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Arcuate kisspeptin neurons display two types of calcium events: Regulation by sex, gonads, and senktide.

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 · 4 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § METHODS ↔ R/9_shiny_app/05-app/app-baseline-sig.R, lines 1533 to 1615 · score 0.86 · Linear mixed models, Kenward Roger, Post hoc comparisons, freedom, fit, marginal · Report a mismatch
  2. [2] § METHODS ↔ matlab/1_import_moco/td_moco2.m, the whole file · a weak match · score 0.65 · rigid motion correction, NoRMCorre, translation, frame, filter, MATLAB · Report a mismatch
  3. [3] § METHODS ↔ matlab/5_slow/custardWrapper3.m, the whole file · a weak match · score 0.61 · threshold factor, minimum event, noise, Custard, algorithm, window · Report a mismatch
  4. [4] § METHODS ↔ matlab/4_custard/custardWrapper3.m, the whole file · a weak match · score 0.61 · threshold factor, minimum event, noise, Custard, algorithm, window · Report a mismatch
  5. [5] § METHODS ↔ matlab/5_slow/custardWrapper3.m, the whole file · a weak match · score 0.53 · Custard algorithm, event detection, window, peak, fast · Report a mismatch

Paper

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The authors' code

MATLAB · 129 lines · 4.8 KB · MIT · 2 matches

  1. function events = custardWrapper3( timing, fluo_matrix, params_in, run_params_fields, detailed )
  2. % version 3 adapted for multiple runs on the same data
  4. % expects params in seconds, converts to points based on timing
  6. % params_in contains multiple sets of custard parameters
  7. % all inputs in real units, converted to points below
  8. % timing 1 x npoints
  9. % fluo nROIs x npoints
  10. % params_in, detailed structure with params for multiple runs
  11. % detailed, optional flag for showing details of custard algorithm
  12. % params_name = run_params_fields{iRun}; % names of the param sets to
  13. % run
  14. % custard_params = params_in.(params_name); % param sets
  15. % forceThresh = custard_params.forceThresh; % dF/F0,
  16. % set to -1 for autothresh
  17. % tXrise = custard_params.FWHMfraction; % fraction of peak for width
  18. % psig = custard_params.psig; % ttest sig for test windows
  19. % win = custard_params.win; % seconds, duration of test windows
  20. % minWidth = custard_params.minWidth;
  22. events.start=datetime;
  24. detailFlag=false;
  25. if nargin==5
  26. detailFlag=detailed;
  27. end
  29. % handle multiple sets of custard params: EZ, Medium, Strict
  30. nRuns = length(run_params_fields);
  32. % handle run based changes in data
  33. if ndims(fluo_matrix)==3
  34. [nRuns2,nROIs,nPoints]=size(fluo_matrix);
  35. runMatrixFlag=true;
  36. %validate runs
  37. if nRuns2~=nRuns
  38. runMatrixFlag=false;
  39. disp('custardWrapper: nRuns must match first dimension of fluo_matrix')
  40. end
  41. else
  42. [nROIs,nPoints]=size(fluo_matrix);
  43. runMatrixFlag=false;
  44. end
  46. for iRun = 1:nRuns % loop over the analysis parameter sets
  48. params_name = run_params_fields{iRun};
  49. custard_params = params_in.(params_name);
  51. forceThresh = custard_params.forceThresh; % dF/F0, -1 for autothresh
  52. % if forceThresh==-1 % future version might enable this
  53. % threshold_type= custard_params.threshold_type;
  54. % threshold_factor= custard_params.threshold_factor;
  55. % end
  56. tXrise = custard_params.FWHMfraction; % fraction of peak for width
  57. psig = custard_params.psig; % ttest sig for test windows
  58. win = custard_params.win; % seconds, duration of test windows
  59. minWidth = custard_params.minWidth; % seconds, minimum event width
  61. noFig=true;
  62. if isfield(params_in,'showPlots') && params_in.showPlots == 1
  63. noFig = false;
  64. end
  66. dt = mean( diff( timing ) );
  67. minWidthPoints = floor( minWidth/dt );
  68. npnts = round( win/dt );
  70. silent=1;
  71. parstor=nan(nROIs,nPoints);
  72. for iROI=1:nROIs
  73. if runMatrixFlag
  74. parstor(iROI,:)=squeeze( fluo_matrix( iRun, iROI, : ) ); % iRun is the root loop
  75. else
  76. parstor(iROI,:) = squeeze( fluo_matrix( iROI, : ) );
  77. end
  78. end
  79. thresholds=nan(nROIs,1);
  80. parfor iROI=1:nROIs
  81. % for iROI=1:nROIs
  82. if forceThresh == 0
  83. % noise_estimate = TDnoise2( squeeze(parstor(iROI,:)), dt, threshold_type );
  84. % minHeight = noise_estimate * threshold_factor;
  85. minHeight=0;
  86. else
  87. minHeight = forceThresh;
  88. end
  89. thresholds(iROI) = minHeight;
  90. custardOutput(iROI) = TDcustard2( npnts, squeeze(parstor(iROI,:)), psig, minHeight, minWidthPoints, tXrise, noFig, detailFlag, silent );
  91. end
  92. % add the thresholds for each ROI to the output of the event detection
  93. custard_params.thresholds = thresholds;
  95. events.params = params_in;
  96. events.analyses = run_params_fields;
  97. if ~isempty(custardOutput)
  98. % fast output data structure
  99. for iROI=1:nROIs
  100. events.(params_name)(iROI).pks = custardOutput(iROI).peak;
  101. events.(params_name)(iROI).locs = ( custardOutput(iROI).peakLoc-1 ) * dt;
  102. events.(params_name)(iROI).w = custardOutput(iROI).peakWidth * dt;
  103. events.(params_name)(iROI).p = custardOutput(iROI).peakAmp;
  105. wxPk = zeros( length(custardOutput(iROI).peak), 2 );
  106. wxPk(:,1) = (custardOutput(iROI).t50riseIndex-1) * dt;
  107. wxPk(:,2) = (custardOutput(iROI).t50fallIndex-1) * dt;
  108. events.(params_name)(iROI).wxPk = wxPk;
  109. events.(params_name)(iROI).custard = custardOutput(iROI);
  110. events.(params_name)(iROI).custard_params = custard_params;
  111. events.(params_name)(iROI).run_date = datetime;
  112. end
  113. else
  114. for iROI=1:nROIs
  115. events.(params_name)(iROI).pks = [];
  116. events.(params_name)(iROI).locs = [];
  117. events.(params_name)(iROI).w = [];
  118. events.(params_name)(iROI).p = [];
  119. events.(params_name)(iROI).wxPk = [];
  120. events.(params_name)(iROI).custard = custardOutput;
  121. events.(params_name)(iROI).custard_params = custard_params;
  122. events.(params_name)(iROI).run_date = datetime;
  123. end
  124. end
  125. end % runs: loop over multiple custard runs!
  126. events.data = []; % fluo_matrix;
  127. events.timing = []; % timing;
  129. events.end=datetime;

