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基于网络的脑状态识别的动态功能连接分析及其在颞叶癫痫中的应用。

Dynamic Functional Connectivity Analysis Using Network-Based Brain State Identification, Application on Temporal Lobe Epilepsy.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2023 Jul;2023:1-4. doi: 10.1109/EMBC40787.2023.10339957.

Abstract

Epilepsy is a brain network disorder caused by discharges of interconnected groups of neurons and resulting brain dysfunction. The brain network can be characterized by intra- and inter-regional functional connectivity (FC). However, since the BOLD signal is inherently non-stationary, the FC is evidenced to be varying over time. Considering the dynamic characteristics of the functional network, we aimed to obtain dynamic brain states and their properties using network-based analyses for the comparison of healthy control and temporal lobe epilepsy (TLE) groups and also lateralization of TLE patients. We used dwelling time, transition time, and brain network connection in each state as the dynamic features for this purpose. Results showed a significant difference in dwelling time and transition time between the healthy control group and both left TLE and right TLE groups and also a significant difference in brain network connections between the left and right TLE groups.

摘要

癫痫是一种由相互连接的神经元群放电引起的脑网络紊乱,导致大脑功能障碍。脑网络可以通过区域内和区域间的功能连接(FC)来描述。然而,由于 BOLD 信号本质上是非平稳的,因此 FC 被证明随时间变化。考虑到功能网络的动态特征,我们旨在使用基于网络的分析来获得动态脑状态及其特性,以比较健康对照组和颞叶癫痫(TLE)组,以及 TLE 患者的侧化。为此,我们使用驻留时间、转换时间和每个状态中的脑网络连接作为动态特征。结果表明,健康对照组与左 TLE 组和右 TLE 组之间在驻留时间和转换时间上存在显著差异,左 TLE 组和右 TLE 组之间在脑网络连接上也存在显著差异。

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