OSCR

Transient infrared laser exposure modulates calcium activity in cortical dendritic spines.

Code ↔ Paper

8 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 8 matches
  1. [1] § Results › Anatomical Variation in Calcium Response ↔ SpineGCaMPAnalysis.m, lines 89–229 · score 0.70 · unreactive soma, DS responding, Cohen, rank, ANOVA, phenotype
  2. [2] § Methods › Infrared Laser Exposure ↔ SpineGCaMPAnalysis.m, lines 515–548 · score 0.68 · pulse energy, pulse train, single pulse, SP, PT, temperatures
  3. [3] § Methods › Data Analysis › Signal feature extraction ↔ ExtendedImagingAnalysis.m, lines 60–140 · score 0.66 · low activity, high activity, mCardinal, fluorescence, spiking, actin
  4. [4] § Methods › Microscopy › Confocal imaging ↔ ExtendedImagingAnalysis.m, lines 60–140 · score 0.61 · low activity, high activity, mCardinal, sham, actin, Bonferroni
  5. [5] § Results › Anatomical Variation in Calcium Response ↔ SpineGCaMPAnalysis.m, lines 89–229 · score 0.57 · Reactive neurons, rank, SEM, unreactive, phenotypes, SPME
  6. [6] § Methods › Data Analysis › Image processing and signal measurement ↔ ActinDataExtraction.m, lines 1–111 · score 0.57 · affine, imregtform, imwarp, transform, Multimodal, segmentation
  7. [7] § Results › Concurrent Calcium and F-Actin Dynamics ↔ ExtendedImagingAnalysis.m, lines 199–267 · score 0.55 · individual neuron, mCardinal, reaction, Spearman, correlation, alpha
  8. [8] § Results › Infrared-Induced Calcium Activity Mediated Through Extracellular Entry ↔ SpineGCaMPAnalysis.m, lines 455–513 · score 0.53 · CaFree, AP5, CNQX, TRP, RR, Kruskal

Paper

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The authors' code

MATLAB · 705 lines · 28 KB · no license · 4 matches

  1. clc, clear, close all
  3. % Load Data
  4. load("Phys Temp.mat"), PhysTemp = ExpData;
  5. load("Room Temp.mat"), RoomTemp = ExpData.PulseTrain;
  6. RoomTemp = rmfield(RoomTemp,'Post');
  7. load("Pharmacology.mat"), Pharma = ExpData.PulseTrain;
  8. Pharma = rmfield(Pharma,'Post');
  9. clear ExpData
  11. DoseType = {'Single', 'Multi'};
  12. artifactpts = 31:5:51;
  14. Colors = [0 0 0;
  15. .5 .5 0;
  16. 0 .5 0;];
  17. yellow_lut = [linspace(0,1,256)', linspace(0,1,256)',zeros(256,1)];
  19. % Separate Room Temp experiments into experimental categories
  20. mask = contains({RoomTemp.Single.filename},'30A');
  21. RoomTemp.Subthreshold.Single = RoomTemp.Single(mask);
  22. RoomTemp.Subthreshold.Multi = RoomTemp.Multi(mask);
  23. RoomTemp.Threshold.Single = RoomTemp.Single(~mask);
  24. RoomTemp.Threshold.Multi = RoomTemp.Multi(~mask);
  26. % Good ol Peak counting. This is the way. [labels,locs height,prom,width]
  27. % Soma = [loction, height, prominance, width ]
  28. % Dendrites = [DendriteLabel, loction, height, prominance, width, bAP]
  29. % Spines = [SpineLabel, ParentDendrite, loction, height, prominance, width, bAP, Reactive, NeuronParent]
  31. PhysTemp.PulseTrain.Single = ExtractPks(PhysTemp.PulseTrain.Single,752,artifactpts(1));
  32. PhysTemp.PulseTrain.Multi = ExtractPks(PhysTemp.PulseTrain.Multi,900,artifactpts);
  33. PhysTemp.SinglePulse.Single = ExtractPks(PhysTemp.SinglePulse.Single,752,artifactpts(1));
  34. PhysTemp.SinglePulse.Multi = ExtractPks(PhysTemp.SinglePulse.Multi,900,artifactpts);
  36. RoomTemp.Subthreshold.Single = ExtractPks(RoomTemp.Subthreshold.Single,752,artifactpts(1));
  37. RoomTemp.Subthreshold.Multi = ExtractPks(RoomTemp.Subthreshold.Multi,900,artifactpts);
  38. RoomTemp.Threshold.Single = ExtractPks(RoomTemp.Threshold.Single,752,artifactpts(1));
  39. RoomTemp.Threshold.Multi = ExtractPks(RoomTemp.Threshold.Multi,900,artifactpts);
  41. Pharma.Single = ExtractPks(Pharma.Single,752,artifactpts(1));
  42. Pharma.Multi = ExtractPks(Pharma.Multi,899,artifactpts);
  44. % Append Data
  45. PhysTemp.PulseTrain = AppendData(PhysTemp.PulseTrain);clc
  46. PhysTemp.SinglePulse = AppendData(PhysTemp.SinglePulse);
  47. RoomTemp.Subthreshold = AppendData(RoomTemp.Subthreshold);
  48. RoomTemp.Threshold = AppendData(RoomTemp.Threshold);
  50. % Peak Stats
  51. PhysTemp.PulseTrain.SpinePkStats = SpinePeakStats(PhysTemp.PulseTrain);
  52. PhysTemp.SinglePulse.SpinePkStats = SpinePeakStats(PhysTemp.SinglePulse);
  53. RoomTemp.Subthreshold.SpinePkStats = SpinePeakStats(RoomTemp.Subthreshold);
  54. RoomTemp.Threshold.SpinePkStats = SpinePeakStats(RoomTemp.Threshold);
  55. Pharma.SpinepkStats = SpinePeakStats(Pharma);
  58. %% Panel 3A
  59. close all
  60. ExpTyp = PhysTemp.SinglePulse.Single;
  61. neuron = 4;
  62. figure('Theme','light');
  63. subplot(8,1,1:5),
  64. spines = RemoveDC(ExpTyp(neuron).Spines,200);
  65. t = (1:length(spines))/7.5;
  66. imagesc(spines'), colormap("hot"),
  67. axpos = get(gca,"Position");
  68. c =colorbar; c.Label.String = '\DeltaF/F'; c.Location = 'manual';
  69. c.Position = [axpos(1)+axpos(3)+.01 axpos(2) .02 axpos(4)];
  70. xline(30*7.5,'LineWidth',2,LineStyle="--",Alpha=1,Color='w')
  71. set(gca, 'XTick', [], 'YTick', [],'FontSize',24)
  73. ylabel('Denditic Spines')
  75. subplot(8,1,6)
  76. mask = ~ExpTyp(neuron).SpinePks(:,end-2);
  77. xline(ExpTyp(neuron).SpinePks(mask,3),'Color','r','LineWidth',2,Alpha=1 );
  78. xline(ExpTyp(neuron).SomaPks(:,1),'LineWidth',2,Alpha=1 );
  79. xlim([t(1) t(end)]), xline(30,'LineWidth',2,LineStyle="--",Alpha=1 )
  80. set(gca, 'XTick', [], 'YTick', [])
  82. subplot(8,1,7:8)
  83. soma = RemoveDC(ExpTyp(neuron).Soma,200);
  84. plot(t,soma,'Color','k','LineWidth',2),
  85. xlim([t(1) t(end)]), xline(30,'LineWidth',2,LineStyle="--",Alpha=1 )
  86. set(gca, 'FontSize',24)
  87. ylabel('\DeltaF/F'), xlabel('time (sec)')
  89. %% Figure 3 comparing Soma, and Spine Responses
  90. close all
  92. SomaLabels = []; SomaMean =[]; SomaPhenotype= []; SpineLabels =[];
  93. SpineMean =[]; SpinePhenotype= []; SpikePercentage = [];
  94. duration = 75;
  95. % Mean Plot responses
  96. for i = 1:4
  97. if i ==1, PulseType = PhysTemp.PulseTrain; ExposureType = PulseType.SingleAppened;
  98. stimpt = round(30*7.5); thresh = stimpt+duration;
  99. ReactiveFraction = PulseType.SpinePkStats.SingleRespondingFraction(:,2);
  100. elseif i ==2, PulseType = PhysTemp.SinglePulse; ExposureType = PulseType.SingleAppened;
  101. ReactiveFraction = PulseType.SpinePkStats.SingleRespondingFraction(:,2);
  102. elseif i ==3, PulseType = PhysTemp.PulseTrain; ExposureType = PulseType.MultiAppened;
  103. stimpt = round(50*7.5); thresh = stimpt+duration;
  104. ReactiveFraction = PulseType.SpinePkStats.MultiRespondingFraction(:,2);
  105. else, PulseType = PhysTemp.SinglePulse; ExposureType = PulseType.MultiAppened;
  106. ReactiveFraction = PulseType.SpinePkStats.MultiRespondingFraction(:,2);
  107. end
  109. soma = RemoveDC(ExposureType.Soma,225);
  110. spines = RemoveDC(ExposureType.Spines,225);
  111. SomaMean = [SomaMean; mean(soma(stimpt:thresh,:))'];
  112. SpineMean = [SpineMean; mean(spines(stimpt:thresh,:))'];
  115. mask = median(sum(soma(stimpt:thresh,:))')<sum(soma(stimpt:thresh,:))'; % Reactive neurons are above the median of the AUC
  116. SomaLabels = [SomaLabels; ones(size(mask))*i];
  117. SomaPhenotype = logical([SomaPhenotype; mask]);
  119. SpikePercentage = [SpikePercentage; ReactiveFraction];
  120. pv = ranksum(ReactiveFraction(mask),ReactiveFraction(~mask));
  122. % Panel C
  123. mask = (PulseType.Labels.Structure=='Spine') & ismember(PulseType.Labels.NeuronID,find(mask));
  124. temp = (PulseType.Labels.Structure=='Soma') | (PulseType.Labels.Structure=='Dendrite');
  125. mask(temp) = [];
  126. SpineLabels = [SpineLabels; ones(size(mask))*i];
  127. SpinePhenotype = logical([SpinePhenotype; mask]);
  128. end
  130. % 3B
  131. Labels = SomaLabels+SomaPhenotype*4;
  132. TickLabels = {'PTSE_(_-_)' 'SPSE_(_-_)' 'PTME_(_-_)' 'SPME_(_-_)'...
  133. 'PTSE_(_+_)' 'SPSE_(_+_)' 'PTME_(_+_)' 'SPME_(_+_)'};
  135. Colors = [repmat([.5 .5 .5],[4 1]); repmat([1 .5 .5],[4 1])];
  136. f1 = figure(Theme='light');
  137. boxchart(Labels, SomaMean,'GroupByColor',Labels,'ColorGroupLayout',...
  138. 'overlaid', 'BoxEdgeColor','k','MarkerSize',10, 'MarkerStyle',...
  139. '.','MarkerColor','k','BoxFaceAlpha', 1);
  140. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  142. colororder(Colors), ylabel('\DeltaF/F'),
  143. set(gca, 'XTick', 1:max(Labels), 'XTickLabels', TickLabels)
  144. ax = gca; ax.FontSize = 24; ax.TickDir = 'none'; theme('light')
  146. % Anderson-Darling normality test
  147. for i = 1:max(Labels)
  148. data = SomaMean(Labels==i);
  149. isnormal(i) = adtest(data);
  150. end, isnormal
  152. % P Vals and heat map
  153. dFSoma = PairwiseTest(SomaMean,Labels,TickLabels,'rank');
  154. mask = dFSoma.P_Value<0.05;
  155. figure(f1), hold on
  156. sigstar(dFSoma.Pairs(mask), dFSoma.P_Value(mask)); hold off;
  157. legend('Reactive Soma', 'Unreactive Soma')
  159. for i = 5:8
  160. mask = Labels==i;
  161. M = mean(SomaMean(mask))*100;
  162. sem = std(SomaMean(mask))/sqrt(sum(mask))*100;
  163. sprintf('%s: %.2f+/-%.2f', TickLabels{i}, M, sem)
  164. end
  166. % Panel 3C
  167. Labels = SpineLabels+SpinePhenotype*4;
  169. figure('Theme','light');
  170. swarmchart(SpineLabels(~SpinePhenotype),SpineMean(~SpinePhenotype),5,'black','filled'), hold on
  171. swarmchart(SpineLabels(SpinePhenotype),SpineMean(SpinePhenotype),5,'red','filled'), hold off
  172. set(gca, 'XTick', 1:4, 'XTickLabels', {'PTSE' 'SPSE' 'PTME' 'SPME'})
  173. ylabel("\DeltaF/F")
  174. ax = gca; ax.FontSize = 30; ax.TickDir = 'none';
  175. legend('Unreactive (-)','Reactive (+)',"Location", "northwest",...
  176. "Color",'none','EdgeColor','none');
  178. % Kolmgrov-smirnov normality test & effect size
  179. for i = 1:max(SpineLabels)
  180. spines = SpineLabels==i;
  181. isnormal(i) = kstest(SpineMean(spines));
  182. x = spines & SpinePhenotype;
  183. x = SpineMean(x);
  184. y = spines & ~SpinePhenotype;
  185. y = SpineMean(y);
  186. meanEffectSize(x,y,"Effect","cohen")
  187. d(i,1) = ans.Effect;
  188. CI = abs(ans.ConfidenceIntervals-ans.Effect);
  189. d(i,2) = mean(CI);
  190. end, isnormal
  192. [~,~,stats] =anova1(SpineMean,Labels,"off");
  193. c = multcompare(stats,'Display','off','CriticalValueType','bonferroni');
  194. pvVal = orderpvalues(c);
  195. plotheatmap(TickLabels, pvVal);
  196. title('ANOVA P-Values')
  198. dFSpines = PairwiseTest(SpineMean,Labels,TickLabels,'ttest');
  201. for i = 5:8
  202. mask = Labels==i;
  203. M = mean(SpineMean(mask))*100;
  204. sem = std(SpineMean(mask))/sqrt(sum(mask))*100;
  205. sprintf('%s: %.2f+/-%.2f', TickLabels{i}, M, sem)
  206. end
  208. % 3D Spiking activity striated by soma phenotype
  209. close all
  210. f1 = figure(Theme='light');
  211. Labels = SomaLabels+SomaPhenotype*4;
  212. boxchart(Labels, SpikePercentage*100,'GroupByColor',Labels,'ColorGroupLayout',...
  213. 'overlaid', 'BoxEdgeColor','k','MarkerSize',10, 'MarkerStyle',...
  214. '.','MarkerColor','k','BoxFaceAlpha', .5),
  215. colororder(Colors), ylabel('% DS Responding/Neuron'), ylim([0 100])
  216. set(gca, 'XTick', 1:max(Labels), 'XTickLabels', TickLabels,'FontSize',24)
  219. PhenoSpike = PairwiseTest(SpikePercentage,Labels,TickLabels,'rank');
  221. % Anderson-Darling normality test
  222. for i = 1:max(Labels)
  223. data = SpikePercentage(Labels==i);
  224. isnormal(i) = adtest(data);
  225. end, isnormal
  227. mask = PhenoSpike.P_Value<0.05;
  228. figure(f1), hold on
  229. sigstar(PhenoSpike.Pairs(mask), PhenoSpike.P_Value(mask)); hold off; % Add significance markers
  232. %% Figure 4
  233. close all
  235. % 4B Distances from Soma for each experimental protocol
  236. Distances = horzcat([PhysTemp.PulseTrain.Single.SpineDistance],...
  237. [PhysTemp.PulseTrain.Multi.SpineDistance],...
  238. [PhysTemp.SinglePulse.Single.SpineDistance],...
  239. [PhysTemp.SinglePulse.Multi.SpineDistance]);
  241. % Labels for all Spines
  242. Labels = horzcat(ones(1,length([PhysTemp.PulseTrain.Single.SpineEventMask])),...
  243. ones(1,length([PhysTemp.PulseTrain.Multi.SpineEventMask]))*2,...
  244. ones(1,length([PhysTemp.SinglePulse.Single.SpineEventMask]))*3,...
  245. ones(1,length([PhysTemp.SinglePulse.Multi.SpineEventMask]))*4);
  247. % Mask of activated spines
  248. mask = logical(horzcat([PhysTemp.PulseTrain.Single.SpineEventMask],...
  249. [PhysTemp.PulseTrain.Multi.SpineEventMask],...
  250. [PhysTemp.SinglePulse.Single.SpineEventMask],...
  251. [PhysTemp.SinglePulse.Multi.SpineEventMask]));
  254. figure('Theme','light');
  255. set(gcf, 'Units', 'inches', 'Position', [5, 5, 5.5, 4]);
  256. swarmchart(Labels(~mask),Distances(~mask),10,'black','filled'), hold on
  257. swarmchart(Labels(mask),Distances(mask)',10,'red','filled'), hold off
  258. set(gca, 'XTick', 1:4, 'XTickLabels', {'PTSE' 'PTME' 'SPSE' 'SPME'})
  259. ylabel("Distance (\mum)")
  260. legend('Unreactive','Reactive',"Location", "northwest",...
  261. "Color",'none','EdgeColor','none');
  262. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  264. for i = 1:4
  265. imask = Labels == i;
  266. [r(i)] = corr(Distances(imask)', mask(imask)', 'Type', 'Pearson');
  267. end
  268. Labels = Labels+mask*4;
  270. TickLabels = {'PTSE (-)' 'PTME (-)' 'SPSE (-)' 'SPME (-)'...
  271. 'PTSE (+)' 'PTME (+)' 'SPSE (+)' 'SPME (+)'};
  273. [~,~,stats] = anova1(Distances, Labels,"off");
