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Brainstem neurons coordinate the bladder and urethral sphincter for urination.

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The authors' code

MATLAB · 198 lines · 6.9 KB · Apache-2.0

  1. name={'1'};
  2. cc_peak_mouse=[];cc_all=[];cc_all_rand=[];
  3. for mouseNum=1:size(name,2)
  4. data1 = xlsread([name{mouseNum} '.xlsx'],1);
  5. % data2 = xlsread('data1.xlsx',2);
  7. %%%
  8. testdata1=cell(1,size(data1,2));
  9. for i=1:size(data1,2)
  10. ind=~isnan(data1(:,i));
  11. testdata1{i}=data1(ind,i);
  12. end
  14. % testdata2=cell(1,6);
  15. % for i=1:size(data2,2)
  16. % ind=~isnan(data2(:,i));
  17. % testdata2{i}=data2(ind,i);
  18. % end
  20. %%%
  21. fiber_data_pool=testdata1(2:2:end);
  22. pressure_data_pool=testdata1(1:2:end);
  24. fiber_data=[];pressure_data=[];
  25. for j=1:size(fiber_data_pool,2)
  26. fiber_data=[fiber_data fiber_data_pool{j}];
  28. StimulusData=pressure_data_pool{j};
  29. calclium_stimu_Time=(0:length(StimulusData)-1)/800;
  30. threshold = (max(StimulusData) - mean(StimulusData))*0.2+mean(StimulusData);
  31. PosAbove=find(StimulusData>threshold);
  32. diff_stimu = PosAbove(2:end) - PosAbove(1:end-1);
  33. if PosAbove(1)>=2
  34. ind_find = [PosAbove(1); PosAbove];
  35. end
  36. % plot(StimulusData)
  37. % hold on
  38. % plot(ind_find,StimulusData(ind_find),'r+')
  39. % 计算出压力的rate
  40. StimulusData_erzhi=zeros(length(StimulusData),1);
  41. StimulusData_erzhi(ind_find)=1;
  42. interval=20;pressure_data_curve=[];
  43. for i=1:length(StimulusData)/interval
  44. pressure_data_curve(i)=sum(StimulusData_erzhi((i-1)*interval+1:i*interval));
  45. end
  47. pressure_data_curve_envelop=envelope(pressure_data_curve,20,'rms');
  48. % figure
  49. % plot(pressure_data_curve_envelop)
  50. pressure_data=[pressure_data pressure_data_curve_envelop'];
  51. end
  53. %%%
  54. fs1=2000;
  55. fs2=800;
  56. data_sec=size(fiber_data,1)/fs1;
  57. data_length=fs2*data_sec;
  58. clear cc;
  59. for i=1:size(fiber_data,2)
  60. fiber_resample=fiber_data(1:50:end,i);
  61. presure_resample=pressure_data(1:1:end,i);
  62. fiber_mean=mean(fiber_resample);
  63. fiber_std=std(fiber_resample);
  64. fiber_zscore=(fiber_resample-fiber_mean)/fiber_std;
  65. presure_mean=mean(presure_resample);
  66. presure_std=std(presure_resample);
  67. presure_zscore=(presure_resample-presure_mean)/presure_std;
  68. [c,lags] = xcorr(fiber_zscore,presure_zscore,'coeff');
  69. cc(i,:)=c;
  70. end
  72. %%%%
  74. clear cc_rand cc_rand_all pos_rand_all;
  76. pressure_data_long=reshape(pressure_data(1:1:end,:),[size(pressure_data(1:1:end,:),1)*size(pressure_data(1:1:end,:),2), 1]);
  77. for n=1:10
  78. for i=1:size(fiber_data,2)
  79. fiber_resample=fiber_data(1:50:end,i);
  80. pos_rand_all(n,i)=randsample(length(pressure_data_long)-size(pressure_data(1:1:end,:),1),1);
  81. presure_resample=pressure_data_long(pos_rand_all(n,i)+1:pos_rand_all(n,i)+size(pressure_data(1:1:end,:),1));
  82. fiber_mean=mean(fiber_resample);
  83. fiber_std=std(fiber_resample);
  84. fiber_zscore=(fiber_resample-fiber_mean)/fiber_std;
  85. presure_mean=mean(presure_resample);
  86. presure_std=std(presure_resample);
  87. presure_zscore=(presure_resample-presure_mean)/presure_std;
  88. [c,lags] = xcorr(fiber_zscore,presure_zscore,'coeff');
  89. cc_rand_all(n,i,:)=c;
  90. end
  91. end
  92. cc_rand=squeeze(mean(cc_rand_all,1));
  93. %%%%
  94. lags_time=lags/40;
  95. cc_peak=zeros(1,size(cc,1));
  96. cc_peak_rand=zeros(1,size(cc,1));
  97. figure;
  98. %set(gcf,'visible','off')
  99. for j=1:size(cc,1)
