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Female Mice Show Stronger Time-of-Day Modulation of Astrocytic Ca<sup>2+</sup> Activity in the Sleep-Regulatory Ventrolateral Preoptic Nucleus.

Code ↔ Paper

2 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 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results ↔ Functions/segWithBaseCorr.m, lines 1–43 · score 0.57 · baseline correction, event amplitude, event frequency, detection, traces, decay
  2. [2] § Results ↔ Functions/pairCrossCorr.m, the whole file · a weak match · score 0.54 · pairwise Pearson, correlation curve, threshold, signals, active

Paper

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The authors' code

MATLAB · 174 lines · 8.6 KB · CC0-1.0 · 1 match

  1. function [meanFreq,realEvts,diffToBaseAll,varList] = ...
  2. segWithBaseCorr(signalMat,dt,dtev,minDepth,maxEvThresh,Tev,cfDown,smoothParam,evNumVec,GlobalEventsTime)
  3. % This function segments the input signals in signalMat and extract the
  4. % segmented events and inter-events features detailed in *Outputs*.
  5. % ----------------------------------------------------------------------- %
  6. % *** Inputs ***
  7. % * signalMat * = matrix containning the signals to segment
  8. % each trace must be in one column i.e. size(signalMat) = N x nT where N is
  9. % the number of time samples and nT the number of traces.
  10. % with N the length in time and nT the number of traces
  11. % * dt * = signal time sampling
  12. % * dtev * = caracteristic event time
  13. % (to be removed around peaks to build the signal outside of peaks)
  14. % * minDepth * = min variation level to detect the peaks
  15. % * maxEvThresh * = min threshold for event detection if std(noise) is too high
  16. % * Tev * = coef to noise for event detection threshold
  17. % then the event detection threshold is t_ev = min(noiseCoef*std(signal noise),minEvThresh)
  18. % * cfDown * = coef to compensate slower decay than increase for the events
  19. % (to build the signal outside of peaks)
  20. % * smoothParam * = smoothing level for the spline for baseline computation
  21. % * evNumVec / GlobalEventsTime * OPTIONAL = Global events number (outputs
  22. % from globalEvSeg) - NECESSARY for Graphs and spatial correlations
  23. % analysis
  24. % ----------------------------------------------------------------------- %
  25. % *** Outputs ***
  26. % meanFreq = mean event frequency for each trace in signalMat
  27. % realEvts{:,1} = beginning of events
  28. % realEvts{:,2} = peak of events
  29. % realEvts{:,3} = end of events
  30. % realEvts{:,4} = global event number (for spatial analysis in image data)
  31. % realEvts{:,5} = events amplitude
  32. % realEvts{:,6} = number of sub peaks
  33. % realEvts{:,7} = sub peak frequency
  34. % varList = list of traces with active events (vector)
  35. % diffToBaseAll = matrix of size(*signalMat*) containing the corrected
  36. % signals (i.e. with baseline correction)
  37. % ----------------------------------------------------------------------- %
  38. % L. Zonca, Jan. 2022
  39. % ----------------------------------------------------------------------- %
  41. [N,nT] = size(signalMat);
  42. % Colormap for the plots
  43. cmap = hsv(max(10,nT));
  45. % Time vector
  46. timeVec = 1:N;
  47. % Global envents info (for spatial analysis when image data)
  48. if ~exist('evNumVec','var') || ~exist('GlobalEventsTime','var')
  49. evNumVec = zeros(size(timeVec));
  50. GlobalEventsTime = [1 N];
  51. else
  52. GlobalEventsTime = GlobalEventsTime';
  53. end
  55. % Initiate outputs
  56. meanFreq = zeros(nT,1);
  57. realEvts = cell(nT,7);
  58. varList = 1:nT;
  59. diffToBaseAll = zeros(size(signalMat));
  61. for traceIdx = 1:nT
  62. signal = signalMat(:,traceIdx);
  63. % plot the trace
  64. figure
  65. plot(timeVec*dt,signal,'LineWidth',1,'color',cmap(traceIdx,:))
  66. % Detect peaks
  67. noEvtsPeriods = timeVec;
  68. [eventsVal,eventsTime] = findpeaks(signal,'MinPeakProminence',minDepth);
  69. % Extract signal outside of peaks for baseline fit
  70. for ev = 1:length(eventsTime)
  71. if eventsTime(ev)>dtev && eventsTime(ev)+cfDown*dtev<=timeVec(end)
  72. noEvtsPeriods = setdiff(noEvtsPeriods, eventsTime(ev)-dtev:eventsTime(ev)+cfDown*dtev);
  73. elseif eventsTime(ev)>dtev
  74. noEvtsPeriods = setdiff(noEvtsPeriods, eventsTime(ev)-dtev:timeVec(end));
  75. elseif eventsTime(ev)+cfDown*dtev<=timeVec(end)
  76. noEvtsPeriods = setdiff(noEvtsPeriods, 1:eventsTime(ev)+cfDown*dtev);
  77. end
  78. end
  79. % Process only if detected events
  80. if ~isempty(eventsTime)
  81. % Correct boundaries of the signal outside of peaks at beginning and end
  82. if eventsTime(1)<dtev
