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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Martin, Joshua M. [1, 2] ; Andriano, Danyal Wainstein [3] ; Mota, Natalia B. [1] ; Mota-Rolim, Sergio A. [1] ; Araujo, John Fontenele [4] ; Solms, Mark [3] ; Ribeiro, Sidarta [1]
Total Authors: 7
Affiliation:
[1] Univ Fed Rio Grande do Norte, Brain Inst, Natal, RN - Brazil
[2] Humboldt Univ, Berlin Sch Mind & Brain, Berlin - Germany
[3] Univ Cape Town, Cape Town - South Africa
[4] Univ Fed Rio Grande do Norte, Dept Physiol & Behav, Natal, RN - Brazil
Total Affiliations: 4
Document type: Journal article
Source: PLoS One; v. 15, n. 7 JUL 23 2020.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Oswaldo Baffa Filho
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC