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A 3D Human Neuron-on-Chip Platform to Monitor Neuronal Injury Responses.

Code ↔ Paper

3 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 3 matches
  1. [1] § Methods › Neuronal Activity and Network Analysis ↔ Pipeline_20240207.m, lines 158–223 · score 0.73 · firing rate, active neurons, photobleaching, Deconvolution, window, mFR
  2. [2] § Results › Weight‐Drop Injury Elicits Persistent Neuronal Silencing and Biphasic Temporal Network Activity Dynamics ↔ Grap_parameters.m, lines 62–124 · score 0.59 · weighted clustering coefficient, weighted node, matrices, GSI, phase, cell
  3. [3] § Results › Weight‐Drop Injury Elicits Persistent Neuronal Silencing and Biphasic Temporal Network Activity Dynamics ↔ Pipeline_20240207.m, lines 158–223 · score 0.55 · firing rate, active neurons, mFR, min, rise, spiking

Paper

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

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The authors' code

MATLAB · 223 lines · 7.8 KB · no license · 2 matches

  1. folders = dir('*_*');
  2. masterroot = cd;
  4. for i = 1:length(folders)
  5. cd(masterroot)
  6. folderlocation = [masterroot,'\',folders(i).name];
  7. filename = [folders(i).name,'_extract.mat'];
  8. [F_full,jason] = AutoCalcium(folderlocation);
  9. save(filename,"F_full","jason");
  10. notification = [num2str(i),'/',num2str(length(folders)),'done'];
  11. disp(notification)
  12. % [F_full_cor,F_dff,cc,spk,spk_th,waveform_para,Active_neurons] = Autoprocessing(F_full);
  13. % filename2 = [folders(i).name,'.mat'];
  14. % save(filename2,"F_full","F_full_cor","F_dff","cc","spk","spk_th","waveform_para","Active_neurons","jason");
  15. end
  17. %%
  18. files = dir("*extract.mat");
  19. fit_limit = 3;
  21. for i = 1:length(files)
  22. load(files(i).name);
  23. [F_full_cor,F_dff,cc,spk,spk_th,waveform_para,Active_neurons] = Autoprocessing(F_full,fit_limit);
  24. filename2 = [files(i).name,'.mat'];
  25. save(filename2,"F_full","F_full_cor","F_dff","cc","spk","spk_th","waveform_para","Active_neurons","jason");
  26. end
  28. %% Region growth segementation + extraction
  29. function [F_full,jason] = AutoCalcium(folderlocation)
  30. cd(folderlocation)
  31. %% Read/stack files
  32. files = dir('*Z001.tif');
  33. % Img = imread(files(1).name);
  34. % Img = im2double(Img);
  36. num_images = length(files);
  37. Astack = [];
  38. for k = 1:num_images
  39. A = imread(files(k).name);
  40. A = im2double(A);
  41. Astack = cat(3,Astack,A);
  42. end
  43. Amax = max(Astack,[],3);
  45. %% creat seeds
  46. % tophat filter
  47. se = strel('disk',3);
  48. tophatFiltered = imtophat(Amax,se);
  49. contrastAdjusted = imadjust(tophatFiltered);
  50. % figure();imshow(contrastAdjusted)
  52. %histogram based threshold estimation -> used to zeros out background
  53. [Hi,Xi] = hist(double(contrastAdjusted(:)),100);%assume higher = cells
  54. cumh = cumsum(Hi);
  55. Ni = cumh/max(cumh);
  56. thi = Xi(find(Ni>=0.95));
  57. Ith = contrastAdjusted;
  58. Ith(contrastAdjusted<thi(1)) = 0;
  60. %% local maxmium initial seeds
  61. TF1 = islocalmax(Ith,1,'MinProminence',0.01,'FlatSelection','center');
  62. TF2 = islocalmax(Ith,2,'MinProminence',0.01,'FlatSelection','center');
  63. TF = TF1.*TF2;
  64. %imshow(TF)
  65. index_lmax = find(TF(:) == 1);
  66. [pixel_size_m,pixel_size_n] = size(Amax);
  68. [seed_row,seed_col] = ind2sub([pixel_size_m pixel_size_n],index_lmax);
  69. num_seed = length(index_lmax);
  70. dynamic_seed = zeros(num_seed,num_images);
  71. for i = 1:num_seed
  72. dynamic_seed(i,:) = Astack(seed_row(i),seed_col(i),:);
  73. end
  74. % diagnal filter
  75. % filter_diag = diag(1:1:num_seed);
  76. % for i = 1:num_seed-1
  77. % filter_diag(i+1,1:(i+1)) = 1;
  78. % end
  80. %% Region growth - intensity
  81. final_mask = zeros(pixel_size_m,pixel_size_n);
  82. for i = 1:num_seed
  83. seed_l = seed_row(i);
  84. seed_m = seed_col(i);
  85. skip_list = 0;
  86. if final_mask(seed_l,seed_m) == 0
  87. if ismember(seed_l*seed_m,skip_list) == 0
  88. seed_mean = contrastAdjusted(seed_l,seed_m);
  89. seed_mask = zeros(pixel_size_m,pixel_size_n);
  90. seed_mask(seed_l,seed_m) = 1;
  91. P = imdilate(seed_mask, strel('disk', 1));
  92. B = P.*~seed_mask;
  93. B = B.*~final_mask;
  94. index_merge = find(B(:) == 1);
  95. merge_fail = 0;
  96. switch_merge = 1;
  97. while switch_merge == 1
  98. for k = 1:length(index_merge)
  99. [merge_row,merge_col] = ind2sub([pixel_size_m pixel_size_n],index_merge(k));
  100. merge_mean = contrastAdjusted(merge_row,merge_col);
  101. if abs(merge_mean/seed_mean-1) < 0.25 %growing th
  102. seed_mask(merge_row,merge_col) = 1;
  103. seed_mean = sum(contrastAdjusted.*seed_mask,"all")/sum(seed_mask,"all");
  104. if ismember(index_merge(k),index_lmax) == 1
  105. skip_list = [skip_list;index_merge(k)];
  106. end
  107. else
  108. merge_fail = merge_fail + 1;
  109. end
  110. end
  111. if sum(seed_mask,'all') <= 200
  112. if merge_fail ~= length(index_merge)
  113. switch_merge = 1;
  114. seed_mask = imfill(seed_mask,"holes");
  115. P = imdilate(seed_mask, strel('disk', 1));
  116. B = P.*~seed_mask;
  117. B = B.*~final_mask;
  118. index_merge = find(B(:) == 1);
  119. merge_fail = 0;
  120. else
  121. switch_merge = 0;
  122. end
  123. else
  124. switch_merge = 0;
  125. end
  126. end
  127. seed_mask = imfill(seed_mask,"holes");
  128. seed_mask = bwareaopen(seed_mask,10);
  129. final_mask = final_mask + seed_mask.*i;
  130. end
  131. end
  132. end
  133. % final_mask = imerode(final_mask,strel('disk', 1));
  134. % final_mask = imdilate(final_mask, strel('disk', 1));
  136. % rgb = label2rgb(final_mask,'jet',[.5 .5 .5]);
  137. % figure();imshow(rgb)
  138. %% Extracting traces
  139. A_full = reshape(Astack,[pixel_size_m*pixel_size_n,num_images]);
  140. list_mask = unique(final_mask);
  141. num_cells = length(unique(final_mask)) - 1;
  142. F_full = zeros(num_cells,num_images);
  143. for i = 1:num_cells