  274. c = multcompare(stats,'Display','off');
  275. p = orderpvalues(c);
  276. plotheatmap(TickLabels, p)
  277. title('ANOVA Bonferroni Post-Hoc P-Values')
  279. % Figure 4 C & D
  280. PTSS = PhysTemp.PulseTrain.SpinePkStats.Post_Single;
  281. PTMS = PhysTemp.PulseTrain.SpinePkStats.Post_Multi;
  282. SPSS = PhysTemp.SinglePulse.SpinePkStats.Post_Single;
  283. SPMS = PhysTemp.SinglePulse.SpinePkStats.Post_Multi;
  285. PkStats = vertcat(PTSS, PTMS, SPSS, SPMS);
  286. Labels = [ones(length(PTSS),1); ones(length(PTMS),1)*2; ...
  287. ones(length(SPSS),1)*3; ones(length(SPMS),1)*4];
  288. IRPks = horzcat(PkStats, Labels);
  290. PTSS = PhysTemp.PulseTrain.SpinePkStats.Single_bAP;
  291. PTMS = PhysTemp.PulseTrain.SpinePkStats.Multi_bAP;
  292. SPSS = PhysTemp.SinglePulse.SpinePkStats.Single_bAP;
  293. SPMS = PhysTemp.SinglePulse.SpinePkStats.Multi_bAP;
  295. PkStats = vertcat(PTSS, PTMS, SPSS, SPMS);
  296. Labels = [ones(length(PTSS),1); ones(length(PTMS),1)*2; ...
  297. ones(length(SPSS),1)*3; ones(length(SPMS),1)*4];
  298. bAPPks = horzcat(PkStats, Labels);
  299. mask = bAPPks(:,3)>30 & bAPPks(:,7)==1;
  300. post_bAPPks = bAPPks(mask,:);
  301. pre_bAPPks = bAPPks(~mask,:);
  304. figure('Theme','light'); set(gcf, 'Units', 'inches', 'Position', [5, 5, 5.5, 4]);
  305. swarmchart(IRPks(:,end),IRPks(:,4),5,'red','filled'), hold on
  306. swarmchart(post_bAPPks(:,end),post_bAPPks(:,4),5,'black','filled'), hold off
  307. set(gca, 'XTick', 1:4, 'XTickLabels', {'PTSE' 'PTME' 'SPSE' 'SPME'})
  308. ylabel("\DeltaF/F (A.U.)")
  309. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  310. legend('ICS','bAP',"Location", "best",...
  311. "Color",'none','EdgeColor','none');
  313. TickLabels = {'PTSE bAP' 'PTME bAP' 'SPSE bAP' 'SPME bAP'...
  314. 'PTSE ICS' 'PTME ICS' 'SPSE ICS' 'SPME ICS'};
  316. Data = [post_bAPPks(:,4); IRPks(:,4)];
  317. Labels = [post_bAPPks(:,end); IRPks(:,end)+4];
  319. for i = 1:max(Labels)
  320. neurons = Labels==i;
  321. tempstruct(i) = datastatsJake(Data(neurons));
  322. isnormal(i) = jbtest(Data(neurons));
  323. end, dFstats = struct2table(tempstruct); clear tempstruct
  324. dFstats.Labels = TickLabels'; isnormal
  326. [~,~,stats] = anova1(Data, Labels,"off");
  327. c = multcompare(stats,'CriticalValueType','bonferroni','Display','off');
  328. p = orderpvalues(c);
  329. plotheatmap(TickLabels, p);
  330. title('Kruskal-Wallis P-Values')
  331. disp(dFstats)
  334. figure('Theme','light'); set(gcf, 'Units', 'inches', 'Position', [5, 5, 5.5, 4]);
  335. swarmchart(IRPks(:,end),IRPks(:,5),5,'red','filled'), hold on
  336. swarmchart(post_bAPPks(:,end),post_bAPPks(:,5),5,'black','filled'), hold off
  337. set(gca, 'XTick', 1:4, 'XTickLabels', {'PTSE' 'PTME' 'SPSE' 'SPME'})
  338. ylabel("FWHM (Sec)")
  339. legend('ICS','bAP',"Location", "best",...
  340. "Color",'none','EdgeColor','none');
  341. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  343. Data = [post_bAPPks(:,5); IRPks(:,5)];
  344. Labels = [post_bAPPks(:,end); IRPks(:,end)+4];
  347. for i = 1:max(Labels)
  348. neurons = Labels==i;
  349. tempstruct(i) = datastatsJake(Data(neurons));
  350. isnormal(i) = jbtest(Data(neurons));
  351. end, FWHMstats = struct2table(tempstruct); clear tempstruct
  352. FWHMstats.Labels = TickLabels'; isnormal
  354. [~,~,stats] = anova1(Data, Labels,"off");
  355. c = multcompare(stats,'CriticalValueType','bonferroni','Display','off');
  356. p = orderpvalues(c);
  357. plotheatmap(TickLabels, p);
  358. disp(FWHMstats)
  360. % Figure 4E
  361. PkCount = horzcat(PhysTemp.PulseTrain.SpinePkStats.SingleRespondingFraction,...
  362. PhysTemp.PulseTrain.SpinePkStats.MultiRespondingFraction,...
  363. PhysTemp.SinglePulse.SpinePkStats.SingleRespondingFraction,...
  364. PhysTemp.SinglePulse.SpinePkStats.MultiRespondingFraction);
  365. PkCount(:,[3 7]) = [];
  368. [j,k] = size(PkCount);
  369. PTPkCount = reshape(PkCount, [j*k 1])*100;
  370. PTLabels = reshape(repmat(1:k,[j 1]), size(PTPkCount));
  371. PTLabels(PTLabels==4) = 1; PTLabels(PTLabels==5) = 4; PTLabels(PTLabels==6) = 5;
  372. Colors = [0.5, 0.5, 0.5; % Gray
  373. 1, 0, 0; % Bright Red
  374. 0.8, 0, 0; % Darker Red
  375. 1, 0.6, 0; % Light Orange
  376. 0.8, 0.4, 0;]; % Darker Orange
  378. figure('Theme','light'); set(gcf, 'Units', 'inches', 'Position', [1, 1, 7, 6]);
  379. boxchart(PTLabels, PTPkCount,'GroupByColor',PTLabels,'ColorGroupLayout',...
  380. 'overlaid', 'BoxEdgeColor','k','MarkerSize',10, 'MarkerStyle',...
  381. '.','MarkerColor','k','BoxFaceAlpha', 1)
  382. %violinplot(PTLabels, PTPkCount,'GroupByColor',PTLabels,'ColorGroupLayout',...
  383. % 'overlaid','FaceAlpha', 1,'EdgeColor','k','DensityScale','count')
  384. colororder(Colors)
  385. %b = gbar(PkCount, Labels)
  386. ylabel("% DS Responding/Neuron"), ylim([0 100]), xlim([0.5 max(PTLabels)+0.5])
  387. set(gca, 'XTick', 1:5, 'XTickLabels', {'Pre Stim' 'PTSE_3_0' 'PTME_3_0' 'SPSE_3_0' 'SPME_3_0'})
  388. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  390. [~,~,stats] = kruskalwallis(PTPkCount, PTLabels,'off');
  391. c = multcompare(stats,'Display','off',CriticalValueType='dunn-sidak');
  392. pairs = num2cell(c(:, 1:2),2); mask = c(:,6)<0.05;
  393. sigstar(pairs(mask), c(mask,6)); hold off; % Add significance markers
  395. p = orderpvalues(c);
  396. plotheatmap({'Pre Stim' 'PTSE' 'PTME' 'SPSE' 'SPME'}, p);
  397. title('Kruskal-Wallis P-Values')
  399. for i = 1:max(PTLabels)
  400. neurons = PTLabels==i;
  401. tempstruct(i) = datastatsJake(PTPkCount(neurons));
  402. isnormal(i) = adtest(PTPkCount(neurons));
  403. end, RespFraction = struct2table(tempstruct); clear tempstruct
  404. isnormal
  405. RespFraction.Labels = {'Pre Stim' 'PTSE' 'PTME' 'SPSE' 'SPME'}';
  406. disp(RespFraction)
  408. % Figure 4F Room Temperature Data
