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REM sleep favors the restructuring of problem-related semantic associations.

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  1. [1] § Results › SemNet restructuring is associated with EEG markers indexing a richer cognitive state during REM sleep ↔ Additionnal data/With sigma frequency bands/Script_EEG2.R, the whole file · a weak match · score 0.51 · frequency band, beta, N2, N3, slope, theta

Paper

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The authors' code

R · 90 lines · 3.7 KB · no license · 1 match

  1. #########################################################
  2. ## Analyses at the EEG level (with other frequency bands)
  3. ## NB : complexity measure remains the same as the one computed in Script_EEG
  5. ############
  6. ## Load data
  7. repertoire_travail <- "/Users/Theophile/Documents/A_transferer_DDE/CREAsleep/Final/Final/PNAS/Final/Final/Revision/Final/Script"
  9. # Weight_EEG2_NREM.csv Weight_EEG2_REM.csv Weight_EEG2_N1.csv Weight_EEG2_N2.csv Weight_EEG2_N3.csv
  10. # Efficiency_EEG2_NREM.csv Efficiency_EEG2_REM.csv Efficiency_EEG2_N1.csv Efficiency_EEG2_N2.csv Efficiency_EEG2_N3.csv
  11. # CC_EEG2_NREM.csv CC_EEG2_REM.csv CC_EEG2_N1.csv CC_EEG2_N2.csv CC_EEG2_N3.csv
  12. # EV_EEG2_NREM.csv EV_EEG2_REM.csv EV_EEG2_N1.csv EV_EEG2_N2.csv EV_EEG2_N3.csv
  13. dir <- "Weight_EEG2_REM.csv"
  14. Table <- read.table(paste(repertoire_travail, dir, sep = '/'),
  15. header = T,sep = ';', dec = '.', na.strings = c('', 'NA', '999999', 'NaN'), stringsAsFactors = T)
  17. Table$Subject <- as.factor(Table$Subject)
  18. Table$Group <- as.factor(Table$Group)
  20. ############
  21. ## Statistic
  22. # Rel_D Rel_T Rel_A Rel_B, Comp_T, Slope
  23. m1 <- aov(DeltaMetric_Z ~ Rel_T * SD_Z + Error(Subject),
  24. Table, na.action=na.omit)
  25. summary(m1)
  27. ## Plot figure
  28. # Heatmap
  29. Data <- read.table('/Users/Theophile/Documents/A_transferer_DDE/CREAsleep/Final/Final/PNAS/Final/Final/Revision/Final/Script/Heatmaps2.csv',
  30. header = T,sep = ';', dec = '.', na.strings = c('', 'NA', '999999', 'NaN'), stringsAsFactors = T)
  32. # REM vs NREM
  33. Data <- Data[-which(Data$Stage == 'N1'),]
  34. Data <- Data[-which(Data$Stage == 'N2'),]
  35. Data <- Data[-which(Data$Stage == 'N3'),]
  37. Data <- Data[which(Data$Metric == 'CC'),] # Weight, Efficiency, CC, EV
  39. Data$EEG <- fct_relevel(Data$EEG, c("Complexity", "Beta", "Alpha", "Theta", "Delta", "Slope"))
  40. Data$Stage <- fct_relevel(Data$Stage, c("NREM", "REM"))
  42. ggplot(Data, aes(x = Stage, y = EEG, fill = Value)) +
  43. geom_tile() +
  44. scale_fill_gradient2(low = "darkred", high = "darkgreen", mid = "white",
  45. midpoint = 0, limit = c(min(Data$Value),max(Data$Value)), space = "Lab",
  46. name="%") +
  47. theme(axis.text.x = element_text(angle = 45, vjust = 1,
  48. size = 12, hjust = 1)) +
  49. theme_bw() +
  50. theme(axis.line = element_line(colour = "black"),
  51. panel.grid.major = element_blank(),
  52. panel.grid.minor = element_blank(),
  53. panel.border = element_blank(),
  54. panel.background = element_blank()) +
  55. xlab("") + ylab("") +
  56. ggtitle("") +
  57. theme(axis.text.x = element_text(color = "black", size = 15)) +
  58. theme(axis.text.y = element_text(color = "black", size = 15))
  61. # N1 vs N2 vs N3
  62. Data <- Data[-which(Data$Stage == 'REM'),]
  63. Data <- Data[-which(Data$Stage == 'NREM'),]
  65. Data <- Data[which(Data$Metric == 'CC'),] # Weight, Efficiency, CC, EV
  67. Data$EEG <- fct_relevel(Data$EEG, c("Complexity", "Beta", "Alpha", "Theta", "Delta", "Slope"))
  68. Data$Stage <- fct_relevel(Data$Stage, c("N1", "N2", "N3"))
  70. ggplot(Data, aes(x = Stage, y = EEG, fill = Value)) +
  71. geom_tile() +
  72. scale_fill_gradient2(low = "darkred", high = "darkgreen", mid = "white",
  73. midpoint = 0, limit = c(min(Data$Value),max(Data$Value)), space = "Lab",
  74. name="%") +
  75. theme(axis.text.x = element_text(angle = 45, vjust = 1,
  76. size = 12, hjust = 1)) +
  77. theme_bw() +
  78. theme(axis.line = element_line(colour = "black"),
  79. panel.grid.major = element_blank(),
  80. panel.grid.minor = element_blank(),
  81. panel.border = element_blank(),
  82. panel.background = element_blank()) +
  83. xlab("") + ylab("") +
  84. ggtitle("") +
  85. theme(axis.text.x = element_text(color = "black", size = 15)) +
  86. theme(axis.text.y = element_text(color = "black", size = 15))

