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Frequency-specific cortical subnetworks support fast human swallowing.

Code ↔ Paper

8 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 8 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Online methods › Preprocessing and trial definition ↔ preprocessing/ft_CleanTrials.m, the whole file · a weak match · score 0.81 · absolute amplitude threshold, temporal gradient, NaNs, rejection, score, preprocessing
  2. [2] § Online methods › Source Analysis and ROI definition ↔ beamformer/ft_Beamformer_extractROI.m, lines 1–39 · score 0.81 · warped MNI, single shell, beamformer, SPM12, LCMV, definition
  3. [3] § Online methods › Preprocessing and trial definition ↔ ft_CleanTrials.m, the whole file · a weak match · score 0.80 · absolute amplitude threshold, temporal gradient, NaNs, rejection, score, 100
  4. [4] § Online methods › Connectivity analysis ↔ connectivity/ft_PSI_singleSub.m, lines 133–171 · score 0.79 · cross spectrum, band width, Fourier, smoothing, tapsmofrq, mtmfft
  5. [5] § Online methods › Connectivity analysis ↔ ft_PSI_singleSub.m, lines 133–171 · score 0.79 · cross spectrum, band width, Fourier, smoothing, tapsmofrq, mtmfft
  6. [6] § Online methods › Functional connectivity, seed-to-ROI, and network analyses ↔ ft_GraphMetrics.m, lines 1–41 · score 0.77 · Graph metrics, weighted graphs, threshold density, proportionally thresholded, global, matrices
  7. [7] § Results › Global network organisation was largely preserved during swallowing ↔ ft_GraphMetrics.m, lines 1–41 · score 0.71 · proportional threshold density, Graph metrics, global efficiency, connectivity matrices, strength, weighted
  8. [8] § Online methods › Statistics and reproducibility ↔ ft_Orthogonalized_wPLI.m, the whole file · a weak match · score 0.61 · recomputing debiased, symmetric orthogonalization, wPLI, connectivity, ROI

Paper

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

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The authors' code

MATLAB · 79 lines · 2.1 KB · MIT · 2 matches

  1. function outFile = ft_GraphMetrics(groupFile, varargin)
  2. % Compute basic weighted graph metrics from a group connectivity matrix.
  4. p = inputParser;
  5. p.addParameter('OutDir', '', @(x)ischar(x)||isstring(x));
  6. p.addParameter('ThresholdDensity', [], @(x)isnumeric(x));
  7. p.parse(varargin{:});
  8. opt = p.Results;
  10. S = load(groupFile);
  11. if isfield(S,'group_mean')
  12. W = S.group_mean;
  13. elseif isfield(S,'wpli_mat')
  14. W = S.wpli_mat;
  15. elseif isfield(S,'psi_mat')
  16. W = abs(S.psi_mat);
  17. else
  18. error('Input file must contain group_mean, wpli_mat, or psi_mat.');
  19. end
  21. W = abs(W);
  22. W(1:size(W,1)+1:end) = 0;
  23. if ~isempty(opt.ThresholdDensity)
  24. W = proportional_threshold_local(W,opt.ThresholdDensity);
  25. end
  27. strength = sum(W,2,'omitnan');
  28. D = weight_to_distance_local(W);
  29. [globalEfficiency, charPath] = efficiency_path_local(D);
  31. T = table(mean(strength,'omitnan'), globalEfficiency, charPath, nnz(W)/(numel(W)-size(W,1)), ...
  32. 'VariableNames', {'MeanStrength','GlobalEfficiency','CharacteristicPathLength','Density'});
  34. if isempty(opt.OutDir)
  35. opt.OutDir = fileparts(groupFile);
  36. end
  37. if ~exist(opt.OutDir,'dir'); mkdir(opt.OutDir); end
  38. [~,name] = fileparts(groupFile);
  39. outFile = fullfile(opt.OutDir, [name '_graph_metrics.xlsx']);
  40. writetable(T,outFile);
  41. end
  43. function Wt = proportional_threshold_local(W,density)
  44. if density <= 0 || density > 1; error('ThresholdDensity must be in (0,1].'); end
  45. n = size(W,1);
  46. mask = triu(true(n),1);
  47. vals = W(mask);
  48. vals = vals(isfinite(vals) & vals>0);
  49. if isempty(vals); Wt = W*0; return; end
  50. k = max(1,round(density*numel(vals)));
  51. sv = sort(vals,'descend');
  52. thr = sv(min(k,numel(sv)));
  53. Wt = W .* (W>=thr);
  54. Wt(1:n+1:end) = 0;
  55. end
  57. function D = weight_to_distance_local(W)
  58. D = 1 ./ (W + eps);
  59. D(W<=0 | ~isfinite(W)) = inf;
  60. D(1:size(D,1)+1:end) = 0;
  61. end
  63. function [Eglob,L] = efficiency_path_local(D)
  64. n = size(D,1);
  65. for k = 1:n
  66. for i = 1:n
  67. for j = 1:n
  68. if D(i,j) > D(i,k) + D(k,j)
  69. D(i,j) = D(i,k) + D(k,j);
  70. end
  71. end
  72. end
  73. end
  74. mask = ~eye(n) & isfinite(D);
  75. invD = zeros(size(D));
  76. invD(mask) = 1 ./ D(mask);
  77. Eglob = sum(invD(:)) / (n*(n-1));
  78. L = mean(D(mask),'omitnan');
  79. end

ft_GraphMetrics.m, under MIT · at the source

Overview

Authors: Paul Muhle1,2, Jonas von Itter1,2, Anne Jung1,2, Bendix Labeit3, Ivy Cheng2,4,5, Inga Claus1, Andreas Wollbrink2, Joachim Gross2, Rainer Dziewas6, Sonja Suntrup-Krueger1,2
ORCID iDs: Paul Muhle, Bendix Labeit, Andreas Wollbrink
  1. Department of Neurology, University Hospital Münster, Münster, Germany
  2. Institute for Biomagnetism and Biosignalanalysis, University Hospital Münster, Münster, Germany
  3. Department of Neurology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
  4. Academic Unit of Human Communication, Learning, and Development, The University Of Hong Kong, Hong Kong, Hong Kong
  5. Centre for Gastrointestinal Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
  6. Department of Neurology, Klinikum Osnabrück, Osnabrück, Germany
Institutions: University of Münster (Germany); University Hospital Münster (Germany); Heinrich Heine University Düsseldorf (Germany); University of Manchester (United Kingdom); University of Hong Kong (Hong Kong SAR China); Klinikum Osnabrück (Germany)
Journal: Communications biology, volume 9, issue 1, article 1059
Dates: received 4 March 2026; accepted 29 July 2026; published online 7 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s42003-026-10751-6 · PMID 42567925 · PMCID PMC13451196 · OpenAlex W7201888225
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: MEG (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Connectivity, Graphs, fMRI & imaging, Physiology & signal measures
Keywords: Motor cortex, Sensory processing, Neural circuits
MeSH: Cerebral Cortex*, Deglutition*, Nerve Net*, Adult, Brain Mapping, Female, Humans, Magnetoencephalography, Male, Somatosensory Cortex, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (German Research Foundation) (SU922/1-1)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

Fast, highly constrained sensorimotor acts require rapid coordination of distributed cortical systems on subsecond timescales. Here, we used source-resolved magnetoencephalography to characterise time-locked cortical connectivity during voluntary swallowing in 74 healthy adults. Cluster-based network statistics revealed focal swallowing-related connectivity changes confined to anatomically selective subnetworks. Undirected phase-lagged connectivity identified theta- and low-gamma weighted phase lag index (wPLI) effects involving somatosensory, motor, supramarginal, and insular regions. Directed connectivity revealed sparse high-gamma phase slope index (PSI) subnetworks centred on the primary somatosensory cortex and anterior insula. Time-window analyses demonstrated temporally evolving low-gamma interactions between posterior parietal and insular regions, while laterality analyses showed rightward theta-band directed asymmetries during later swallowing phases. In contrast, global graph-theoretical metrics and node-level hub measures remained largely stable after correction for multiple comparisons. These findings indicate that voluntary swallowing is supported by focal, frequency-specific, and temporally structured cortical interactions rather than broad global network reconfiguration.