  410. PkCount = horzcat(RoomTemp.Subthreshold.SpinePkStats.SingleRespondingFraction,...
  411. RoomTemp.Subthreshold.SpinePkStats.MultiRespondingFraction,...
  412. RoomTemp.Threshold.SpinePkStats.SingleRespondingFraction,...
  413. RoomTemp.Threshold.SpinePkStats.MultiRespondingFraction);
  415. PkCount(:,[3, 5, 7]) = []; % pre exposure indexes for later time points
  417. [j,k] = size(PkCount);
  418. RTPkCount = reshape(PkCount, [j*k 1])*100;
  419. RTLabels = reshape(repmat(1:k,[j 1]), size(RTPkCount));
  422. titles = {'Pre Stim' 'PTSE_S_u_b' 'PTME_S_u_b' 'SPSE_T_h_r_e_s_h' 'SPME_T_h_r_e_s_h'};
  423. Colors = [0.5, 0.5, 0.5; % Gray
  424. 1, 0, 0; % Bright Red
  425. 0.8, 0, 0; % Darker Red
  426. 0.5, 0.8, 1;
  427. .1, .3, 1;];
  429. figure('Theme','light'); set(gcf, 'Units', 'inches', 'Position', [1, 1, 7, 6]);
  430. boxchart(RTLabels, RTPkCount,'GroupByColor',RTLabels,'ColorGroupLayout',...
  431. 'overlaid', 'BoxEdgeColor','k','MarkerSize',10, 'MarkerStyle',...
  432. '.','MarkerColor','k','BoxFaceAlpha', 1)
  433. colororder(Colors)
  434. ylabel("% DS Responding/Neuron"), ylim([0 100])
  435. set(gca, 'XTick', 1:5, 'XTickLabels',titles)
  436. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  438. [~,~,stats] = kruskalwallis(RTPkCount, RTLabels,'off');
  439. c = multcompare(stats,'Display','off',CriticalValueType='dunn-sidak');
  440. pairs = num2cell(c(:, 1:2),2); mask = c(:,6)<0.05;
  441. sigstar(pairs(mask), c(mask,6)); hold off; % Add significance markers
  443. p = orderpvalues(c);
  444. plotheatmap(titles, p)
  445. title('Kruskal-Wallis P-Values')
  447. for i = 1:max(PTLabels)
  448. neurons = PTLabels==i;
  449. tempstruct(i) = datastatsJake(RTPkCount(neurons));
  450. isnormal(i) = adtest(RTPkCount(neurons));
  451. end, RTRespFraction = struct2table(tempstruct); clear tempstruct
  452. RTRespFraction.Labels = titles';
  453. disp(RTRespFraction), isnormal
  455. %% Figure 6 Physiological Mechanism
  456. close all
  457. PharmaGroups ={'Control','CaFree','AP5', 'CNQX', 'RR','Gd','TRP'};
  459. CountLabels = []; PkCount = []; spines = Pharma.Multi;
  460. Locs = []; LocLabels = [];
  461. Colors = orderedcolors("gem");
  462. Colors([2, 5], :) = Colors([5, 2], :);
  464. ex = [3 1 1 1 1 1 1 1 1];
  465. for i = 1:length(PharmaGroups)
  467. mask = contains({Pharma.Multi.filename},PharmaGroups{i});
  468. temp = spines(mask);
  469. TempLocs = vertcat(temp.SpinePks);
  470. TempLocs = TempLocs(~TempLocs(:,6),3); % Time of firing that is not an AP
  471. Locs = [Locs; TempLocs(TempLocs>30)];
  472. LocLabels = [LocLabels; ones(numel(TempLocs(TempLocs>30)),1)*i];
  473. PkCount = [PkCount; Pharma.SpinepkStats.MultiRespondingFraction(mask,2)*100];
  474. CountLabels = [CountLabels; ones(sum(mask),1)*i];
  477. figure('Theme','light'); set(gcf, 'Units', 'inches', 'Position', [1, 1, 6, 2]);
  478. histogram(TempLocs,24,'Normalization','countdensity',...
  479. 'FaceColor', Colors(i,:), 'FaceAlpha', 1), %hold on
  480. ylim([0 15]), xlim([0 120]),
  481. xlabel('Time (sec)'), ylabel('Counts/5 Sec')
  482. xline(artifactpts-1)
  483. set(gca, "FontSize", 20)
  484. legend(PharmaGroups{i},"Location", "best",...
  485. "Color",'none','EdgeColor','none');
  486. end
  488. for i = 1:max(CountLabels)
  489. neurons = CountLabels==i;
  490. tempstruct(i) = datastatsJake(PkCount(neurons));
  491. isnormal(i) = adtest(PkCount(neurons));
  492. end, PRespFraction = struct2table(tempstruct); clear tempstruct
  493. PRespFraction.Labels = PharmaGroups';
  494. disp(PRespFraction),
  496. figure('Theme','light');
  497. set(gcf, 'Units', 'inches', 'Position', [1, 1, 7, 6]);
  498. boxchart(CountLabels, PkCount,'GroupByColor',CountLabels,'ColorGroupLayout','overlaid', ...
  499. 'BoxEdgeColor','k','MarkerSize',10, 'MarkerStyle',...
  500. '.','MarkerColor','k','BoxFaceAlpha', 1)
  501. colororder(Colors)
  502. ylabel("% DS Responding/Neuron"), ylim([0 119])
  503. set(gca, 'XTick', 1:i, 'XTickLabels',PharmaGroups)
  504. set(gca, 'YTick', 0:20:100)%, 'XTickLabels',PharmaGroups)
  505. ax = gca; ax.FontSize = 24; ax.TickDir = 'none';
  507. [~,~,stats] = kruskalwallis(PkCount, CountLabels,'off');
  508. c = multcompare(stats,'Display','off',CriticalValueType='bonferroni');
  509. pairs = num2cell(c(:, 1:2),2); mask = c(:,end)<0.05;
  510. sigstar(pairs(mask), c(mask,6)); hold off; % Add significance markers
  512. p = orderpvalues(c);
  513. plotheatmap(PharmaGroups, p);
  515. %% Energy Calculations
  516. [Area,~] = SpotSizeCalc(0.22,1.33,1.33,400,200,0);
  517. PTAmps = [ 28 28 30 28 27 28 28 28 28 27.5 30 30 30 30 30];
  518. z = 0.02; %cm
  519. lambda = [1860 1880]; alpha = [14.19 31.08]; % Hale & Querry 1973 (1/cm)
  520. alpha = linterp(lambda, alpha, 1875);
  522. % Pulse Train
  523. %m = (121.9-88.7)/(37-28); b = 121.9-m*37;
  524. X = [28 37];
  525. Y = [88.7 121.9];
  526. Period = 0.01;
  527. %PT_Energy = mean(linterp(X,Y,PTAmps)*Period*100);
  528. PT_Energy = 3.52*.25*100 % 7/10 CW measurement I=28.7A
  529. PT_H = PT_Energy*exp(-alpha*z)*1e5/Area;
  531. % Single Pulse
  532. SPAmps = [ 25 25 25 25 24 24 24 23.5 25 25 23 24 24 23 23.5];
  533. X = [23 35];
  534. Y = [233.9 256.7];
  535. Period = 0.1; %seconds
  536. SP_Energy = mean(linterp(X,Y,SPAmps)*Period)
  537. SP_Energy = 2.92*8; % 7/10 CW measurement I=24.2A
  538. SP_H = SP_Energy*exp(-alpha*z)*1e5/Area;
  541. rho = .9932; % density of water at 37C
  542. cp = 4.18; % isobaric specific heat kJ/(kg*K) from engineering toolbox
  543. mean((alpha*SP_H*2)./(rho*cp))+30 % Temperature conversions
  546. % Pulse train energy at 37C: 91.28 +/- 4.17 mJ
  547. % Single 8 ms pulse energy at 37C: 27.02 +/- 0.83 mJ
  548. % Pulse train energy at ~18C: 121.9 mJ
  550. %% Functions
  551. function [ExperimentType] = AppendData(ExperimentType)
  553. ExperimentType.SingleAppened.Soma = [];
