Abnormal brain functional network dynamics in sleep‐related hypermotor epilepsy

• Published in: CNS neuroscience & therapeutics Vol. 29; no. 2; pp. 659 - 668
• Main Authors: Wan, Xinyue, Zhang, Pengfei, Wang, Weina, Wu, Xintong, Tan, Qiaoyue, Su, Xiaorui, Zhang, Simin, Yang, Xibiao, Li, Shuang, Shao, Hanbing, Yue, Qiang, Gong, Qiyong
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.02.2023
• Subjects: Brain; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; China; Cluster analysis; Convulsions & seizures; dynamic functional network connectivity; Epilepsy; Epilepsy - diagnostic imaging; Ethnicity; Functional magnetic resonance imaging; Humans; independent component analysis; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; network‐based statistics; Neural networks; Neural Pathways - diagnostic imaging; Neuroimaging; resting‐state functional magnetic resonance imaging; Seizures; Sleep; sleep‐related hypermotor epilepsy; Statistical analysis; Temporal variations; China; resting-state functional magnetic resonance imaging; independent component analysis; sleep-related hypermotor epilepsy; network-based statistics; dynamic functional network connectivity
• ISSN: 1755-5930; 1755-5949; 1755-5949
• DOI: 10.1111/cns.14048

Aims This study aimed to use resting‐state functional magnetic resonance imaging (rs‐fMRI) to determine the temporal features of functional connectivity states and changes in connectivity strength in sleep‐related hypermotor epilepsy (SHE). Methods High‐resolution T1 and rs‐fMRI scanning were performed on all the subjects. We used a sliding‐window approach to construct a dynamic functional connectivity (dFC) network. The k‐means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network‐based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed. Results After k‐means clustering, the SHE patients mainly exhibited two dFC states. The frequency of state 1 was higher, which was characterized by stronger connections within the networks; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks. SHE patients had greater fractional time and a mean dwell time in state 2 and had a larger number of state transitions. The NBS results showed that SHE patients had increased connectivity strength between networks. None of the properties was correlated with illness duration among patients with SHE. Conclusion The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures. After k‐means clustering, the SHE patients mainly have two dFC states. The frequency of state 1 was higher, which is characterized by stronger connections within the network, including executive control, default mode, sensorimotor, and visual network; state 2 occurred at a relatively low frequency, characterized by stronger connections between networks, including sensorimotor, visual, and auditory networks. It turns out that SHE patients showed preference in state 2.