Abnormal brain functional network dynamics in sleep‐related hypermotor epilepsy

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 perfor...

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Vydáno v:CNS neuroscience & therapeutics Ročník 29; číslo 2; s. 659 - 668
Hlavní autoři: Wan, Xinyue, Zhang, Pengfei, Wang, Weina, Wu, Xintong, Tan, Qiaoyue, Su, Xiaorui, Zhang, Simin, Yang, Xibiao, Li, Shuang, Shao, Hanbing, Yue, Qiang, Gong, Qiyong
Médium: Journal Article
Jazyk:angličtina
Vydáno: England John Wiley & Sons, Inc 01.02.2023
Témata:
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
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Shrnutí: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.
Bibliografie:Xinyue Wan, Pengfei Zhang contributed equally to this work.
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ISSN:1755-5930
1755-5949
1755-5949
DOI:10.1111/cns.14048