OSCR

Conservation of Neuron-Astrocyte Correlated Activity in Developing Sensory Pathways.

Code ↔ Paper

9 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 9 matches
  1. [1] § Methods › Method Details › Tamoxifen Injections ↔ SCwave_Tracking/tracking_framework.m, lines 214–280 · score 0.81 · mGluR3, GCaMP6s, mGluR5, CreER, Aldh1l1, tamoxifen
  2. [2] § Methods › Method Details › Image Processing › Two‐Photon Imaging ↔ DualAnalysisSC.m, lines 48–91 · score 0.78 · peak prominence, peak distance, peak width, msbackadj, findpeaks, trace
  3. [3] § Results › Retinal Waves Induce Coordinated Increases in Calcium Among SC Astrocytes ↔ SCwave_Tracking/tracking_framework.m, lines 214–280 · score 0.63 · GCaMP6s, CreER, Aldh1l1, tamoxifen, Day, brain
  4. [4] § Methods › Method Details › Image Processing › Two‐Photon Imaging ↔ twoP_SCIC_AnalysisROIs.m, lines 55–77 · score 0.62 · peak prominence, peak width, findpeaks, ROIs, median, threshold
  5. [5] § Methods › Method Details › Image Processing › Two‐Photon Imaging ↔ SCGrid_Rasterplots.m, lines 151–165 · score 0.60 · peak prominence, peak width, findpeaks, ROIs, threshold, frames
  6. [6] § Methods › Method Details › Image Processing › Two‐Photon Imaging ↔ twoP_SCIC_AnalysisROIs.m, lines 55–77 · score 0.58 · peak prominence, peak width, findpeaks, median, threshold, IC
  7. [7] § Methods › Method Details › Tamoxifen Injections ↔ grpSCAnalysis.m, lines 172–203 · score 0.57 · GCAMP6s, CreER, Aldh1l1, tamoxifen
  8. [8] § Results › Spatial and Temporal Coordination of Astrocyte and Neuronal Activity in the Developing SC ↔ grpSCAnalysis.m, lines 172–203 · score 0.55 · jRGECO1a, GCAMP6s, Aldh1l1, Thy1, neurons, SC
  9. [9] § Methods › Method Details › Image Processing › Widefield Tracking of Calcium Waves ↔ SCwave_Tracking/CINDA-master/CINDA-master/cs2mex.c, lines 1902–1998 · score 0.54 · cost circulation, formulates, tracking, trajectory, CINDA, edges

