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Deltas' and spindles' cross-area synchronization and ripple subtypes.

Code ↔ Paper

15 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 15 matches · 9 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and methods › Detection of spindles and delta waves ↔ nonrem_oscillations/Required_Functions/FindDeltaWavesRGS14.m, lines 1–47 · score 0.75 · FindDeltaWaves, sleep periods, 1–6 Hz, threshold, channel, Matlab
  2. [2] § Materials and methods › Time–frequency Granger causality ↔ nonrem_oscillations/Spectrograms&GrangerCausality/functions/createauto_timefreq.m, the whole file · a weak match · score 0.70 · ft_connectivityanalysis, ft_freqanalysis, Granger Causality, Hanning, Fourier, FieldTrip
  3. [3] § Materials and methods › Time–frequency Granger causality ↔ nonrem_oscillations/Spectrograms&GrangerCausality/functions/stats_high_granger.m, the whole file · a weak match · score 0.70 · randomized trials, frequency matrix, Granger Causality, pixel, transform, permutation
  4. [4] § Materials and methods › Dimensionality reduction of ripple features and ripple clustering › Number of peaks ↔ Adrian/c3split_sweep_analysis.m, the whole file · a weak match · score 0.64 · Akaike information criterion, Bayesian information criterion, AIC, BIC, clustered
  5. [5] § Materials and methods › Time–frequency spectrograms ↔ nonrem_oscillations/Spectrograms&GrangerCausality/spectrogram_automation.m, the whole file · a weak match · score 0.58 · notch filter, ft_preprocessing, spectrograms, FieldTrip, channels
  6. [6] § Materials and methods › Time–frequency spectrograms ↔ Pelin/spectrogram_automation.m, the whole file · a weak match · score 0.58 · notch filter, ft_preprocessing, spectrograms, FieldTrip, channels
  7. [7] § Materials and methods › Dimensionality reduction of ripple features and ripple clustering › Peak-to-peak distance ↔ nonrem_oscillations/Spectrograms&GrangerCausality/functions/delta_specs.m, the whole file · a weak match · score 0.57 · peak distance, peak2peak, amplitude
  8. [8] § Materials and methods › Dimensionality reduction of ripple features and ripple clustering › Peak-to-peak distance ↔ Pelin/functions/delta_specs.m, the whole file · a weak match · score 0.57 · peak distance, peak2peak, amplitude
  9. [9] § Results › Hippocampal-cortical oscillation interactions during ripple types ↔ Pelin/revision2025/extract_values_from_figFiles_Vehicle.m, lines 1–44 · score 0.57 · 0–20 Hz, Extracted Granger, PFC HPC, cluster
  10. [10] § Materials and methods › Ripple detection ↔ spiking_activity/Required_Functions/Ripple_Analysis_All.m, the whole file · a weak match · score 0.55 · post trials, workspace, offset, presleep, split, treatment
  11. [11] § Materials and methods › Detection of oscillation sequences ↔ nonrem_oscillations/Required_Functions/FindDeltaWavesRGS14.m, lines 1–47 · score 0.55 · find delta waves, find ripples, duration, NonREM, sleep, peaks
  12. [12] § Materials and methods › Cortical activity during ripples ↔ spiking_activity/Required_Functions/Ripple_Analysis_All.m, the whole file · a weak match · score 0.53 · firing activity, vector, spike, window, neuron, timestamps
  13. [13] § Materials and methods › Dimensionality reduction of ripple features and ripple clustering › Number of peaks ↔ Adrian/CBD_Main_script.m, lines 26–57 · score 0.52 · CovarianceType, fitgmdist, diagonal, GMM, vehicle, clustered
  14. [14] § Materials and methods › Ripple detection ↔ nonrem_oscillations/Required_Functions/swr_check_thr.m, lines 1–97 · score 0.51 · 100–300 Hz, consecutive, epochs, detection, thresholding, bandpass
  15. [15] § Materials and methods › Slow oscillation phase ↔ nonrem_oscillations/Spindle_analysis_Kopal.m, lines 93–202 · score 0.51 · 0.5–4 Hz, angle, phase, 360 deg, Hilbert, bandpass

