Boosting sleep slow waves suppresses dreaming

Juan, Elsa; Arslan, Ceren; Regnath, Franziska; Talamini, Lucia M.

doi:10.1101/2023.03.10.532054

Cited by 2 publications

(3 citation statements)

References 20 publications

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“…CLAS has recently shown applications in enhancing theta wave (3-7 Hz) activity within REM sleep (Harrington et al, 2020), resulting in extended suppression of theta wave after a short boost and prolonged beta power enhancement. This possibility of CLAS to manipulate theta oscillatory activity during REM sleep, might extend recent advances in the use of CLAS during NREM sleep to modulate dream experiences (Juan et al, 2023), paving the way for new, physiology-focused nightmare therapies.…”

Section: Conclusion Limitations and Future Outlookmentioning

confidence: 90%

“…Altogether, while these initial studies exploring CLAS as a non-invasive therapeutic method have modest clinical outcomes, they highlight the usability of the technique in populations with disturbed sleep, and illuminate how CLAS as a form of therapy might present a new and safe way to improve deep sleep (Talamini & Juan, 2020). A recent study indeed demonstrated that using CLAS to boost posterior SO during NREM sleep decreased the likelihood of dreaming, thus opening the possibility to suppress nightmares in clinical populations (Juan et al, 2023).…”

Section: Clinical Applicationsmentioning

confidence: 99%

“…For instance, Cox et al (2014) extracted the instantaneous SO phase using a Hilbert-transform and fitted a sine wave to the most recent signal segment, while a phase-locked loop (PLL) approach was employed by Santostasi et al (2016) to continuously track SO phase alterations. More recently, Talamini & Van Poppel (2019) utilized an approach to model the full oscillatory dynamics of the signal through a sine fitting procedure on raw data (Pathak et al, 2021;Juan et al, 2023). This avoids signal distortions due to filtering and other data transformations and enhancing phase targeting performance throughout the 360 o phase range.…”

Section: Detection Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

Esfahani,

Farboud,

Ngo

et al. 2023

Neuroscience & Biobehavioral Reviews

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Section: Conclusion Limitations and Future Outlookmentioning

confidence: 90%

Section: Clinical Applicationsmentioning

confidence: 99%

Section: Detection Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

Esfahani,

Farboud,

Ngo

et al. 2023

Neuroscience & Biobehavioral Reviews

Self Cite

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We are the sensors of consciousness! A review and analysis on how awakenings during sleep influence dream recall

Stucky

2024

Preprint

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Purpose: Since the 1930s, researchers have awakened people from different stages of sleep to record what they have experienced. While some aspects, including asking whether participants had dreams or thoughts before awakening, largely remain the same, others, such as the method of awakening, vary greatly. In addition, it is often assumed that the influence of participant characteristics, such as personality traits, motivation, memory, and attention, is reduced by collecting experiences immediately after they occur, rather than through delayed morning recall. However, the extent to which these variables influence dream recall upon awakening has not yet been thoroughly investigated. Materials and methods: To explore possible contextual and individual influences, this review analyzed 69 awakening studies conducted between 2000 and 2024 and utilized the DREAM database. Differences between sleep stages were quantified and experiences analyzed using the categories 'with recall', 'without recall', and 'no report'. Results: Similar levels of null reports were found in NREM stage 2 and stage 3. Significant factors affecting dream recall included the method of awakening (lower recall with an alarm compared to calling the participant's name), the number of study days (reduced recall for multiple days) and the sleep environment (higher recall at home compared to the laboratory), along with participant characteristics beyond age, sex and study design. Recall rates from NREM sleep are particularly sensitive to the method of awakening and interindividual differences. Conclusion: Both the awakening procedure and participant characteristics influence the amount of reported sleep experiences, which can impact study outcomes, such as the identification of neural correlates of consciousness. Therefore, greater emphasis needs to be placed on how experiences are collected and on participant characteristics, such as openness to experience or familiarity with different states of consciousness.

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Boosting sleep slow waves suppresses dreaming

Cited by 2 publications

References 20 publications

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices

We are the sensors of consciousness! A review and analysis on how awakenings during sleep influence dream recall

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