custardWrapper3.m at commit 6c35cb4, under MIT · at the source

Overview

Authors: R. Anthony DeFazio1, Christian R. Burgess1,2, Laura L. Burger1, Amanda G. Gibson3, Suzanne M. Moenter1,4,5,6
ORCID iDs: R. Anthony DeFazio, Christian R. Burgess, Laura L. Burger, Amanda G. Gibson, Suzanne M. Moenter
  1. Department of Molecular and Integrative Physiology University of Michigan Ann Arbor Michigan USA
  2. Michigan Neuroscience Institute University of Michigan Ann Arbor Michigan USA
  3. Department of Psychology and Neuroscience Regis University Denver Colorado USA
  4. Department of Internal Medicine University of Michigan Ann Arbor Michigan USA
  5. Department of Obstetrics and Gynecology University of Michigan Ann Arbor Michigan USA
  6. The Reproductive Sciences Program University of Michigan Ann Arbor Michigan USA
Institutions: University of Michigan (United States); Regis University (United States); Michigan Medicine (United States)
Journal: Journal of neuroendocrinology, volume 38, issue 11, article e70298
Dates: received 28 May 2026; accepted 7 October 2026; published online 27 October 2026; in print November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/jne.70298 · PMID 42935294 · PMCID PMC13711095 · OpenAlex W7223973855
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: optical imaging (calcium, voltage, 2-photon) (modality), mouse (organism), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: arcuate, calcium imaging, GnRH, pulse generator, reproduction
MeSH: Arcuate Nucleus of Hypothalamus*, Calcium*, Calcium Signaling*, Kisspeptins*, Neurons*, Peptide Fragments*, Substance P*, Action Potentials, Animals, Dynorphins, Female, Gonadotropin-Releasing Hormone, Hypothalamic-Pituitary-Gonadal Axis, Male, Mice, Neurokinin B (* major topic)
Topic: Hypothalamic control of reproductive hormones (Reproductive Medicine, Medicine), according to OpenAlex
Funding: Eunice Kennedy Shriver National Institute of Child Health and Human Development (R37HD034860)
Citations: not cited yet (Europe PMC); 71 references in the paper

Abstract

The pattern of gonadotropin‐releasing hormone (GnRH) release controls reproduction in vertebrates and is regulated by feedback from the gonads. Evidence is accumulating that steroid‐sensitive hypothalamic arcuate kisspeptin–neurokinin B–dynorphin (KNDy) neurons drive GnRH pulses. KNDy neurons exhibit coordinated, large amplitude calcium signals in vivo that precede LH pulses; coordinated events persist in brain slice recordings of these cells. We used cre‐lox to target GCaMP6f expression to KNDy neurons. Brain slice calcium imaging was done to identify putative unitary calcium events. Newly devised analyses identified two types of calcium events (fast and slow) in intact and gonadectomized male and female adult mice. Neurokinin B and the neurokinin‐3 receptor agonist senktide are strong activators of action potential firing in KNDy neurons. The effect of senktide on calcium events over 5‐min baseline and treatment periods was determined. Gonadectomy unexpectedly increased GCaMP6f fluorescence; illumination levels were adjusted to standardize initial levels. Senktide increased the frequency of fast and slow events; this effect was enhanced in gonadectomized mice for fast events. Senktide increased fast event amplitude only in gonadectomized mice. For slow events, in contrast, senktide increased amplitude in all groups, with the increase being larger in gonadectomized than intact mice. Differential regulation by gonadectomy and senktide modulation of fast and slow events suggests two separate mechanisms for these events, which likely also differ from the large amplitude events observed in vivo in KNDy neurons.