  144. index_temp = find(final_mask(:) == list_mask(i+1));
  145. num_index = length(index_temp);
  146. dynamic_pixel = zeros(num_index,num_images);
  147. for k = 1:num_index
  148. dynamic_pixel(k,:) = A_full(index_temp(k),:);
  149. end
  150. F_full(i,:) = mean(dynamic_pixel,1);
  151. end
  152. %% Mask infos
  153. jason = regionprops(final_mask,'Area','Centroid');
  154. jason(find(vertcat(jason.Area) == 0)) = [];
  155. end
  157. %%post processing
  158. function [F_full_cor,F_dff,cc,spk,spk_th,waveform_para,Active_neurons] = Autoprocessing(F_full,fit_limit)
  159. %% Correction for photobleaching
  160. [num_cells,num_images] = size(F_full);
  161. F_full_cor = zeros(num_cells,num_images);
  162. for i = 1:num_cells
  163. ROIp = F_full(i,:);
  164. x = 1:1:num_images;
  165. ft = fittype('a*exp(-b*t) + c','indep','t');
  166. Int_min = min(ROIp,[],'all');
  167. try
  168. mdl{i} = fit(x',ROIp',ft,'start',[Int_min-10^(-fit_limit),10^(-fit_limit),Int_min+10^(-fit_limit)]);%[Int_min-0.00001,0.001,Int_min+0.00001]
  169. %plot(mdl,x',ROIp')
  170. catch
  171. fit_limit_2 = fit_limit + 20;
  172. mdl{i} = fit(x',ROIp',ft,'start',[Int_min-10^(-fit_limit_2),10^(-fit_limit_2),Int_min+10^(-fit_limit_2)]);
  173. end
  175. for k = 1:num_images
  176. bleach_cor = (mdl{i}.a + mdl{i}.c)/mdl{i}(k);
  177. F_full_cor(i,k) = F_full(i,k)*bleach_cor;
  178. end
  179. end
  180. %% df/f0
  181. F_dff = zeros(num_cells,num_images);
  182. for i = 1:num_cells
  183. ROIp = F_full_cor(i,:);
  184. f0 = prctile(ROIp,8);
  185. ROIp = (ROIp-f0)./f0;
  186. F_dff(i,:) = ROIp;
  187. end
  188. %% deconvolution
  189. cc = zeros(num_cells,num_images);
  190. spk = zeros(num_cells,num_images);
  191. for i = 1:num_cells
  192. ROIp = F_dff(i,:);
  193. spkmin = 0.5*GetSn(ROIp);
  194. lam = choose_lambda(exp(-1/(20*0.5)),GetSn(ROIp),0.99); % Fz = 20
  195. [cc(i,:),spk(i,:),~] = deconvolveCa(ROIp,'ar1','method','thresholded','optimize_pars',true,'maxIter',20,...
  196. 'window',150,'lambda',lam,'smin',spkmin);
  197. end
  198. %% spike th
  199. spk_th = spk;
  200. spk_th(find(spk < 0.001)) = 0;
  201. spk_th(find(spk_th > 0)) = 1;
  202. %% mean fire rate, Hz, per cell
  203. waveform_para = zeros(num_cells,5);
  204. timelength = 300;
  205. MFR = sum(spk_th,2)/(timelength);
  206. waveform_para(:,1) = MFR;
  207. %% active neurons, %, TH = 5 spikes/min
  208. spk_th_cell = sum(spk_th,2);
  209. spk_th_cell(find(spk_th_cell < 5*(timelength/60))) = 0;
  210. spk_th_cell(find(spk_th_cell > 0)) = 1;
  211. Active_neurons = sum(spk_th_cell,'all')/height(spk_th_cell);
  212. %% Waveform
  213. for i = 1:num_cells
  214. %rise time
  215. waveform_para(i,2) = mean(risetime(cc(i,:),20),'all');
  216. %fall time
  217. waveform_para(i,3) = mean(falltime(cc(i,:),20),'all');
  218. %pulsewidth
  219. waveform_para(i,4) = mean(pulsewidth(cc(i,:),20),'all');
  220. %amptitute
  221. waveform_para(i,5) = mean(cc(i,find(spk_th(i,:) == 1)),"all");
  222. end
  223. end

