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Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure

机译:特发性全身性癫痫发作的特发性全身性癫痫中的动态功能网络连接

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摘要

Idiopathic generalized epilepsy (IGE) has been linked with disrupted intra‐network connectivity of multiple resting‐state networks (RSNs); however, whether impairment is present in inter‐network interactions between RSNs, remains largely unclear. Here, 50 patients with IGE characterized by generalized tonic–clonic seizures (GTCS) and 50 demographically matched healthy controls underwent resting‐state fMRI scans. A dynamic method was implemented to investigate functional network connectivity (FNC) in patients with IGE‐GTCS. Specifically, independent component analysis was first carried out to extract RSNs, and then sliding window correlation approach was employed to obtain dynamic FNC patterns. Finally, ‐mean clustering was performed to characterize six discrete functional connectivity states, and state analysis was conducted to explore the potential alterations in FNC and other dynamic metrics. Our results revealed that state‐specific FNC disruptions were observed in IGE‐GTCS and the majority of aberrant functional connectivity manifested itself in default mode network. In addition, temporal metrics derived from state transition vectors were altered in patients including the total number of transitions across states and the mean dwell time, the fraction of time spent and the number of subjects in specific FNC state. Furthermore, the alterations were significantly correlated with disease duration and seizure frequency. It was also found that dynamic FNC could distinguish patients with IGE‐GTCS from controls with an accuracy of 77.91% (  Hum Brain Mapp 38:957–973, 2017. ©
机译:特发性全身性癫痫(IGE)与多个静止状态网络(RSN)的网络内部连接中断有关。但是,在RSN之间的网络间交互中是否存在损害尚不清楚。在这里,对50例以全身性强直-阵挛性癫痫发作(GTCS)为特征的IGE患者和50例在人口统计学上相匹配的健康对照者进行了静息状态fMRI扫描。实施了一种动态方法来调查IGE-GTCS患者的功能网络连接(FNC)。具体来说,首先进行独立分量分析以提取RSN,然后使用滑动窗口相关方法获得动态FNC模式。最后,进行了均值聚类以表征六个离散的功能连接状态,并进行了状态分析以探索FNC和其他动态指标的潜在变化。我们的结果表明,在IGE-GTCS中观察到了特定于状态的FNC中断,并且大多数异常功能连接都表现在默认模式网络中。另外,从患者的状态转换向量得出的时间指标发生了变化,包括跨状态的转换总数和平均停留时间,花费的时间比例以及处于特定FNC状态的受试者数量。此外,这些变化与疾病持续时间和癫痫发作频率显着相关。还发现动态FNC可以将IGE‐GTCS患者与对照患者区分开,准确率为77.91%((Hum Brain Mapp 38:957–973,2017.©

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