796 Quantitative EEG Analysis in Angelman Syndrome. (3rd May 2021)

• Record Type: Journal Article
• Authors: Hosamane, Nishitha; Levin, Yuval; McNair, Taylor; Sidorov, Michael
• Is Part Of: Sleep. Volume 44(2021)Supplement 2
• Page Start: A310
• Page End: A310
• Publication Date: 2021-05-03
• Subjects: Sleep -- Physiological aspects -- Periodicals; Sleep disorders -- Periodicals; Sommeil -- Aspect physiologique -- Périodiques; Sommeil, Troubles du -- Périodiques; Sleep disorders; Sleep -- Physiological aspects; Sleep -- physiological aspects; Sleep Wake Disorders; Psychophysiology; Electronic journals; Periodicals; 616.8498
• DOI: 10.1093/sleep/zsab072.793
• Languages: English
• ISSNs: 0161-8105

Abstract

Abstract: Introduction: Angelman syndrome (AS) is a neurodevelopmental disorder resulting from decreased expression of the maternal copy of the imprinted UBE3A gene on chromosome 15. This disorder is characterized by intellectual disability, impaired speech and motor skills, and sleep abnormalities but currently lacks any treatment. However, mouse models have shown that un-silencing the dormant paternal copy of UBE3A has been an effective mechanism to restore the functionality of the UBE3A protein, thus clinical trials using this approach are on the near horizon. Developing biomarkers is essential for assessing responses to treatment when clinical trials begin, and quantitative EEG analysis has shown great promise as a biomarker for AS. Methods: Here, we sought to define EEG biomarkers directly linked to sleep impairments seen in up to 90% of individuals with AS (Trickett). We analyzed nine overnight sleep studies from patients with AS with age and sex matched Down syndrome and neurotypical controls. We specifically examined low-frequency delta rhythms and sleep spindles during NREM sleep. Results: We confirmed that low- delta rhythms are increased during overnight sleep in AS, and that this biomarker appears more reliable than possible changes in sleep spindles. Conclusion: Our results suggest that quantitative measurement of delta rhythms during sleep can be used as a potential biomarker for treatments in Angelman syndrome clinical trials. Support (if any):