OSCR

Dynamics of Dentate Gyrus Place Cells and Dentate Spikes during Spatial and Nonspatial Changes in Environments.

Code ↔ Paper

19 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 19 matches · 9 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › Dentate spike analyses. ↔ Plotting and helper functions/getThetaEpochs_per_shank.m, the whole file · a weak match · score 0.93 · maximal theta power, sustained theta, theta epochs, theta detection, cycle lengths, longest
  2. [2] § Materials and Methods › Place cell analyses. ↔ Main functions/place_cell_metrics.m, lines 597–721 · score 0.84 · linear mixed model, Global remapping, Spatial correlation, fitlme, anova, Fisher
  3. [3] § Materials and Methods › Dentate spike analyses. ↔ GENERAL_LAB_CODE/find_lfp_events_in_power_ts.m, the whole file · a weak match · score 0.82 · amplitude envelope, entire LFP, cycle lengths, events detected, wavelet, power
  4. [4] § Materials and Methods › Spike sorting and unit selection. ↔ matlab/+bc/+qm/runAllQualityMetrics.m, lines 1–67 · score 0.76 · refractory period violations, quality metrics, Spike sorting, Bombcell, score, waveform
  5. [5] § Materials and Methods › Data acquisition. ↔ py_bombcell/bombcell/extract_raw_waveforms.py, lines 163–301 · score 0.75 · acquisition board, SpikeGLX, PXIe, streams, NI, neural
  6. [6] § Results › DG place cell responses to social and nonsocial olfactory stimuli ↔ Main functions/place_cell_metrics.m, lines 597–721 · score 0.74 · post hoc, global remapping, Nonsocial Odor, Fox Odor, Spatial correlation, Novel Room
  7. [7] § Materials and Methods › Spike sorting and unit selection. ↔ matlab/+bc/+qm/saveQMetrics.m, the whole file · a weak match · score 0.73 · refractory period violations, quality metrics, noise ratio, Bombcell, score, waveform
  8. [8] § Materials and Methods › Dentate spike analyses. ↔ dentate_spike_detection_classification/evaluate_scoring.m, lines 1–51 · score 0.69 · standard deviation threshold, Dentate spikes, scorers, detection, CSD, scored
  9. [9] § Materials and Methods › Dentate spike analyses. ↔ dentate_spike_detection_classification/dentateSpikeClustering.m, the whole file · a weak match · score 0.68 · CSD depth profiles, dentate spikes, dorsal, sink, clustering, classified
  10. [10] § Materials and Methods › Dentate spike analyses. ↔ dentate_spike_detection_classification/getDS_CSD_per_shank_v4.m, lines 2–45 · score 0.67 · standard deviation threshold, detected dentate spikes, CSD, detection, LFP, channel
  11. [11] § Materials and Methods › Behavior. ↔ Plotting and helper functions/getCircularTimemaps.m, the whole file · a weak match · score 0.65 · Reward Zone, Nonsocial Odor, Stimulus Zone, Fox Odor, Novel Room, circular
  12. [12] § Materials and Methods › Dentate spike analyses. ↔ Plotting and helper functions/getCircularTimemaps.m, the whole file · a weak match · score 0.61 · Reward Zone, Stimulus Zone, position bin, Social Odor, Novel Room, circle
  13. [13] § Results › DS1s were differentially modulated by social stimuli ↔ Main functions/place_cell_metrics.m, lines 723–835 · score 0.57 · post hoc, Nonsocial Odor, Fox Odor, Wald, tailed, fit
  14. [14] § Materials and Methods › Dentate spike analyses. ↔ GENERAL_LAB_CODE/get_ratemap_circtrack.m, the whole file · a weak match · score 0.56 · spikes occur, enforced, firing rate maps, velocity, spent, smoothed
  15. [15] § Materials and Methods › Place cell analyses. ↔ Main functions/place_cell_metrics.m, lines 57–206 · score 0.54 · rate ratio, spatial correlation, rate map, metric, Firing rate, log
  16. [16] § Materials and Methods › Dentate spike analyses. ↔ GENERAL_LAB_CODE/get_2d_ratemap.m, lines 1–37 · score 0.54 · spikes occur, enforced, firing rate maps, velocity, spent, Raw
  17. [17] § Materials and Methods › Behavior. ↔ Main functions/dentate_spike_metrics.m, lines 606–668 · score 0.53 · Reward Zone, Stimulus Zone, Fox Odor, Social Odor, Novel Room, Empty
  18. [18] § Materials and Methods › Place cell analyses. ↔ GENERAL_LAB_CODE/FMAToolbox/General/CircularDistribution.m, the whole file · a weak match · score 0.53 · Gaussian kernel, standard deviation, smoothed, circular, binned
  19. [19] § Materials and Methods › Dentate spike analyses. ↔ GENERAL_LAB_CODE/FMAToolbox/FMAToolbox.m, the whole file · a weak match · score 0.52 · peri event, PETHs, histograms, Firing rate, smoothed, window

