OSCR

Respiratory pauses highlight sleep architecture in mice.

Code ↔ Paper

23 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 23 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Brain state labeling ↔ detectors/detectStates/SleepScoreMaster/SleepScoreMaster.m, lines 1–86 · score 0.74 · TheStateEditor, motion signal, power spectra, GUI, episodes, hippocampal
  2. [2] § Methods › Pressure sensor data analysis › Software tools ↔ MouseBreathMetricsMaster/helpers/MouseBreathMetricsWrapper.m, the whole file · a weak match · score 0.68 · mouse BreathMetrics, MATLAB toolbox, Zelano, built, written, airflow
  3. [3] § Methods › LFP spectral analysis during NREM packets ↔ externalPackages/FMAToolbox/Analyses/MTPointSpectrum.m, the whole file · a weak match · score 0.63 · multi taper, Chronux, padding, resolution, spectrogram, band
  4. [4] § Methods › Brain state labeling ↔ detectors/detectStates/SleepScoreMaster/ClusterStates_GetMetrics.m, lines 102–151 · score 0.62 · 4–90 Hz, power spectrum, slow wave, slope, LFP
  5. [5] § Methods › Brain state labeling ↔ detectors/detectStates/SleepScoreMaster/PickSWTHChannel.m, lines 198–235 · score 0.62 · 4–90 Hz, power spectrum, slow wave, slope, LFP
  6. [6] § Methods › Surgeries › Silicon probe implantations (12 mice) ↔ analysis/SharpWaveRipples/bz_DetectSWR.m, lines 2–50 · score 0.61 · sharp wave ripples, pyramidal, layer, probes, properties, LFP
  7. [7] § Methods › Surgeries › Silicon probe implantations (12 mice) ↔ analysis/SharpWaveRipples/detect_swr/detect_swr.m, lines 1–48 · score 0.61 · sharp wave ripples, pyramidal, layer, probes, properties, LFP
  8. [8] § Results › Respiratory features predict brain states ↔ MouseBreathMetricsMaster/breathmetrics-master/breathmetrics_functions/getSecondaryRespiratoryFeatures.m, lines 103–239 · score 0.60 · breathing rate, respiratory features, breathing cycles, variations, volumes, variability
  9. [9] § Methods › Animals ↔ preprocessing/old/preprocessing_AnimalMetadataText.m, lines 34–165 · score 0.60 · C57Bl6, anesthetized, males, fibers, implanted, weight
  10. [10] § Methods › LFP spectral analysis during NREM packets ↔ analysis/SpectralAnalyses/bz_WaveSpec.m, lines 1–65 · score 0.59 · frequency resolution, LFP sampled, downsampled, spectrogram, spaced, spectral
  11. [11] § Methods › Brain state labeling ↔ GUITools/TheStateEditor/TheStateEditor.m, lines 1126–1240 · score 0.58 · 0.5–4 Hz, 5–10 Hz, offline, GUI, Lab, raw
  12. [12] § Methods › Pressure sensor data analysis › Software tools ↔ MouseBreathMetricsMaster/breathmetrics-master/breathmetrics.m, lines 1–90 · score 0.58 · Zelano lab, respiration recorded, BreathMetrics, airflow, MATLAB, mouse
  13. [13] § Methods › Microarousals detection and associated NREM packets ↔ externalPackages/CircularStats/circ_ksdensity.m, lines 1–60 · score 0.57 · median absolute deviation, standard deviation
  14. [14] § Methods › Data acquisition ↔ io/bz_getDigitalIn.m, the whole file · a weak match · score 0.55 · Intan RHD2000, digital, board, amplified, voltage, analog
  15. [15] § Methods › Surgeries › Nasal implants (28 mice) ↔ preprocessing/old/preprocessing_AnimalMetadataText.m, lines 34–165 · score 0.55 · strain, surface, DV, ML, AP, implantation
  16. [16] § Methods › Pressure sensor data analysis › Detection of pauses after inhalation and late exhalation components ↔ MouseBreathMetricsMaster/helpers/FindPausesFromBmChunk_FromMaxima.m, lines 460–568 · score 0.54 · putative inhalation, exhalation windows, Pauses, trough, signal, peak
  17. [17] § Methods › Data acquisition ↔ externalPackages/read_Intan_RHD2000_file.m, lines 86–133 · score 0.54 · Intan RHD2000, board, digital, amplified, voltage, sensor
  18. [18] § Methods › Statistical analyses ↔ externalPackages/IoSR-Surrey-MatlabToolbox-4bff1bb/+iosr/+statistics/boxPlot.m, lines 371–460 · score 0.54 · upper whiskers, prctile, Box, alpha, median, MATLAB
  19. [19] § Methods › Automated brain state prediction from respiratory features › Network parameters training ↔ analysis/lfp_general/bz_GradDescCluster.m, lines 1–119 · score 0.52 · gradient descent, minimizing, iterations, probabilities, animals
  20. [20] § Methods › Surgeries › All surgeries ↔ preprocessing/metadata/BWMetadataSystem/bz_AnimalMetadataTextTemplate.m, lines 34–77 · score 0.52 · subcutaneous, analgesic, intraperitoneal, isoflurane, kg, anesthetized
  21. [21] § Methods › Pressure sensor data analysis › Detection of respiratory components across states ↔ MouseBreathMetricsMaster/helpers/MouseBreathMetricsWrapper.m, the whole file · a weak match · score 0.51 · correctRespirationToBaseline, sniff, chunks, BreathMetrics, breathing, cycles
  22. [22] § Methods › Detection of respiration-based NREM packets ↔ RespirationBasedStatePrediction.ipynb, lines 148–202 · score 0.51 · state prediction, respiration features, activation, layer, CNN
  23. [23] § Methods › Brain state labeling ↔ detectors/bz_EMGFromLFP.m, lines 1–49 · score 0.51 · 300–600 Hz, EMG, shanks, 300 Hz, correlation, band