Paper

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The authors' code

MATLAB · 280 lines · 11 KB · no license · 2 matches

  1. %% Tracking of SC waves
  2. %Code was developed by Xuelong Mi in the lab of Guoqiang Yu and adapted by
  3. %Vered Kellner
  4. close all
  5. clearvars -except dataStruct
  6. f=1;
  7. %% setting
  8. startup; % initialize
  9. preset = 1; % default
  10. opts = util.parseParam(preset,1);
  12. cellAns=questdlg('Cell type?','','Neuron','Astrocyte','Neuron');
  13. useList={'Left','Right','Both'};
  14. useAns=listdlg('ListString',useList,'PromptString','Use?');
  15. %% large-impact paramters
  16. if strcmp(cellAns,'Astrocyte')
  17. opts.lenFilter = 20; % the minimum duration of signal wave. Will filter out short signal waves - was 3 for neurons
  18. opts.maxJump = 20; % allowed max jump (due to merging, some detection will not be found) - was 8 for neurons
  19. % opts.lenFilter = 10; % the minimum duration of signal wave. Will filter out short signal waves - was 3 for neurons
  20. % opts.maxJump = 10; % allowed max jump (due to merging, some detection will not be found) - was 8 for neurons
  21. % % % For example, if one signal wave appear at frame 1 and frame 5, then we at
  22. % least set this parameter to 4 to link them.
  24. opts.jumpProb = 0.9; % Set from 0 to 1. It's the false negative probability - was 0.5 for neurons
  25. % (the probablity I didn't detect the signal in one frame.) But due to the
  26. % merging, I set it to 0.5. Large this parameter, more easily to link
  27. % detections for large jump.
  28. %% other parameters
  29. opts.thrARScl = 0.2; % bottom-up to check detection, start from (opts.thrARScl x noise) was 3 for neurons, 0.2 for astro
  30. opts.smoXY = 5; % smooth parameter for select region was 1 for neurons
  31. opts.minCir = 0.05; % minimum circularity for one detection was 0.05
  32. opts.stepRatio = 0.1; % the step for bottom-up. Should be small if SNR is low. Now is 0.1 x noise one step
  33. opts.cut = 30; % parameter for calculating dF. If the F0 change strongly, should be small.
  34. opts.movAvgWin = 10; % parameter for calculating dF. If the F0 change strongly, should be small.
  35. opts.growIoU = 2; % parameter for calculating IoU. grow the region then judge IoU.
  37. else
  38. opts.lenFilter = 20; %was 20 for neurons
  39. opts.maxJump = 20;
  40. opts.jumpProb = 0.9;
  41. %% other parameters
  42. opts.thrARScl = 0.1; % bottom-up to check detection, start from (opts.thrARScl x noise) was 1 for neurons then 2 then 0.5
  43. opts.smoXY = 3; % smooth parameter for select region was 2 for neurons
  44. opts.minCir = 0.05; % minimum circularity for one detection - was 0.05
  45. opts.stepRatio = 0.1; % the step for bottom-up. Should be small if SNR is low. Now is 0.1 x noise one step
  46. opts.cut = 30; % parameter for calculating dF. If the F0 change strongly, should be small.
  47. opts.movAvgWin = 10; % parameter for calculating dF. If the F0 change strongly, should be small.
  48. opts.growIoU = 2; % parameter for calculating IoU. grow the region then judge IoU.
  49. end
  50. opts.minSize = 100; % minimum number of pixels for one detection in each frame
  51. opts.maxSize = 5000; % maximize number of pixels for one detection in each frame
  53. % linking parameters
  54. opts.IoULimit = 0.01; % IoU limitation for linking together
  55. opts.zThr = 0.2; % filter small-score - was 3 for neurons was 0.2 for dim was 0.5
  58. %% file path
  59. p0 = 'D:\Data\RAW WF data\'; % folder name
  60. f0 = uigetfile([p0,'*.czi']); % file name
  62. %% load data
  63. load('random_Seed');
  64. rng(s);
  65. [folder, name, ext] = fileparts(strcat(p0,'\',f0));
  66. path0 = [p0,name,'\'];
  67. % if ~exist(path0,'dir') && ~isempty(path0)
  68. % mkdir(path0);
  69. % end
  70. [datOrg,opts] = burst.prep1(p0,f0,[],opts); % read data
  72. %% 1. registration
  73. % datReg = ui.algo.registrate_CC(datOrg);
