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.2020 Jul 23;15(7):e0228903.
doi: 10.1371/journal.pone.0228903. eCollection 2020.

Structural differences between REM and non-REM dream reports assessed by graph analysis

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Structural differences between REM and non-REM dream reports assessed by graph analysis

Joshua M Martin et al. PLoS One..

Abstract

Dream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 133 dream reports obtained from 20 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream report complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Word graph analysis applied to dream reports.
Dream report represented as a directed word graph. Nodes indicated in red, edges indicated as black arrows. There are two components in this graph: one with three nodes and the other with 22 nodes. LCC and LSC measures are derived from the larger component.
Fig 2
Fig 2. Illustration of the sliding window method.
This example uses a window length of 15 words and an overlap of 10 words. While graphs from the first two windows are shown here, the window is applied across the entire dream report, after which an overall average is calculated.
Fig 3
Fig 3. Illustration of random shuffling.
Word order from the dream report is randomly shuffled 1000 times. The abbreviations “mr” and “sdr” denote the respective mean (mrLCC, mrLSC) and standard deviation (sdrLCC, sdrLSC) scores calculated from this distribution of 1000 shuffled reports. An overall measure of random-like quality is then estimated using the average scores of LCC and LSC based upon this iteration.
Fig 4
Fig 4. Stacked bar plot showing prevalence of dream reports and type of mentation recalled.
See this image and copyright information in PMC

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