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

MATLAB · 156 lines · 5.8 KB · GPL-3.0 · 2 matches

  1. function delta = FindDeltaWavesRGS14(filtered,k)
  3. %FindDeltaWaves - Find cortical delta waves (1-6Hz waves).
  4. %
  5. % USAGE
  6. %
  7. % delta = FindDeltaWaves(filtered,<options>)
  8. %
  9. % filtered delta-band filtered LFP <a href="matlab:help samples">samples</a> (one channel). This must
  10. % be restricted to slow wave sleep periods for the algorithm
  11. % to perform best.
  12. % <options> optional list of property-value pairs (see table below)
  13. %
  14. % =========================================================================
  15. % Properties Values
  16. % -------------------------------------------------------------------------
  17. % 'thresholds' thresholds for z-scored minimum peak and trough amplitudes
  18. % (default = [1 2 0 1.5], see NOTE below)
  19. % 'durations' min and max wave durations in ms (default = [150 500])
  20. % =========================================================================
  21. %
  22. % OUTPUT
  23. %
  24. % delta for each delta wave, times and z-scored amplitudes of
  25. % the beginning, peak and trough of the wave, in a Nx6
  26. % matrix [start_t peak_t end_t start_z peak_z end_z]
  27. %
  28. % NOTE
  29. %
  30. % To be selected, candidate delta waves must fulfill one of two amplitude
  31. % conditions. The peak and trough must both exceed a threshold, but one can
  32. % compensate for the other, i.e. a large peak requires a smaller trough and
  33. % vice versa.
  34. %
  35. % More precisely, there are two thresholds for the peak, p and P (p<P), and
  36. % two for the trough, t and T (t<T). The amplitude must fulfill one of the
  37. % following conditions:
  38. %
  39. % peak > p and trough > T
  40. % or peak > P and trough > t
  41. %
  42. % Thresholds are given in SDs, and the above conditions relate to absolute
  43. % values.
  44. %
  45. % SEE
  46. %
  47. % See also FilterLFP, FindRipples.
  49. % Copyright (C) 2012-2017 Michaël Zugaro, 2012-2015 Nicolas Maingret,
  50. %
  51. % This program is free software; you can redistribute it and/or modify
  52. % it under the terms of the GNU General Public License as published by
  53. % the Free Software Foundation; either version 3 of the License, or
  54. % (at your option) any later version.
  56. % Default values
  57. if ~isempty(find(k==[2,3,6],1))
  58. highPeak = 2; % Threshold for filtered signal (number of SDs)
  59. lowPeak = 1;
  60. elseif ~isempty(find(k==[1,4,8],1))
  61. highPeak = 2.15;
  62. lowPeak = 1.15;
  63. elseif k==5
  64. highPeak = 1.75;
  65. lowPeak = 0.75;
  66. else
  67. highPeak = 1.8;
  68. lowPeak = 0.8;
  69. end
  70. if k==5
  71. highTrough = 1.3;
  72. else
  73. highTrough = 1.5;
  74. end
  75. lowTrough = 0;
  76. minDuration = 150; % min time between successive zero crossings (in ms)
  77. maxDuration = 450; % max time between successive zero crossings (in ms)
  79. % % Check number of parameters
  80. % if nargin < 1 | mod(length(varargin),2) ~= 0,
  81. % error('Incorrect number of parameters (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).');
  82. % end
  83. %
  84. % % Check parameter sizes
  85. % if ~isdmatrix(filtered,'@2'),
  86. % error('Parameter ''filtered'' is not a Nx2 matrix (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).');
  87. % end
  88. %
  89. % % Parse parameter list
  90. % for i = 1:2:length(varargin),
  91. % if ~ischar(varargin{i}),
  92. % error(['Parameter ' num2str(i+2) ' is not a property (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).']);
  93. % end
  94. % switch(lower(varargin{i})),
  95. % case {'thresholds','amplitudes'},
  96. % thresholds = varargin{i+1};
  97. % if ~isdvector(thresholds,'#4'),
  98. % error('Incorrect value for property ''thresholds'' (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).');
  99. % end
  100. % lowPeak = thresholds(1);
  101. % highPeak = thresholds(2);
  102. % lowTrough = thresholds(3);
  103. % highTrough = thresholds(4);
  104. % if lowPeak > highPeak || lowTrough > highTrough,
  105. % error('Inconsistent amplitude thresholds (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).');
  106. % end
  107. % case 'durations',
  108. % durations = varargin{i+1};
  109. % if ~isdvector(durations,'#2','<','>0'),
  110. % error('Incorrect value for property ''durations'' (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).');
  111. % end
  112. % if durations(2) < 1,
  113. % warning('Delta wave min and max durations are less than 1 ms, assuming seconds.');
  114. % durations = durations * 1000;
  115. % end
  116. % minDuration = durations(1); maxDuration = durations(2);
  117. % otherwise,
  118. % error(['Unknown property ''' num2str(varargin{i}) ''' (type ''help <a href="matlab:help FindDeltaWaves">FindDeltaWaves</a>'' for details).']);
  119. % end
  120. % end
  122. % Find local minima and maxima corresponding to beginning, peak and end of delta waves
  123. % This is done by finding zero crossings of the (z-scored) derivative of the signal
  125. % Differentiate, filter and z-score signal
  126. z = filtered;
  127. z(:,2) = [diff(filtered(:,2));0];
  128. z = FilterLFP(z,'passband',[0 6],'order',8);
  129. z(:,2) = zscore(z(:,2));
  131. % Find positions (in # samples) of zero crossings
  132. [up,down] = ZeroCrossings(z);
  133. down = find(down);
  134. up = find(up);
  135. if down(1) < up(1), down(1) = []; end
  137. % List positions (in # samples) of successive up,down,up crossings in an Nx3 matrix
  138. n = length(up);
  139. where = [up(1:n-1) down(1:n-1) up(2:n)];
  141. % List positions but also z-scored amplitudes in an Nx6 matrix (positions then amplitudes)
  142. z = filtered;
  143. z(:,2) = zscore(filtered(:,2));
  144. delta = z(where,:);
  145. delta = reshape(delta,size(where,1),6);
  147. % Discard waves that are too long or too short
  148. duration = delta(:,3) - delta(:,1);
  149. delta(duration<minDuration/1000|duration>maxDuration/1000,:) = [];
  151. % Threshold z-scored peak and trough amplitudes
  152. peak = delta(:,5);
  153. trough = delta(:,6);
  154. case1 = peak > highPeak & trough <= -lowTrough;
  155. case2 = peak >= lowPeak & trough < -highTrough;
  156. delta = delta(case1|case2,:);

