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Slow wave canonical activity deviation concept: Toward a slow wave-based EEG-fMRI reference map for health-associated network function.

Code ↔ Paper

9 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 9 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § STAR★Methods › Method details › Spindle detection ↔ Tools/swa_getInfoDefaults.m, the whole file · a weak match · score 0.91 · spline wavelet, 0.3–3 s, 10–16 Hz, power ratio, median, squaring · Report a mismatch
  2. [2] § STAR★Methods › Method details › EEG preprocessing ↔ Templates/swa_preprocessingTemplate.m, lines 173 to 225 · score 0.71 · linked mastoids, Independent component, EEGLAB, preprocessed, ICA, window · Report a mismatch
  3. [3] § STAR★Methods › Method details › SWE detection ↔ Tools/swa_getInfoDefaults.m, the whole file · a weak match · score 0.68 · zero crossing, amplitude threshold, envelope, detection, 0.25 s, algorithm · Report a mismatch
  4. [4] § STAR★Methods › Method details › fMRI preprocessing ↔ src/halfpipe/workflows/reports/func.py, lines 22 to 164 · score 0.61 · MNI space, fMRIPrep, T1w, segmented, volumes · Report a mismatch
  5. [5] § STAR★Methods › Quantification and statistical analysis › Event-related fMRI analyses ↔ src/halfpipe/tui/standards.py, lines 18 to 72 · score 0.57 · aCompCor, motion parameters, CSF, derivatives, component, mask · Report a mismatch
  6. [6] § STAR★Methods › Quantification and statistical analysis › Event-related fMRI analyses ↔ src/halfpipe/ui/setting/vals.py, lines 27 to 87 · score 0.57 · aCompCor, motion parameters, CSF, derivatives, component · Report a mismatch
  7. [7] § STAR★Methods › Method details › Sleep scoring ↔ Tools/swa_sleep_statistics.m, lines 156 to 210 · score 0.53 · sleep scored, wake, REM, N1, N3, N2 · Report a mismatch
  8. [8] § Results › How to create a template of SWE fMRI representation in healthy humans? ↔ src/halfpipe/tui/standards.py, lines 18 to 72 · score 0.52 · aCompCor, motion parameters, CSF, derivatives, signal, template · Report a mismatch
  9. [9] § STAR★Methods › Method details › Spindle detection ↔ SS/swa_FindSSChannels.m, lines 45 to 84 · score 0.50 · median, absolute, wavelet, Power, smoothing, crossings · Report a mismatch

Paper

Loaded from Europe PMC by your browser; OSCR does not store it: doi.org · Europe PMC