  100. % subplot(15,10,j);
  101. set(gca,'FontSize',5);
  102. mean_trial=cc(:,:);
  103. mean_trial_rand=cc_rand(:,:);
  104. plot(lags_time,cc(j,:),'r');
  105. hold on;
  106. plot(lags_time,cc_rand(j,:),'k');
  107. set(gca,'xlim',[-80 80]);
  108. set(gca,'ylim',[-1 1]);
  109. xlabel('Lags (s)');
  110. ylabel('Cross correlation');
  111. title(['Mouse ' num2str(j)]);
  112. cc_peak(j)=max(cc(j,:));
  113. cc_peak_rand(j)=max(cc_rand(j,:));
  114. box off
  115. ax = gca;
  116. ax.XAxis.TickDirection = 'out';
  117. ax.YAxis.TickDirection = 'out';
  118. end
  120. subplot(2,4,1)
  121. plot(lags_time,mean(cc),'r')
  122. hold on;
  123. plot(lags_time,mean(cc_rand),'k');
  124. title('average')
  125. box off
  127. cc_all=[cc_all;mean(cc)];
  128. cc_all_rand=[cc_all_rand;mean(cc_rand)];
  130. %
  131. subplot(2,4,2);
  132. set(gca,'FontSize',5);
  133. hold on;
  134. for k=1:length(cc_peak)
  135. plot([1 2],[cc_peak(k), cc_peak_rand(k)],'color',0.7*[1 1 1],'LineWidth',3);
  136. end
  137. plot(ones(1,length(cc_peak)),cc_peak,'o','color',0.7*[1 1 1],'LineWidth',3,'MarkerSize',20,'MarkerFace',[1 0 0]);
  138. plot(2*ones(1,length(cc_peak_rand)),cc_peak_rand,'o','color',0.7*[1 1 1],'LineWidth',3,'MarkerSize',20,'MarkerFace',[0 0 0]);
  140. set(gca,'ylim',[0 1]);
  141. set(gca,'xlim',[0 3]);
  142. set(gca,'xtick',1:2);
  143. set(gca,'xticklabel',{'data','shuffled'});
  144. ylabel('Correlation coefficient');
  145. set(gcf,'Position',[100,100,1600,800])
  146. saveas(gcf,name{mouseNum},'tif')
  147. print('-depsc2','-painters',[name{mouseNum} '.eps']);
  148. %close
  149. cc_peak_mouse=[cc_peak_mouse;[mean(cc_peak),mean(cc_peak_rand)]];
  150. end
  151. save('cc','cc_peak_mouse','cc_all','cc_all_rand','lags_time')
  152. %% avearge
  153. load('cc.mat')
  154. figure
  155. subplot(3,4,1)
  156. data_plot{1,1}=cc_all;
  157. data_plot{2,1}=cc_all_rand;
  158. colorsem=[1 0 0;0 0 0]*0.1;
  159. colorcodes=[1 0 0;0 0 0];
  160. for k=1:size(data_plot,1)
  161. DataGroupMean_NB=mean(data_plot{k,1},1)';
  162. DataGroupSem_NB=sem(data_plot{k,1})';
  163. set(gca,'FontSize',10);
  164. Time=lags_time';
  165. hf=fill([Time;Time(end:-1:1)],[DataGroupMean_NB+DataGroupSem_NB;DataGroupMean_NB(end:-1:1)-DataGroupSem_NB(end:-1:1)]',colorsem(k,:),'edgealpha',0,'facealpha',0.1);
  166. set(hf,'edgec',colorsem(k,:));
  167. hold on;
  168. plot(Time,DataGroupMean_NB,'color',colorcodes(k,:),'LineWidth',1);
  169. end
  170. xlim([-50 50])
  171. ylim([-0.4 1])
  172. xlabel('Time (min)')
  173. box off
  174. set(gca,'FontSize',15);
  177. subplot(3,4,2)
  178. set(gca,'FontSize',15);
  179. hold on;
  180. for k=1:length(cc_peak_mouse)
  181. plot([1 2],[cc_peak_mouse(k,1), cc_peak_mouse(k,2)],'color',0.7*[1 1 1],'LineWidth',2);
  182. end
  183. plot(ones(1,length(cc_peak_mouse)),cc_peak_mouse(:,1),'o','color',0.7*[1 1 1],'LineWidth',2,'MarkerSize',10,'MarkerFace',[1 0 0]);
  184. plot(2*ones(1,length(cc_peak_mouse)),cc_peak_mouse(:,2),'o','color',0.7*[1 1 1],'LineWidth',2,'MarkerSize',10,'MarkerFace',[0 0 0]);
  186. p=signrank(cc_peak_mouse(:,1), cc_peak_mouse(:,2));
  187. %p=ranksum(cc_peak_mouse(:,1), cc_peak_mouse(:,2)); %un-paired
  188. text(1.5,max(max(cc_peak_mouse))*0.8,['p = ' num2str(p)])
  189. set(gca,'ylim',[0 1]);
  190. set(gca,'xlim',[0 3]);
  191. set(gca,'xtick',1:2);
  192. set(gca,'xticklabel',{'data','shuffled'});
  193. ylabel('Correlation coefficient');
  194. set(gcf,'Position',get(0,'ScreenSize'))
  195. saveas(gcf,name{mouseNum},'tif')
  196. print('-depsc2','-painters',[name{mouseNum} 'average.eps']);
  197. %close