  75. %% 2. background
  76. [dFOrg] = burst.actTopTracking(datOrg,opts); % read data
  78. %% find movement times (from Travis / Vered)
  79. Y=datOrg;
  80. norms = [];
  81. regto = fft2(mean((Y),3));
  82. t = size(Y,3);
  83. parfor j = 1:t
  84. [output, Greg] = dftregistration(regto,fft2(Y(:,:,j)),1) %the last number will determine how sensitive this is
  85. norms(j) = norm([1 0 output(3); 0 1 output(4); 0 0 1],2);
  86. end
  87. hit = zeros(size(norms));
  88. for j = 1:t
  89. if norms(j) > 1
  90. %look 50 ahead
  91. if j>5 && j < t-30
  92. if sum(norms(j+1:j+30)-1) > 0
  93. hit(j-5:j+30) = 1;
  94. elseif hit(j-5) == 1
  95. % hit(i:i+5) = 1;
  96. % hit(i+6:i+56)=0;
  97. hit(j:j+1) = 1;
  98. hit(j+2:j+30)=0;
  99. else
  100. hit(j) = 0;
  101. end
  102. end
  103. end
  104. end
  105. figure; plot(norms-1); hold on; plot(hit)
  106. mvmInd=find(hit>0);
  108. %% 3. detection
  109. tic
  110. zScoreMap = burst.detect(dFOrg,opts);
  111. toc
  113. %% 4. linking
  114. tic
  115. [evtLst,~,evtLen] = burst.linking(zScoreMap,opts);
  116. toc
  118. %% 5. filter
  119. evtLen2=evtLen;
  120. evtLst2=evtLst;
  121. evtLst = evtLst(evtLen>opts.lenFilter);
  122. evtLen = evtLen(evtLen>opts.lenFilter);
  124. %% feature
  125. [H,W,T] = size(datOrg);
  126. movFea = cell(numel(evtLst),1);
  127. % determine if events are in left or right SC
  128. figure; imagesc(squeeze(mean(datOrg,3)))
  129. lh = imline(gca);
  130. [s] = round(lh.getPosition); x = s(1);
  131. % % Original scale dF, without stabilization (we use square root to stabilize
  132. % % data), and smooth filter to denoise.
  133. dF = imgaussfilt(dFOrg.*(2*datOrg-dFOrg)*opts.maxValueDat,3);
  134. for i = 1:numel(evtLst)
  136. vel = nan(1,T);
  137. % first row of dir: positive - north, negative - south
  138. % second row of dir: positive - east, negative - west
  139. dir = nan(2,T);
  141. [ih,iw,it] = ind2sub([H,W,T],evtLst{i});
  142. t0 = min(it);
  143. t1 = max(it);
  144. ts = unique(it); %active frames for this event
  145. centroid = nan(2,T);
  146. for j = 1:numel(ts)
  147. curIt = ts(j);
  148. curIh = ih(it==curIt);
  149. curIw = iw(it==curIt);
  150. curPix = sub2ind([H,W],curIh,curIw);
  151. weight = dFOrg(:,:,curIt);
  152. weight = weight(curPix);
  153. weight = weight/sum(weight);
  154. centroidX = weight'*curIh;
  155. centroidY = weight'*curIw;
  156. centroid(1,curIt) = H+1-centroidX;
  157. centroid(2,curIt) = centroidY;
  158. end
  160. vel(t0) = 0;
  161. dir(:,t0) = 0;
  162. pret = t0;
  163. t = t0+1;
  164. while(t<=t1)
  165. if(~isnan(centroid(1,t)))
  166. prePos = centroid(:,pret);
  167. curPos = centroid(:,t);
  168. move = curPos-prePos;
  169. dur = t-pret;
  170. vel(pret+1:t) = sqrt(sum(move.^2))/dur;
  171. dir(:,pret+1:t) = repmat(move/sqrt(sum(move.^2)),1,dur);
  172. pret = t;
  173. end
  174. t = t+1;
  175. end
  176. if useAns==1
  177. evtLoc='Left';
  178. elseif useAns==2
  179. evtLoc='Right';
  180. elseif useAns==3
  181. if nanmedian(centroid(2,:))>x
  182. evtLoc='Right';
  183. else
  184. evtLoc='Left';
  185. end
  186. end
  187. movFea{i}.centroid = centroid;
  188. movFea{i}.vel = vel;
  189. movFea{i}.dir = dir;
  190. movFea{i}.frms=ts; %active frames
  191. movFea{i}.Side=evtLoc; %left or right
  192. movFea{i}.amplitude = median(dF(evtLst{i}));
  193. end
  195. %% show
  196. tic
  197. ov = zeros(3*H,W,3,T);
  198. ov(1:H,:,1,:) = datOrg;
  199. ov(1:H,:,2,:) = datOrg;
  200. ov(1:H,:,3,:) = datOrg;
  201. ov(H+1:2*H,:,:,:) = double(plt.regionMapWithData_label(evtLst,movFea,datOrg*0.5,0.5))/255;
  202. ov(2*H+1:end,:,1,:) = dFOrg/max(dFOrg(:));
  203. ov(2*H+1:end,:,2,:) = ov(2*H+1:end,:,1,:);
  204. ov(2*H+1:end,:,3,:) = ov(2*H+1:end,:,1,:);
  205. zzshow(ov);
  206. toc
  207. %% manually filter out events - VK added Jan 2022
  208. %need to compare the above video with raw video, write down in notepad
