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Respiration as a dynamic modulator of sensory sampling.

Code ↔ Paper

5 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 5 matches
  1. [1] § Methods › Respiration preprocessing and phase extraction ↔ Code/fig1d.m, lines 13 to 64 · score 0.65 · peak prominence, breathing rates, findpeaks, troughs, respiration · Report a mismatch
  2. [2] § Methods › MEG preprocessing and segmentation ↔ Code/fig1d.m, lines 66 to 156 · score 0.65 · ft_preprocessing, outliers, eye, segmented, artefacts, channels · Report a mismatch
  3. [3] § Results › Distinct oscillatory signatures of behaviourally relevant stimulus characteristics ↔ Code/fig2.m, lines 454 to 552 · score 0.56 · Individual alpha power, posterior alpha, BF10, Violin, uncued, Figure 2 · Report a mismatch
  4. [4] § Methods › Respiration phase-dependent fitting of the psychometric function ↔ Code/fig1bc.m, lines 267 to 349 · score 0.56 · cumulative Gaussian, Psignifit, sigmoid, fitted, refitting, threshold · Report a mismatch
  5. [5] § Results › Respiration-driven spectral changes within the RMBO network mediate perception ↔ Code/fig3bcd.m, lines 100 to 205 · score 0.54 · 1–40 Hz, power spectra, respiration phase, FDR, ROIs, onset · Report a mismatch

