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Journal of Sleep Research
Article . 2025 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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PubMed Central
Article . 2025
License: CC BY NC ND
Data sources: PubMed Central
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The Future of Parasomnias

Authors: Claudia Picard‐Deland; Matteo Cesari; Ambra Stefani; Jean‐Baptiste Maranci; Birgit Hogl; Isabelle Arnulf;

The Future of Parasomnias

Abstract

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.

Keywords

The Future of Sleep Medicine, Parasomnias, Polysomnography, Humans, Electroencephalography, REM Sleep Behavior Disorder, Actigraphy, Dreams

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
hybrid
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