FindDeltaWavesRGS14.m at commit 95cc6cb, under GPL-3.0 · at the source

Overview

Authors: Adrian Aleman-Zapata1, Yixiao Zhang1, Pelin Özsezer1, Kopal Agarwal1, Abdelrahman Rayan1, Irene Navarro-Lobato1, Lisa Genzel1
ORCID iDs: Adrian Aleman-Zapata, Yixiao Zhang, Kopal Agarwal
  1. Donders Institute for Brain, Cognition and Behaviour, Radboud University, Postbus 9010, 6500GL Nijmegen, The Netherlands
Institutions: Radboud University Nijmegen (Netherlands); Donders Institute for Brain, Cognition and Behaviour (Netherlands)
Journal: Sleep, volume 49, issue 9, article zsag168
Dates: received 10 December 2025; accepted 27 May 2026; published online 19 June 2026; in print September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/sleep/zsag168 · PMID 42319042 · PMCID PMC13553294 · OpenAlex W7165142621
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), rat (organism)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, Single-unit activity, calcium imaging
Keywords: hippocampal ripples, memory consolidation, sleep, prefrontal cortex, delta waves, spindles, neural synchronization
MeSH: Cortical Synchronization*, Delta Rhythm*, Hippocampus*, Memory Consolidation*, Sleep*, Animals, Electroencephalography, Male, Prefrontal Cortex, Rats (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NWO-Vidi
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