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 5 matches between paragraphs and lines of code.

syncytium2/KNDy-Ca-1

License: MIT
State: the link answers, verified on 1 November 2026
Evidence: files inventoried
Commit: 6c35cb4b057a6d2446adf41086562093f6e12e8c, 15 October 2026
Languages: MATLAB (138), R (39), Python (2)
Size: 204 files, 179 scripts
Software Heritage: not archived
Found in: the text, “METHODS”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (7 files), Image Processing Toolbox (6 files), tidyverse (6 files), Signal Processing Toolbox (3 files), ggplot2 (2 files), afex (1 file), cowplot (1 file), easystats (1 file), emmeans (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), multcomp (1 file), rstatix (1 file)
Availability: 1 check, the latest on 1 November 2026: the link answers
  • 1 November 2026: the link answers
181 files
At the source: github.com/syncytium2/KNDy-Ca-1

gibson-amandag/ArcKissCa_analysis

License: none: the authors keep all their rights
State: the link answers, verified on 1 November 2026
Evidence: files inventoried
Commit: e0b329ecd15b41a4c82438de4c79cebb70becb66, 11 September 2026
Languages: R (119)
Size: 124 files, 119 scripts
Software Heritage: not archived
Found in: the text, “METHODS”
Holds: README, 28 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (18 files), nlme (3 files), emmeans (2 files), ggplot2 (2 files), afex (1 file), broom (1 file), easystats (1 file), ggpubr (1 file), lme4 (1 file), lmerTest (1 file), rstatix (1 file)
Availability: 1 check, the latest on 1 November 2026: the link answers
  • 1 November 2026: the link answers
120 files
At the source: github.com/gibson-amandag/ArcKissCa_analysis

Tracing map

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  • 298 scripts, each with its path and the digest of its content;
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Data

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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.

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Version 1, 1 November 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 16 MeSH terms, 1 funder, 53 references.

Cite

This paper

DeFazio, R. A., Burgess, C. R., Burger, L. L., Gibson, A. G., & Moenter, S. M. (2026). Arcuate kisspeptin neurons display two types of calcium events: Regulation by sex, gonads, and senktide. Journal of neuroendocrinology, 38(11), e70298. https://doi.org/10.1111/jne.70298

BibTeX

@article{defazio2026arcuate,
author = {DeFazio, R. Anthony and Burgess, Christian R. and Burger, Laura L. and Gibson, Amanda G. and Moenter, Suzanne M.},
title = {{Arcuate kisspeptin neurons display two types of calcium events: Regulation by sex, gonads, and senktide}},
journal = {Journal of neuroendocrinology},
year = {2026},
month = nov,
volume = {38},
number = {11},
pages = {e70298},
publisher = {Wiley},
issn = {0953-8194},
doi = {10.1111/jne.70298},
url = {https://doi.org/10.1111/jne.70298},
pmid = {42935294},
pmcid = {PMC13711095}
}

RIS

TY - JOUR
AU - DeFazio, R. Anthony
AU - Burgess, Christian R.
AU - Burger, Laura L.
AU - Gibson, Amanda G.
AU - Moenter, Suzanne M.
TI - Arcuate kisspeptin neurons display two types of calcium events: Regulation by sex, gonads, and senktide
T2 - Journal of neuroendocrinology
J2 - J Neuroendocrinol
PY - 2026
DA - 2026/11/01
VL - 38
IS - 11
SP - e70298
SN - 0953-8194
PB - Wiley
DO - 10.1111/jne.70298
UR - https://doi.org/10.1111/jne.70298
LA - en
ER -

CSL-JSON

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"id": "10.1111/jne.70298",
"type": "article-journal",
"title": "Arcuate kisspeptin neurons display two types of calcium events: Regulation by sex, gonads, and senktide",
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"author": [
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"family": "DeFazio",
"given": "R. Anthony"
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"container-title-short": "J Neuroendocrinol",
"volume": "38",
"issue": "11",
"page": "e70298",
"DOI": "10.1111/jne.70298",
"PMID": "42935294",
"PMCID": "PMC13711095",
"ISSN": "0953-8194",
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"URL": "https://doi.org/10.1111/jne.70298",
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"issued": {
"date-parts": [
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2026,
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1
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]
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}

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