Paper

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The authors' code

MATLAB · 854 lines · 35 KB · no license · 4 matches

  1. function place_cell_metrics(cellStruct, frType)
  2. % Purpose: to compute place cell metrics (spatial correlation, rate
  3. % overlap) from cellStruct, store in table (long) format, plot metrics, and
  4. % run statistics
  5. % - note: the saving functions have been commented out. uncomment them if
  6. % you would like to save tables, figure, and/or stats
  7. %
  8. % Inputs:
  9. % cellStruct - data structure containing the cells from all recordings
  10. % frType - whether to use the mean or peak firing rate for analyses
  11. %
  12. % Outputs:
  13. % Fig. 1 - ratemaps (Fig. 2 from paper)
  14. % Fig. 2 - global remapping (Fig. 3 from paper)
  15. % Fig. 3 - rate remapping (Fig. 4 from paper)
  16. %
  17. % Peyton Demetrovich
  18. % Colgin Lab 4/8/26
  20. %% initialize
  21. comparisons = {'A-B', 'B-B''', 'B''-A''', 'A-B''', 'A-A'''};
  22. spatBinSz = 6;
  23. cellType = {'dentate'};
  24. combos = [1 2;
  25. 2 3;
  26. 3 4;
  27. 1 3;
  28. 1 4];
  30. if strcmp(cellType, 'granule')
  31. minAvgFr = 0.1;
  32. maxAvgFr = 1;
  33. elseif strcmp(cellType, 'mossy')
  34. minAvgFr = 1;
  35. maxAvgFr = 10;
  36. else
  37. minAvgFr = 0.1;
  38. maxAvgFr = 10;
  39. end
  41. % data containers
  42. cellID =[];
  43. ratID = {};
  44. condID = {};
  45. dayID = {};
  46. comboID = {};
  47. uID = [];
  48. rateMaps = [];
  49. spatCorr = [];
  50. rateOverlap = [];
  51. signedLogRatio = [];
  52. fieldChange = [];
  53. fieldProp = [];
  54. frA = [];
  55. frB = [];
  56. uCntr = 0;
  57. %% GET DATA
  58. for r = 1:length(cellStruct.rat)
  59. fprintf('%s\n', cellStruct.rat(r).name)
  60. for c=1:length(cellStruct.rat(r).cond)
  62. if c > length(cellStruct.rat(r).cond)
  63. continue
  64. end
  66. for d = 1:length(cellStruct.rat(r).cond(c).day)
  67. fprintf('\t%s Day %d\n', cellStruct.rat(r).cond(c).name, d)
  68. fprintf('\t\t%s\n', cellStruct.rat(r).cond(c).day(d).name)
  70. dgPCu = false(length(cellStruct.rat(r).cond(c).day(d).smRms_1d_v2),1);
  71. meanFrs = cellfun(@mean, cellStruct.rat(r).cond(c).day(d).smRms_1d_v2);
  72. isPC = cell2mat(cellStruct.rat(r).cond(c).day(d).isPlaceCell);
  74. for u = 1:length(cellStruct.rat(r).cond(c).day(d).smRms_1d_v2)
  75. if (r==2 & c==2 & d== 1 & u==33) | (r==2 & c==6 & d==1 & u==31) | (r==2 & c==1 & d==1 & u==23) | (r==2 & c==1 & d==1 & u==32)% duplicate cells
  76. continue
  77. end
  79. uRateMaps = zeros(4,360/spatBinSz); %for storing rate maps from all begins for this unit
  80. uTimeMaps = zeros(4,360/spatBinSz);
  82. if ismember(cellStruct.rat(r).cond(c).day(d).session(1).uID(u).cellType, {'dentate'}) & ...
  83. any(meanFrs(u,:) >= minAvgFr) & all(meanFrs(u,:) < maxAvgFr) & any(isPC(u,:))
  85. for b = 1:4
  86. uRateMaps(b,:) = cellStruct.rat(r).cond(c).day(d).smRms_1d_v2{u,b}; %get all of the ratemaps for the spat corr comparisons
  87. uTimeMaps(b,:) = cellStruct.rat(r).cond(c).day(d).timePerBin_v2{u,b};
  88. end %begin
  90. dgPCu(u) = true;
  91. uCntr = uCntr +1;
  93. for cb = 1%:size(combos,1)
  94. cellID = [cellID; uCntr];
  95. ratID = [ratID; cellStruct.rat(r).name];
  96. condID = [condID; cellStruct.rat(r).cond(c).name];
  97. dayID = [dayID; cellStruct.rat(r).cond(c).day(d).name];
  98. comboID = [comboID; comparisons{cb}];
  99. uID = [uID; u];
  101. b1 = combos(cb,1); %begins to compare
  102. b2 = combos(cb,2);
  104. rm1 = uRateMaps(b1,:); %ratemaps to compare
  105. rm2 = uRateMaps(b2,:);
  107. pf1 = cellStruct.rat(r).cond(c).day(d).smPfs_1d_v3(u,b1);
  108. pf2 = cellStruct.rat(r).cond(c).day(d).smPfs_1d_v3(u,b2);
  110. rateMaps = [rateMaps; {[rm1; rm2]}];
  112. scMat = corrcoef(rm1,rm2);
  113. spatCorr = [spatCorr; scMat(2)];
  115. fieldRateb1 = getStrongestFieldRate(pf1, frType);
  116. fieldRateb2 = getStrongestFieldRate(pf2, frType);
  118. hasA = ~isnan(fieldRateb1);
  119. hasB = ~isnan(fieldRateb2);
  121. if hasA & hasB % field in both
  122. fieldProp = [fieldProp; 1];
  123. elseif ~hasA & hasB % gained field
  124. fieldProp = [fieldProp; 2];
  125. elseif hasA & ~hasB % lost field
  126. fieldProp = [fieldProp; 3];
  127. elseif ~hasA & ~hasB % field in neither
  128. fieldProp = [fieldProp; 4];
  129. end
  131. % if hasA & hasB
  132. % fieldBoth = 1;
  133. % fieldGain = 0;
  134. % fieldLoss = 0;
  135. % fieldNeither = 0;
  136. % elseif ~hasA & hasB
  137. % fieldBoth = 0;
  138. % fieldGain = 1;
  139. % fieldLoss = 0;
  140. % fieldNeither = 0;
  141. % elseif hasA & ~hasB
  142. % fieldBoth = 0;
  143. % fieldGain = 0;
  144. % fieldLoss = 1;
  145. % fieldNeither = 0;
  146. % elseif ~hasA & ~hasB
  147. % fieldBoth = 0;
  148. % fieldGain = 0;
  149. % fieldLoss = 0;
  150. % fieldNeither = 1;
  151. % end
  154. fc = log(fieldRateb2 / fieldRateb1);
  155. fieldChange = [fieldChange; fc];
  157. if strcmp(frType, 'mean')
  158. fr1 = nanmean(uRateMaps(b1,:));
  159. fr2 = nanmean(uRateMaps(b2,:));
  160. frA= [frA; fr1];
  161. frB= [frB; fr2];
  162. else
  163. fr1 = max(uRateMaps(b1,:));
  164. fr2 = max(uRateMaps(b2,:));
  165. frA= [frA; fr1];
  166. frB= [frB; fr2];
  167. end
  168. ro = min([fr1 fr2]) / max([fr1 fr2]); % Calculate the rate ratio with the lower FR as numerator
  169. rateOverlap = [rateOverlap; ro];
  170. slr = log(fr2/fr1); % signed log ratio
  171. signedLogRatio = [signedLogRatio; slr];
  173. end %combos
  174. end % good unit
  175. end %unit
  176. if strcmp(cellType, 'granule')
  177. cellStruct.rat(r).cond(c).day(d).granU = dgPCu;
  178. elseif strcmp(cellType, 'mossy')
  179. cellStruct.rat(r).cond(c).day(d).mossyU = dgPCu;
  180. else
  181. cellStruct.rat(r).cond(c).day(d).dgPcU = dgPCu;
  182. end
  184. end %day
  186. end % cond
  188. end %rat
  190. % fisher r-to-z transformation for spatial correlation
  191. spatCorr_z = atanh(spatCorr);
  193. % logit transform for rate overlap
  194. % rateOverlap_log = log( (rateOverlap ./ (1 - rateOverlap)) );
  196. % magnitude of log ratio
  197. % logRatioMag = abs(signedLogRatio);
  199. % create table
  200. % table_for_stats = table(cellID, uID, strrep(dayID, '_', '-'), ratID, condID, comboID, rateMaps, spatCorr, spatCorr_z, rateOverlap, rateOverlap_log, ...
  201. % 'VariableNames', {'Cell', 'Unit', 'Day', 'Rat', 'Condition', 'Session', 'RateMaps', 'SpatCorr', 'SpatCorr_norm', 'RateOverlap', 'RateOverlap_logit'});
  203. table_for_stats = table(cellID, uID, strrep(dayID, '_', '-'), ratID, condID, comboID, rateMaps, frA, frB, spatCorr, spatCorr_z, rateOverlap, signedLogRatio, fieldChange, fieldProp, ...
  204. 'VariableNames', {'Cell', 'Unit', 'Day', 'Rat', 'Condition', 'Session', 'RateMaps', 'FiringRateA', 'FiringRateB', 'SpatCorr', 'SpatCorr_norm', 'RateOverlap', 'signedLogRatio', 'infieldChange', 'FieldBehavior'});
  206. save(['remappingTable_' date '.mat'], 'table_for_stats')
  207. %% plot rate maps from table
  208. cols = {[0.0 0.45 0.70] [0.85 0.37 0.01] [0.93 0.69 0.13] [0.0 0.62 0.45] [0.80 0.47 0.74] [0.8203 0.7031 0.5469]};
  209. rats = {'Rat452', 'Rat451', 'Rat501', 'Rat504', 'Rat503'};
  210. conditionNames = {'Empty', 'Social', 'Social Odor', 'Nonsocial Odor', 'Fox Odor', 'Novel Room'};
  211. comparisons = {'A-B'};
  212. conditions = {'Empty' 'Social' 'SocialOdor' 'NonSocialOdor' 'FoxOdor' 'NovelRoom'};
  213. num_conditions = numel(conditions);
  215. figure, theme(gcf, 'light'), clf
  216. pltCntr = 1;
  217. for c=1:num_conditions
  218. % get data from specific condition and session across animals
  219. pullDataInds = strcmp(table_for_stats.Condition, conditions{c}) & strcmp(table_for_stats.Session, comparisons{1});
  220. cond_data = table_for_stats(pullDataInds,:);
  222. tmpRms = cond_data.RateMaps(:);
  224. % extract the rows from each cell
  225. firstRows = cell2mat(cellfun(@(x) x(1,:), tmpRms, 'UniformOutput', false));
  226. secondRows = cell2mat(cellfun(@(x) x(2,:), tmpRms, 'UniformOutput', false));
  228. % concatenate, normalize, then split
  229. catRows = [firstRows secondRows];
  230. catRows_norm = peak_rate_normalize_rows(catRows);
  231. firstRows_norm = catRows_norm(:,1:60);
  232. secondRows_norm = catRows_norm(:,61:120);
  234. % sort by first session
  235. [~, maxInd] = max(firstRows_norm,[],2);
  236. [~,sortInd] = sort(maxInd);
  238. % plot
  239. subplot(3,4,pltCntr)
  240. imagesc(firstRows_norm(sortInd,:))
  241. % title([' A'])
  242. xlabel('Position (bins)')
  243. ylabel({conditionNames{c}; 'Cell ID'})
  244. pltCntr = pltCntr + 1;
  246. subplot(3,4,pltCntr)
  247. imagesc(secondRows_norm(sortInd,:))
  248. xlabel('Position (bins)')
  249. yticks('')
  250. % title([' B'])
  251. if pltCntr == 12
  252. clbr = colorbar;
  253. clbr.Position = [0.91 0.1164 0.0100 0.0907];
  254. clbr.Ticks = [0.1 1];
  255. clbr.TickLabels = {'Min', 'Max'};
  256. ylabel(clbr, 'Firing rate')
  257. end
  258. pltCntr = pltCntr + 1;
  260. end
  261. colormap jet
  262. SetFigureDefaults()
  264. % add zone bars to ratemaps
  265. for i=1:11
  266. h = subplot(3,4,i);
  267. switch i
  268. case {1,2}
  269. add_highlight_above_axes(h, [45/6 135/6; 180/6 270/6], 0.005, 0.005, [0 0 0; cols{1}]);
  270. case {3,4}
  271. add_highlight_above_axes(h, [45/6 135/6; 180/6 270/6], 0.005, 0.005, [0 0 0; cols{2}]);
  272. case {5,6}
  273. add_highlight_above_axes(h, [45/6 135/6; 180/6 270/6], 0.005, 0.005, [0 0 0; cols{3}]);
  274. case {7,8}
  275. add_highlight_above_axes(h, [45/6 135/6; 180/6 270/6], 0.005, 0.005, [0 0 0; cols{4}]);
  276. case {9,10}
  277. add_highlight_above_axes(h, [45/6 135/6; 180/6 270/6], 0.005, 0.005, [0 0 0; cols{5}]);
  278. case {11}
  279. add_highlight_above_axes(h, [45/6 135/6; 180/6 270/6], 0.005, 0.005, [0 0 0; cols{6}]);
  280. end
  281. end
  282. savefig(gcf,['allCells_' date])
  283. saveas(gcf, ['allCells_' date], 'png')
  284. saveas(gcf, ['allCells_' date], 'epsc')
  286. %% rat-specific marker dotplots
  287. markers = {'o', '>', 'd', 's', '<'};
  288. metric = {'SpatCorr', 'signedLogRatio'};
  289. colorInds = [1,2,3,4,5,6];
  291. % subplot(3,3,1:4)
  292. for m=1:length(metric)
  293. figure, theme(gcf, "light");
  294. for b=1%:length(comparisons)
  295. subplot(3,3,[1 4])
  296. hold on;
  298. % dummy vals for legend
  299. scatter(nan, nan, 'Marker', 'o','MarkerEdgeColor', 'k', 'LineWidth', 1, 'DisplayName', 'Rat 452');
  300. scatter(nan, nan, 'Marker', '>','MarkerEdgeColor', 'k', 'LineWidth', 1, 'DisplayName', 'Rat 451');
  301. scatter(nan, nan, 'Marker', 'd','MarkerEdgeColor', 'k', 'LineWidth', 1, 'DisplayName', 'Rat 501');
  302. scatter(nan, nan, 'Marker', 's','MarkerEdgeColor', 'k', 'LineWidth', 1, 'DisplayName', 'Rat 504');