  209. %which roi numbers to keep and input here
  210. userAns=inputdlg('Which events to keep?');
  211. keepInd=str2num(userAns{:});
  212. keepInd=unique(keepInd);
  214. %% create structure for each mouse
  215. dataStruct(f).Cell=cellAns;
  216. sensorList={'GCaMP3','GCaMP6s','RGECO','GCaMP6short'};
  217. sensorAns=listdlg('ListString',sensorList,'PromptString','Sensor:');
  218. dataStruct(f).Sensor=sensorList{sensorAns};
  219. promoterList={'GLAST-CreER','SNAP25','Aldh1l1-CreER','Thy1'};
  220. promoterAns=listdlg('ListString',promoterList,'PromptString','Promoter:');
  221. dataStruct(f).Promoter=promoterList{promoterAns};
  222. dataStruct(f).Name=name;
  223. ageList={'5','6','7','8','9','10','11','12','13','14','15'};
  224. ageAns=listdlg('ListString',ageList,'PromptString','Age (days):');
  225. dataStruct(f).Age=ageList{ageAns};
  226. sexList={'Male','Female','?'};
  227. sexAns=listdlg('ListString',sexList,'PromptString','Sex:');
  228. dataStruct(f).Sex=sexList{sexAns};
  229. tmxList={'None','Once','Twice','4HT twice'};
  230. tmxAns=listdlg('ListString',tmxList,'PromptString','Tamoxifen injection:');
  231. dataStruct(f).Tamoxifen=tmxList{tmxAns};
  232. manipList={'None','LY','Vehicle','MPEP','KOfull','KOhet','KOcontrol','Pre','LY+MPEP'};
  233. manipAns=listdlg('ListString',manipList,'PromptString','Manipulation:');
  234. dataStruct(f).Manipulation=manipList{manipAns};
  235. switch manipAns
  236. case 1
  237. dataStruct(f).ManipDetails='None';
  238. case 2
  239. lyList={'Pre','immediate','10min','30min','1hr','3hr','5.5hr'};
  240. lyAns=listdlg('ListString',lyList);
  241. dataStruct(f).ManipDetails=lyList{lyAns};
  242. case 3
  243. vehList={'Pre','24hr CTEP','Saline 30min','Saline immediate','Saline 3hr','ddH2O immediate','ddH2O 30min'};
  244. vehAns=listdlg('ListString',vehList);
  245. dataStruct(f).ManipDetails=vehList{vehAns};
  246. case 4
  247. mpepList={'Pre','immediate','10min','30 min','1-2hr','4-6hr','8-9hr','19-21hr','@5min','@3min','3x & @5min'};
  248. mpepAns=listdlg('ListString',mpepList);
  249. dataStruct(f).ManipDetails=mpepList{mpepAns};
  250. case 5
  251. KOList={'FMR1','FMR1cKO','IP3R2','mGluR5','Grm3KO','Grm3cKO','mGluR5+MPEP','mGluR5+LY','mGluR5+MPEP+LY','mGluR3+MPEP','mGluR3+LY','mGluR3+LY+MPEP'};
  252. KOAns=listdlg('ListString',KOList);
  253. dataStruct(f).ManipDetails=KOList{KOAns};
  254. case 6
  255. KOList={'FMR1','FMR1cKO','IP3R2','mGluR5','Grm3KO','Grm3cKO','mGluR5+MPEP','mGluR5+LY','mGluR5+MPEP+LY','mGluR3+MPEP','mGluR3+LY','mGluR3+LY+MPEP'};
  256. KOAns=listdlg('ListString',KOList);
  257. dataStruct(f).ManipDetails=KOList{KOAns};
  258. case 7
  259. KOList={'FMR1','FMR1cKO','IP3R2','mGluR5','Grm3KO','Grm3cKO','mGluR5+MPEP','mGluR5+LY','mGluR5+MPEP+LY','mGluR3+MPEP','mGluR3+LY','mGluR3+LY+MPEP'};
  260. KOAns=listdlg('ListString',KOList);
  261. dataStruct(f).ManipDetails=KOList{KOAns};
  262. case 8
  263. dataStruct(f).ManipDetails='None';
  264. case 9
  265. lyMPList={'Pre','immediate','10min','30min','1hr','3hr','5.5hr'};
  266. lyAns=listdlg('ListString',lyMPList);
  267. dataStruct(f).ManipDetails=lyMPList{lyAns};
  268. end
  269. cmntList={'Good','OK','Slightly cloudy','Pretty cloudy','Bad','1024x1024','No/low activity','Animal moving a lot','pinworms'};
  270. cmntAns=listdlg('ListString',cmntList,'PromptString','Brain state');
  271. dataStruct(f).comment=cmntList{cmntAns};
  272. % useList={'Left','Right','Both'};
  273. % useAns=listdlg('ListString',useList,'PromptString','Use?');
  274. dataStruct(f).use=useList{useAns};
  276. dataStruct(f).Events.Duration=evtLen(keepInd); %frames
  277. dataStruct(f).Events.Area=evtLst(keepInd); %pixels in 3D use [x,y,t]=ind2sub([H,W,T],evtlst{i}) to extract coordinates of pixel
  278. dataStruct(f).Events.WaveInfo=movFea(keepInd); %centroid, direction, velocity, active frames, side, amp
  279. dataStruct(f).Events.Frm=T;
  280. dataStruct(f).Events.MvmInd=mvmInd;