  83. noEvtsPeriods = [1 noEvtsPeriods];
  84. end
  85. noEvtSig = zeros(size(signal));
  86. noEvtSig(noEvtsPeriods,:) = signal(noEvtsPeriods,:);
  87. noEvtSig(GlobalEventsTime,:) = ...
  88. repmat((mean(signal)-min(signal))/2+min(signal),...
  89. length(GlobalEventsTime),1);
  90. if ~ismember(length(signal(:,1)),noEvtsPeriods)
  91. noEvtSig(end,:) = (mean(signal)-min(signal))/2+min(signal);
  92. noEvtsPeriods = [union(noEvtsPeriods,GlobalEventsTime), length(signal(:,1))];
  93. else noEvtsPeriods = union(noEvtsPeriods,GlobalEventsTime);
  94. end
  95. % --- OUPUT 1 --- Mean oscillation frequency (between peaks) ---
  96. if length(eventsTime)>=2
  97. periodes = (eventsTime(2:end)-eventsTime(1:end-1))*dt;
  98. frequences = 1./periodes;
  99. meanFreq(traceIdx) = mean(frequences);
  100. end
  101. hold on
  102. % Plot detected peaks
  103. plot(eventsTime*dt,eventsVal,'.','MarkerSize',20,'color',cmap(traceIdx,:))
  104. % Plot signal used for baseline fit
  105. plot(noEvtsPeriods*dt,noEvtSig(noEvtsPeriods,:),'LineWidth',1.5,'color','r')
  106. % Fit the baseline outside of events
  107. baseLine = fit(noEvtsPeriods',noEvtSig(noEvtsPeriods,:),'smoothingspline','SmoothingParam',smoothParam);
  108. % Plot the fit
  109. plot(noEvtsPeriods*dt,baseLine(noEvtsPeriods),'k','LineWidth',1.5)
  110. baseLineInt = interp1(unique(noEvtsPeriods),baseLine(unique(noEvtsPeriods)),timeVec,'nearest');
  111. % Signal correction: remove baseline
  112. diffToBase = signal-baseLineInt';
  113. diffToBaseAll(:,traceIdx) = diffToBase;
  114. % Plot corrected signal
  115. plot(timeVec*dt,signal-baseLineInt','color','g','LineWidth',1.5)
  116. plot(timeVec*dt,zeros(size(timeVec)),'k','LineWidth',1.5)
  118. % --- OUTPUT 2 --- beginning-peak-end times / amplitude / number of supeaks - sub peak frequency ---
  119. % Detect event boundaries using corrected signal (diffToBase)
  120. signDiffToBase = diffToBase(2:end).*diffToBase(1:end-1);
  121. newEvents = find(signDiffToBase <= 0);
  123. realEvts{traceIdx,1} = []; % beginning of events
  124. realEvts{traceIdx,2} = []; % peak of events
  125. realEvts{traceIdx,3} = []; % end of events
  126. realEvts{traceIdx,4} = []; % global event number (for spatial analysis)
  127. realEvts{traceIdx,5} = []; % events amplitude
  128. realEvts{traceIdx,6} = []; % number of sub peaks inside events
  129. realEvts{traceIdx,7} = []; % sub peak frequency
  130. % Define minimal event amplitude proportional to noise std dev of this astrocyte
  131. noiseAmpli = std(diffToBase(noEvtsPeriods));
  132. minEvAmpli = min(Tev*noiseAmpli,maxEvThresh);
  133. for ev = 1:length(newEvents)
  134. [evVal,evTime] = max(diffToBase(newEvents(max(1,ev-1)):newEvents(ev)));
  135. if evVal > minEvAmpli % If peak amplitude is too small, discard event
  136. realEvts{traceIdx,1} = [realEvts{traceIdx,1}; newEvents(max(1,ev-1))];
  137. realEvts{traceIdx,2} = [realEvts{traceIdx,2}; newEvents(max(1,ev-1))+evTime-1];
  138. realEvts{traceIdx,3} = [realEvts{traceIdx,3}; newEvents(ev)];
  139. realEvts{traceIdx,4} = [realEvts{traceIdx,4}; evNumVec(newEvents(max(1,ev-1))+evTime-1)];
  140. realEvts{traceIdx,5} = [realEvts{traceIdx,5}; diffToBase(newEvents(max(1,ev-1))+evTime-1)];
  141. try
  142. [subPeakVal,subPeakTime] = findpeaks(diffToBase(newEvents(max(1,ev-1)):newEvents(ev)),...
  143. 'MinPeakProminence',minEvAmpli/1.5);
  144. if ~isempty(subPeakTime)
  145. realEvts{traceIdx,6} = [realEvts{traceIdx,6}; numel(subPeakVal)];
  146. plot(timeVec(newEvents(max(1,ev-1))+subPeakTime-1)*dt,...
  147. diffToBase(newEvents(max(1,ev-1))+subPeakTime-1),...
  148. 'x','color',cmap(mod(ev,length(cmap)),:),'MarkerSize',8,'LineWidth',2)
  149. if numel(subPeakVal)>1
  150. realEvts{traceIdx,7} = [realEvts{traceIdx,7}; 1/(mean((subPeakTime(2:end)-subPeakTime(1:end-1)))*dt)];
  151. end
  152. end
  153. catch
  154. warning(['Skipped evt ' num2str(ev)])
  155. end
  156. end
  157. end
  158. plot(realEvts{traceIdx,2}*dt,realEvts{traceIdx,5}+baseLineInt(realEvts{traceIdx,2})','.','MarkerSize',20,'color',cmap(traceIdx,:))
  159. title(num2str(minEvAmpli))
  160. plot(noEvtsPeriods*dt,baseLine(noEvtsPeriods)+minEvAmpli,'k--','LineWidth',1.)
  161. plot(noEvtsPeriods*dt,baseLine(noEvtsPeriods)-minEvAmpli,'k--','LineWidth',1.)
  162. xlim([min(timeVec*dt),max(timeVec*dt)])
  163. xlabel('Time (s)')
  164. ylabel(['Trace # ' num2str(traceIdx)])
  165. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  166. % Keep this trace only if it has active events
  167. if length(realEvts{traceIdx,2})<1
  168. varList = setdiff(varList,traceIdx);
  169. close
  170. end
  171. else varList = setdiff(varList,traceIdx);
  172. close
  173. end
  174. end