Pipeline_20240207.m at commit 09e15a6, no license · at the source

Overview

Authors: Ruiping Tang1,2, Charles‐Francois Latchoumane1,2, Avi Chopra1, Md Marzan Sarkar1,2, Chunki Kim3, Nathan Gonsalves1,2, Hsueh‐Fu Wu4,5, John C Sentmanat6, Alan Liu7, Isha Mhatre‐Winters3, Aditya Mishra8, Andrei G Fedorov6, Jay M Patel9,10,11, Nadja Zeltner4,5,12, Steven L Stice1,2, Jason R Richardson3, Lohitash Karumbaiah1,2,12
ORCID iDs: Chunki Kim, Lohitash Karumbaiah
  1. Regenerative Bioscience Center, University of Georgia, Athens, Georgia, USA
  2. Edgar L. Rhodes Center For Animal & Dairy Science, College of Agricultural and Environmental Sciences, University of Georgia, Athens, Georgia, USA
  3. Isakson Center for Neurological Disease Research, Department of Physiology and Pharmacology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA
  4. Center For Molecular Medicine, University of Georgia, Athens, Georgia, USA
  5. Department of Biochemistry and Molecular Biology, Franklin College of Arts and Sciences, University of Georgia, Athens, Georgia, USA
  6. G. W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
  7. Optics11 Life Inc. Boston, MA, USA
  8. Department of Statistics, University of Georgia, Athens, Georgia, USA
  9. Department of Orthopedics, Emory University School of Medicine, Atlanta, Georgia, USA
  10. Department of Biomedical Engineering, Georgia Institute of Technology‐Emory University, Atlanta, Georgia, USA
  11. Atlanta VA Medical Center, Emory University School of Medicine, Decatur, Georgia, USA
  12. Division of Neuroscience, Biomedical and Translational Sciences Institute, University of Georgia, Athens, Georgia, USA
Institutions: University of Georgia (United States); Georgia Institute of Technology (United States); Emory University (United States); Atlanta VA Medical Center (United States)
Journal: Advanced healthcare materials, volume 15, issue 32, article e03457
Dates: received 12 July 2025; accepted 14 July 2026; published online 30 July 2026; in print 26 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/adhm.202503457 · PMID 42529940 · PMCID PMC13507570 · OpenAlex W4413196782
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.06.667201
Status: code verified
Categories: optical imaging (calcium, voltage, 2-photon) (modality), human (organism), traumatic brain injury (population)
Methods: Machine learning, Connectivity, Graphs, fMRI & imaging, Single-unit activity, calcium imaging, Statistics
Keywords: calcium imaging, neurodegeneration, Neuron‐on‐chip, new approach methodologies (NAMs), traumatic brain injury
MeSH: Brain Injuries, Traumatic*, Lab-On-A-Chip Devices*, Neurons*, Calcium, Cells, Cultured, Cytokines, Humans, Microphysiological Systems, Prefrontal Cortex (* major topic)
Topic: Neuroscience and Neural Engineering (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Science Foundation (NSF) - Engineering Research Center (ERC) for Cell Manufacturing Technologies (NSF-ERC1648035); National Institute of Health (NIH)- National Institute of Neurological Disorders and Stroke (R21NS130468, R01ES033892, R01NS099596); Dianne Isakson Distinguished Professorship award; NIEHS NIH HHS (R01 ES033892); NINDS NIH HHS (R01 NS099596, R21 NS130468); NICHD NIH HHS (P2C HD086843); Alliance for Regenerative Rehabilitation Research and Training technology development award
Citations: not cited yet (Europe PMC); 157 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 3 matches between paragraphs and lines of code.

Thorowen/Calcium-imaging-data-processing

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 09e15a61778b85ece72983fb07588d22aeb9f612, 22 May 2026
Languages: MATLAB (4)
Size: 4 files, 4 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Curve Fitting Toolbox (1 file), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/Thorowen/Calcium-imaging-data-processing

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

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  • 3 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Code and data availability statement

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Read it in the paper: doi.org/10.1002/adhm.202503457.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 17 authors, 5 keywords, 9 MeSH terms, 7 funders, 152 references.

