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从立体脑电图信号中识别癫痫发作起始区:一种基于连通性图论的方法。

Identification of the Epileptogenic Zone from Stereo-EEG Signals: A Connectivity-Graph Theory Approach.

作者信息

Panzica Ferruccio, Varotto Giulia, Rotondi Fabio, Spreafico Roberto, Franceschetti Silvana

机构信息

Neurophysiology and Diagnostic Epileptology Operative Unit, "C. Besta" Neurological Institute IRCCS Foundation , Milan , Italy.

出版信息

Front Neurol. 2013 Nov 6;4:175. doi: 10.3389/fneur.2013.00175.

Abstract

In the context of focal drug-resistant epilepsies, the surgical resection of the epileptogenic zone (EZ), the cortical region responsible for the onset, early seizures organization, and propagation, may be the only therapeutic option for reducing or suppressing seizures. The rather high rate of failure in epilepsy surgery of extra-temporal epilepsies highlights that the precise identification of the EZ, mandatory objective to achieve seizure freedom, is still an unsolved problem that requires more sophisticated methods of investigation. Despite the wide range of non-invasive investigations, intracranial stereo-EEG (SEEG) recordings still represent, in many patients, the gold standard for the EZ identification. In this contest, the EZ localization is still based on visual analysis of SEEG, inevitably affected by the drawback of subjectivity and strongly time-consuming. Over the last years, considerable efforts have been made to develop advanced signal analysis techniques able to improve the identification of the EZ. Particular attention has been paid to those methods aimed at quantifying and characterizing the interactions and causal relationships between neuronal populations, since is nowadays well assumed that epileptic phenomena are associated with abnormal changes in brain synchronization mechanisms, and initial evidence has shown the suitability of this approach for the EZ localization. The aim of this review is to provide an overview of the different EEG signal processing methods applied to study connectivity between distinct brain cortical regions, namely in focal epilepsies. In addition, with the aim of localizing the EZ, the approach based on graph theory will be described, since the study of the topological properties of the networks has strongly improved the study of brain connectivity mechanisms.

摘要

在局灶性耐药性癫痫的背景下,手术切除癫痫灶(EZ),即负责癫痫发作起始、早期发作组织和传播的皮质区域,可能是减少或抑制癫痫发作的唯一治疗选择。颞叶外癫痫的癫痫手术失败率相当高,这突出表明,精确识别癫痫灶作为实现无癫痫发作的强制性目标,仍然是一个未解决的问题,需要更复杂的研究方法。尽管有广泛的非侵入性检查,但在许多患者中,颅内立体脑电图(SEEG)记录仍然是识别癫痫灶的金标准。在这种情况下,癫痫灶的定位仍然基于对SEEG的视觉分析,不可避免地受到主观性缺点的影响,并且非常耗时。在过去几年中,人们付出了相当大的努力来开发先进的信号分析技术,以改善癫痫灶的识别。特别关注那些旨在量化和表征神经元群体之间的相互作用和因果关系的方法,因为如今人们普遍认为癫痫现象与脑同步机制的异常变化有关,并且初步证据表明这种方法适用于癫痫灶的定位。本综述的目的是概述应用于研究不同脑皮质区域之间连通性的不同脑电图信号处理方法,即在局灶性癫痫中。此外,为了定位癫痫灶,将描述基于图论的方法,因为对网络拓扑特性的研究极大地改进了对脑连通性机制的研究。

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Clin Neurophysiol. 2012 Jun;123(6):1067-87. doi: 10.1016/j.clinph.2012.01.011. Epub 2012 Feb 21.
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Graph analysis of epileptogenic networks in human partial epilepsy.人类部分性癫痫致痫网络的图分析。
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