基于难治性癫痫患者脑网络特征的立体脑电图引导射频热凝毁损术预后预测

doi:10.3969/j.issn.0258-8021.2023.06.002

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (3057 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The outcome of radiofrequency thermocoagulation (RFTC) in different patients with refractory epilepsy is usually largely different. This study aimed to investigate graph theory indexes of brain networks and establish a RFTC prognosis prediction model. Based on the stereo-electroencephalography (SEEG) signals of 45 patients with refractory epilepsy before RFTC, a time-variant multi-variate autoregressive model was constructed. Spectrum-weighted time-variant partial directed coherence was computed to build an effective connectivity network of the brain and graph theory indexes of the effective connectivity network were analyzed. According to the Engel classification at least three months after RFTC, the patients were divided into RFTC responder group (Engel I & II) and RFTC non-responder group (Engel III). The graph theory indexes were used for statistical analysis between the two groups and for establishing prognosis prediction by support vector machine (SVM). The normalized average clustering coefficient (P=0.000) and small-worldness (P=0.022) of the patients in RFTC responder group were significantly higher than those in RFTC non-responder group, and the normalized characteristic path length was significantly lower than those in the RFTC non-responder group (P=0.032) (The normalized average clustering coefficient, the small-worldness and the normalized characteristic path length of the patients in the RFTC responder group were 0.995 3±0.000 2, 0.853 0±0.006 2 and 1.168 8±0.008 5, respectively. The normalized average clustering coefficient, small-worldness and normalized characteristic path length of the patients in RFTC non-responder group were 0.994 0±0.000 2, 0.833 5±0.005 6 and 1.194 4±0.008 0, respectively. Based on the three indexes, the accuracy of the prognosis prediction reached 81.97% by SVM. The RFTC prognosis prediction model based on the graph theory indexes (normalized average clustering coefficient, normalized characteristic path length, and small-worldness) of the effective connectivity networks before RFTC could effectively predict the postoperative outcome.

Key words： epilepsy stereo-electroencephalography guided radiofrequency thermocoagulation effective connectivity network graph theory support vector machine

Received: 23 August 2022

PACS:

R318

Corresponding Authors: ^*E-mail:pmzhang@sjtu.edu.cn

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yang Shuyao
	Xie Yuhai
	Gong Yuchen
	Liu Qiangqiang
	Zhang Puming

Cite this article:

Yang Shuyao,Xie Yuhai,Gong Yuchen, et al. Stereo-Electroencephalography Guided Radiofrequency Thermocoagulation Prognosis Prediction Based on Brain Network Features of Patients with Refractory Epilepsy[J]. Chinese Journal of Biomedical Engineering, 2023, 42(6): 651-658.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2023.06.002 OR http://cjbme.csbme.org/EN/Y2023/V42/I6/651