Paper

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

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The authors' code

MATLAB · 102 lines · 5.2 KB · MIT · 2 matches

  1. function Sniffing = PreliminarySniffExamination(t_Input,Data_Input,Fs,ValidSniffInt,SniffIntToUse , PLOT);
  2. %% Wrapper function for examining sniff trace using MouseBreathmetrics - a Matlab toolbox written by Giulio Casali (ROUX LAB, CNRS) -
  3. %based on the redundant/intensive exploitation of "breathmetrics" (Zelano 2018) designed to extract sniff cycles embedded inside human airflow traces;
  5. % These parameters are the ones used in ROUX team -
  6. % These are the features of the resistors used in the voltage-divider built
  7. % by Pascal Ravassard.
  8. % Important to know: The original signal was collected at 20kHz and with original signal using [-4,4 psi] mapped between 0-5V;
  9. % The signal fed into the intan board is shifted to 0-3.3V.
  10. % It is important to deal with these parameters correctly if the amplitude of the waveforms need to be examined thoroughly.
  12. %% Inputs:
  13. % 1) t_Input = vector of time-stamps
  14. % 2) Data_Input = vector of raw pressure data in Volts same as t_Input;
  15. % 3) Fs = sampling rate of the raw signal;
  16. % 4) SniffIntToUse = [ intervals of interest for examining sniffing traces]
  18. %% Outputs:
  19. % 1) Sniffing: a mat structure
  22. %% Hardcore parameters:
  23. Params.ImpossibleLength = 5; % in seconds - if cycles are longer than this duration it means an error occurred- better to discard this window and exclude from future analyses;
  25. %% Output
  26. % 1) Sniffing
  28. %% 1) Format Input data;
  29. % 1A) Turn signal in columsn;
  30. t = reshape(t_Input,[],1);
  31. Data = reshape(Data_Input,[],1);
  33. % 1B) Create output structure
  34. Sniffing = [];
  35. Sniffing.SampleRate = Fs;
  36. Sniffing.TimeWindow =[0,ceil(t(end)) ] ;
  37. Sniffing.Ts = reshape(EqualBinning([ Sniffing.TimeWindow ] , Sniffing.SampleRate^-1 ) , [],1) ;%% bmObj.time ;
  39. %% 2) Convert signal in PSI - for this the parameters of the voltage-divider should be checked.
  40. % 2A) Here the features used in ROUX lab are described - but it is something it can vary across setups/labs ecc...
  41. PressureSignalParameters =[];
  42. PressureSignalParameters.OffsetToAddTo3_3V = 0.0;
  43. PressureSignalParameters.R2 = 180 *10^3;
  44. PressureSignalParameters.R1 = 100 *10^3;
  46. % 2B) Calibration of the signal is performed here;
  47. CalibratedSniffSignal = CalibrateRawPressureSignal([t,Data],PressureSignalParameters , false ,SniffIntToUse );
  48. t = CalibratedSniffSignal(:,1) ; % ts over time
  49. Data = CalibratedSniffSignal(:,2) ; % Raw Voltage Signal over time
  50. clear CalibratedSniffSignal;
  52. % 2C) Formattin the signal for breathmetrics - the inverted signal is because the human signal ...
  53. % is with the opposite polarity (inhalation upwards, exhalation downwards) - so first invert the signal to detect inhalation/exhalation peaks correctly
  54. InvertedData = -1*(Data) ;
  56. %% 3) MOUSE-BREATHMETRICS
  57. % 3A) The actual computation occurs here with the ivnerted data - in this exampl;
  58. bmObj = RunBreathMetricsOnEachChunk([t,InvertedData] , Sniffing , SniffIntToUse ) ;
  60. % 3B) Update now the ivnerted signal wit the real one;
  61. bmObj.rawRespiration = Data;
  62. InvertedBreathMetrics = breathmetrics(bmObj.rawRespiration, Sniffing.SampleRate, 'rodentAirflow');
  63. InvertedBreathMetrics.correctRespirationToBaseline('sliding', 0, true);
  64. bmObj.rawRespiration = (InvertedBreathMetrics.rawRespiration) ;
  65. bmObj.smoothedRespiration = (InvertedBreathMetrics.smoothedRespiration) ;
  66. bmObj.baselineCorrectedRespiration = (InvertedBreathMetrics.baselineCorrectedRespiration) ;
  67. clear InvertedBreathMetrics ;
  69. %% 4) Post-hoc curing of the signal -
  70. % 4A) - Curing cycles using minimum threshold duration, overlapping cycles
  71. % or cycles which require stitching
  72. [bmObj , PostProcessesSniffIntToUse , PostProcessesGoodSniffInt ] = RemoveExtraCyclesFromSniffTrace(bmObj , Params.ImpossibleLength ,Sniffing ,SniffIntToUse , SniffIntToUse, SniffIntToUse) ;
  74. %4B) Now re-interpolate cycles relative to the Sniffing.Ts (
  75. Sniffing.bmObj = UpdataBmBobj(bmObj, Sniffing.Ts , PostProcessesGoodSniffInt, Sniffing.TimeWindow) ;
  76. clear bmObj;
  79. Sniffing.SniffIntToUse =SniffIntToUse;
  80. Sniffing.PostProcessesGoodSniffInt =PostProcessesGoodSniffInt;
  82. %% Plot the 2 seconds for inspection
  83. if PLOT
  84. hold on ; ;ylim([-1,1]*5);
  85. ThisTimeWindow = [10]+[0,2];
  86. PlotXY(Restrict([Sniffing.bmObj.time,Sniffing.bmObj.rawRespiration],ThisTimeWindow),'k')
  87. PlotXY(Restrict([Sniffing.bmObj.time(Sniffing.bmObj.inhalePeaks),Sniffing.bmObj.rawRespiration(Sniffing.bmObj.inhalePeaks)],ThisTimeWindow),'r.') ; % inhale peaks
  88. PlotXY(Restrict([Sniffing.bmObj.time(Sniffing.bmObj.exhaleTroughs),Sniffing.bmObj.rawRespiration(Sniffing.bmObj.exhaleTroughs)],ThisTimeWindow),'b.') ; % exhale peaks
  89. PlotIntervals( Restrict( Sniffing.bmObj.time([Sniffing.bmObj.inhaleOnsets Sniffing.bmObj.inhaleOffsets ] ) , ThisTimeWindow) , 'color','r','alpha',0.2)
  90. PlotIntervals( Restrict( Sniffing.bmObj.time([Sniffing.bmObj.exhaleOnsets Sniffing.bmObj.exhaleOffsets ] ) , ThisTimeWindow) , 'color','b','alpha',0.2);
  91. PlotIntervals( Restrict( Sniffing.bmObj.time(RemoveNansFromHorizontalRows([Sniffing.bmObj.inhalePauseOnsets Sniffing.bmObj.exhaleOnsets ] )) , ThisTimeWindow) , 'color','k','alpha',0.2);
  92. PlotIntervals( Restrict( Sniffing.bmObj.time(RemoveNansFromHorizontalRows([Sniffing.bmObj.exhalePauseOnsets Sniffing.bmObj.CycleEnd ] )) ,ThisTimeWindow) , 'color','k','alpha',0.2);
  93. xlabel('Time (s)');
  94. ylabel('Pressure (PSI)');
  95. xlim(ThisTimeWindow);
  96. end;
  97. end