segWithBaseCorr.m at commit e68a897, under CC0-1.0 · at the source

Overview

Authors: Félix Camille Bellier1,2, Lou Zonca3,4, David Holcman4, Frédéric Chauveau2, Nathalie Rouach1, Armelle Rancillac1
  1. Neuroglial Interactions in Cerebral Physiology and Pathologies, Center for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM Université PSL, PSL‐Neuro Paris France
  2. IRBA (Institut de Recherche Biomédicale Des Armées) Brétigny‐sur‐Orge France
  3. Toulouse Neuroimaging Center, INSERM UMR1214 Toulouse France
  4. Group of Applied Mathematics and Computational Biology, Ecole Normale Supérieure, PSL University Paris France
Institutions: Centre National de la Recherche Scientifique (France); Inserm (France); Collège de France (France); Université Paris Sciences et Lettres (France); Centre Interdisciplinaire de Recherche en Biologie (France); École Normale Supérieure - PSL (France); Toulouse NeuroImaging Center (France)
Journal: Glia, volume 74, issue 8, article e70181
Dates: received 18 March 2026; accepted 21 May 2026; published online 7 June 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/glia.70181 · PMID 42252582 · PMCID PMC13243727 · OpenAlex W7163821820
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Preprocessing, Graphs, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: AstroNet, calcium imaging, network, NREM sleep, sex differences, sleep regulation, VLPO
MeSH: Astrocytes*, Calcium*, Calcium Signaling*, Circadian Rhythm*, Preoptic Area*, Sex Characteristics*, Sleep*, Animals, Female, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Time Factors (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Agence Nationale de la Recherche (French National Research Agency) (ANR‐10‐IDEX‐0001)
Citations: not cited yet (Europe PMC); 32 references in the paper