Cite

This paper

Tang, R., Latchoumane, C., Chopra, A., Sarkar, M. M., Kim, C., Gonsalves, N., Wu, H., Sentmanat, J. C., Liu, A., Mhatre‐Winters, I., Mishra, A., Fedorov, A. G., Patel, J. M., Zeltner, N., Stice, S. L., Richardson, J. R., & Karumbaiah, L. (2026). A 3D Human Neuron-on-Chip Platform to Monitor Neuronal Injury Responses. Advanced healthcare materials, 15(32), e03457. https://doi.org/10.1002/adhm.202503457

BibTeX

@article{tang20263d,
author = {Tang, Ruiping and Latchoumane, Charles‐Francois and Chopra, Avi and Sarkar, Md Marzan and Kim, Chunki and Gonsalves, Nathan and Wu, Hsueh‐Fu and Sentmanat, John C and Liu, Alan and Mhatre‐Winters, Isha and Mishra, Aditya and Fedorov, Andrei G and Patel, Jay M and Zeltner, Nadja and Stice, Steven L and Richardson, Jason R and Karumbaiah, Lohitash},
title = {{A 3D Human Neuron-on-Chip Platform to Monitor Neuronal Injury Responses}},
journal = {Advanced healthcare materials},
year = {2026},
month = jul,
volume = {15},
number = {32},
pages = {e03457},
publisher = {Wiley},
issn = {2192-2640},
doi = {10.1002/adhm.202503457},
url = {https://doi.org/10.1002/adhm.202503457},
pmid = {42529940},
pmcid = {PMC13507570}
}

RIS

TY - JOUR
AU - Tang, Ruiping
AU - Latchoumane, Charles‐Francois
AU - Chopra, Avi
AU - Sarkar, Md Marzan
AU - Kim, Chunki
AU - Gonsalves, Nathan
AU - Wu, Hsueh‐Fu
AU - Sentmanat, John C
AU - Liu, Alan
AU - Mhatre‐Winters, Isha
AU - Mishra, Aditya
AU - Fedorov, Andrei G
AU - Patel, Jay M
AU - Zeltner, Nadja
AU - Stice, Steven L
AU - Richardson, Jason R
AU - Karumbaiah, Lohitash
TI - A 3D Human Neuron-on-Chip Platform to Monitor Neuronal Injury Responses
T2 - Advanced healthcare materials
J2 - Adv Healthc Mater
PY - 2026
DA - 2026/07/30
VL - 15
IS - 32
SP - e03457
SN - 2192-2640
PB - Wiley
DO - 10.1002/adhm.202503457
UR - https://doi.org/10.1002/adhm.202503457
LA - en
ER -

CSL-JSON

{
"id": "10.1002/adhm.202503457",
"type": "article-journal",
"title": "A 3D Human Neuron-on-Chip Platform to Monitor Neuronal Injury Responses",
"container-title": "Advanced healthcare materials",
"author": [
{
"family": "Tang",
"given": "Ruiping"
},
{
"family": "Latchoumane",
"given": "Charles‐Francois"
},
{
"family": "Chopra",
"given": "Avi"
},
{
"family": "Sarkar",
"given": "Md Marzan"
},
{
"family": "Kim",
"given": "Chunki"
},
{
"family": "Gonsalves",
"given": "Nathan"
},
{
"family": "Wu",
"given": "Hsueh‐Fu"
},
{
"family": "Sentmanat",
"given": "John C"
},
{
"family": "Liu",
"given": "Alan"
},
{
"family": "Mhatre‐Winters",
"given": "Isha"
},
{
"family": "Mishra",
"given": "Aditya"
},
{
"family": "Fedorov",
"given": "Andrei G"
},
{
"family": "Patel",
"given": "Jay M"
},
{
"family": "Zeltner",
"given": "Nadja"
},
{
"family": "Stice",
"given": "Steven L"
},
{
"family": "Richardson",
"given": "Jason R"
},
{
"family": "Karumbaiah",
"given": "Lohitash"
}
],
"container-title-short": "Adv Healthc Mater",
"volume": "15",
"issue": "32",
"page": "e03457",
"DOI": "10.1002/adhm.202503457",
"PMID": "42529940",
"PMCID": "PMC13507570",
"ISSN": "2192-2640",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/adhm.202503457",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
30
]
]
}
}

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