MouseBreathMetricsWrapper.m at commit 392515d, under MIT · at the source

Overview

Authors: Giulio Casali1, Camille Miermon1, Geoffrey Terral1, Pascal Ravassard1, Tim Gervois1, Tiphaine Dolique1, Evan R Harrell1, Alena Spitsyn1, Edith Lesburguères1, David Jarriault2, Frédéric Gambino1, Nicolas Chenouard3,4, Lisa Roux1
ORCID iDs: Giulio Casali, Geoffrey Terral, Tim Gervois, Evan R Harrell, Frédéric Gambino, Nicolas Chenouard, Lisa Roux
  1. Univ. Bordeaux, CNRS, Interdisciplinary Institute for Neuroscience, IINS, UMR 5297, Bordeaux, France
  2. Univ. of Bordeaux, INRAE, Bordeaux INP, NutriNeuro, UMR 1286, Bordeaux, France
  3. Sorbonne Univ., INSERM, CNRS, Paris Brain Institute, Institut du Cerveau, ICM, U1127, UMR 7225, Paris, France
  4. Present Address: Univ. Bordeaux, INSERM, Neurocentre Magendie, U1215, Bordeaux, France
Institutions: Centre National de la Recherche Scientifique (France); Université de Bordeaux (France); Institut Interdisciplinaire de Neuroscience (France); Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (France); NutriNeuro (France); Institut Polytechnique de Bordeaux (France); Inserm (France); Sorbonne Université (France); Institut du Cerveau (France); Neurocentre Magendie (France)
Journal: Nature communications, volume 17, issue 1, article 6620
Dates: received 19 November 2024; accepted 4 May 2026; published online 19 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73106-z · PMID 42156372 · PMCID PMC13381926 · OpenAlex W4393219879
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2024.03.27.586921
Status: code verified
Categories: EEG (modality), extracellular electrophysiology (units, LFP) (modality), mouse (organism)
Methods: Spectral & time-frequency, Connectivity, Statistics, Preprocessing, Evoked potentials, Physiology & signal measures
Keywords: Neurophysiology, Consolidation, Sleep, Locus coeruleus, Non-REM sleep
MeSH: Respiration*, Sleep*, Animals, Brain, Electroencephalography, Hippocampus, Local Field Potential Measurement, Male, Mice, Mice, Inbred C57BL, Sleep Stages, Sleep, REM, Wakefulness (* major topic)
Topic: Neuroscience of respiration and sleep (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: European Research Council (ERC-StG-851560, 851560)
Citations: cited by 1 paper (Europe PMC); 91 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 23 matches between paragraphs and lines of code.