Paper

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The authors' code

MATLAB · 166 lines · 5.6 KB · no license · 2 matches

  1. clearvars
  2. addpath(genpath('D:\RESCUE\'));
  3. addpath('C:\Program Files\MATLAB\Toolboxes\fieldtrip-20240111\fieldtrip-20240111\');
  4. ft_defaults;
  5. ft_warning off;
  7. datapath = 'D:\RESCUE\_data\';
  8. outpath = 'D:\RESCUE\_out\';
  10. ids = [2549 3051 3066 3048 3033 3050 3067 3064 3062 3063 2521 2550 2448 2314 2168 ...
  11. 2524 2480 2299 2749 1566 2214 2348 1822 3090 3047 3081 3053 2330 3078 3105]; % full
  13. %% extract respiration frequencies for rest and task
  14. conds = {'nasal','oral'};
  15. respfreqs = zeros(3,numel(ids));
  16. for isub = 1:numel(ids)
  17. % rest
  18. for icond = 1:2
  19. load([outpath 'phasevec_' num2str(ids(isub)) '_rest_' conds{icond} '.mat'],'phasevec');
  20. tmp = phasevec(~isnan(phasevec));
  21. [~, peaks] = findpeaks(tmp, 'MinPeakProminence',1);
  22. [~, troughs] = findpeaks(-tmp, 'MinPeakProminence',1);
  23. for k = 2:numel(peaks)
  24. diff(k-1) = peaks(k)-peaks(k-1);
  25. end
  26. respfreqs(icond,isub) = 1/(mean(diff)/600); % breathing rate (Hz)
  27. end
  29. % task
  30. tmpfreqs = [];
  31. for irun = 1:6
  32. load([outpath 'phasevec_' num2str(ids(isub)) '_task_' num2str(irun) '.mat'],'phasevec');
  33. tmp = phasevec(~isnan(phasevec));
  34. [~, peaks] = findpeaks(tmp, 'MinPeakProminence',1);
  35. [~, troughs] = findpeaks(-tmp, 'MinPeakProminence',1);
  36. for k = 2:numel(peaks)
  37. diff(k-1) = peaks(k)-peaks(k-1);
  38. end
  39. tmpfreqs(irun) = 1/(mean(diff)/600); % breathing rate (Hz)
  40. end
  41. respfreqs(3,isub) = mean(tmpfreqs);
  42. end
  44. % plot frequencies for rest (nasal, oral) and task
  45. addpath('D:\RespSpeech');
  46. cmap = {'#57315A','#8C2B59','#8C8474'};
  47. figure;
  48. v = violinplot(respfreqs');
  49. for k = 1:3
  50. v(1,k).ViolinColor = hex2rgb(cmap{k});
  51. v(1,k).ScatterPlot.SizeData = 25;
  52. v(1,k).ScatterPlot.MarkerFaceAlpha = 1;
  53. v(1,k).ScatterPlot.MarkerEdgeColor = [1 1 1];
  54. end
  55. ylim([0.1 0.5]);
  56. xlabel('condition');
  57. ylabel('frequency (Hz)');
  58. set(gca,'XTickLabel',{'nasal (rest)','oral (rest)','task mean'});
  59. set(gca,'YTick',[0.1:0.1:0.5]);
  60. title('respiration frequencies');
  62. % test
  63. [p,h,stats] = signrank(respfreqs(1,:),respfreqs(2,:)) % nasal > oral *
  64. [p,h,stats] = signrank(respfreqs(1,:),respfreqs(3,:)) % nasal < task **
  66. %% NEW 251112 - R1 Analysis for saccadic patterns across conditions
  67. ids = [2549 3051 3066 3048 3033 3050 3067 3064 3062 3063 2521 2550 2448 2314 2168 ...
  68. 2524 2480 2299 2749 1566 2214 2348 1822 3090 3047 3081 3053 2330 3078 3105];
  70. % create one full data set of segmented horizontal eye movements for each participant
  71. for isub = 1:numel(ids)
  72. trials = [];
  74. for irun = 1:6
  76. % load raw data sampled at 600 Hz
  77. idfiles = dir(fullfile([datapath 'A' num2str(ids(isub))], '*.ds'));
  78. fn = [datapath 'A' num2str(ids(isub)) '\' idfiles(irun+1).name];
  79. cfg = [];
  80. cfg.dataset = fn;
  81. cfg.continuous = 'yes';
  82. raw = ft_preprocessing(cfg);
  84. % crop to pupil
  85. rawpup = ft_selectdata(struct('channel',{{'UADC005'}}),raw);
  87. % actual trial definition
  88. cfg = [];
  89. cfg.dataset = fn;
  90. cfg.trialfun = 'dk_trialfun_rescue';
  91. cfg.trialdef.eventtype = 'frontpanel trigger';
  92. cfg.trialdef.eventvalue = [16 17 18 19 21 22 23 24];
  93. cfg.trialdef.prestim = 1.85; %1.25;
  94. cfg.trialdef.poststim = 0.65; %0.75;
  95. cfg_stimonset = ft_definetrial(cfg);
  97. trials = [trials; cfg_stimonset.trl(11:end,:)]; % crop first ten trials
  98. segrawpup = ft_redefinetrial(cfg_stimonset, rawpup);
  99. all{irun} = segrawpup;
  100. end
  102. cfg = [];
  103. cfg.keepsampleinfo = 'no';
  104. fullsegrawpup = ft_appenddata(cfg,all{1},all{2},all{3},all{4},all{5},all{6});
  105. save([outpath 'fullsegrawpup_' num2str(ids(isub)) '.mat'], 'fullsegrawpup');
  106. end
  108. % determine the number of saccades within each category
  109. saccnum = zeros(numel(ids),4);
  110. for isub = 1:numel(ids)
  111. isub
  112. load([outpath 'fullsegrawpup_' num2str(ids(isub)) '.mat'], 'fullsegrawpup');
  114. % crop to prestimulus time window
  115. cfg = [];
  116. cfg.latency = [-1.6 0];
  117. prestimrawpup = ft_selectdata(cfg, fullsegrawpup);
  119. % trial selection
  120. trlinfo = prestimrawpup.trialinfo(:,1);
  121. uu = find(trlinfo == 16 | trlinfo == 24); % uncued, untimed
  122. ut = find(trlinfo == 18 | trlinfo == 22); % uncued, timed
  123. cu = find(trlinfo == 17 | trlinfo == 23); % cued, untimed
  124. ct = find(trlinfo == 19 | trlinfo == 21); % cued, timed
  126. cfg = [];
  127. cfg.trials = uu;
  128. uupup = ft_selectdata(cfg, prestimrawpup);
  129. cfg.trials = ut;
  130. utpup = ft_selectdata(cfg, prestimrawpup);
  131. cfg.trials = cu;
  132. cupup = ft_selectdata(cfg, prestimrawpup);
  133. cfg.trials = ct;
  134. ctpup = ft_selectdata(cfg, prestimrawpup);
  136. % run the artefact detection
  137. cfg = [];
  138. cfg.artfctdef.zvalue.channel = 'all';
  139. cfg.artfctdef.zvalue.cutoff = 5;
  140. uusacc = ft_artifact_zvalue(cfg, uupup);
  141. utsacc = ft_artifact_zvalue(cfg, utpup);
  142. cusacc = ft_artifact_zvalue(cfg, cupup);
  143. ctsacc = ft_artifact_zvalue(cfg, ctpup);
  145. % save number of saccades
  146. saccnum(isub,1) = length(uusacc.artfctdef.zvalue.artifact);
  147. saccnum(isub,2) = length(utsacc.artfctdef.zvalue.artifact);
  148. saccnum(isub,3) = length(cusacc.artfctdef.zvalue.artifact);
  149. saccnum(isub,4) = length(ctsacc.artfctdef.zvalue.artifact);
  150. end
  152. % remove outlier
  153. saccnum(19,:) = [];
  154. writematrix(saccnum,[outpath 'saccnum.txt']);
  156. median(saccnum)

