OSCR

Theta oscillations tag episodic memories for sleep-dependent consolidation.

Code ↔ Paper

14 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 14 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Materials and methods › Data analysis › EEG. ↔ tools/fun_so_spindle_coupling.m, lines 1–40 · score 0.90 · 0.5–4 Hz, circStats, spindle coupling density, fun_slow_oscillations, coupling phase, detected spindle
  2. [2] § Materials and methods › Statistical analysis ↔ code/figure_2C.m, lines 106–146 · score 0.85 · activity predicts later, Discovery Rate, cross correlation, theta power, xcorr, lagged
  3. [3] § Materials and methods › Data analysis › EEG. ↔ tools/fun_slow_oscillations.m, lines 111–129 · score 0.82 · negative zero crossings, 0.5–1.25 Hz, fun slow oscillations, peak amplitude, 0.5 Hz, event
  4. [4] § Materials and methods › Data analysis › EEG. ↔ tools/fun_sleep_spindles.m, lines 114–157 · score 0.79 · complex Morlet wavelet, fun sleep spindles, peak frequency, FWHM, bandwidth, empirically
  5. [5] § Results › Theta oscillations support the tagging of memories for sleep-dependent consolidation ↔ code/figure_2C.m, lines 106–146 · score 0.77 · classification accuracy, early theta, cross correlation, predicted later, theta power, lags
  6. [6] § Materials and methods › Data analysis › EEG. ↔ tools/fun_otsu_criterion.m, the whole file · a weak match · score 0.71 · empirically determined, sleep spindles, variance, class, median, wavelet
  7. [7] § Materials and methods › Statistical analysis ↔ code/figure_2E.m, lines 88–126 · score 0.70 · RobustOpts, robust linear, memory retention, fitlm, fitting, permutation
  8. [8] § Materials and methods › Statistical analysis ↔ code/figure_2B.m, lines 148–213 · score 0.59 · ft_freqstatistics, FieldTrip, permutation, TFRs, cluster, wake
  9. [9] § Materials and methods › Data analysis › EEG. ↔ code/figure_2B.m, lines 105–142 · score 0.59 · Theta power, 1–4 s, FieldTrip, 3–8 Hz, edge, TFRs
  10. [10] § Materials and methods › Statistical analysis ↔ code/figure_2A.m, lines 96–115 · score 0.57 · ft_timelockstatistics, Cohen, FieldTrip, zero, classification
  11. [11] § Materials and methods › Data analysis › EEG. ↔ tools/fun_detect_artifacts.m, lines 278–402 · score 0.56 · Hjorth parameters, sleep stage, mobility, epochs, activity, channel
  12. [12] § Results › Theta oscillations at learning predict slow oscillation-spindle coupling activity during sleep ↔ code/figure_2E.m, lines 33–56 · score 0.52 · robust linear regression, memory retention, permutation, cluster, coupled
  13. [13] § Results › Theta oscillations at learning predict slow oscillation-spindle coupling activity during sleep ↔ code/figure_2D.m, lines 72–95 · score 0.52 · robust linear regression, theta power, permutation, cluster, coupled
  14. [14] § Results › Theta oscillations at learning predict slow oscillation-spindle coupling activity during sleep ↔ code/figure_2E.m, lines 33–56 · score 0.50 · robust linear regression, memory retention, model, coupled