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The authors' code

MATLAB · 131 lines · 6.3 KB · no license · 2 matches

  1. function Info = swa_getInfoDefaults(Info, type, method)
  2. % Get the current default detection parameters for slow waves (SW), spindles
  3. % (SS), or saw-tooth waves (ST)
  5. % 'method' input is only used for slow wave detection where there are distinct
  6. % defaults for the envelope and "massimini detection criteria" ('mdc');
  8. % check inputs
  9. if nargin < 3
  10. method = 'envelope';
  11. end
  13. switch type
  14. case 'SW'
  16. % filter parameters
  17. % ^^^^^^^^^^^^^^^^^
  18. Info.Parameters.Filter_Apply = true;
  19. Info.Parameters.Filter_Method = 'Chebyshev'; % 'Chebyshev'/'Buttersworth'
  20. Info.Parameters.Filter_hPass = 0.5;
  21. Info.Parameters.Filter_lPass = 4;
  22. Info.Parameters.Filter_order = 2;
  24. % reference detection
  25. % ^^^^^^^^^^^^^^^^^^^
  26. Info.Parameters.Ref_Method = []; % canonical wave method ('envelope', 'diamond', 'midline', etc)
  27. Info.Parameters.Ref_Electrodes = false; % logical array of electrodes used for each canonical
  28. Info.Parameters.Ref_InspectionPoint = 'MNP'; % 'MNP'/'ZC'
  29. Info.Parameters.Ref_UseInside = 1; % 0/1 Use interior head channels or all
  30. Info.Parameters.Ref_UseStages = []; % whether or not to use sleep scoring information
  31. Info.Parameters.Ref_AmplitudeCriteria = 'relative'; % relative/absolute
  32. Info.Parameters.Ref_AmplitudeRelative = 5; % Standard deviations from mean negativity
  33. Info.Parameters.Ref_AmplitudeAbsolute = 60;
  34. Info.Parameters.Ref_AmplitudeMax = 250; % maximum amplitude in case of artefacts
  35. Info.Parameters.Ref_WaveLength = [0.25 1.25]; % Length criteria between zero crossings
  36. Info.Parameters.Ref_SlopeMin = 0.90; % Percentage cut-off for slopes
  37. Info.Parameters.Ref_Peak2Peak = []; % Only for channel thresholding
  39. % channel detection
  40. % ^^^^^^^^^^^^^^^^^
  41. Info.Parameters.Channels_Correlate2 = 'mean'; % which canonical wave (main, mean, or all)
  42. Info.Parameters.Channels_Detection = 'correlation'; % correlation/threshold
  43. Info.Parameters.Channels_Threshold = 0.9; % amount to adjust threshold if using threshold method
  44. Info.Parameters.Channels_ClusterTest = true;
  45. Info.Parameters.Channels_WinSize = 0.100;
  47. % travelling parameters
  48. % ^^^^^^^^^^^^^^^^^^^^^
  49. Info.Parameters.Travelling_GS = 40; % size of interpolation grid
  50. Info.Parameters.Travelling_MinDelay = 40; % minimum travel time (ms)
  51. Info.Parameters.Travelling_RecalculateDelay = true; % set to false if delay maps are manually calculated outside of algorith (e.g. smoothing)
  53. % option specific defaults
  54. % ^^^^^^^^^^^^^^^^^^^^^^^^
  55. if isempty(method)
  56. method = 'envelope';
  57. end
  59. switch lower(method)
  60. case 'envelope'
  61. % set envelope specific defaults
  62. Info.Parameters.Ref_Method = 'Envelope';
  64. case 'mdc'
  65. Info.Parameters.Ref_Method = 'diamond';
  66. Info.Parameters.Ref_AmplitudeCriteria = 'absolute';
  67. Info.Parameters.Ref_InspectionPoint = 'ZC';
  68. Info.Parameters.Ref_AmplitudeAbsolute = 80;
  69. Info.Parameters.Ref_Peak2Peak = 140; % Only for channel thresholding
  70. Info.Parameters.Channels_Detection = 'threshold'; % correlation/threshold
  71. Info.Parameters.Channels_Threshold = 1; % amount to adjust threshold if using threshold method
  73. end
  75. case 'ST'
  77. % Reference and Filter Parameters
  78. Info.Parameters.Ref_Method = 'Midline';
  79. Info.Parameters.Ref_Electrodes = []; % logical array of electrodes used for each canonical
  80. Info.Parameters.Filter_Apply = false; % No filter needed for CWT method...
  82. % Wavelet Parameters for Detection
  83. Info.Parameters.CWT_hPass = 2;
  84. Info.Parameters.CWT_lPass = 5;
  86. Info.Parameters.CWT_StdThresh = 1.75; % Explore defaults
  87. Info.Parameters.CWT_AmpThresh = [];
  88. Info.Parameters.CWT_ThetaAlpha = 1.2;
  90. Info.Parameters.Burst_Length = 1; % Maximum time between waves in seconds
  91. Info.Parameters.Burst_Adjust = 0.75; % Adjust the criteria for wave if found in a burst
  93. Info.Parameters.Channels_WinSize = 0.060; % in seconds
  94. Info.Parameters.Channel_Adjust = 0.9;
  96. Info.Parameters.Travelling_GS = 40; % Gridsize for delay map
  97. Info.Parameters.Travelling_MinDelay = 20; % minimum travel time in ms
  99. case 'SS'
  101. % Reference / Canonical Parameters
  102. Info.Parameters.Ref_Method = 'Midline';
  103. Info.Parameters.Ref_Electrodes = []; % logical array of electrodes used for each canonical
  104. Info.Parameters.Filter_Apply = false; % No filter needed for CWT method...
  105. Info.Parameters.Ref_UseStages = []; % whether or not to use sleep scoring information
  107. % Filter Parameters
  108. Info.Parameters.Filter_Method = 'Chebyshev'; % 'Chebyshev'/'Buttersworth'
  109. Info.Parameters.Filter_band = [10, 16];
  110. Info.Parameters.Filter_checkrange = 2;
  111. Info.Parameters.Filter_Window = 0.150; % length of smoothing window for root mean square of power
  112. Info.Parameters.Filter_order = 2;
  114. Info.Parameters.Wavelet_name = 'fbsp1-1-3'; % b-spline wavelet; else use 'morl'
  115. Info.Parameters.Wavelet_norm = 1; % normalise according to median entire time coefficients
  117. % Spindle Criteria
  118. Info.Parameters.Ref_AmplitudeCriteria = 'relative'; % relative/absolute
  119. Info.Parameters.Ref_AmplitudeMetric = 'median';
  120. Info.Parameters.Ref_AmplitudeRelative = [4, 2]; % Standard deviations from mean negativity [high, low]
  121. Info.Parameters.Ref_AmplitudeAbsolute = 15;
  122. Info.Parameters.Ref_NeighbourRatio = 3; % minimum tolerance for spindle/neighbour power ratio
  124. Info.Parameters.Ref_WaveLength = [0.3 3]; % time above threshold power
  125. Info.Parameters.Ref_MinWaves = 3; % minimum number of spindle waves
  127. Info.Parameters.Channels_Method = 'power'; % wavelet or power (FFT) method
  128. Info.Parameters.Channels_WinSize = 0.150; % window around ref spindle to search around (in seconds)
  129. Info.Parameters.Channels_Threshold = 0.75; % adjustment to ref criteria
  131. end