tracking_framework.m at commit cda515b, no license · at the source

Overview

Authors: Vered Kellner1,2, Patrick Parker1, Dongeun Heo1, Xuelong Mi3, Mikhail Coen1, Guoqiang Yu3, Gesine Saher4, Loyal A Goff1,5,6, Dwight E Bergles1,6,7
ORCID iDs: Vered Kellner, Dongeun Heo, Dwight E Bergles
  1. The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland, USA
  2. Department of Neurophysiology and Neuropharmacology, Center for Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria
  3. Virginia Tech Research Center, Arlington, Virginia, USA
  4. Department of Neurogenetics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
  5. Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
  6. Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland, USA
  7. Department of Otolaryngology Head and Neck Surgery, Johns Hopkins University, Baltimore, Maryland, USA
Institutions: Johns Hopkins University (United States); Medical University of Vienna (Austria); Virginia Tech (United States); Max Planck Society (Germany); Max Planck Institute for Multidisciplinary Sciences (Germany); Johns Hopkins Medicine (United States)
Journal: Glia, volume 74, issue 5, article e70141
Dates: received 23 May 2024; accepted 24 January 2026; published online 26 March 2026; in print May 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1002/glia.70141 · PMID 41889203 · PMCID PMC13022517 · OpenAlex W7141629547
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: astrocyte, brain development, in vivo, inferior colliculus, metabotropic glutamate receptor, spontaneous activity, superior colliculus
MeSH: Astrocytes*, Neurons*, Superior Colliculi*, Action Potentials, Animals, Animals, Newborn, Calcium, Glutamic Acid, Inferior Colliculi, Mice, Mice, Inbred C57BL, Mice, Transgenic, Receptor, Metabotropic Glutamate 5, Receptors, Metabotropic Glutamate (* major topic)
Topic: Hearing, Cochlea, Tinnitus, Genetics (Sensory Systems, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (F32DC017364, NS050274, DC008860); NIDCD NIH HHS (R01 DC008860); NIH HHS (F32DC017364); Deutsche Forschungsgemeinschaft (SA2114/2); NINDS NIH HHS (P30 NS050274)
Citations: not cited yet (Europe PMC); 75 references in the paper

Abstract

Neurons in developing sensory organs exhibit prolonged burst firing before the onset of sensory experience. This activity promotes neuronal survival and maturation in central sensory pathways. Within the auditory system, periodic bursts of synaptic glutamate release activate metabotropic glutamate receptors (mGluRs) on astrocytes, resulting in spatially and temporally correlated calcium transients; however, whether this phenomenon occurs in other sensory modalities is unknown. Using in vivo calcium imaging in the midbrain of awake mouse pups before eyelid opening, we show that retina wave‐induced burst firing of visual afferents induces correlated waves of astrocyte activity in the superior colliculus (SC), a visual processing region. Glutamate sensor imaging revealed that each neuronal burst resulted in glutamate transients at astrocyte membranes in both developing sensory regions. Calcium transients in SC astrocytes resulted from activation of astrocytic mGluR5 and mGluR3, similar to astrocyte events in the nearby inferior colliculus (IC), which are induced by neuronal burst firing in the cochlea. Astrocyte calcium increased with each neuronal wave in the SC, but only the largest neuronal events triggered astrocyte responses in the IC. Astrocyte transcriptomic analysis suggested differential expression of mGluR3 and mGluR5 between these sensory regions, in accordance with the greater dependence on mGluR5 in IC astrocytes. Despite differences in receptor contribution and temporal features of activity, astrocytes in these different regions exhibited similar overall calcium activity. Thus, neuronal burst firing in developing sensory organs provides a conserved mechanism to synchronize neuronal and astrocyte activity in the brain at a critical stage of development.