Paper

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The authors' code

MATLAB · 166 lines · 4.4 KB · no license · 3 matches

  1. % Denis et al (2026)
  2. % Figure_2E: Correlation between SO-spindle coupling and memory retention
  3. %
  4. % Requires: FieldTrip
  6. clear
  8. rng(11) % Set seed to match manuscript results
  9. %% Load coupling data
  11. start_folder = 'R:\YACL\Projects\memopt\memopt1\analysis\osf';
  12. addpath(fullfile(start_folder, 'auxillary'))
  14. load(fullfile(start_folder, 'data', 'sosp_grouplevel_data.mat'))
  16. % Define channel neighbours (for forming clusters)
  18. cfg = [];
  19. cfg.layout = 'mo1_layout.lay';
  20. layout = ft_prepare_layout(cfg);
  22. cfg = [];
  23. cfg.method = 'triangulation';
  24. cfg.layout = layout;
  25. neighbours = ft_prepare_neighbours(cfg);
  27. chan_neighbours = psg_neighbours(neighbours, layout.label(1:14));
  29. %% Load memory data
  31. memory = readtable(fullfile(start_folder, 'data', 'memory_retention.csv'));
  33. %% Configure test
  35. % Using the approach of Mylonas et al (2020) doi: 10.1038/s41386-020-00833-2
  36. % for fitting complex models (robust linear regression) within a cluster-based
  37. % permutation framework
  39. % Config
  40. cfg = [];
  41. cfg.nperm = 1000;
  42. cfg.p = .05;
  43. cfg.pctile = 95;
  44. cfg.labels = {All_cp{1, 1}.chan};
  45. cfg.neighbours = chan_neighbours;
  47. % Set the DV: which coupling metric do you want to use
  48. cp_metric = 'couplingDensity1';
  50. % Extract coupling data
  51. for i = 1:length(All_cp)
  52. coupling(i, :) = [All_cp{i, 1}.(cp_metric)];
  53. end
  55. % Set the IV: Sleep or wake memory retention?
  56. retention = memory.sleep_retention;
  58. %% RLM at each electrode
  60. for chan_i = 1:length(cfg.labels)
  62. % Make a table
  63. test_table = array2table([coupling(:, chan_i) retention], 'VariableNames', {'Spindles' 'Memory'});
  65. % Fit robust linear model
  66. test_rlm = fitlm(test_table, 'Spindles ~ Memory', 'RobustOpts', 'on');
  68. % Extract statistic
  69. term_names = test_rlm.CoefficientNames{end};
  71. stat(chan_i, :) = test_rlm.Coefficients.tStat(end); % t-values
  72. pval(chan_i, :) = test_rlm.Coefficients.pValue(end); % p-values
  74. end
  76. % Find clusters (uncorrected)
  77. fprintf('Finding clusters...\n')
  79. test_results.stat = stat';
  80. test_results.pval = pval';
  82. test_clusters = findclusters(test_results, cfg);
  84. %% Permutation test
  86. n_sub = length(retention); % Number of participants
  88. %%% Permutation test
  89. fprintf('Shuffling data over %d permutations...\n', cfg.nperm)
  91. % Permute perm times
  93. for perm = 1:cfg.nperm
  95. l = fprintf('Permutation %d of %d\n', perm, cfg.nperm);
  97. % shuffle memory retention across participants
  98. retention_perm = retention(randperm(n_sub), :);
  100. for chan_i = 1:length(cfg.labels)
  102. % Make table to feed model
  103. perm_table = array2table([coupling(:, chan_i) retention_perm], 'VariableNames', {'Spindles' 'Memory'});
  105. % Fit the robust linear model
  106. perm_rlm = fitlm(perm_table, 'Spindles ~ Memory', 'RobustOpts', 'on');
  108. % Extract statistic
  109. term_names = perm_rlm.CoefficientNames{end};
  111. stat_perm(chan_i, :) = perm_rlm.Coefficients.tStat(end); % t-values
  112. pval_perm(chan_i, :) = perm_rlm.Coefficients.pValue(end); % p-values
  114. end
  116. % Find clusters
  117. perm_results.stat = stat_perm';
  118. perm_results.pval = pval_perm';
  120. perm_clusters = findclusters(perm_results, cfg);
  122. statmax_pos(perm, :) = [perm_clusters.pos.cond.statmax]';
  123. statmax_neg(perm, :) = [perm_clusters.neg.cond.statmax]';
  124. fprintf(repmat('\b', 1, l));
  126. end
  128. %% Find significant clusters
  130. % Determine significance of test results against permuted null distribution
  131. [cluster_stat, test_stat] = sigclusters(test_clusters, cfg, statmax_pos, statmax_neg, {'Spindles'});
  133. %% Plot the results
  135. if ~isempty(cluster_stat) & any([cluster_stat.pValue] < .05)
  137. % Find the significant electrodes
  138. cluster_idx = find([cluster_stat.pValue] < .05);
  139. sig_chans = ismember(cfg.labels, cluster_stat(cluster_idx).labels);
  141. % Average coupling activity across electrodes in cluster
  142. cp_cluster = mean(coupling(:, sig_chans), 2);
  144. figure;
  145. subplot(1, 2, 1)
  146. royDanScatter(cp_cluster, retention);
  148. % Topography of significant electrodes
  149. subplot(1, 2, 2)
  150. ft_plot_layout(layout, 'chanindx', sig_chans, 'box', 'no', 'label', 'no', ...
  151. 'point', 'yes', ...
  152. 'pointsymbol', '.', ...
  153. 'pointsize', 20);
  154. axis(gca, 'square')
  155. axis(gca, 'off')
  157. else
  159. warning('No significant clusters found!!')
  160. end