  303. scatter(nan, nan, 'Marker', '<','MarkerEdgeColor', 'k', 'LineWidth', 1, 'DisplayName', 'Rat 503');
  304. legend('show');
  305. legend('autoupdate', 'off');
  306. legend location southwest
  308. for i = 1:numel(rats)
  309. if m == 1
  310. pullData = strcmp(table_for_stats.Rat, rats{i}) & strcmp(table_for_stats.Session, comparisons{b});
  311. rat_data = table_for_stats(pullData, :);
  312. else
  313. pullData = strcmp(stableTable.Rat, rats{i}) & strcmp(stableTable.Session, comparisons{b});
  314. rat_data = stableTable(pullData, :);
  315. end
  317. for j = 1:num_conditions
  318. cond = conditions{j};
  319. % rat and cond-specific data
  320. cond_data = rat_data(strcmp(rat_data.Condition, cond), :);
  321. if ~isempty(cond_data)
  322. x_jitter = j + 0.3 * (rand(height(cond_data),1) - 0.5); % Add jitter
  323. scttr = scatter(x_jitter, cond_data.(metric{m}), 100, ...
  324. 'Marker', markers{i}, ...
  325. 'MarkerEdgeColor', 'k', ...
  326. 'MarkerFaceColor', cols{colorInds(j)}, ...
  327. 'DisplayName', rats{i}, 'LineWidth', 1);
  328. end
  329. end
  330. end
  332. for i=1:num_conditions
  333. % plot medians for each condition
  334. if m == 1
  335. pullData = strcmp(table_for_stats.Condition, conditions{i}) & strcmp(table_for_stats.Session, comparisons{b});
  336. cond_data = table_for_stats(pullData,:);
  337. else
  338. pullData = strcmp(stableTable.Condition, conditions{i}) & strcmp(stableTable.Session, comparisons{b});
  339. cond_data = stableTable(pullData,:);
  340. end
  342. scatter(i, nanmedian(cond_data.(metric{m})),100, 'Marker', 'x',...
  343. 'MarkerEdgeColor', 'k', 'LineWidth', 2);
  345. if i==1 & m==1
  346. % kmeans thresholding
  347. [~, C] = kmeans(cond_data.(metric{m}), 2, 'Replicates', 50);
  348. sc_thresh = mean(C); % approximate boundary
  349. yline(sc_thresh,'r--');
  350. text(-1,sc_thresh+.075, {'Stability'; 'Threshold'}, 'Color', 'r')
  351. end
  353. end
  355. xlim([-1, num_conditions + 1]);
  356. xticks('');
  357. ylabel(metric{m})
  358. if m==1
  359. ylim([-1 1])
  360. else
  361. ylim([-2 2])
  362. end
  363. end
  364. if m == 1 % create table of stable cells using threshold from empty condition spatial correlations
  365. stableTable = table_for_stats(table_for_stats.SpatCorr > sc_thresh,:);
  366. % unstableTable = table_for_stats(table_for_stats.SpatCorr < sc_thresh,:);
  367. end
  368. end
  369. %% plot firing rates
  370. % figure, clf
  371. % for c=1:length(conditions)
  372. % % pullData = strcmp(table_for_stats.Condition, conditions{c});
  373. % % cond_dataA = table_for_stats.FiringRateA(pullData,:);
  374. % % cond_dataB = table_for_stats.FiringRateB(pullData,:);
  375. % pullData = strcmp(stableTable.Condition, conditions{c});
  376. % cond_dataA = stableTable.FiringRateA(pullData,:);
  377. % cond_dataB = stableTable.FiringRateB(pullData,:);
  378. % subplot(2,3,c)
  379. % scatter(cond_dataA, cond_dataB, 30, 'filled', 'MarkerFaceColor', cols{c})
  380. % hold on
  381. % plot([0 10], [0 10], 'k--')
  382. % xlim([0 10])
  383. % ylim([0 10])
  384. % xlabel('Firing Rate in A')
  385. % ylabel('Firing Rate in B')
  386. % title(conditionNames{c})
  387. % end
  388. % %
  389. % % % loglog scale
  390. % logax = logspace(-2, 2, 10);
  391. % figure, clf
  392. % for c=1:length(conditions)
  393. % pullData = strcmp(table_for_stats.Condition, conditions{c});
  394. % cond_dataA = table_for_stats.FiringRateA(pullData,:);
  395. % cond_dataB = table_for_stats.FiringRateB(pullData,:);
  396. % % pullData = strcmp(stableTable.Condition, conditions{c});
  397. % % cond_dataA = stableTable.FiringRateA(pullData,:);
  398. % % cond_dataB = stableTable.FiringRateB(pullData,:);
  399. % subplot(2,3,c)
  400. % loglog(cond_dataA, cond_dataB, 'o', 'LineStyle', 'none', 'MarkerFaceColor', cols{c}, 'MarkerEdgeColor', 'none');
  401. % hold on
  402. % plot(logax, logax, 'k--')
  403. % xlabel('Firing Rate in A')
  404. % ylabel('Firing Rate in B')
  405. % title(conditionNames{c})
  406. % end
  407. %% field behavior per condition
  408. % figure, theme(gcf, 'light'), clf
  409. % for c=1:length(conditions)
  410. % pullData = strcmp(table_for_stats.Condition, conditions{c});
  411. % cond_dataA = table_for_stats.FieldBehavior(pullData,:);
  412. % % pullData = strcmp(stableTable.Condition, conditions{c});
  413. % % cond_dataA = stableTable.FieldBehavior(pullData,:);
  414. % subplot(2,3,c)
  415. % histogram(cond_dataA, 'FaceColor', cols{c}, 'EdgeColor', cols{c});
  416. % xticks([1:4])
  417. % xticklabels({'Field in Both' 'Gained Field' 'Lost Field' 'Field in Neither'})
  418. % ylabel('# Cells')
  419. % title(conditionNames{c})
  420. % ylim([0 15])
  421. % xlim([0.25 4.75])
  422. % end
  423. %% cumulative probability
  424. tileNum = [2,3,5,6,8];
  425. condCount = length(unique(table_for_stats.Condition));
  426. distTest = 'ks'; % either 'ks' for 2-sample kolmogorov-smirnov or 'ad' for 2-sample anderson-darling
  428. for m = 1:length(metric)
  429. cdfStat = {
  430. 'Comparison', 'test-type', 'stat', 'p' 'p_BF';
  431. 'Empty vs Social', [distTest], nan, nan, nan;
  432. 'Empty vs Social Odor', [distTest], nan, nan, nan;
  433. 'Empty vs Nonsocial Odor', [distTest], nan, nan, nan;
  434. 'Empty vs FoxOdor', [distTest], nan, nan, nan;
  435. 'Empty vs NovelRoom', [distTest], nan, nan, nan;
  436. 'Social Odor vs Fox Odor', [distTest], nan, nan, nan;
