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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Abstract Full text Figures/Tables Video References Cited by

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.	梁賽蘭, 王多琎. 動態因果模型在腦網絡研究中的應用進展. 中國生物醫學工程學報, 2022, 41(1): 86-99.
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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.	薛開慶, 羅程, 田銀, 等. 基于彌散張量成像的兒童失神癲癇認知控制網絡的結構連接研究. 中國生物醫學工程學報, 2013, 32(4): 426-432.
8.	李素姣, 劉蘇, 藍賀, 等. 腦肌電信號同步耦合分析方法研究進展. 生物醫學工程學雜志, 2019, 36(2): 334-337, 342.
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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, 李穎, 趙營鴿, 等. 基于腦功能網絡的混合情緒因素對錯誤記憶影響的研究. 生物醫學工程學雜志, 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.	左銳, 張旭, 魏婧, 等. 基于腦網絡信息傳遞特性的癲癇病灶區定位. 北京生物醫學工程, 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.	李尊鈺, 袁冠前, 黃平, 等. 基于立體定向腦電圖的顳葉致癇網絡獨立有效相干分析. 生物醫學工程學雜志, 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.	吳雪瑞, 侯錦, 徐婷, 等. 癲癇腦電網絡應用及研究進展. 現代醫學, 2020, 48(11): 1470-1474.
20.	趙國光. 癲癇腦網絡外科精準治療的探索. 中華醫學雜志, 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.	閆錚, 高小榕, 應俊. 基于認知功能連接的信息流增益計算方法及應用. 電子與信息學報, 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.	閆秀鵬, 王海祥, 周文靜, 等. 基于高頻顱內腦電的致癇區定位和網絡分析. 北京生物醫學工程, 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.	潘多濤, 史洪巖, 黃明忠, 等. 流行病防治過程SIR模型的穩定性分析. 生物醫學工程學雜志, 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. 江錄偉, 錢若兵, 傅先明, 等. 難治性癲癇患者腦網絡介數及其異常腦區間功能連接變化研究. 國際神經病學神經外科學雜志, 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. 梁賽蘭, 王多琎. 動態因果模型在腦網絡研究中的應用進展. 中國生物醫學工程學報, 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. 薛開慶, 羅程, 田銀, 等. 基于彌散張量成像的兒童失神癲癇認知控制網絡的結構連接研究. 中國生物醫學工程學報, 2013, 32(4): 426-432.
8. 李素姣, 劉蘇, 藍賀, 等. 腦肌電信號同步耦合分析方法研究進展. 生物醫學工程學雜志, 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, 李穎, 趙營鴿, 等. 基于腦功能網絡的混合情緒因素對錯誤記憶影響的研究. 生物醫學工程學雜志, 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. 左銳, 張旭, 魏婧, 等. 基于腦網絡信息傳遞特性的癲癇病灶區定位. 北京生物醫學工程, 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. 李尊鈺, 袁冠前, 黃平, 等. 基于立體定向腦電圖的顳葉致癇網絡獨立有效相干分析. 生物醫學工程學雜志, 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. 吳雪瑞, 侯錦, 徐婷, 等. 癲癇腦電網絡應用及研究進展. 現代醫學, 2020, 48(11): 1470-1474.
20. 趙國光. 癲癇腦網絡外科精準治療的探索. 中華醫學雜志, 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. 閆錚, 高小榕, 應俊. 基于認知功能連接的信息流增益計算方法及應用. 電子與信息學報, 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. 閆秀鵬, 王海祥, 周文靜, 等. 基于高頻顱內腦電的致癇區定位和網絡分析. 北京生物醫學工程, 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. 潘多濤, 史洪巖, 黃明忠, 等. 流行病防治過程SIR模型的穩定性分析. 生物醫學工程學雜志, 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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