fig1d.m, no license · at the source

Overview

Authors: Nikos Chalas1,2, Martina Saltafossi1,2, Teresa Berther1,2, Elio Balestrieri1,2, Omid Abbasi1,2, Daniel S. Kluger1,2
ORCID iDs: Daniel S. Kluger
  1. Institute for Biomagnetism and Biosignal Analysis, University of Münster,Münster, Germany
  2. Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster,Münster, Germany
Institutions: University of Münster (Germany)
Journal: Nature communications, volume 17, issue 1, article 3261
Dates: received 28 July 2025; accepted 23 March 2026; published online 7 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-71604-8 · PMID 41946728 · PMCID PMC13062087 · OpenAlex W4411754679
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.26.661828
Status: code verified (how the code is found and verified)
Categories: MEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Sensory processing, Perception
MeSH: Brain*, Respiration*, Adult, Arousal, Female, Humans, Magnetoencephalography, Male, Photic Stimulation, Pupil, Visual Perception, Young Adult (* major topic)
Topic: Advanced Chemical Sensor Technologies (Biomedical Engineering, Engineering), according to OpenAlex
Funding: European Research Council (101162169); Innovative Medical Research - Grant ID KL 1 2 22 01 IZKF - Grant ID Klu3/002/26; Deutsche Forschungsgemeinschaft (491157081)
Citations: cited by 3 papers (Europe PMC); 70 references in the paper

Abstract

Respiration dynamically modulates sensory perception by orchestrating transient states of the brain and the body. Using simultaneous recordings of high-density magneto- encephalography (MEG), respiration, and pupillometry, we show that human perceptual sensitivity to near-threshold visual stimuli was enhanced during inspiration, coinciding with respiration-modulated increases in arousal neuromodulation and cortical excitability. Participants adapted their breathing patterns to align with predictable stimulus onset, and this adaptive respiratory alignment correlated with improved performance. We further reveal that respiration-modulated changes in alpha and beta oscillations reflect distinct shifts in sensory and motor excitability, respectively. Crucially, respiration-resolved multivariate Granger causality analyses demonstrate that the breathing rhythm systematically shapes directed information flow within a widespread interoceptive network. This respiration-brain coupling was flexibly adjusted based on stimulus predictability, highlighting a mechanism for active sensing which integrates internal bodily rhythms with external sensory demands to optimise perception.

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

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OSF qasvp

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (8)
Size: 30 files, 8 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (7 files), Statistics and Machine Learning Toolbox (4 files), CircStat (3 files), fdr_bh (Benjamini-Hochberg FDR) (1 file), Signal Processing Toolbox (1 file), psignifit (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
8 files
At the source: osf.io/qasvp

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 2 keywords, 12 MeSH terms, 3 funders, 70 references.

Cite

This paper

Chalas, N., Saltafossi, M., Berther, T., Balestrieri, E., Abbasi, O., & Kluger, D. S. (2026). Respiration as a dynamic modulator of sensory sampling. Nature communications, 17(1), 3261. https://doi.org/10.1038/s41467-026-71604-8

BibTeX

@article{chalas2026respiration,
author = {Chalas, Nikos and Saltafossi, Martina and Berther, Teresa and Balestrieri, Elio and Abbasi, Omid and Kluger, Daniel S.},
title = {{Respiration as a dynamic modulator of sensory sampling}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {3261},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71604-8},
url = {https://doi.org/10.1038/s41467-026-71604-8},
pmid = {41946728},
pmcid = {PMC13062087}
}

RIS

TY - JOUR
AU - Chalas, Nikos
AU - Saltafossi, Martina
AU - Berther, Teresa
AU - Balestrieri, Elio
AU - Abbasi, Omid
AU - Kluger, Daniel S.
TI - Respiration as a dynamic modulator of sensory sampling
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/07
VL - 17
IS - 1
SP - 3261
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71604-8
UR - https://doi.org/10.1038/s41467-026-71604-8
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13062087",
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