  437. 'Social Odor vs Nonsocial Odor', [distTest], nan, nan, nan;
  438. };
  439. for b=1%:size(combos,1)
  441. if m == 1
  442. pullData = strcmp(table_for_stats.Condition, 'Empty') & strcmp(table_for_stats.Session, comparisons{b});
  443. control_data = table_for_stats.(metric{m})(pullData,:);
  444. else
  445. pullData = strcmp(stableTable.Condition, 'Empty') & strcmp(stableTable.Session, comparisons{b});
  446. control_data = stableTable.(metric{m})(pullData,:);
  447. end
  449. control_data(isnan(control_data)) = [];
  451. for c = 2:condCount
  452. if m == 1
  453. pullData = strcmp(table_for_stats.Condition, conditions{c}) & strcmp(table_for_stats.Session, comparisons{b});
  454. cond_data = table_for_stats.(metric{m})(pullData,:);
  455. else
  456. pullData = strcmp(stableTable.Condition, conditions{c}) & strcmp(stableTable.Session, comparisons{b});
  457. cond_data = stableTable.(metric{m})(pullData,:);
  458. end
  460. cond_data(isnan(cond_data)) = [];
  462. if isempty(cond_data)
  463. continue
  464. end
  466. figure(m+1),
  467. subplot(3,3,tileNum(c-1)), cla
  468. [control_f, x, flo, fup] = ecdf(control_data, 'Alpha', 0.05, 'Bounds','on');
  469. stairs(x, control_f, 'Color', cols{1}, 'DisplayName', conditions{1}, 'LineWidth', 2); % CDF
  470. hold on;
  471. plot(x, flo, '--', x, fup, '--', 'Color', cols{1}); % 95% CI
  473. [cond_f, x, flo, fup] = ecdf(cond_data, 'Alpha', 0.05, 'Bounds','on');
  474. stairs(x, cond_f, 'Color', cols{c}, 'DisplayName', conditions{c}, 'LineWidth', 2); % CDF
  475. plot(x, flo, '--', x, fup, '--', 'Color', cols{c}); % 95% CI
  477. switch distTest
  478. case 'ks'
  479. if m == 1 % spatial corr
  480. [~,p,stat] = kstest2(control_data, cond_data,'Tail', 'smaller'); % If the data values in x1 tend to be larger than those in x2, the empirical distribution function of x1 tends to be smaller than that of x2, and vice versa.
  481. else % rate ratios
  482. [~,p,stat] = kstest2(control_data, cond_data);
  483. end
  484. cdfStat{c,3} = stat;
  485. cdfStat{c,4} = p;
  486. cdfStat{c,5} = min(1, p .* (size(cdfStat,1)-1));
  487. if c==6
  488. format shortE
  489. title({ [conditionNames{c} ' vs ' conditionNames{1}]; ['p = ' num2str(p) ', ks2stat = ' num2str(round(stat,3))] })
  490. format default
  491. else
  492. title({ [conditionNames{c} ' vs ' conditionNames{1}]; ['p = ' num2str(round(p,3)) ', ks2stat = ' num2str(round(stat,3))] })
  493. end
  494. case 'ad'
  495. [stat, p] = ad2test(control_data, cond_data);
  496. cdfStat{c,3} = stat;
  497. cdfStat{c,4} = p;
  498. cdfStat{c,5} = min(1, p .* (size(cdfStat,1)-1));
  499. title({ [conditionNames{c} ' vs ' conditionNames{1}]; ['p = ' num2str(round(p,3)) ', ad2stat = ' num2str(round(stat,3))] })
  500. end
  502. ylabel('Cumulative Probabilty')
  503. xlabel(metric{m})
  504. if m==1
  505. xlim([-1 1])
  506. else
  507. xlim([-2 2])
  508. end
  509. axis square
  510. box off
  511. yticks([0 0.5 1])
  513. if c == 3
  514. if m == 1
  515. pullData = strcmp(table_for_stats.Condition, 'SocialOdor') & strcmp(table_for_stats.Session, comparisons{b});
  516. so_data = table_for_stats.(metric{m})(pullData,:);
  518. pullData = strcmp(table_for_stats.Condition, 'NonSocialOdor') & strcmp(table_for_stats.Session, comparisons{b});
  519. nso_data = table_for_stats.(metric{m})(pullData,:);
  521. pullData = strcmp(table_for_stats.Condition, 'FoxOdor') & strcmp(table_for_stats.Session, comparisons{b});
  522. fo_data = table_for_stats.(metric{m})(pullData,:);
  523. else
  524. pullData = strcmp(stableTable.Condition, 'SocialOdor') & strcmp(stableTable.Session, comparisons{b});
  525. so_data = stableTable.(metric{m})(pullData,:);
  527. pullData = strcmp(stableTable.Condition, 'NonSocialOdor') & strcmp(stableTable.Session, comparisons{b});
  528. nso_data = stableTable.(metric{m})(pullData,:);
  530. pullData = strcmp(stableTable.Condition, 'FoxOdor') & strcmp(stableTable.Session, comparisons{b});
  531. fo_data = stableTable.(metric{m})(pullData,:);
  532. end
  534. so_data(isnan(so_data)) = [];
  535. nso_data(isnan(nso_data)) = [];
  536. fo_data(isnan(fo_data)) = [];
  538. subplot(3,3,9), cla
  539. [so_f, x, flo, fup] = ecdf(so_data, 'Alpha', 0.05, 'Bounds','on');
  540. stairs(x, so_f, 'Color', cols{3}, 'DisplayName', conditions{3}, 'LineWidth', 2); % CDF
  541. hold on;
  542. plot(x, flo, '--', x, fup, '--', 'Color', cols{3}); % 95% CI
  544. [nso_f, x, flo, fup] = ecdf(nso_data, 'Alpha', 0.05, 'Bounds','on');
  545. stairs(x, nso_f, 'Color', cols{4}, 'DisplayName', conditions{4}, 'LineWidth', 2); % CDF
  546. plot(x, flo, '--', x, fup, '--', 'Color', cols{4}); % 95% CI
  548. [fo_f, x, flo, fup] = ecdf(fo_data, 'Alpha', 0.05, 'Bounds','on');
  549. stairs(x, fo_f, 'Color', cols{5}, 'DisplayName', conditions{5}, 'LineWidth', 2); % CDF
  550. plot(x, flo, '--', x, fup, '--', 'Color', cols{5}); % 95% CI
  553. switch distTest
  554. case 'ks'
  555. [~,p1,stat1] = kstest2(so_data, nso_data);
  556. cdfStat{8,3} = stat1;
  557. cdfStat{8,4} = p1;
  558. cdfStat{8,5} = min(1, p1 .* (size(cdfStat,1)-1));
  560. [~,p2,stat2] = kstest2(so_data, fo_data);
  561. cdfStat{7,3} = stat2;
  562. cdfStat{7,4} = p2;
  563. cdfStat{7,5} = min(1, p2 .* (size(cdfStat,1)-1));
  565. title({ ['SO vs NSO: p = ' num2str(round(p1,3)) ', ks2stat = ' num2str(round(stat1,3))]; ['SO vs FO: p = ' num2str(round(p2,3)) ', ks2stat = ' num2str(round(stat2,3))] })
