Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised
Dynamic network properties of the interictal brain determine whether seizures appear focal or generalised
复制标题
DOI：
10.1038/s41598-020-63430-9
复制
发表时间：
2019-03
影响因子：
4.6
通讯作者：
W. Woldman;Helmut Schmidt;E. Abela;F. Chowdhury;A. Pawley;Sharon Jewell;M. Richardson;John R. Terry
中科院分区：
文献类型：
--
作者：
W. Woldman;Helmut Schmidt;E. Abela;F. Chowdhury;A. Pawley;Sharon Jewell;M. Richardson;John R. Terry
目前的解释性概念表明，癫痫发作是由大脑网络的持续动力学引起的。目前尚不清楚大脑网络特性如何决定局灶性或全身性癫痫发作，或者如何以临床有用的方式描述网络特性。了解网络特性将有助于阐明癫痫产生机制，并允许量化癫痫发作的局部或全身程度。功能性脑网络估计头皮脑电图片段无发作间期放电（68人癫痫，38名对照）。使用计算机模型模拟简化的大脑动力学。我们介绍：临界耦合（Cc），网络产生癫痫发作的能力;发作指数（OI），一个地区产生癫痫发作的趋势;参与指数（PI），一个地区参与癫痫发作的趋势。与对照组相比，两组患者的CC均较低。局灶性发作的OI和PI较全身性发作的OI和PI变化更大。在局灶性病例中，具有最高OI和PI的区域对应于发作侧。可以使用计算机模型估计来自头皮EEG的发作间期功能网络的特性，并用于预测癫痫发作的可能性和发作模式。这可能会提供潜在的，以加强诊断，通过量化的癫痫发作类型使用发作间期记录。
Current explanatory concepts suggest seizures emerge from ongoing dynamics of brain networks. It is unclear how brain network properties determine focal or generalised seizure onset, or how network properties can be described in a clinically-useful manner. Understanding network properties would cast light on seizure-generating mechanisms and allow to quantify to which extent a seizure is focal or generalised. Functional brain networks were estimated in segments of scalp-EEG without interictal discharges (68 people with epilepsy, 38 controls). Simplified brain dynamics were simulated using a computer model. We introduce: Critical Coupling (Cc), the ability of a network to generate seizures; Onset Index (OI), the tendency of a region to generate seizures; and Participation Index (PI), the tendency of a region to become involved in seizures. Ccwas lower in both patient groups compared with controls. OI and PI were more variable in focal-onset than generalised-onset cases. In focal cases, the regions with highest OI and PI corresponded to the side of seizure onset. Properties of interictal functional networks from scalp EEG can be estimated using a computer model and used to predict seizure likelihood and onset patterns. This may offer potential to enhance diagnosis through quantification of seizure type using inter-ictal recordings.