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 8 matches between paragraphs and lines of code.

Zenodo 20393275

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (12)
Size: 16 files, 12 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
14 files
At the source:

muhlep/swallowing-meg-connectivity

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: f9560aa27912a3aab1c9bb28d8a4f5b77e5610ee, 7 January 2026
Languages: MATLAB (9)
Size: 16 files, 9 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff
Not found: environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
11 files
At the source: github.com/muhlep/swallowing-meg-connectivity/tree/v1.0.0

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 21 scripts, each with its path and the digest of its content;
  • 8 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

Cortical networks were visualised using complementary approaches. Seed-to-ROI connectivity was displayed as circular and chord diagrams, while whole-network topology was rendered on inflated cortical surfaces with BrainNet Viewer50. Node positions correspond to the 21 predefined ROIs (Supplementary Fig. 4; Supplementary Table 1). Connectivity strength was encoded by edge colour and thickness, and directed interactions were indicated by arrows (Figs. 2–5). Exploratory whole-network visualisations were based on thresholded group-average connectivity differences and are intended for descriptive illustration rather than inferential interpretation. All analyses were implemented in MATLAB using FieldTrip51, the Brain Connectivity Toolbox18, BrainNet Viewer50, and publicly available plotting functions (circularGraph, chordPlot). All custom MATLAB scripts for preprocessing, source analysis, and connectivity estimation are publicly available at [10.5281/zenodo.20393275]. Due to institutional and data-protection regulations applying to high-dimensional neurophysiological datasets, raw MEG recordings are not publicly deposited in an unrestricted repository. De-identified derived datasets and analysis outputs supporting the findings of this study are available from the corresponding author upon reasonable request and subject to institutional data-sharing regulations. Source data underlying the figures are provided in Supplementary Data 1–8. Supplementary Data 5 contains the numerical source data underlying Fig. 5.

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, 10 authors, 3 keywords, 11 MeSH terms, 1 funder, 51 references.

Cite

This paper

Muhle, P., von Itter, J., Jung, A., Labeit, B., Cheng, I., Claus, I., Wollbrink, A., Gross, J., Dziewas, R., & Suntrup-Krueger, S. (2026). Frequency-specific cortical subnetworks support fast human swallowing. Communications biology, 9(1), 1059. https://doi.org/10.1038/s42003-026-10751-6

BibTeX

@article{muhle2026frequency,
author = {Muhle, Paul and von Itter, Jonas and Jung, Anne and Labeit, Bendix and Cheng, Ivy and Claus, Inga and Wollbrink, Andreas and Gross, Joachim and Dziewas, Rainer and Suntrup-Krueger, Sonja},
title = {{Frequency-specific cortical subnetworks support fast human swallowing}},
journal = {Communications biology},
year = {2026},
month = aug,
volume = {9},
number = {1},
pages = {1059},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10751-6},
url = {https://doi.org/10.1038/s42003-026-10751-6},
pmid = {42567925},
pmcid = {PMC13451196}
}

RIS

TY - JOUR
AU - Muhle, Paul
AU - von Itter, Jonas
AU - Jung, Anne
AU - Labeit, Bendix
AU - Cheng, Ivy
AU - Claus, Inga
AU - Wollbrink, Andreas
AU - Gross, Joachim
AU - Dziewas, Rainer
AU - Suntrup-Krueger, Sonja
TI - Frequency-specific cortical subnetworks support fast human swallowing
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/08/07
VL - 9
IS - 1
SP - 1059
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10751-6
UR - https://doi.org/10.1038/s42003-026-10751-6
LA - en
ER -

CSL-JSON

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