swa_getInfoDefaults.m at commit 6e5f295, no license · at the source

Overview

Authors: Merve Ilhan-Bayrakcı1, Oliver Tüscher1,2,3,4,5, Albrecht Stroh1,6
ORCID iDs: Merve Ilhan-Bayrakcı
  1. Leibniz Institute for Resilience Research, Mainz, Germany
  2. Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
  3. Institute of Molecular Biology (IMB), Mainz, Germany
  4. Department of Psychiatry, Psychotherapy and Psychosomatics, University Medicine Halle of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
  5. German Center for Mental Health (DZPG), Site Halle-Jena-Magdeburg, Halle (Saale), Germany
  6. Institute of Physiology I, University Hospital Muenster, Muenster, Germany
Institutions: Leibniz Institute for Resilience Research (Germany); Johannes Gutenberg University Mainz (Germany); University Medical Center of the Johannes Gutenberg University Mainz (Germany); Institute of Molecular Biology (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); Martin Luther University Halle-Wittenberg (Germany); University of Münster (Germany); University Hospital Münster (Germany)
Journal: iScience, volume 29, issue 4, article 115455
Dates: received 4 September 2025; accepted 19 March 2026; published online 21 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.isci.2026.115455 · PMID 42006354 · PMCID PMC13089066 · OpenAlex W7139987742
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: EEG (modality), fMRI (modality)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Smoothing, state filtering, decompositions, fMRI & imaging, Physiology & signal measures, Machine learning
Keywords: neuroscience, behavioral neuroscience, sensory neuroscience, cognitive neuroscience, techniques in neuroscience
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Leibniz Association; Boehringer Ingelheim Foundation; State of Rhineland-Palatinate; European Commission (H2020-SC1-2017-CNECT-2, 777084); national institutes of health (PO1 HL14454, R01 HL144801, R01 HL151389, R01 HL126523); MEXT | Japan Science and Technology Agency (JST) (JPMJMS2292-1-05); Deutsche Forschungsgemeinschaft (TRR 128, SFB1193, SFB1080)
Citations: not cited yet (Europe PMC); 92 references in the paper

Abstract

Functional network architecture associated with health varies across individuals, limiting neuroimaging sensitivity for detecting early network maladaptations and identifying at-risk individuals. To increase sensitivity, we use slow wave events (SWEs) as neurophysiological markers in EEG-informed fMRI analyses. Neural excitability and network state impact SWE spatiotemporal dynamics. Analyzing simultaneous EEG-fMRI from two healthy cohorts (N = 24), we generated individual SWE-related BOLD maps. We created cohort-level probabilistic maps and derived reference maps revealing consistently engaged regions, including the cingulate cortex, thalamus, hippocampus, and cerebellum. Stratifying SWEs by synchronization efficiency revealed distinct BOLD patterns. High synchronization SWEs recruited widespread cortical and subcortical networks, whereas low-synchronization SWEs engaged predominantly posterior cortical regions, refining the reference maps for physiologically distinct SWE subtypes. Building on this, we propose the “slow wave canonical activity deviation (SloCAD)” concept to quantify individual deviations from canonical SWE activation, providing a foundation for studying early network dysfunction in pathological conditions.

