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利用有向网络分析研究致痫灶的连通性和中心性特征。

Connectivity and Centrality Characteristics of the Epileptogenic Focus Using Directed Network Analysis.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2019 Jan;27(1):22-30. doi: 10.1109/TNSRE.2018.2886211. Epub 2018 Dec 11.

Abstract

Accurate epileptogenic focus localization is required prior to surgical resection of brain tissue for the treatment of patients with antiepileptic drug-resistant (intractable) epilepsy. This clinical need is only partially fulfilled through a subjective, and at times inconclusive, the evaluation of the recorded electroencephalogram (EEG) at seizures' onset (the so-called gold standard for focus localization in epilepsy). We herein present a novel method of multivariate analysis of the EEG that appears to be very promising for an objective and robust localization of the epileptogenic focus at seizures' onset. Using the measure of generalized partial directed coherence, combined with surrogate data analysis, we first estimated from multichannel intracranial EEG the statistically significant causal interactions between brain regions at the onset of 92 clinical seizures from nine patients with temporal lobe intractable epilepsy. From the networks that were formed based on the thus derived interactions, a set of centrality metrics was estimated per network node (brain site). Brain sites located anatomically within the epileptogenic focus were shown to be associated with greater inward centrality values than non-focal brain regions at high frequencies ( γ band), and particular inward centrality metrics accurately localized the focus in all nine patients. In addition to focus localization from seizure (ictal) onset, the developed novel framework for analysis of EEG could be employed to identify the changes of the focal network over time, peri-ictally and interictally, and thus shed light onto the dynamics of ictogenesis, which could then have a significant impact on automated prediction and closed-loop control of seizures by neuromodulation.

摘要

准确的致痫灶定位是抗癫痫药物耐药(难治性)癫痫患者进行手术切除脑组织治疗的前提。这种临床需求仅通过对发作时记录的脑电图(EEG)的主观、有时不确定的评估部分得到满足(这是癫痫灶定位的所谓“金标准”)。我们在此提出了一种新的 EEG 多变量分析方法,该方法似乎非常有希望实现发作时致痫灶的客观和稳健定位。我们使用广义部分定向相干性度量,结合替代数据分析,首先从 9 名颞叶耐药性癫痫患者的 92 例临床发作的多通道颅内 EEG 中估计,在发作开始时大脑区域之间存在统计学显著的因果相互作用。根据由此衍生的相互作用形成的网络,为每个网络节点(大脑部位)估计了一组中心性度量。与非病灶区域相比,位于致痫灶内的脑区在高频(γ 波段)时表现出更大的内向中心性值,特定的内向中心性度量可以在所有 9 名患者中准确定位病灶。除了从发作(癫痫发作)开始定位病灶外,开发的 EEG 分析新框架还可用于识别焦点网络随时间的变化,包括发作期、发作间期和发作间,从而揭示癫痫发作的动力学,这可能对神经调节的癫痫自动预测和闭环控制产生重大影响。

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