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

Joshua M. Martin, Danyal Wainstein, Natalia B. Mota, Sergio A. Mota-Rolim, View ORCID ProfileJohn Fontenele Araújo, Mark Solms, View ORCID ProfileSidarta Ribeiro
doi: https://doi.org/10.1101/2020.01.28.922740
Joshua M. Martin
1Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
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Danyal Wainstein
2The University of Cape Town, Cape Town, South Africa
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Natalia B. Mota
1Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
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Sergio A. Mota-Rolim
1Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
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John Fontenele Araújo
3Department of Physiology and Behavior, Federal University of Rio Grande do Norte, Natal, Brazil
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  • ORCID record for John Fontenele Araújo
Mark Solms
2The University of Cape Town, Cape Town, South Africa
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Sidarta Ribeiro
1Brain Institute, Federal University of Rio Grande do Norte, Natal, Brazil
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  • ORCID record for Sidarta Ribeiro
  • For correspondence: sidartaribeiro@neuro.ufrn.br
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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 125 dream reports obtained from 19 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 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 28, 2020.
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Structural differences between REM and non-REM dream reports assessed by graph analysis
Joshua M. Martin, Danyal Wainstein, Natalia B. Mota, Sergio A. Mota-Rolim, John Fontenele Araújo, Mark Solms, Sidarta Ribeiro
bioRxiv 2020.01.28.922740; doi: https://doi.org/10.1101/2020.01.28.922740
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Structural differences between REM and non-REM dream reports assessed by graph analysis
Joshua M. Martin, Danyal Wainstein, Natalia B. Mota, Sergio A. Mota-Rolim, John Fontenele Araújo, Mark Solms, Sidarta Ribeiro
bioRxiv 2020.01.28.922740; doi: https://doi.org/10.1101/2020.01.28.922740

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