Script_EEG2.R, no license · at the source

Overview

Authors: Théophile Bieth1,2, Nicolas Decat3, Yoed N Kenett4, Marie Scuccimarra1, Marcela Ovando-Tellez1, Alizée Lopez-Persem1, Célia Lacaux1,3, Saoussen Ben Younes1, Arthur Le Coz1,3, Sarah Moreno-Rodriguez1, Isabelle Arnulf1,3, Emmanuelle Volle1, Delphine Oudiette1,3
ORCID iDs: Théophile Bieth, Yoed N Kenett, Marcela Ovando-Tellez, Alizée Lopez-Persem, Arthur Le Coz, Sarah Moreno-Rodriguez, Emmanuelle Volle, Delphine Oudiette
  1. Sorbonne University, Institut du Cerveau—Paris Brain Institute -ICM-, Inserm, CNRS, AP-HP Hôpital de la Pitié-Salpêtrière, Paris, France
  2. Neurology department, Pitié-Salpêtrière Hospital, AP-HP, Paris, France
  3. AP-HP, Pitié-Salpêtrière Hospital, Service des Pathologies du Sommeil, National Reference Center for Narcolepsy, Paris, France
  4. Faculty of Data and Decision Sciences, Technion—Israel Institute of Technology, Haifa, Israel
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Sorbonne Université (France); Assistance Publique – Hôpitaux de Paris (France); Pitié-Salpêtrière Hospital (France); Institut du Cerveau (France); Technion – Israel Institute of Technology (Israel)
Journal: Communications biology, volume 9, issue 1, article 1171
Dates: received 7 August 2025; accepted 15 May 2026; published online 3 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10354-1 · PMID 42236577 · PMCID PMC13542217 · OpenAlex W4412626006
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/2d8mf_v1
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Graphs, Complexity, Physiology & signal measures
Keywords: Problem solving, REM sleep
MeSH: Memory*, Problem Solving*, Semantics*, Sleep, REM*, Cognition, Electroencephalography, Female, Humans, Male, Wakefulness, Young Adult (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Agence Nationale de la Recherche (French National Research Agency) (ANR-19-CE37-001-01)
Citations: cited by 1 paper (Europe PMC); 122 references in the paper

