经外科学杂志, 2018, 45(1): 30-35.
3. Wille C, Steinhoff B J, Altenmuller D M, et al. Chronic high-frequency deep-brain stimulation in progressive myoclonic epilepsy in adulthood-report of five cases. Epilepsia, 2011, 52(3): 489-496.
4. 梁赛兰, 王多琎. 动态因果模型在脑网络研究中的应用进展. 中国生物医学工程学报, 2022, 41(1): 86-99.
5. Lu A C, Beenhakker M. Casting a wide net to catch seizures. Epilepsy Curr, 2019, 19(4): 258-260.
6. Dong L, Luo C, Zhu Y T, et al. Complex dischargeaffecting networks in juvenile myoclonic epilepsy: a simultaneous EEG-fMRI study. Hum Brain Mapp, 2016, 37(10): 3515-3529.
7. 薛开庆, 罗程, 田银, 等. 基于弥散张量成像的儿童失神癫痫认知控制网络的结构连接研究. 中国生物医学工程学报, 2013, 32(4): 426-432.
8. 李素姣, 刘苏, 蓝贺, 等. 脑肌电信号同步耦合分析方法研究进展. 生物医学工程学杂志, 2019, 36(2): 334-337, 342.
9. Kaminski M J, Blinowska K J. A new method of the description of the information flow in the brain structures. Biol Cybern, 1991, 65(3): 203-210.
10. Li F L, Chen B, Li H, et al. The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(7): 725-733.
11. Baccala L A, Sameshima K. Partial directed coherence: a new concept in neural structure determination. Biol Cybern, 2001, 84(6): 463-474.
12. Pascual-Marqui R D, Biscay R J, Bosch-Bayard J, et al. Assessing direct paths of intracortical causal information flow of oscillatory activity with the isolated effective coherence (iCoh). Front Hum Neurosci, 2014, 8: 448.
13. 倪召兵, 李颖, 赵营鸽, 等. 基于脑功能网络的混合情绪因素对错误记忆影响的研究. 生物医学工程学杂志, 2021, 38(5): 828-837.
14. Sporns O, Zwi J D. The small world of the cerebral cortex. Neuroinformatics, 2004, 2(2): 145-162.
15. 左锐, 张旭, 魏婧, 等. 基于脑网络信息传递特性的癫痫病灶区定位. 北京生物医学工程, 2018, 37(4): 331-336.
16. Hao S, Subramanian S, Jordan A, et al. Computing network-based features from intracranial EEG time series data: application to seizure focus localization// 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago: IEEE, 2014: 5812-5815.
17. 李尊钰, 袁冠前, 黄平, 等. 基于立体定向脑电图的颞叶致痫网络独立有效相干分析. 生物医学工程学杂志, 2019, 36(4): 541-547.
18. Li C S, Sohrabpour A, Jiang H T, et al. High-frequency hubs of the ictal cross-frequency coupling network predict surgical outcome in epilepsy patients. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1290-1299.
19. 吴雪瑞, 侯锦, 徐婷, 等. 癫痫脑电网络应用及研究进展. 现代医学, 2020, 48(11): 1470-1474.
20. 赵国光. 癫痫脑网络外科精准治疗的探索. 中华医学杂志, 2021, 101(41): 3361-3364.
21. Li C S, Jacobs D, Hilton T, et al. Epileptogenic source imaging using cross-frequency coupled signals from scalp EEG. IEEE Trans Biomed Eng, 2016, 63(12): 2607-2618.
22. Friston K J, Frith C D, Frackowiak R S J. Time-dependent changes in effective connectivity measured with PET. Hum Brain Mapp, 1993, 1: 69-79.
23. Wilke C, van Drongelen W, Kohrman M, et al. Neocortical seizure foci localization by means of a directed transfer function method. Epilepsia, 2010, 51(4): 564-572.
24. Wilke C, Worrell G A, He Bin. Analysis of epileptogenic network properties during ictal activity//2009 31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis: IEEE, 2009: 2220-2223.
25. Ren Y, Cong F Y, Ristaniemi T, et al. Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy. J Neurol, 2019, 266(4): 844-859.
26. 闫铮, 高小榕, 应俊. 基于认知功能连接的信息流增益计算方法及应用. 电子与信息学报, 2014, 36(11): 2756-2761.
27. Benjamin O, Fitzgerald T H, Ashwin P, et al. A phenomenological model of seizure initiation suggests network structure may explain seizure frequency in idiopathic generalised epilepsy. J Math Neurosci, 2012, 2(1): 1.
28. Sinha N, Dauwels J, Kaiser M, et al. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain, 2017, 140(2): 319-332.
29. Olmi S, Petkoski S, Guye M, et al. Controlling seizure propagation in large-scale brain networks. PLoS Comput Biol, 2019, 15(2): e1006805.
30. Zweiphenning W J E M, Keijzer H M, van Diessen E, et al. Increased gamma and decreased fast ripple connections of epileptic tissue: A high-frequency directed network approach. Epilepsia, 2019, 60(9): 1908-1920.
31. 闫秀鹏, 王海祥, 周文静, 等. 基于高频颅内脑电的致痫区定位和网络分析. 北京生物医学工程, 2017, 36(3): 229-236.
32. Nissen I A, Millan A P, Stam C J, et al. Optimization of epilepsy surgery through virtual resections on individual structural brain networks. Sci Rep, 2021, 11(1): 19025.
33. 潘多涛, 史洪岩, 黄明忠, 等. 流行病防治过程SIR模型的稳定性分析. 生物医学工程学杂志, 2015, 32(5): 1013-1018.

Journal of Biomedical Engineering

Localization of epileptogenic zone based on reconstruction of dynamical epileptic network and virtual resection

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