Calcium_EMG_crosscorrelation.m at commit afdb515, under Apache-2.0 · at the source

Overview

Authors: Xing Li1, Xianping Li2,3, Jun Li1, Han Qin4, Shanshan Liang2, Jun Li2, Tingliang Jian2, Xia Wang5, Lingxuan Yin1, Chunhui Yuan3, Xiang Liao5, Hongbo Jia1,6,7, Xiaowei Chen2,4, Jiwei Yao3
ORCID iDs: Han Qin, Xia Wang, Hongbo Jia, Xiaowei Chen, Jiwei Yao
  1. Advanced Institute for Brain and Intelligence, School of Physical Science and Technology, Guangxi University, Nanning, China
  2. Brain Research Center and State Key Laboratory of Trauma and Chemical Poisoning, Third Military Medical University, Chongqing, China
  3. Department of Urology, PLA Naval Medical Center, Naval Medical University, Shanghai, China
  4. LFC Laboratory and Chongqing Institute for Brain and Intelligence, Guangyang Bay Laboratory, Chongqing, China
  5. Center for Neurointelligence, School of Medicine, Chongqing University, Chongqing, China
  6. Leibniz Institute for Neurobiology, Magdeburg, Germany
  7. Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
Institutions: Guangxi University (China); Army Medical University (China); Second Military Medical University (China); Chongqing Institute for Brain and Intelligence (China); Chongqing University (China); Chinese Academy of Sciences (China); Suzhou Institute of Biomedical Engineering and Technology (China); Leibniz Institute for Neurobiology (Germany)
Journal: eLife, volume 13, article RP103224
Dates: published online 6 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.103224 · PMID 42559936 · PMCID PMC13446904 · OpenAlex W4404909933
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), cellular / molecular (subfield)
Methods: Preprocessing, Connectivity, Statistics, Machine learning, Spectral & time-frequency, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Mouse
MeSH: Brain Stem*, Neurons*, Urethra*, Urinary Bladder*, Urination*, Animals, Estrogen Receptor alpha, Female, Mice (* major topic)
Topic: Urinary Bladder and Prostate Research (Urology, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (31925018, 32127801); National Key Research and Development Program of China (2021YFA0805000); Guangxi Talent Program (Highland of Innovation Talents); Jiangsu Provincial Big Science Facility Initiative (BM2022010); Suzhou Science and Technology Plan Project (SZS2022008)
Citations: cited by 3 papers (Europe PMC); 72 references in the paper

Abstract

Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and external urethral sphincter (EUS) that is tightly controlled by a complex neural network. However, the specific subpopulation of neurons that accounts for such coordination remains unidentified, limiting the development of target-specific therapies for certain urination disorders, for example, detrusor–sphincter dyssynergia. Here, we find that cells expressing estrogen receptor 1 (ESR1+) in the pontine micturition center (PMC) initiate voiding when activated and suspend ongoing voiding when suppressed, each at 100% reliability. Transection of the pelvic nerve does not impair PMCESR1+ neurons’ control of the EUS via the pudendal nerve, whereas transection of the pudendal nerve does not impair their control of the bladder via the pelvic nerve. Anatomically, PMCESR1+ neurons consist of three distinct spinal-projection-based subpopulations: one targeting the sacral parasympathetic nucleus, one innervating the dorsal gray commissure, and a third that projects to both regions, thereby enforcing the coordination of bladder contraction and sphincter relaxation in a rigid temporal sequence. Thus, we identify a cell type in the brainstem that controls the bladder–urethra coordination for urination.