Abstract

REM sleep is considered a ‘hyperassociative’ state enhancing the connections between weakly related concepts, thereby facilitating memory restructuring — a critical process proposed in creative problem-solving. We empirically explore this hypothesis by testing participants on a riddle before and after a 90-minute incubation period including rapid eye movement (REM) sleep, only non-REM sleep, or wakefulness. We quantified memory restructuring by computing differences in problem-related semantic memory network (SemNets) properties between pre- and post-incubation. For each sleep stage, we extracted fine-grained electroencephalogram (EEG) measures linked to cognitive richness. REM sleep, compared to wake, reshaped the dominant problem representation by combining semantically remote concepts in memory and reducing strong but solution-irrelevant associations of ideas. Such REM sleep-related restructuring was associated with EEG measures indexing a richer cognitive state. While REM drove memory restructuring, it alone did not boost problem-solving success, suggesting that more extensive restructuring or additional processes are necessary for creative breakthroughs.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF 2apgj

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (7)
Size: 75 files, 7 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
7 files
At the source: osf.io/2apgj/?view_only=c4c15039b6b04a0aafb8e731e996f632

Code availability

We used open software and toolboxes available online: semantic networks metrics were computed using the Brain Connectivity Toolbox (BCT, version 2019-03-03)95, EEG analysis were performed with the FOOOF and NICE toolbox, and statistical analyses were done using Rstudio (v 1.4.1717).

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 7 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

The study reported in this article was not formally preregistered. Experimental material is available in the Supplementary Information associated with this article, and in the method of our previous article 23 and its public persistent repository (https://osf.io/pk9nc/?view_only=4c4432106624412391bb4f1016a79ac5)121. The data sets generated and/or analyzed during the current study, and the scripts written is available in a public persistent repository (https://osf.io/2apgj/?view_only=c4c15039b6b04a0aafb8e731e996f632)122. Data sharing will be anonymized and will not include participants’ personnal information.

We used open software and toolboxes available online: semantic networks metrics were computed using the Brain Connectivity Toolbox (BCT, version 2019-03-03)95, EEG analysis were performed with the FOOOF and NICE toolbox, and statistical analyses were done using Rstudio (v 1.4.1717).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 2 keywords, 11 MeSH terms, 1 funder, 102 references.

Cite

This paper

Bieth, T., Decat, N., Kenett, Y. N., Scuccimarra, M., Ovando-Tellez, M., Lopez-Persem, A., Lacaux, C., Ben Younes, S., Le Coz, A., Moreno-Rodriguez, S., Arnulf, I., Volle, E., & Oudiette, D. (2026). REM sleep favors the restructuring of problem-related semantic associations. Communications biology, 9(1), 1171. https://doi.org/10.1038/s42003-026-10354-1

BibTeX

@article{bieth2026rem,
author = {Bieth, Théophile and Decat, Nicolas and Kenett, Yoed N and Scuccimarra, Marie and Ovando-Tellez, Marcela and Lopez-Persem, Alizée and Lacaux, Célia and Ben Younes, Saoussen and Le Coz, Arthur and Moreno-Rodriguez, Sarah and Arnulf, Isabelle and Volle, Emmanuelle and Oudiette, Delphine},
title = {{REM sleep favors the restructuring of problem-related semantic associations}},
journal = {Communications biology},
year = {2026},
month = jun,
volume = {9},
number = {1},
pages = {1171},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10354-1},
url = {https://doi.org/10.1038/s42003-026-10354-1},
pmid = {42236577},
pmcid = {PMC13542217}
}

RIS

TY - JOUR
AU - Bieth, Théophile
AU - Decat, Nicolas
AU - Kenett, Yoed N
AU - Scuccimarra, Marie
AU - Ovando-Tellez, Marcela
AU - Lopez-Persem, Alizée
AU - Lacaux, Célia
AU - Ben Younes, Saoussen
AU - Le Coz, Arthur
AU - Moreno-Rodriguez, Sarah
AU - Arnulf, Isabelle
AU - Volle, Emmanuelle
AU - Oudiette, Delphine
TI - REM sleep favors the restructuring of problem-related semantic associations
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/06/03
VL - 9
IS - 1
SP - 1171
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10354-1
UR - https://doi.org/10.1038/s42003-026-10354-1
LA - en
ER -

CSL-JSON

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