Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic–clonic seizure. Issue 2 (11th October 2016)

• Record Type: Journal Article
• Authors: Liu, Feng; Wang, Yifeng; Li, Meiling; Wang, Wenqin; Li, Rong; Zhang, Zhiqiang; Lu, Guangming; Chen, Huafu
• Is Part Of: Human brain mapping. Volume 38:Issue 2(2017)
• Page Start: 957
• Page End: 973
• Publication Date: 2016-10-11
• Subjects: IGE‐GTCS -- resting‐state fMRI -- dynamic functional network connectivity -- clustering and state analyses -- default mode network; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23430
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: 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, k ‐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% ( P < 0.001). Taken together, this study not only provided novel insights into the pathophysiological mechanisms of IGE‐GTCS but also suggested that the dynamic FNC analysis was a promising avenue to deepen our understanding of this disease. Hum Brain Mapp 38:957–973, 2017 . ©2016 Wiley Periodicals, Inc.