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

Repository

Its files are read in the Code ↔ Paper reader above.

xieyangshuying/Brainstem-neurons-coordinate-the-bladder-and-urethral-sphincter-for-urination

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: afdb51517ddaf7d0a963764edeb43050c3bf44e7, 5 August 2026
Languages: MATLAB (6)
Size: 8 files, 6 scripts
Software Heritage: not checked
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (4 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
8 files
At the source: github.com/xieyangshuying/Brainstem-neurons-coordinate-the-bladder-and-urethral-sphincter-for-urination

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

Datasets cited

Data availability

The source data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.vdncjszbc. The source code supporting the findings of this study is openly hosted in a public repository on GitHub and can be accessed at: https://github.com/xieyangshuying/Brainstem-neurons-coordinate-the-bladder-and-urethral-sphincter-for-urination, copy archived at xieyangshuying, 2026.

The following dataset was generated:

Li X, Li X, Li J, Qin H, Liang S, Li J, Jian T, Wang X, Yin L, Yuan C, Liao X, Jia H, Chen X, Yao J. 2026. Data from: Brainstem neurons coordinate the bladder and urethral sphincter for urination. Dryad Digital Repository.

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, pages, dates, 14 authors, 1 keyword, 9 MeSH terms, 5 funders, 72 references.

Cite

This paper

Li, X., Li, X., Li, J., Qin, H., Liang, S., Li, J., Jian, T., Wang, X., Yin, L., Yuan, C., Liao, X., Jia, H., Chen, X., & Yao, J. (2026). Brainstem neurons coordinate the bladder and urethral sphincter for urination. eLife, 13, RP103224. https://doi.org/10.7554/elife.103224

BibTeX

@article{li2026brainstem,
author = {Li, Xing and Li, Xianping and Li, Jun and Qin, Han and Liang, Shanshan and Li, Jun and Jian, Tingliang and Wang, Xia and Yin, Lingxuan and Yuan, Chunhui and Liao, Xiang and Jia, Hongbo and Chen, Xiaowei and Yao, Jiwei},
title = {{Brainstem neurons coordinate the bladder and urethral sphincter for urination}},
journal = {eLife},
year = {2026},
month = aug,
volume = {13},
pages = {RP103224},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.103224},
url = {https://doi.org/10.7554/elife.103224},
pmid = {42559936},
pmcid = {PMC13446904}
}

RIS

TY - JOUR
AU - Li, Xing
AU - Li, Xianping
AU - Li, Jun
AU - Qin, Han
AU - Liang, Shanshan
AU - Li, Jun
AU - Jian, Tingliang
AU - Wang, Xia
AU - Yin, Lingxuan
AU - Yuan, Chunhui
AU - Liao, Xiang
AU - Jia, Hongbo
AU - Chen, Xiaowei
AU - Yao, Jiwei
TI - Brainstem neurons coordinate the bladder and urethral sphincter for urination
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/08/06
VL - 13
SP - RP103224
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.103224
UR - https://doi.org/10.7554/elife.103224
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.103224",
"type": "article-journal",
"title": "Brainstem neurons coordinate the bladder and urethral sphincter for urination",
"container-title": "eLife",
"author": [
{
"family": "Li",
"given": "Xing"
},
{
"family": "Li",
"given": "Xianping"
},
{
"family": "Li",
"given": "Jun"
},
{
"family": "Qin",
"given": "Han"
},
{
"family": "Liang",
"given": "Shanshan"
},
{
"family": "Li",
"given": "Jun"
},
{
"family": "Jian",
"given": "Tingliang"
},
{
"family": "Wang",
"given": "Xia"
},
{
"family": "Yin",
"given": "Lingxuan"
},
{
"family": "Yuan",
"given": "Chunhui"
},
{
"family": "Liao",
"given": "Xiang"
},
{
"family": "Jia",
"given": "Hongbo"
},
{
"family": "Chen",
"given": "Xiaowei"
},
{
"family": "Yao",
"given": "Jiwei"
}
],
"container-title-short": "eLife",
"volume": "13",
"page": "RP103224",
"DOI": "10.7554/elife.103224",
"PMID": "42559936",
"PMCID": "PMC13446904",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.103224",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
6
]
]
}
}

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