Consciousness and CognitionNeurosciencePeer-reviewed journal article
EEG Microstates reveal distinct network dynamics in lucid and non-lucid REM sleep
Xinlin WangiD, Emma PetersiD, Johannes Strelen, Nathan Lockhart, Michael S. Franklin, Stephen P. LaBergeiD, Thomas W. KoenigiD, Martin DresleriD, Daniel ErlacheriD
Abstract
During lucid dreaming, the dreamer is aware that they are dreaming. In past years, numerous studies have explored the characteristics of lucid dreams, which link lucid dreams to vivid visual perception, positive emotions, and wake-like metacognition and executive functions. To determine brain network activity associated with the conscious experience during lucid dreaming, we analyzed 39 sleep recordings (non-lucid REM vs. lucid REM) with 32-channel polysomnography from 8 lucid dreamers, using EEG microstate analysis. We found that microstates A and G dominated during lucid REM sleep compared to non-lucid REM sleep, and microstates B, C, and D dominated during non-lucid REM sleep compared to lucid REM sleep. We explored the correlation of our microstate maps with previous findings based on topographical similarities of the microstate maps in our study. This suggests that in our study, microstate A might be associated with emotional processing, microstate B with visual processing, microstate C with salience network activity, microstate D with executive functions, and microstate G with the default mode network. Our results suggest that lucidity during REM sleep is associated with increased self-visualization, metacognition, and executive processing, along with decreased emotional processing and reduced default mode network activity. Additionally, we found the inverse relationship between the presence of microstates/networks associated with regions that serve specific functions and evidence for the function being used. This might indicate the inhibitory function of the EEG microstates during sleep. Our study provides novel insight into the distinct network dynamics in lucid and non-Lucid REM sleep.
- organism: human
- assay: electronic health records
- research focus: cell type annotation
- approach: statistical model
- Citations
- 1
- FWCI
- 4.12
- Field percentile
- top 13%
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