  554. ExperimentType.SingleAppened.Dendrites = [];
  555. ExperimentType.SingleAppened.Spines = [];
  557. ExperimentType.MultiAppened.Soma = [];
  558. ExperimentType.MultiAppened.Dendrites = [];
  559. ExperimentType.MultiAppened.Spines = [];
  561. temp = 0; DendriteParent = []; SpineNeuronParent = [];SpineParent =[];
  562. for N = 1:length(ExperimentType.Single)
  563. time = 752;
  564. ExperimentType.SingleAppened.Soma = [ExperimentType.SingleAppened.Soma ExperimentType.Single(N).Soma(1:time,1)];
  565. ExperimentType.SingleAppened.Dendrites = [ExperimentType.SingleAppened.Dendrites ExperimentType.Single(N).Dendrites(1:time,:)];
  566. ExperimentType.SingleAppened.Spines = [ExperimentType.SingleAppened.Spines ExperimentType.Single(N).Spines(1:time,:)];
  569. time = 900;
  570. ExperimentType.MultiAppened.Soma = [ExperimentType.MultiAppened.Soma ExperimentType.Multi(N).Soma(1:time,1)];
  571. ExperimentType.MultiAppened.Dendrites = [ExperimentType.MultiAppened.Dendrites ExperimentType.Multi(N).Dendrites(1:time,:)];
  572. ExperimentType.MultiAppened.Spines = [ExperimentType.MultiAppened.Spines ExperimentType.Multi(N).Spines(1:time,:) ];
  575. sz = size(ExperimentType.Single(N).Dendrites,2);
  576. DendriteParent = [DendriteParent; ones(sz,1)*N];
  577. sz = size(ExperimentType.Single(N).SpineParent,2);
  578. SpineNeuronParent = [SpineNeuronParent; ones(sz,1)*N];
  579. SpineParent = [SpineParent ExperimentType.Single(N).SpineParent+temp];
  580. temp = max(SpineParent);
  581. end
  583. NeuronID = [(1:N)'; DendriteParent; SpineNeuronParent];
  584. DendriteID = [nan(N,1); (1:length(DendriteParent))'; SpineParent'];
  585. Structure = categorical([repelem("Soma", N),...
  586. repelem("Dendrite", length(DendriteParent)),...
  587. repelem("Spine", length(SpineParent))])';
  589. ExperimentType.Labels = table(NeuronID, DendriteID, Structure);
  590. end
  592. function [Traces, trough] = RemoveDC(Traces,Frame)
  593. smoothed = smoothdata(Traces,1,"movmean",30);
  594. trough = min(smoothed(Frame-100:Frame,:),[],1);
  595. Traces = (Traces-trough)./trough;
  596. end
  598. function ExpTyp = ExtractPks(ExpTyp,Frames,artifactpts)
  600. for N = 1:length(ExpTyp)
  602. % Soma
  603. Soma = ExpTyp(N).Soma(1:Frames);
  604. Fs = length(Soma)/ExpTyp(N).ndinfo.duration; % sampling Fq
  605. time = (1:length(Soma))/Fs;
  606. frame = round(30*Fs);
  608. Soma = RemoveDC(Soma,frame);
  609. [Somalocs, ~, height, width] = FindDerivativePks(Soma,artifactpts, Fs, 1);
  611. ExpTyp(N).SomaPks = [Somalocs, height, width];
  614. % Spines
  615. Spines = ExpTyp(N).Spines(1:Frames,:);
  616. Spines = RemoveDC(Spines,frame);
  617. [spinelocs, spinelabel, pkheight, FWHM, bAPlabel] ...
  618. = FindDerivativePks(Spines,artifactpts, Fs, 3, Somalocs);
  619. SomaLabel = ones(numel(spinelabel),1)*N;
  620. SpineParent = ExpTyp(N).SpineParent(spinelabel)';
  622. UniqueSpines = unique(spinelabel.*~bAPlabel);
  623. ActiveSpine = ismember(spinelabel, UniqueSpines);
  624. SpinePks = [spinelabel, SpineParent, spinelocs, pkheight, FWHM, bAPlabel, ActiveSpine, SomaLabel];
  626. SpineEventMask = zeros(size(Spines,2),1);
  627. SpineEventMask(UniqueSpines(2:end)) = 1;
  629. ExpTyp(N).SpinePks = SpinePks;
  630. ExpTyp(N).SpineEventMask = SpineEventMask';
  631. ExpTyp(N).NumberSpineEvent = sum(~bAPlabel);% number of independent spine events
  634. end
  635. end
  637. function [SPkS] = SpinePeakStats(ExpTyp)
  639. timethresh = 30; % Seconds
  641. % Single
  642. if ~isempty(ExpTyp.Single)
  643. PksSpine = vertcat(ExpTyp.Single.SpinePks);
  644. ICS = ~logical(PksSpine(:,end-2)); % not bAP events
  645. ActiveSpines = logical(PksSpine(:,end-1)); % Active Spines only
  646. timemask = PksSpine(:,3)>timethresh; % After Stimulation
  648. for N = 1:max(PksSpine(:,end))
  649. NeuronMask = PksSpine(:,end)==N;
  650. TotalSpines = size(ExpTyp.Single(N).Spines,2);
  652. postmask = ICS & ActiveSpines & NeuronMask & timemask;
  653. premask = ICS & ActiveSpines & NeuronMask & ~timemask;
  655. SpikeFreq(N, 1) = numel(PksSpine(premask,1))/30;
  656. SpikeFreq(N, 2) = numel(PksSpine(postmask,1))/70;
  658. RespondingFraction(N, 1) = numel(unique(PksSpine(premask,1)))/TotalSpines;
  659. RespondingFraction(N, 2) = numel(unique(PksSpine(postmask,1)))/TotalSpines;
  660. end
  661. SPkS.SingleSpikeFreq = SpikeFreq;
  662. SPkS.SingleRespondingFraction = RespondingFraction;
  663. clear SpikeFreq RespondingFraction,
  665. % Peaks before the stimulus that are not AP
  666. mask = ICS & ~timemask;
  667. SPkS.Pre_Spine = PksSpine(mask,:);
  669. % Peaks after the stimulus that are not AP
  670. mask = ICS & timemask;
  671. SPkS.Post_Single = PksSpine(mask,:);
  672. SPkS.Single_bAP = PksSpine(~ICS,:);
  673. end
  675. % Multi
  676. if ~isempty(ExpTyp.Multi)
  677. PksSpine = vertcat(ExpTyp.Multi.SpinePks);
  678. ICS = ~logical(PksSpine(:,end-2)); % not bAP events
  679. ActiveSpines = logical(PksSpine(:,end-1)); % Active Spines only
  680. timemask = PksSpine(:,3)>timethresh; % After Stimulation
  682. for N = 1:max(PksSpine(:,end))
  683. NeuronMask = PksSpine(:,end)==N;
  684. TotalSpines = size(ExpTyp.Multi(N).Spines,2);
  686. postmask = ICS & ActiveSpines & NeuronMask & timemask;
  687. premask = ICS & ActiveSpines & NeuronMask & ~timemask;
  689. SpikeFreq(N, 1) = numel(PksSpine(premask,1))/30;
  690. SpikeFreq(N, 2) = numel(PksSpine(postmask,1))/90;
  692. RespondingFraction(N, 1) = numel(unique(PksSpine(premask,1)))/TotalSpines;
  693. RespondingFraction(N, 2) = numel(unique(PksSpine(postmask,1)))/TotalSpines;
  694. end
  695. SPkS.MultiSpikeFreq = SpikeFreq;
  696. SPkS.MultiRespondingFraction = RespondingFraction;
  699. % Peaks after the stimulus that are not AP
  700. mask = ICS & timemask;
  701. SPkS.Post_Multi = PksSpine(mask,:);
  702. SPkS.Multi_bAP = PksSpine(~ICS,:);
  703. end
  705. end

