{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T10:30:47Z","timestamp":1789641047533,"version":"4.0.0"},"reference-count":41,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T00:00:00Z","timestamp":1686268800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia Open"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Cognitive comorbidities are common in epilepsy; however, symptomatic treatment is currently the only available effective therapy. Sleep, cognition, and epilepsy are closely associated. Therefore, many studies on epilepsy and cognition have focused on sleep structures, such as sleep spindles, which are considered windows to understanding the sleeping brain. This study aimed to investigate the relationship between sleep spindles and the severity of cognitive impairment in adult epilepsy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Fifty\u2010seven adults with epilepsy underwent overnight sleep electroencephalogram recordings and cognitive testing. Slow (9\u201312\u2009Hz) and fast (12\u201315\u2009Hz) spindle characteristics during N2 sleep were calculated using a convolutional neural network\u2010based sleep staging system and automatic spindle detection algorithm. Repeated\u2010measures analysis of variance was used to analyze differences in fast and slow spindle densities among subgroups of patients based on cognitive impairment severity.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A significant between\u2010group effect was observed for both slow and fast spindle densities. Multiple comparisons showed that slow and fast spindle densities of the severe cognitive impairment subgroup were lower than those of the noncognitive impairment subgroup (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05). Simple\u2010effect analysis revealed differences in slow spindle density distributed among the EEG channels Fp1, Fp2, F3, C3, P4, O1, O2, F8, T4, T5, T6, Fz, and Cz (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05). Differences in fast spindle density were distributed among the channels Fp1, Fp2, F3, C3, O1, O2, F7, F8, T4, T5, T6, and Fz (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>Significant differences in topographical distribution of fast and slow spindle densities were observed at the scalp level among patients with different cognitive statuses. Compared with patients with no cognitive impairment, those with severe cognitive impairment had lower slow and fast spindle densities over multiple scalp regions during N2 sleep. This study provides a reference for objective assessment of cognitive dysfunction in epilepsy patients.<\/jats:p><\/jats:sec>","DOI":"10.1002\/epi4.12768","type":"journal-article","created":{"date-parts":[[2023,6,1]],"date-time":"2023-06-01T07:50:12Z","timestamp":1685605812000},"page":"980-990","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Differences in the topographical distribution of sleep spindles among adult epilepsy with cognitive impairment"],"prefix":"10.1002","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1814-8009","authenticated-orcid":false,"given":"Yajin","family":"Huang","sequence":"first","affiliation":[{"name":"The Second Clinical Medical College Lanzhou University  Lanzhou China"},{"name":"Department of Neurology, Epilepsy Center Lanzhou University Second Hospital, Lanzhou University  Lanzhou 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