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 9 matches between paragraphs and lines of code.

Mensen/swa-matlab

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 6e5f2955c90f474a51ea71e6a48305713aa3a70e, 20 July 2020
Languages: MATLAB (53), C (2)
Size: 65 files, 55 scripts
Software Heritage: not archived
Found in: the text, “Key resources table”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (8 files), Wavelet Toolbox (8 files), EEGLAB (5 files), Statistics and Machine Learning Toolbox (4 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
56 files
At the source: github.com/Mensen/swa-matlab

HALFpipe/HALFpipe

License: GPL-3.0
State: the link answers, verified on 9 November 2026
Evidence: files inventoried
Commit: b65f53d305936a2660ba420bbdff6c156416b3be, 18 August 2026
Languages: Python (455), Shell (5)
Size: 611 files, 460 scripts
Software Heritage: not archived
Found in: the text, “Key resources table”
Holds: README, license file, environment (Dockerfile, pyproject.toml, recipes/conda_build_config.yaml), tests, continuous integration
Not found: CITATION.cff, documentation
Tools: NumPy (90 files), Nipype (86 files), NiBabel (51 files), pandas (38 files), Nilearn (17 files), FSL (14 files), SciPy (14 files), fMRIPrep (13 files), TemplateFlow (9 files), AFNI (6 files), NetworkX (4 files), Numba (4 files), PyBIDS (4 files), ANTs (3 files), Matplotlib (3 files), DataLad (2 files), FreeSurfer (1 file), seaborn (1 file), statsmodels (1 file)
Availability: 2 checks, the latest on 9 November 2026: the link answers
  • 9 November 2026: the link answers
  • 27 September 2026: the link answers
462 files
At the source: github.com/HALFpipe/HALFpipe

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

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Data

Datasets cited

Data and code availability

• Data: The Bergmann et al.27 data (dataset 1) reported in this study cannot be deposited in a public repository because they are subject to ethical restrictions. To request access, contact the Ethics Committee of the Medical Faculty, Christian-Albrechts-University of Kiel (Project: “Schlafbedingte Gedächtniskonsolidierung assoziativen Lernens: eine kombinierte fMRT EEG Studie,” Reference A 108/08; ). This paper analyzes existing, publicly available data (dataset 2) available via OpenNeuro (OpenNeuro: ds003574; DOI: https://openneuro.org/datasets/ds003574/versions/1.0.2).28,29 Reference masks generated in this study have been deposited in OSF (OSF: 6adf2; DOI: https://doi.org/10.17605/OSF.IO/6ADF2) and are publicly available at https://osf.io/6adf2/. • Code: This paper does not report original code. • Additional information: Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 funders, 91 references.

Cite

This paper

Ilhan-Bayrakcı, M., Tüscher, O., & Stroh, A. (2026). Slow wave canonical activity deviation concept: Toward a slow wave-based EEG-fMRI reference map for health-associated network function. iScience, 29(4), 115455. https://doi.org/10.1016/j.isci.2026.115455

BibTeX

@article{ilhanbayrakc2026slow,
author = {Ilhan-Bayrakcı, Merve and Tüscher, Oliver and Stroh, Albrecht},
title = {{Slow wave canonical activity deviation concept: Toward a slow wave-based EEG-fMRI reference map for health-associated network function}},
journal = {iScience},
year = {2026},
month = mar,
volume = {29},
number = {4},
pages = {115455},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.115455},
url = {https://doi.org/10.1016/j.isci.2026.115455},
pmid = {42006354},
pmcid = {PMC13089066}
}

RIS

TY - JOUR
AU - Ilhan-Bayrakcı, Merve
AU - Tüscher, Oliver
AU - Stroh, Albrecht
TI - Slow wave canonical activity deviation concept: Toward a slow wave-based EEG-fMRI reference map for health-associated network function
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/03/21
VL - 29
IS - 4
SP - 115455
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.115455
UR - https://doi.org/10.1016/j.isci.2026.115455
LA - en
ER -

CSL-JSON

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21
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