Hippocampal ripples, critical for sleep-related memory consolidation, are heterogeneous events with various sources and functions. However, their specific roles in supporting different forms of memory remain poorly understood. Here, we applied principal component analysis to identify ripple sub-types and relate them to hippocampal–cortical interactions, as well as their role in consolidating simple and complex semantic-like memories in rats. Three main ripple types were identified: medium-sized, large-sized, and small-sized ripples. Small-sized ripples were associated with increased prefrontal cortex to hippocampus connectivity, followed hippocampal delta waves, and were related to simple learning. In contrast, large-sized ripples exhibited increased hippocampus to prefrontal cortex connectivity, occurred during hippocampal spindles together as a doublet with a small-sized ripple, and were related to complex memory consolidation. Finally, learning induced heightened coupling between hippocampal delta and spindle oscillations and their cortical counterparts, consequently leading to an increased synchronization of ripples with cortical oscillations. Together, these results underscore that distinct ripple subtypes and their specific patterns of hippocampal–cortical coordination during sleep underpin different memory consolidation processes.

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 15 matches between paragraphs and lines of code.

genzellab/RGS14

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 95cc6cb5785a15a3e65ae80fd40fe7cad5ed9039, 8 May 2025
Languages: MATLAB (50)
Size: 69 files, 50 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: FieldTrip (11 files), Signal Processing Toolbox (9 files), Statistics and Machine Learning Toolbox (8 files), Image Processing Toolbox (2 files), boundedline (1 file), Open Ephys analysis tools (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
52 files
At the source: github.com/genzellab/RGS14

genzellab/RGS14_clusters

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 4a287c820deb2c24743d1d8dad522ef3e4d5916e, 3 April 2026
Languages: MATLAB (135), Python (6)
Size: 205 files, 141 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (13 files), Statistics and Machine Learning Toolbox (13 files), Signal Processing Toolbox (7 files), Matplotlib (5 files), SciPy (5 files), NumPy (4 files), Image Processing Toolbox (2 files), Parallel Computing Toolbox (1 file), Plotly (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
142 files
At the source: github.com/genzellab/RGS14_clusters

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

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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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 191 scripts, each with its path and the digest of its content;
  • 15 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

Datasets cited

Data availability

All data are available at https://osf.io/7xw43/ . Code can be found on the following GitHub links: https://github.com/genzellab/RGS14 and https://github.com/genzellab/RGS14_clusters

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, 7 authors, 7 keywords, 10 MeSH terms, 1 funder, 64 references.

Cite

This paper

Aleman-Zapata, A., Zhang, Y., Özsezer, P., Agarwal, K., Rayan, A., Navarro-Lobato, I., & Genzel, L. (2026). Deltas' and spindles' cross-area synchronization and ripple subtypes. Sleep, 49(9), zsag168. https://doi.org/10.1093/sleep/zsag168

BibTeX

@article{alemanzapata2026deltas,
author = {Aleman-Zapata, Adrian and Zhang, Yixiao and Özsezer, Pelin and Agarwal, Kopal and Rayan, Abdelrahman and Navarro-Lobato, Irene and Genzel, Lisa},
title = {{Deltas' and spindles' cross-area synchronization and ripple subtypes}},
journal = {Sleep},
year = {2026},
month = sep,
volume = {49},
number = {9},
pages = {zsag168},
publisher = {Oxford University Press},
issn = {0161-8105},
doi = {10.1093/sleep/zsag168},
url = {https://doi.org/10.1093/sleep/zsag168},
pmid = {42319042},
pmcid = {PMC13553294}
}

RIS

TY - JOUR
AU - Aleman-Zapata, Adrian
AU - Zhang, Yixiao
AU - Özsezer, Pelin
AU - Agarwal, Kopal
AU - Rayan, Abdelrahman
AU - Navarro-Lobato, Irene
AU - Genzel, Lisa
TI - Deltas' and spindles' cross-area synchronization and ripple subtypes
T2 - Sleep
J2 - Sleep
PY - 2026
DA - 2026/09/01
VL - 49
IS - 9
SP - zsag168
SN - 0161-8105
PB - Oxford University Press
DO - 10.1093/sleep/zsag168
UR - https://doi.org/10.1093/sleep/zsag168
LA - en
ER -

CSL-JSON

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"title": "Deltas' and spindles' cross-area synchronization and ripple subtypes",
"container-title": "Sleep",
"author": [
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"family": "Aleman-Zapata",
"given": "Adrian"
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"PMID": "42319042",
"PMCID": "PMC13553294",
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"issued": {
"date-parts": [
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1
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]
}
}

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