Ultradian and circadian modulation of dream recall: EEG correlates and age effects

Chellappa, Sarah Laxhmi; Cajochen, Christian

doi:10.1016/j.ijpsycho.2013.03.006

Cited by 15 publications

(11 citation statements)

References 59 publications

(86 reference statements)

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“…Many studies did attempt to link dream recall or dream content attributes with specific neurophysiological indicators, most commonly with electroencephalograpy (EEG) measures, but such studies provide mixed results at best. For example, EEG correlates of remembering dreaming after REM sleep awakenings vary widely, from power reductions in alpha (Bertolo et al, 2003;Esposito et al, 2004;Chellappa and Cajochen, 2013), delta (Esposito et al, 2004), frontal beta (Chellappa et al, 2011), and 4-to 14-Hz activity (Lehmann et al, 1981), to power increases in 40-Hz (Llinas and Ribary, 1993), occipital beta (Chellappa et al, 2011), and theta (Marzano et al, 2011;Scarpelli et al, 2015). Studies attempting to uncover EEG signatures for specific dream qualities were quite uncommon (e.g.…”

Section: Limits Of Traditional Rem Sleep Dream Studiesmentioning

confidence: 99%

Microdream Neurophenomenology

Nielsen¹

2018

Oxford Handbooks Online

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The fleeting dream images of sleep onset afford a rare glimpse at how experience is transformed from the perceptually grounded consciousness of wakefulness to the hallucinatory simulations of dreaming. These images, or microdreams, are briefer, simpler, and more accessible to phenomenological scrutiny than are the long REM dreams traditionally recorded in the sleep lab. This chapter shows that a focus on microdream phenomenology has thus far contributed to (1) developing a classification system for dreaming’s core phenomenology (Windt`s oneiragogic spectrum), (2) establishing a structure for assessing dreaming’s multiple memory inputs (multi-temporal memory sources), (3) furthering Silberer’s project for sleep onset imagery by uncovering two new types of imagery (autosensory imagery, exosensory imagery), and (4) providing a larger framework for explaining some microdreaming processes (multisensory integration approach). A continued focus on microdream neurophenomenology may help resolve outstanding questions about dreaming’s core features, neurophysiological correlates, and memory sources.

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Section: Limits Of Traditional Rem Sleep Dream Studiesmentioning

confidence: 99%

Microdream Neurophenomenology

Nielsen¹

2018

Oxford Handbooks Online

View full text Add to dashboard Cite

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“…Many studies did attempt to link dream recall or dream content attributes with specific neurophysiological indicators, most commonly with electroencephalograpy (EEG) measures, but such studies provide mixed results at best. For example, EEG correlates of remembering dreaming after REM sleep awakenings vary widely, from power reductions in alpha ( Bertolo et al , 2003 ; Esposito et al , 2004 ; Chellappa and Cajochen, 2013 ), delta ( Esposito et al , 2004 ), frontal beta ( Chellappa et al , 2011 ), and 4- to 14-Hz activity ( Lehmann et al , 1981 ), to power increases in 40-Hz ( Llinas and Ribary, 1993 ), occipital beta ( Chellappa et al , 2011 ), and theta ( Marzano et al , 2011 ; Scarpelli et al , 2015 ). Studies attempting to uncover EEG signatures for specific dream qualities were quite uncommon (e.g.…”

Section: Limits Of Traditional Rem Sleep Dream Studiesmentioning

confidence: 99%

Microdream neurophenomenology

Nielsen

2017

Neuroscience of Consciousness

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Nightly transitions into sleep are usually uneventful and transpire in the blink of an eye. But in the laboratory these transitions afford a unique view of how experience is transformed from the perceptually grounded consciousness of wakefulness to the hallucinatory simulations of dreaming. The present review considers imagery in the sleep-onset transition—“microdreams” in particular—as an alternative object of study to dreaming as traditionally studied in the sleep lab. A focus on microdream phenomenology has thus far proven fruitful in preliminary efforts to (i) develop a classification for dreaming’s core phenomenology (the “oneiragogic spectrum”), (ii) establish a structure for assessing dreaming’s multiple memory inputs (“multi-temporal memory sources”), (iii) further Silberer’s project for classifying sleep-onset images in relation to waking cognition by revealing two new imagery types (“autosensory imagery,” “exosensory imagery”), and (iv) embed a potential understanding of microdreaming processes in a larger explanatory framework (“multisensory integration approach”). Such efforts may help resolve outstanding questions about dream neurophysiology and dreaming’s role in memory consolidation during sleep but may also advance discovery in the neuroscience of consciousness more broadly.

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“…For instance, after awakenings from NREM sleep, two studies found that a successful dream recall was associated with a decreased alpha power (8–12 Hz) in the sleep EEG preceding awakening (Esposito et al, 2004; Marzano et al, 2011), while another study found that successful dream recall was associated with increased alpha power (Takeuchi et al, 2003). The variability of these results (for a review see Chellappa and Cajochen, 2013) may be due to the difficulty in knowing for sure whether or not the reported dream occurred in the few minutes preceding awakening. Studies using such an approach may thus have been biased by dream reports resulting from dreams produced outside the few minutes of sleep investigated.…”

Section: Introductionmentioning

confidence: 99%

Alpha reactivity to first names differs in subjects with high and low dream recall frequency

Ruby¹,

Blochet²,

Eichenlaub³

et al. 2013

Front. Psychol.

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Studies in cognitive psychology showed that personality (openness to experience, thin boundaries, absorption), creativity, nocturnal awakenings, and attitude toward dreams are significantly related to dream recall frequency (DRF). These results suggest the possibility of neurophysiological trait differences between subjects with high and low DRF. To test this hypothesis we compared sleep characteristics and alpha reactivity to sounds in subjects with high and low DRF using polysomnographic recordings and electroencephalography (EEG). We acquired EEG from 21 channels in 36 healthy subjects while they were presented with a passive auditory oddball paradigm (frequent standard tones, rare deviant tones and very rare first names) during wakefulness and sleep (intensity, 50 dB above the subject's hearing level). Subjects were selected as High-recallers (HR, DRF = 4.42 ± 0.25 SEM, dream recalls per week) and Low-recallers (LR, DRF = 0.25 ± 0.02) using a questionnaire and an interview on sleep and dream habits. Despite the disturbing setup, the subjects' quality of sleep was generally preserved. First names induced a more sustained decrease in alpha activity in HR than in LR at Pz (1000–1200 ms) during wakefulness, but no group difference was found in REM sleep. The current dominant hypothesis proposes that alpha rhythms would be involved in the active inhibition of the brain regions not involved in the ongoing brain operation. According to this hypothesis, a more sustained alpha decrease in HR would reflect a longer release of inhibition, suggesting a deeper processing of complex sounds than in LR during wakefulness. A possibility to explain the absence of group difference during sleep is that increase in alpha power in HR may have resulted in awakenings. Our results support this hypothesis since HR experienced more intra sleep wakefulness than LR (30 ± 4 vs. 14 ± 4 min). As a whole our results support the hypothesis of neurophysiological trait differences in high and low-recallers.

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Ultradian and circadian modulation of dream recall: EEG correlates and age effects

Cited by 15 publications

References 59 publications

Microdream Neurophenomenology

Microdream Neurophenomenology

Microdream neurophenomenology

Alpha reactivity to first names differs in subjects with high and low dream recall frequency

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