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

Repository

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Bergles-lab/Kellner-et-al-2024-source-code

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: cda515bd38ac2c243d4b5b40060e9ed6de280c36, 14 April 2024
Languages: MATLAB (324), C++ (5), C/C++ (4), C (2), Python (1), Jupyter (1), Java (1)
Size: 407 files, 338 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: 1 notebook
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (52 files), Statistics and Machine Learning Toolbox (35 files), Signal Processing Toolbox (3 files), shadedErrorBar (2 files), Curve Fitting Toolbox (1 file), NumPy (1 file), Psychtoolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
338 files
At the source: github.com/Bergles-lab/Kellner-et-al-2024-source-code

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

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-NC), from the paper cited above.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request. All original code has been deposited on github (https://github.com/Bergles‐lab/Kellner‐et‐al‐2024‐source‐code (https://github.com/Bergles-lab/Kellner-et-al-2024-source-code)) and is publicly available.

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-NC), 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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 7 keywords, 14 MeSH terms, 5 funders, 74 references.

Cite

This paper

Kellner, V., Parker, P., Heo, D., Mi, X., Coen, M., Yu, G., Saher, G., Goff, L. A., & Bergles, D. E. (2026). Conservation of Neuron-Astrocyte Correlated Activity in Developing Sensory Pathways. Glia, 74(5), e70141. https://doi.org/10.1002/glia.70141

BibTeX

@article{kellner2026conservation,
author = {Kellner, Vered and Parker, Patrick and Heo, Dongeun and Mi, Xuelong and Coen, Mikhail and Yu, Guoqiang and Saher, Gesine and Goff, Loyal A and Bergles, Dwight E},
title = {{Conservation of Neuron-Astrocyte Correlated Activity in Developing Sensory Pathways}},
journal = {Glia},
year = {2026},
month = may,
volume = {74},
number = {5},
pages = {e70141},
publisher = {Wiley},
issn = {0894-1491},
doi = {10.1002/glia.70141},
url = {https://doi.org/10.1002/glia.70141},
pmid = {41889203},
pmcid = {PMC13022517}
}

RIS

TY - JOUR
AU - Kellner, Vered
AU - Parker, Patrick
AU - Heo, Dongeun
AU - Mi, Xuelong
AU - Coen, Mikhail
AU - Yu, Guoqiang
AU - Saher, Gesine
AU - Goff, Loyal A
AU - Bergles, Dwight E
TI - Conservation of Neuron-Astrocyte Correlated Activity in Developing Sensory Pathways
T2 - Glia
J2 - Glia
PY - 2026
DA - 2026/05/01
VL - 74
IS - 5
SP - e70141
SN - 0894-1491
PB - Wiley
DO - 10.1002/glia.70141
UR - https://doi.org/10.1002/glia.70141
LA - en
ER -

CSL-JSON

{
"id": "10.1002/glia.70141",
"type": "article-journal",
"title": "Conservation of Neuron-Astrocyte Correlated Activity in Developing Sensory Pathways",
"container-title": "Glia",
"author": [
{
"family": "Kellner",
"given": "Vered"
},
{
"family": "Parker",
"given": "Patrick"
},
{
"family": "Heo",
"given": "Dongeun"
},
{
"family": "Mi",
"given": "Xuelong"
},
{
"family": "Coen",
"given": "Mikhail"
},
{
"family": "Yu",
"given": "Guoqiang"
},
{
"family": "Saher",
"given": "Gesine"
},
{
"family": "Goff",
"given": "Loyal A"
},
{
"family": "Bergles",
"given": "Dwight E"
}
],
"container-title-short": "Glia",
"volume": "74",
"issue": "5",
"page": "e70141",
"DOI": "10.1002/glia.70141",
"PMID": "41889203",
"PMCID": "PMC13022517",
"ISSN": "0894-1491",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/glia.70141",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
1
]
]
}
}

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