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发作间期癫痫样放电改变了BECTS中与癫痫相关的脑网络结构。

Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS.

作者信息

Dai Xi-Jian, Yang Yang, Wang Yongjun

机构信息

School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, 518020, China.

Shenzhen Kangning Hospital, Shenzhen Mental Health Centre, 1080#, Cuizhu Rd, Luohu District, Shenzhen, 518003, China.

出版信息

Brain Imaging Behav. 2022 Apr;16(2):909-920. doi: 10.1007/s11682-021-00566-w. Epub 2021 Oct 22.

Abstract

To investigate directed information flow of epileptiform activity in benign epilepsy with centrotemporal spikes (BECTS) during ictal epileptiform discharges (IEDs) and non-IEDs periods. In this multi-center study, a total of 188 subjects, including 50 BECTS and 138 normal children's controls (NCs) from three different centers (Center 1: females/males, 38/55; mean age, 9.33 ± 2.6 years; Center 2: females/males,7/10; mean age, 8.59 ± 2.32 years; Center 3: females/males, 14/14; mean age, 13 ± 3.42 years) were recruited. The BECTS were classified into IEDs (females/males, 12/15; mean age, 8.15 ± 1.68 years) and non-IEDs (females/males, 10/13; mean age, 9.09 ± 1.98 years) subgroups depending on presence of central-temporal spikes from an EEG-fMRI examination. Three new methods, structural equation parametric modeling, dynamic causal modeling and granger causality density (GCD) were used to determine optimal network architectures for BECTS. Three multicentric NCs determined a reliable and consistent network architecture by structural equation parametric modeling method. Further analyses were used for IEDs and non-IEDs to determine the brain network architecture by structural equation parametric modeling, dynamic causal modeling and GCD, respectively. The brain network architecture of IEDs substate, non-IEDs substate and NCs are different. IEDs promoted the driving effect of the Rolandic areas with more output information flows, and increased the targeted effect of the top of pre-/post-central gyrus with more input information flows. The information flow arises from the Rolandic areas, and subsequently propagates to the top of pre-/post-central gyrus and thalamus. From non-IEDs status to IEDs status, the thalamus load may play an important role in the modulation and regulation of epileptiform activity. These findings shed new light on pathophysiological mechanism of directed localization of epileptiform activity in BECTS.

摘要

为研究中央颞区棘波良性癫痫(BECTS)在发作期癫痫样放电(IEDs)和非IEDs期间癫痫样活动的定向信息流。在这项多中心研究中,共招募了188名受试者,包括来自三个不同中心的50名BECTS患者和138名正常儿童对照(NCs)(中心1:女性/男性,38/55;平均年龄,9.33±2.6岁;中心2:女性/男性,7/10;平均年龄,8.59±2.32岁;中心3:女性/男性,14/14;平均年龄,13±3.42岁)。根据脑电图功能磁共振成像检查中中央颞区棘波的有无,将BECTS患者分为IEDs亚组(女性/男性,12/15;平均年龄,8.15±1.68岁)和非IEDs亚组(女性/男性,10/13;平均年龄,9.09±1.98岁)。使用三种新方法,即结构方程参数建模、动态因果建模和格兰杰因果密度(GCD)来确定BECTS的最佳网络架构。三个多中心的NCs通过结构方程参数建模方法确定了可靠且一致的网络架构。进一步分别对IEDs和非IEDs进行分析,以通过结构方程参数建模、动态因果建模和GCD确定脑网络架构。IEDs亚状态、非IEDs亚状态和NCs的脑网络架构不同。IEDs增强了罗兰区的驱动作用,有更多的输出信息流,并增加了中央前/后回顶部的靶向作用,有更多的输入信息流。信息流起源于罗兰区,随后传播到中央前/后回顶部和丘脑。从非IEDs状态到IEDs状态,丘脑负荷可能在癫痫样活动的调制和调节中起重要作用。这些发现为BECTS中癫痫样活动定向定位的病理生理机制提供了新的见解。

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