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We used a sliding\u2010window approach to construct a dynamic functional connectivity (dFC) network. The k\u2010means clustering method was performed to analyze specific FC states and related temporal properties. Finally, the connectivity strength between the components was analyzed using network\u2010based statistics (NBS) analysis. The correlations between the abovementioned measures and disease duration were analyzed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>After k\u2010means 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The patterns of dFC patterns may represent an adaptive and protective mode of the brain to deal with epileptic seizures.<\/jats:p><\/jats:sec>","DOI":"10.1111\/cns.14048","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T07:33:51Z","timestamp":1670916831000},"page":"659-668","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Abnormal brain functional network dynamics in sleep\u2010related hypermotor epilepsy"],"prefix":"10.1111","volume":"29","author":[{"given":"Xinyue","family":"Wan","sequence":"first","affiliation":[{"name":"Huaxi MR Research Center (HMRRC), Department of Radiology West China Hospital of Sichuan University  Chengdu China"},{"name":"Department of Radiology, Huashan Hospital Fudan University  Shanghai 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