buzsakilab/buzcode

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0969ddf7f55ccaca8c71969bee4b21f310840047, 27 August 2026
Languages: MATLAB (1980), C (29), C/C++ (15), Jupyter (5), C++ (1)
Size: 3,062 files, 2,030 scripts
Software Heritage: archived
Found in: “Code availability”
Holds: README, license file, tests, documentation, 4 notebooks
Not found: CITATION.cff, environment file, continuous integration
Tools: EEGLAB (172 files), Statistics and Machine Learning Toolbox (97 files), Chronux (74 files), Signal Processing Toolbox (64 files), CircStat (41 files), Image Processing Toolbox (12 files), FieldTrip (9 files), Matplotlib (5 files), NumPy (5 files), Optimization Toolbox (4 files), Parallel Computing Toolbox (4 files), Neurodata Without Borders (PyNWB, MatNWB) (3 files), SciPy (3 files), boundedline (2 files), scikit-image (2 files), export_fig (1 file), Curve Fitting Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/buzsakilab/buzcode

RouxLaboratory/Casali_2026

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 392515da63612a95ebd7091e390e2c90f459c999, 24 April 2026
Languages: MATLAB (63), Jupyter (2)
Size: 80 files, 65 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (13 files), Signal Processing Toolbox (4 files), h5py (2 files), Keras (2 files), Image Processing Toolbox (2 files), Matplotlib (2 files), NumPy (2 files), SciPy (2 files), TensorFlow (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
67 files
At the source: github.com/RouxLaboratory/Casali_2026.git

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-73106-z.

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;
  • 2,063 scripts, each with its path and the digest of its content;
  • 23 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 statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-73106-z.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 13 MeSH terms, 1 funder, 91 references.

Cite

This paper

Casali, G., Miermon, C., Terral, G., Ravassard, P., Gervois, T., Dolique, T., Harrell, E. R., Spitsyn, A., Lesburguères, E., Jarriault, D., Gambino, F., Chenouard, N., & Roux, L. (2026). Respiratory pauses highlight sleep architecture in mice. Nature communications, 17(1), 6620. https://doi.org/10.1038/s41467-026-73106-z

BibTeX

@article{casali2026respiratory,
author = {Casali, Giulio and Miermon, Camille and Terral, Geoffrey and Ravassard, Pascal and Gervois, Tim and Dolique, Tiphaine and Harrell, Evan R and Spitsyn, Alena and Lesburguères, Edith and Jarriault, David and Gambino, Frédéric and Chenouard, Nicolas and Roux, Lisa},
title = {{Respiratory pauses highlight sleep architecture in mice}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6620},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73106-z},
url = {https://doi.org/10.1038/s41467-026-73106-z},
pmid = {42156372},
pmcid = {PMC13381926}
}

RIS

TY - JOUR
AU - Casali, Giulio
AU - Miermon, Camille
AU - Terral, Geoffrey
AU - Ravassard, Pascal
AU - Gervois, Tim
AU - Dolique, Tiphaine
AU - Harrell, Evan R
AU - Spitsyn, Alena
AU - Lesburguères, Edith
AU - Jarriault, David
AU - Gambino, Frédéric
AU - Chenouard, Nicolas
AU - Roux, Lisa
TI - Respiratory pauses highlight sleep architecture in mice
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/19
VL - 17
IS - 1
SP - 6620
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73106-z
UR - https://doi.org/10.1038/s41467-026-73106-z
LA - en
ER -

CSL-JSON

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