The Future of Parasomnias.
Claudia Picard-Deland, Matteo Cesari, Ambra Stefani, Jean-Baptiste Maranci, Birgit Högl, Isabelle Arnulf
Journal of Sleep Research October 1, 2025 DOI: 10.1111/jsr.70090 (opens in new tab) via PubMed
Summary
AI-generated from the abstractParasomnias, including disorders of arousal and REM sleep behavior disorder, are increasingly diagnosed using home devices such as actigraphy, EEG headbands, and 2D infrared or 3D time-of-flight cameras with automatic analysis. Traditional video-polysomnographic criteria are becoming more accurate, and deep learning methods classify abnormal polysomnographic signals. Big data from clinical, cognitive, brain imaging, DNA, and polysomnography collections reveal factors associated with parasomnias and, for RBD, may predict individual risk of conversion to overt neurodegenerative disease. Dream engineering, targeted memory reactivation, image repetition therapy, and lucid dreaming help alleviate nightmares. RBD has united movement disorder and sleep neurologists; research into nightmares and sleep-wake dissociations has brought together sleep and consciousness scientists.
Study at a glance
| Characteristics | Review Peer reviewed |
|---|---|
| Keywords | Parasomnia Big data Computerised methods Home video Prodromal neurodegeneration |
| Key finding | Argues that new home diagnostic devices and big data approaches are improving the diagnosis and risk prediction of parasomnias, particularly RBD's link to neurodegeneration. |
Abstract
Parasomnias are abnormal behaviours or mental experiences during sleep or the sleep-wake transition. As disorders of arousal (DOA) or REM sleep behaviour disorder (RBD) can be difficult to capture in the sleep laboratory and may need to be diagnosed in large communities, new home diagnostic devices are being developed, including actigraphy, EEG headbands, as well as 2D infrared and 3D time of flight home cameras (often with automatic analysis). Traditional video-polysomnographic diagnostic criteria for RBD and DOA are becoming more accurate, and deep learning methods are beginning to accurately classify abnormal polysomnographic signals in these disorders. Big data from vast collections of clinical, cognitive, brain imaging, DNA and polysomnography data have provided new information on the factors that are associated with parasomnia and, in the case of RBD, may predict the individual risk of conversion to an overt neurodegenerative disease. Dream engineering, including targeted reactivation of memory during sleep, combined with image repetition therapy and lucid dreaming, is helping to alleviate nightmares in patients. On a political level, RBD has brought together specialists in abnormal movements and sleep neurologists, and research into nightmares and sleep-wake dissociations has brought together sleep and consciousness scientists.