figure_2E.m, no license · at the source

Overview

Authors: Dan Denis1, Zhiyi Chen1,2, Manroop Kaur1, Benjamin Clayden1,3, Thomas Schreiner4, Scott A Cairney1
ORCID iDs: Dan Denis, Benjamin Clayden
  1. Department of Psychology, University of York, York, United Kingdom
  2. Department of Psychology and Neuroscience, Baylor University, Waco, Texas, United States of America
  3. Research Department of Early Life Imaging, Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
  4. Department of Psychology, Ludwig-Maximilians-Universität München, München, Germany
Institutions: University of York (United Kingdom); Baylor University (United States); King's College London (United Kingdom); Ludwig-Maximilians-Universität München (Germany)
Journal: PLoS biology, volume 24, issue 8, article e3003938
Dates: received 12 December 2025; accepted 24 July 2026; published online 27 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003938 · PMID 42658809 · PMCID PMC13521381 · OpenAlex W7162550969
Open access: gold, a free copy (OpenAlex)
Preprint: osf.io/tpwvb
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, fMRI & imaging, Physiology & signal measures
MeSH: Memory Consolidation*, Memory, Episodic*, Sleep*, Theta Rhythm*, Adult, Brain, Electroencephalography, Female, Humans, Learning, Male, Wakefulness, Young Adult (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (101169737)
Citations: not cited yet (Europe PMC); 76 references in the paper
Research resources: 60] in MATLAB RRID:SCR_001622, RRID:SCR_002881, RRID:SCR_004849, RRID:SCR_016651, RRID:SCR_022173

Abstract

How does the brain select which experiences to consolidate into long-term memory? Numerous neurobiological frameworks suggest that certain memories are “tagged” at learning for consolidation during later sleep. However, experimental evidence of such a tagging mechanism in the human brain is lacking. Employing multivariate classification of human electroencephalography data, we reliably decoded brain states for episodic memories that are tagged at learning for consolidation across sleep or wakefulness. The tagging of memories for consolidation across sleep (but not wakefulness) was linked to 3–8 Hz theta rhythms during learning. The magnitude of this tagging-related theta response predicted the coupling of slow oscillations to sleep spindles during post-learning sleep (an established neural correlate of sleep-dependent memory processing). In turn, slow oscillation-spindle coupling was associated with better memory performance at the post-sleep test. These findings provide new insights into the neural mechanisms through which our brains determine which information is retained for the future.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 14 matches between paragraphs and lines of code.

OSF tpwvb

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (11)
Size: 20 files, 11 scripts
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (5 files), Statistics and Machine Learning Toolbox (4 files), shadedErrorBar (2 files), EEGLAB (1 file), fdr_bh (Benjamini-Hochberg FDR) (1 file), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
11 files
At the source: doi.org/10.17605/OSF.IO/TPWVB

dandenis73/danalyzer

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 68f4e575e5565b6329c4cabbc43ba1fe67408a30, 11 January 2026
Languages: MATLAB (93)
Size: 112 files, 93 scripts
Software Heritage: not archived
Found in: the text, “EEG.”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (11 files), EEGLAB (9 files), Statistics and Machine Learning Toolbox (6 files), CircStat (2 files), Wavelet Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
95 files
At the source: github.com/dandenis73/danalyzer

The paper's code and data availability statement is in the Data section.

Tracing map

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What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 104 scripts, each with its path and the digest of its content;
  • 14 matches 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

No dataset and no data link were found in the paper.

Data Availability

All data and analysis code supporting the findings of this study are publicly available at the Open Science Framework (https://doi.org/10.17605/OSF.IO/TPWVB).

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, 6 authors, 13 MeSH terms, 1 funder, 73 references, 5 RRIDs.

Cite

This paper

Denis, D., Chen, Z., Kaur, M., Clayden, B., Schreiner, T., & Cairney, S. A. (2026). Theta oscillations tag episodic memories for sleep-dependent consolidation. PLoS biology, 24(8), e3003938. https://doi.org/10.1371/journal.pbio.3003938

BibTeX

@article{denis2026theta,
author = {Denis, Dan and Chen, Zhiyi and Kaur, Manroop and Clayden, Benjamin and Schreiner, Thomas and Cairney, Scott A},
title = {{Theta oscillations tag episodic memories for sleep-dependent consolidation}},
journal = {PLoS biology},
year = {2026},
month = aug,
volume = {24},
number = {8},
pages = {e3003938},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003938},
url = {https://doi.org/10.1371/journal.pbio.3003938},
pmid = {42658809},
pmcid = {PMC13521381}
}

RIS

TY - JOUR
AU - Denis, Dan
AU - Chen, Zhiyi
AU - Kaur, Manroop
AU - Clayden, Benjamin
AU - Schreiner, Thomas
AU - Cairney, Scott A
TI - Theta oscillations tag episodic memories for sleep-dependent consolidation
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/08/27
VL - 24
IS - 8
SP - e3003938
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003938
UR - https://doi.org/10.1371/journal.pbio.3003938
LA - en
ER -

CSL-JSON

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