SpineGCaMPAnalysis.m at commit e3c56b3, no license · at the source

Overview

Authors: Jacob Hardenburger1,2, George Grow1,2, Pratheepa Rasiah1,2, Mona Gerges1,2, Joel Bixler3, Chad Oian3, Bryan Millis1,2, Christopher Valdez4, E Duco Jansen1,2,5, Anita Mahadevan-Jansen1,2,5
ORCID iDs: Pratheepa Rasiah, Joel Bixler, Anita Mahadevan-Jansen
  1. Vanderbilt University, Vanderbilt Biophotonics Center, Nashville, Tennessee, United States
  2. Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
  3. Air Force Research Laboratory, Bioeffects Division, JBSA Fort Sam Houston, Texas, United States
  4. University of Texas Health San Antonio, San Antonio, Texas, United States
  5. Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, United States
Institutions: Vanderbilt University (United States); United States Air Force Research Laboratory (United States); The University of Texas at San Antonio Health Science Center (United States)
Journal: Biophotonics discovery, volume 3, issue 3, article 035001
Dates: received 9 February 2026; accepted 6 July 2026; published online 5 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1117/1.bios.3.3.035001 · PMID 42719458 · PMCID PMC13557455 · OpenAlex W7172539871
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: dendritic spines, laser–tissue interactions, calcium signaling, photothermal, infrared neural stimulation
Topic: Laser Applications in Dentistry and Medicine (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Oak Ridge Institute for Science and Education; Air Force Office of Scientific Research (LRIR 22RHCOR012)
Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