  566. case 'ad'
  567. [stat1, p1] = ad2test(so_data, nso_data);
  568. cdfStat{8,3} = stat1;
  569. cdfStat{8,4} = p1;
  570. cdfStat{8,5} = min(1, p1 .* (size(cdfStat,1)-1));
  572. [stat2, p2] = ad2test(nso_data, fo_data);
  573. cdfStat{7,3} = stat2;
  574. cdfStat{7,4} = p2;
  575. cdfStat{7,5} = min(1, p2 .* (size(cdfStat,1)-1));
  577. title({ ['SO vs NSO: p = ' num2str(round(p1,3)) ', ad2stat = ' num2str(round(stat1,3))]; ['SO vs FO: p = ' num2str(round(p2,3)) ', ad2stat = ' num2str(round(stat2,3))] })
  578. end
  580. axis square
  581. box off
  582. ylabel('Cumulative Probabilty')
  583. xlabel(metric{m})
  584. if m==1
  585. xlim([-1 1])
  586. else
  587. xlim([-2 2])
  588. end
  589. yticks([0 0.5 1])
  590. end
  591. end
  592. end
  593. disp(cdfStat)
  594. % writecell(cdfStat, [metric{m} '_cdfStats_' date '.csv']);
  595. end
  597. %% Linear mixed model statistics
  598. table_for_stats.Cell = categorical(table_for_stats.Cell);
  599. table_for_stats.Rat = categorical(table_for_stats.Rat);
  600. table_for_stats.Rat = reordercats(table_for_stats.Rat, {'Rat452', 'Rat451', 'Rat501', 'Rat504', 'Rat503'});
  601. table_for_stats.Condition = categorical(table_for_stats.Condition);
  602. table_for_stats.Condition = reordercats(table_for_stats.Condition, {'Empty', 'Social', 'SocialOdor', 'NonSocialOdor', 'FoxOdor', 'NovelRoom'});
  603. table_for_stats.Session = categorical(table_for_stats.Session);
  605. % fit model
  606. lme = fitlme(table_for_stats, ...
  607. 'SpatCorr_norm ~ Condition + (1|Rat)', 'Verbose', 1, 'DummyVarCoding', 'reference');
  609. % test for overall effect of condition
  610. anv = anova(lme)
  611. save(['global_remapping_lme_' date '.mat'], 'lme', 'anv')
  613. % get estimated marginal means
  614. % Define categories
  615. conditions = categories(table_for_stats.Condition);
  616. n = numel(conditions);
  618. dummyRat = repmat(table_for_stats.Rat(1), n, 1);
  619. % dummyCell = repmat(stableTable.Cell(1), n, 1);
  621. newTbl = table(categorical(conditions), categorical(dummyRat), ...
  622. 'VariableNames', {'Condition','Rat'});
  624. [yhat, yCI] = predict(lme, newTbl, 'Conditional', false);
  625. % conditions = unique(table_for_stats.Condition);
  626. % sessions = unique(table_for_stats.Session); % unnecessary when no main effect of session
  627. %
  628. % % Create grid of all combinations
  629. % [C, S] = ndgrid(conditions, sessions);
  630. %
  631. % % Create dummy values for Rat and Cell
  632. % n = numel(C);
  633. % dummyRat = repmat("DummyRat", n, 1);
  634. % dummyCell = repmat("DummyCell", n, 1);
  635. %
  636. % % Build table
  637. % newTbl = table(C(:), S(:), categorical(dummyRat), categorical(dummyCell), ...
  638. % 'VariableNames', {'Condition', 'Session', 'Rat', 'Cell'});
  639. %
  640. % % plot
  641. % [yhat, yCI] = predict(lme, newTbl, 'Conditional', false);
  643. figure(2), subplot(3,3,7), cla, hold on
  644. for c=1:length(yhat)
  645. plot([c,c],[yCI(c,1), yCI(c,2)], 'Color', cols{c}, 'LineWidth', 2)
  646. plot(c, yhat(c), 'ko')
  647. end
  648. xticks([1:length(conditions)])
  649. xticklabels(conditionNames)
  650. xtickangle(45)
  651. xlim([-1 7])
  652. ylabel({'Spatial Correlation'; '(Fisher''s r-to-z)'})
  654. % if significant main effect of condition, run post-hoc planned comparisons
  655. if anv.pValue(end) < 0.05
  656. statPlan = {
  657. 'Comparison', 'test-type', 'contrastLabel', 'Fstat', 'pTwo', 'df1','df2', 'Tstat', 'pNew', 'pNew_BF';
  658. 'Empty vs Social', 'one-tailed', [0 1 0 0 0 0], nan, nan,nan, nan, nan,nan,nan;
  659. 'Empty vs Social Odor', 'one-tailed', [0 0 1 0 0 0], nan, nan, nan, nan, nan,nan,nan;
  660. 'Empty vs Nonsocial Odor', 'one-tailed', [0 0 0 1 0 0], nan, nan, nan, nan, nan,nan,nan;
  661. 'Empty vs FoxOdor', 'one-tailed', [0 0 0 0 1 0], nan, nan, nan, nan, nan, nan,nan;
  662. 'Empty vs NovelRoom', 'one-tailed', [0 0 0 0 0 1], nan, nan, nan, nan, nan, nan,nan;
  663. 'Social Odor vs Fox Odor', 'two-tailed', [0 0 1 0 -1 0], nan, nan, nan, nan, nan, nan,nan;
  664. 'Social Odor vs Nonsocial Odor','two-tailed', [0 0 1 -1 0 0], nan, nan, nan, nan ,nan, nan,nan;
  665. };
  667. beta = fixedEffects(lme); % p-by-1
  668. V = lme.CoefficientCovariance; % p-by-p
  669. xpos = [1,2;
  670. 1,3;
  671. 1,4;
  672. 1,5;
  673. 1,6;
  674. 3,5;
  675. 3,4;
  677. ];
  678. % convert F to a signed t using the contrast estimate's direction, then use the t CDF for a "less-than" test.
  679. for i=2:size(statPlan,1)
  680. % H: 1-by-p contrast row (e.g., [0 1 -1 0 ...])
  681. H = statPlan{i,3}; % adjust to your coefficient order
  683. % Wald F-test
  684. [pTwo, Fstat, df1, df2] = coefTest(lme, H);
  686. % Get the contrast estimate and its SE
  687. theta = H * beta; % contrast estimate (β_level - β_ref)
  688. se = sqrt(H * V * H.'); % standard error of the contrast
  690. % Convert to signed t-statistic (df1 must be 1)
  691. if df1 == 1
  692. tStat = theta / se;
  693. % One-tailed p-value for "lower than reference"
  694. if ismember(statPlan{i,2}, {'one-tailed'})
  695. pNew = tcdf(tStat, df2); % uses left tail; small pOne supports θ < 0
  696. else
  697. pNew = 2 * min(tcdf(tStat, df2), 1 - tcdf(tStat, df2)); % two-tailed from t
  698. end
  699. pNew_bf = min(1, pNew .* (size(statPlan,1)-1));
  701. % store values
  702. statPlan{i,4} = Fstat;