[1] Falco-Walter JJ, Scheffer IE, Fisher RS. The new definition and classification of seizures and epilepsy [J]. Epilepsy Research, 2018, 139: 73-79.
[2] Fiest KM, Sauro KM, Wiebe S, et al. Prevalence and incidence of epilepsy: a systematic review and meta-analysis of international studies [J]. Neurology, 2017, 88(3): 296-303.
[3] 中国抗癫痫协会,中华医学会神经外科学分会神经生理学组, 中华医学会神经病学分会癫痫与脑电图学组,等. 癫痫外科术前评估中国专家共识(2022版) [J]. 中华神经外科杂志, 2022, 38(10): 973-979.
[4] Laxer KD, Trinka E, Hirsch LJ, et al. The consequences of refractory epilepsy and its treatment [J]. Epilepsy & Behavior, 2014, 37: 59-70.
[5] Johnson EL. Seizures and epilepsy [J]. Medical Clinics of North America, 2019, 103: 309-324.
[6] Catenoix H, Bourdillon P, Guenot M, et al. The combination of stereo-EEG and radiofrequency ablation [J]. Epilepsy Research, 2018, 142: 117-120.
[7] Panzica F, Varotto G, Rotondi F, et al. Identification of the epileptogenic zone from stereo-EEG signals: a connectivity-graph theory approach [J]. Frontiers in Neurology, 2013, 4(4): 175.
[8] Cossu M, Fuschillo D, Casaceli G, et al. Stereoelectroencephalogra-phy-guided radiofrequency thermocoagu-lation in the epileptogenic zone: a retrospective study on 89 cases [J]. Journal of Neurosurgery, 2015, 123: 1358-1367.
[9] Bourdillon P, Isnard J, Catenoix H, et al. Stereo-electroencephalogra-phy-guided radiofrequency thermocoagulation (SEEG-guided RF-TC) in drug-resistant focal epilepsy: Results from a 10-year experience [J]. Epilepsia, 2017, 58(1): 85-93.
[10] 关宇光,于思科,刘长青,等. 立体定向脑电图引导下射频热凝毁损术治疗药物难治性癫痫 [J]. 中国临床神经外科杂志, 2017, 22(6): 369-371.
[11] McCormick DA, Contreras D. On the cellular and network bases of epileptic seizures [J]. Annual Review of Physiology, 2001, 63: 815-846.
[12] 树海峰,匡永勤,余思逊,等. 基于图论对局灶性癫痫病灶热凝损毁的分析研究 [C]// 第七届CAAE脑电图与神经电生理大会会刊. 北京: 中国抗癫痫协会, 2020: 115-116.
[13] Mao Junwei, Ye Xiaolai, Li Yonghua, et al. Dynamic network connectivity analysis to identify epileptogenic zones based on stereo-electroencephalography [J]. Frontiers in Computational Neuroscience, 2016, 10: 113.
[14] Milde T, Leistritz L, Astolfi L, et al. A new kalman filter approach for the estimation of high-dimensional time-variant multivariate AR models and its application in analysis of laser-evoked brain potentials [J]. Neuroimage, 2010, 50(3): 960-969.
[15] Van Mierlo P, Carrette E, Hallez H, et al. Ictal-onset localization through connectivity analysis of intracranial EEG signals in patients with refractory epilepsy [J]. Epilepsia, 2013, 54(8): 1409-1418.
[16] Rubinov M, Sporns O. Complex network measures of brain connec-tivity: Uses and interpretations [J]. Neuroimage, 2010, 52(3): 1059-1069.
[17] Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: Simple building blocks of complex networks [J]. Science, 2002, 298(5594): 824-827.
[18] Ponten SC, Bartolomei F, Stam CJ. Small-world networks and epilepsy: Graph theoretical analysis of intracerebrally recorded mesial temporal lobe seizures [J]. Clinical Neurophysiology, 2007, 118(4): 918-927.
[19] Bartolomei F, Bettus G, Stam CJ, et al. Interictal network properties in mesial temporal lobe epilepsy: A graph theoretical study from intracerebral recording [J]. Clinical Neurophysiology, 2013, 124: 2345-2353.
[20] 凌淑莹. 耐药性颞叶癲痫患者的脑电网络研究 [D]. 南京:南京大学, 2019.
[21] Liao Wei, Ji Gongjun, Xu Qiang, et al. Functional connectome before and following temporal lobectomy in mesial temporal lobe epilepsy [J]. Scientific Reports, 2016, 6: 23153.
[22] Sinha N, Dauwels J, Kaiser M, et al. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling [J]. Brain, 2017, 140(2): 319-332.
[23] Zhou Fang, Han Ling, Li Chunsheng. Predicting surgical outcomes in epilepsy patients using directed transfer function and computational model [C]//The Fifth International Conference on Biological Information and Biomedical Engineering. Hangzhou:ACM, 2021, 34: 1-5.
[24] Catenoix H, Mauguiere F, Montavont A, et al. Seizures outcome after stereo-electroencephalography-guided thermocoagulations in malformations of cortical development poorly accessible to surgical resection [J]. Neurosurgery, 2015, 77(1): 9-15.
[25] Lagarde S, Buzori S, Trebuchon A, et al. The repertoire of seizure onset patterns in human focal epilepsies: determinants and prognostic values [J]. Epilepsia, 2019, 60(1): 85-95.
[26] Liang Xiulin, Pang Xiaomin, Liu Jinping, et al. Comparison of topological properties of functional brain networks with graph theory in temporal lobe epilepsy with different duration of disease [J]. Annals of Translational Medicine, 2020, 8(22): 1503.