Significance: Infrared neural stimulation (INS) is an optical neuromodulation technique that elicits neural activity through photothermal temperature gradients. Photothermal gradients have been shown to alter neuronal excitability, but the effects on the dendritic spine (DS) structure and function have not been characterized.

Aim: We aim to determine whether INS induces calcium signaling in DS and whether this signaling alters filamentous actin (F-actin).

Approach: We used confocal imaging of cortical neurons expressing a genetically encoded calcium indicator (GCaMP)8f-Syn1 and mCardinal-LifeAct during laser exposure. Fluorescence signals were measured within regions of interest covering the DS. The resulting fluorescence measurements were baseline-normalized, and transient spiking activity in DS was quantified.

Results: Transient thermal gradients induce persistent calcium activity in a fraction of DS, independent of pulse duration but dependent on the absolute temperature and number of laser exposures. There is no relationship between the calcium activity and F-actin. Calcium signaling in DS was not mediated by ionotropic glutamate receptors but depended on extracellular entry.

Conclusions: These results indicate that transient thermal gradients elicit calcium activity in DS, but the induced activity does not alter F-actin, suggesting that transient thermal gradients may be a useful tool for studying calcium dynamics in DS without triggering traditional structural plasticity pathways.

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 8 matches between paragraphs and lines of code.

PanPandaPan615/Photothermal-Spine-Activity

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e3c56b39cc7e8303501d6671dffdd249a8ade5ae, 6 February 2026
Languages: MATLAB (6)
Size: 7 files, 6 scripts
Software Heritage: not archived
Found in: “Code and Data Availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Image Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/PanPandaPan615/Photothermal-Spine-Activity.git

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;
  • 8 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

Datasets cited

Code and Data Availability

The measured data supporting the findings of this article are publicly available at 10.6084/m9.figshare.31286167. The raw image files can be made available upon request. The code used for processing is available at https://github.com/PanPandaPan615/Photothermal-Spine-Activity.git.

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 2, 28 September 2026

  • Funding: added Oak Ridge Institute for Science and Education; Air Force Office of Scientific Research: LRIR 22RHCOR012

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 65 references.

Cite

This paper

Hardenburger, J., Grow, G., Rasiah, P., Gerges, M., Bixler, J., Oian, C., Millis, B., Valdez, C., Jansen, E. D., & Mahadevan-Jansen, A. (2026). Transient infrared laser exposure modulates calcium activity in cortical dendritic spines. Biophotonics discovery, 3(3), 035001. https://doi.org/10.1117/1.bios.3.3.035001

BibTeX

@article{hardenburger2026transient,
author = {Hardenburger, Jacob and Grow, George and Rasiah, Pratheepa and Gerges, Mona and Bixler, Joel and Oian, Chad and Millis, Bryan and Valdez, Christopher and Jansen, E Duco and Mahadevan-Jansen, Anita},
title = {{Transient infrared laser exposure modulates calcium activity in cortical dendritic spines}},
journal = {Biophotonics discovery},
year = {2026},
month = jul,
volume = {3},
number = {3},
pages = {035001},
publisher = {Society of Photo-Optical Instrumentation Engineers},
issn = {3005-4745},
doi = {10.1117/1.bios.3.3.035001},
url = {https://doi.org/10.1117/1.bios.3.3.035001},
pmid = {42719458},
pmcid = {PMC13557455}
}

RIS

TY - JOUR
AU - Hardenburger, Jacob
AU - Grow, George
AU - Rasiah, Pratheepa
AU - Gerges, Mona
AU - Bixler, Joel
AU - Oian, Chad
AU - Millis, Bryan
AU - Valdez, Christopher
AU - Jansen, E Duco
AU - Mahadevan-Jansen, Anita
TI - Transient infrared laser exposure modulates calcium activity in cortical dendritic spines
T2 - Biophotonics discovery
J2 - Biophotonics Discov
PY - 2026
DA - 2026/07/01
VL - 3
IS - 3
SP - 035001
SN - 3005-4745
PB - Society of Photo-Optical Instrumentation Engineers
DO - 10.1117/1.bios.3.3.035001
UR - https://doi.org/10.1117/1.bios.3.3.035001
LA - en
ER -

CSL-JSON

{
"id": "10.1117/1.bios.3.3.035001",
"type": "article-journal",
"title": "Transient infrared laser exposure modulates calcium activity in cortical dendritic spines",
"container-title": "Biophotonics discovery",
"author": [
{
"family": "Hardenburger",
"given": "Jacob"
},
{
"family": "Grow",
"given": "George"
},
{
"family": "Rasiah",
"given": "Pratheepa"
},
{
"family": "Gerges",
"given": "Mona"
},
{
"family": "Bixler",
"given": "Joel"
},
{
"family": "Oian",
"given": "Chad"
},
{
"family": "Millis",
"given": "Bryan"
},
{
"family": "Valdez",
"given": "Christopher"
},
{
"family": "Jansen",
"given": "E Duco"
},
{
"family": "Mahadevan-Jansen",
"given": "Anita"
}
],
"container-title-short": "Biophotonics Discov",
"volume": "3",
"issue": "3",
"page": "035001",
"DOI": "10.1117/1.bios.3.3.035001",
"PMID": "42719458",
"PMCID": "PMC13557455",
"ISSN": "3005-4745",
"publisher": "Society of Photo-Optical Instrumentation Engineers",
"URL": "https://doi.org/10.1117/1.bios.3.3.035001",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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