  703. statPlan{i,5} = pTwo;
  704. statPlan{i,6} = df1;
  705. statPlan{i,7} = df2;
  706. statPlan{i,8} = tStat;
  707. statPlan{i,9} = pNew;
  708. statPlan{i,10} = pNew_bf;
  709. if statPlan{i,10} < 0.05
  710. sigstar(xpos(i-1,:), statPlan{i,10})
  711. end
  712. end
  714. end
  715. disp(statPlan)
  716. writecell(statPlan, ['spatCorr_stats_' date '.csv']);
  717. end
  718. SetFigureDefaults()
  719. savefig(gcf,['spatCorr_plots_' date])
  720. saveas(gcf, ['spatCorr_plots_' date], 'png')
  721. saveas(gcf, ['spatCorr_plots_' date], 'epsc')
  723. %% for stable cell metrics
  724. stableTable.Cell = categorical(stableTable.Cell);
  725. stableTable.Rat = categorical(stableTable.Rat);
  726. stableTable.Rat = reordercats(stableTable.Rat, {'Rat452', 'Rat451', 'Rat501', 'Rat504', 'Rat503'});
  727. stableTable.Condition = categorical(stableTable.Condition);
  728. stableTable.Condition = reordercats(stableTable.Condition, {'Empty', 'Social', 'SocialOdor', 'NonSocialOdor', 'FoxOdor'});
  729. stableTable.Session = categorical(stableTable.Session);
  731. % fit model
  732. % lme = fitlme(stableTable, ...
  733. % 'RateOverlap_logit ~ Condition + (1|Rat)', 'Verbose', 1, 'DummyVarCoding', 'reference');
  735. lme = fitlme(stableTable, ...
  736. 'signedLogRatio ~ Condition + (1|Rat)', 'Verbose', 1, 'DummyVarCoding', 'reference');
  738. % test for overall effect of condition
  739. anv = anova(lme)
  740. save(['rate_remapping_lme_' date '.mat'], 'lme', 'anv')
  742. % get estimated marginal means
  743. % Define categories
  744. conditions = categories(stableTable.Condition);
  745. n = numel(conditions);
  747. dummyRat = repmat(stableTable.Rat(1), n, 1);
  748. % dummyCell = repmat(stableTable.Cell(1), n, 1); % use only if random
  749. % effect of cell
  751. newTbl = table(categorical(conditions), categorical(dummyRat), ...
  752. 'VariableNames', {'Condition','Rat'});
  754. [yhat, yCI] = predict(lme, newTbl, 'Conditional', false);
  756. figure(3), subplot(3,3,7), cla, hold on
  757. for c=1:length(yhat)
  758. plot([c,c],[yCI(c,1), yCI(c,2)], 'Color', cols{c}, 'LineWidth', 2)
  759. plot(c, yhat(c), 'ko')
  760. end
  761. xticks([1:length(conditions)])
  762. xticklabels(conditionNames)
  763. xtickangle(45)
  764. xlim([-1 7])
  765. ylabel('Signed Log FR Ratio')
  766. ylim([-1 1])
  768. % if significant main effect of condition, run post-hoc planned comparisons
  769. if anv.pValue(end) < 0.05
  770. statPlan = {
  771. 'Comparison', 'test-type', 'contrastLabel', 'Fstat', 'pTwo', 'df1','df2', 'Tstat', 'pNew', 'pNew_corrected';
  772. 'Empty vs Social', 'two-tailed', [0 1 0 0 0], nan, nan,nan, nan, nan,nan,nan;
  773. 'Empty vs Social Odor', 'two-tailed', [0 0 1 0 0], nan, nan, nan, nan, nan,nan,nan;
  774. 'Empty vs Nonsocial Odor', 'two-tailed', [0 0 0 1 0], nan, nan, nan, nan, nan,nan,nan;
  775. 'Empty vs FoxOdor', 'two-tailed', [0 0 0 0 1], nan, nan, nan, nan, nan, nan,nan;
  776. 'Social Odor vs Fox Odor', 'two-tailed', [0 0 1 0 -1], nan, nan, nan, nan, nan, nan,nan;
  777. 'Social Odor vs Nonsocial Odor','two-tailed', [0 0 1 -1 0], nan, nan, nan, nan ,nan, nan,nan;
  778. };
  780. beta = fixedEffects(lme); % p-by-1
  781. V = lme.CoefficientCovariance; % p-by-p
  782. xpos = [1,2;
  783. 1,3;
  784. 1,4;
  785. 1,5;
  786. 1,6;
  787. 3,5;
  788. 3,4;
  789. ];
  791. % convert F to a signed t using the contrast estimate's direction, then use the t CDF for a "less-than" test.
  792. for i=2:size(statPlan,1)
  793. % H: 1-by-p contrast row (e.g., [0 1 -1 0 ...])
  794. H = statPlan{i,3}; % adjust to your coefficient order
  796. % Wald F-test
  797. [pTwo, Fstat, df1, df2] = coefTest(lme, H);
  799. % Get the contrast estimate and its SE
  800. theta = H * beta; % contrast estimate (β_level - β_ref)
  801. se = sqrt(H * V * H.'); % standard error of the contrast
  803. % Convert to signed t-statistic (df1 must be 1)
  804. if df1 == 1
  805. tStat = theta / se;
  806. % One-tailed p-value for "lower than reference"
  807. if ismember(statPlan{i,2}, {'one-tailed'})
  808. pNew = tcdf(tStat, df2); % uses left tail; small pOne supports θ < 0
  809. else
  810. pNew = 2 * min(tcdf(tStat, df2), 1 - tcdf(tStat, df2)); % two-tailed from t
  811. end
  812. pNew_bf = min(1, pNew .* (size(statPlan,1)-1));
  814. % store values
  815. statPlan{i,4} = Fstat;
  816. statPlan{i,5} = pTwo;
  817. statPlan{i,6} = df1;
  818. statPlan{i,7} = df2;
  819. statPlan{i,8} = tStat;
  820. statPlan{i,9} = pNew;
  821. statPlan{i,10} = pNew_bf;
  822. if statPlan{i,10} < 0.05
  823. sigstar(xpos(i-1,:), statPlan{i,10})
  824. end
  825. end
  826. end
  827. disp(statPlan)
  828. writecell(statPlan, ['rate_stats_' date '.csv']);
  829. end
  830. SetFigureDefaults()
  831. savefig(gcf,['rate_plots_' date])
  832. saveas(gcf, ['rate_plots_' date], 'png')
  833. saveas(gcf, ['rate_plots_' date], 'epsc')
  835. end
  836. function rates = getStrongestFieldRate(placeFields, mode)
  837. % mode = 'peak' or 'mean'
  839. numCells = numel(placeFields);
  840. rates = nan(numCells,1);
  842. for n = 1:numCells
  843. if isempty(placeFields{n}), continue; end
  845. % find strongest field
  846. [~, idx] = max([placeFields{n}.peakRate]);
  848. if strcmp(mode, 'peak')
  849. rates(n) = placeFields{n}(idx).peakRate;
  850. else
  851. rates(n) = placeFields{n}(idx).meanRate;
  852. end
  853. end
  854. end