Abstract

Astrocytes actively contribute to sleep regulation through intracellular calcium (Ca2+) signaling. Yet, whether astrocytic dynamics within sleep‐promoting hypothalamic nuclei vary across the nycthemeral cycle in a sex‐dependent manner remains unknown. The ventrolateral preoptic area (VLPO) is a key sleep‐promoting nucleus whose neuronal circuitry has been extensively characterized. However, the local astrocytic Ca2+ activity remains poorly defined. Here, we investigated astrocytic Ca2+ signaling in the VLPO of male and female mice across the nycthemeral cycle. Using two‐photon Ca2+ imaging in acute VLPO‐containing brain slices prepared at Zeitgeber Time (ZT)‐2, corresponding to the onset of the rest period, and ZT‐14, corresponding to the beginning of the active period, we combined single‐event analyses with graph‐based network approaches to characterize astrocytic activity across scales. At the level of individual events, spontaneous astrocytic Ca2+ dynamics exhibited marked state dependence and sexual dimorphism. In males, Ca2+ events were smaller and faster at ZT‐14 than at ZT‐2 (shorter duration and accelerated rise and decay time). In contrast, in females, ZT‐14 was characterized by increased event amplitude and frequency, consistent with upregulated Ca2+ signaling during the active phase. At the network level, functional connectivity remained stable in males. Conversely, females exhibited robust network remodeling at ZT‐14, including increased astrocyte recruitment, higher node degree of correlations, and a marked rise in the number and proportion of highly connected astrocytes. Together, these findings reveal sex‐specific astrocytic signaling strategies in the VLPO across the nycthemeral cycle and underscore the need to incorporate sex as a biological variable in astrocyte‐based sleep research.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

louzonca/AstroNet

License: CC0-1.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: e68a8970c6e3db32f4f30f51bfc0b2e79aea5c78, 11 December 2024
Languages: MATLAB (14)
Size: 18 files, 14 scripts
Software Heritage: not archived
Found in: the text, “Astrocytic Ca 2+ Analysis and Functional Network”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (4 files), Statistics and Machine Learning Toolbox (3 files), Signal Processing Toolbox (2 files), Violinplot-Matlab (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
16 files
At the source: github.com/louzonca/AstroNet

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • 2 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 data that support the findings of this study are available from the corresponding author upon reasonable request.

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 3, 28 September 2026

  • Publisher: — → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 7 keywords, 14 MeSH terms, 1 funder, 30 references.

Cite

This paper

Bellier, F. C., Zonca, L., Holcman, D., Chauveau, F., Rouach, N., & Rancillac, A. (2026). Female Mice Show Stronger Time-of-Day Modulation of Astrocytic Ca&lt;sup&gt;2+&lt;/sup&gt; Activity in the Sleep-Regulatory Ventrolateral Preoptic Nucleus. Glia, 74(8), e70181. https://doi.org/10.1002/glia.70181

BibTeX

@article{bellier2026female,
author = {Bellier, Félix Camille and Zonca, Lou and Holcman, David and Chauveau, Frédéric and Rouach, Nathalie and Rancillac, Armelle},
title = {{Female Mice Show Stronger Time-of-Day Modulation of Astrocytic Ca\&lt;sup\&gt;2+\&lt;/sup\&gt; Activity in the Sleep-Regulatory Ventrolateral Preoptic Nucleus}},
journal = {Glia},
year = {2026},
month = aug,
volume = {74},
number = {8},
pages = {e70181},
publisher = {Wiley},
issn = {0894-1491},
doi = {10.1002/glia.70181},
url = {https://doi.org/10.1002/glia.70181},
pmid = {42252582},
pmcid = {PMC13243727}
}

RIS

TY - JOUR
AU - Bellier, Félix Camille
AU - Zonca, Lou
AU - Holcman, David
AU - Chauveau, Frédéric
AU - Rouach, Nathalie
AU - Rancillac, Armelle
TI - Female Mice Show Stronger Time-of-Day Modulation of Astrocytic Ca&lt;sup&gt;2+&lt;/sup&gt; Activity in the Sleep-Regulatory Ventrolateral Preoptic Nucleus
T2 - Glia
J2 - Glia
PY - 2026
DA - 2026/08/01
VL - 74
IS - 8
SP - e70181
SN - 0894-1491
PB - Wiley
DO - 10.1002/glia.70181
UR - https://doi.org/10.1002/glia.70181
LA - en
ER -

CSL-JSON

{
"id": "10.1002/glia.70181",
"type": "article-journal",
"title": "Female Mice Show Stronger Time-of-Day Modulation of Astrocytic Ca&lt;sup&gt;2+&lt;/sup&gt; Activity in the Sleep-Regulatory Ventrolateral Preoptic Nucleus",
"container-title": "Glia",
"author": [
{
"family": "Bellier",
"given": "Félix Camille"
},
{
"family": "Zonca",
"given": "Lou"
},
{
"family": "Holcman",
"given": "David"
},
{
"family": "Chauveau",
"given": "Frédéric"
},
{
"family": "Rouach",
"given": "Nathalie"
},
{
"family": "Rancillac",
"given": "Armelle"
}
],
"container-title-short": "Glia",
"volume": "74",
"issue": "8",
"page": "e70181",
"DOI": "10.1002/glia.70181",
"PMID": "42252582",
"PMCID": "PMC13243727",
"ISSN": "0894-1491",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/glia.70181",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
1
]
]
}
}

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