place_cell_metrics.m at commit fd5d50d, no license · at the source

Overview

Authors: Peyton G Demetrovich1,2, Laura Lee Colgin1,2,3
ORCID iDs: Laura Lee Colgin
  1. Interdisciplinary Neuroscience Program, The University of Texas at Austin, Austin, Texas 78712
  2. Center for Learning and Memory, The University of Texas at Austin, Austin, Texas 78712
  3. Department of Neuroscience, The University of Texas at Austin, Austin, Texas 78712
Institutions: The University of Texas at Austin (United States)
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience, volume 46, issue 32, article e2001252026
Dates: published online 12 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/jneurosci.2001-25.2026 · PMID 42469025 · PMCID PMC13470782 · OpenAlex W7169518659
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: extracellular electrophysiology (units, LFP) (modality), rat (organism), systems (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Spectral & time-frequency, Preprocessing, Connectivity, Single-unit activity, calcium imaging
Keywords: dentate gyrus, dentate spikes, hippocampus, place cells, social behaviors, spatial memory
MeSH: Action Potentials*, Dentate Gyrus*, Environment*, Neurons*, Place Cells*, Space Perception*, Animals, Female, Local Field Potential Measurement, Male, Odorants, Rats, Rats, Long-Evans (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R01 MH131317); HHS | National Institutes of Health (R01MH131317); The University of Texas at Austin Graduate Continuing Fellowship
Citations: not cited yet (Europe PMC); 69 references in the paper

Abstract

The dentate gyrus (DG) is thought to play a key role in the formation of dissociable memory representations for similar contexts. Neurons in the DG receive highly processed spatial and nonspatial sensory information from the medial and lateral entorhinal cortices, respectively. Changes in spatially tuned firing patterns of DG place cells occur after spatial changes to an environment, but the degree to which DG place cells respond to ethologically relevant nonspatial stimuli is largely unknown. Spatial and nonspatial information is thought to be transmitted to the DG during discrete local field potential events called dentate spikes. Here, we tested the extent to which different spatial and nonspatial stimuli modulate place cell firing patterns and dentate spike dynamics. We performed extracellular recordings of DG place cells and local field potentials in rats of both sexes exploring a familiar spatial environment, in which social stimuli and nonsocial odors of varying ethological relevance were presented, and a novel spatial environment. As expected, DG place cells exhibited different firing patterns between familiar and novel environments. Significant changes in firing were not observed, however, with any of the nonspatial stimuli. Surprisingly, the occurrence of dentate spikes associated with lateral entorhinal cortex input increased during exploration of ethologically relevant stimuli, and this increase was greater for social stimuli. Altogether, these results suggest that the DG preferentially responds to social stimuli at the network level, providing novel insights into how spatial and nonspatial information is processed in the DG.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 19 matches between paragraphs and lines of code.

Julie-Fabre/bombcell

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2952cd5e16796635a4dfa61d7e286a4b46014521, 7 September 2026
Languages: MATLAB (80), Python (20), Jupyter (5), C (1)
Size: 173 files, 106 scripts
Software Heritage: not archived
Found in: the text, “Spike sorting and unit selection.”
Holds: README, license file, environment (py_bombcell/pyproject.toml, py_bombcell/requirements.txt), documentation, 6 notebooks
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (20 files), pandas (12 files), Matplotlib (11 files), Statistics and Machine Learning Toolbox (10 files), SciPy (6 files), Phy (4 files), Optimization Toolbox (2 files), Curve Fitting Toolbox (1 file), Signal Processing Toolbox (1 file), Numba (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
108 files
At the source: github.com/Julie-Fabre/bombcell

ColginLab/Demetrovich_Colgin_2026

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: fd5d50da76e2a64120562cc05203e824efde81bf, 29 April 2026
Languages: MATLAB (21)
Size: 22 files, 21 scripts
Software Heritage: not archived
Found in: “Statistics, data, and code availability.”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), EEGLAB (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
22 files
At the source: github.com/ColginLab/Demetrovich_Colgin_2026

ColginLab/ColginLabCode

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 3ceac6adfd22e6f47266eb65e7df044623e9bfa3, 19 May 2020
Languages: MATLAB (1347), C (14), C/C++ (11)
Size: 1,892 files, 1,372 scripts
Software Heritage: not archived
Found in: “Statistics, data, and code availability.”
Holds: README, tests, documentation
Not found: license file, CITATION.cff, environment file, continuous integration
Tools: Statistics and Machine Learning Toolbox (91 files), Chronux (72 files), CircStat (44 files), Signal Processing Toolbox (29 files), Image Processing Toolbox (9 files), EEGLAB (1 file), export_fig (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
1,373 files
At the source: github.com/ColginLab/ColginLabCode

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1,499 scripts, each with its path and the digest of its content;
  • 19 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

No dataset and no data link were found in the paper.

Statistics, data, and code availability

GLMMs or LMMs and two-sample Kolmogorov–Smirnov tests were used to analyze results, as described above for each measure. Statistical tests on each (G)LMM were computed as Wald F tests or converted t tests, as reported. Converted t tests were necessary for one-tailed tests that were used when effects in a specific direction were hypothesized a priori. Post hoc comparisons were computed only when a significant main effect [for (G)LMMs with one fixed effect] or interaction effect [for (G) LMMs with two or more fixed effects] was observed. All reported p values were Bonferroni corrected unless otherwise stated. All data and statistical analyses were performed using MATLAB scripts, which are hosted at https://github.com/ColginLab/Demetrovich_Colgin_2026. Scripts were written using custom, built-in, and/or toolbox-specific functions. Certain custom functions were used from our lab’s code base (https://github.com/ColginLab/ColginLabCode). Data used in this study are available at https://web.corral.tacc.utexas.edu/ColginLab/Demetrovich_Colgin_JNeurosci_2026/.

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 3, 28 September 2026

  • Publisher: n/a → Society for Neuroscience

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 13 MeSH terms, 3 funders, 65 references.

Cite

This paper

Demetrovich, P. G., & Colgin, L. L. (2026). Dynamics of Dentate Gyrus Place Cells and Dentate Spikes during Spatial and Nonspatial Changes in Environments. The Journal of neuroscience : the official journal of the Society for Neuroscience, 46(32), e2001252026. https://doi.org/10.1523/jneurosci.2001-25.2026

BibTeX

@article{demetrovich2026dynamics,
author = {Demetrovich, Peyton G and Colgin, Laura Lee},
title = {{Dynamics of Dentate Gyrus Place Cells and Dentate Spikes during Spatial and Nonspatial Changes in Environments}},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
year = {2026},
month = aug,
volume = {46},
number = {32},
pages = {e2001252026},
publisher = {Society for Neuroscience},
issn = {0270-6474},
doi = {10.1523/jneurosci.2001-25.2026},
url = {https://doi.org/10.1523/jneurosci.2001-25.2026},
pmid = {42469025},
pmcid = {PMC13470782}
}

RIS

TY - JOUR
AU - Demetrovich, Peyton G
AU - Colgin, Laura Lee
TI - Dynamics of Dentate Gyrus Place Cells and Dentate Spikes during Spatial and Nonspatial Changes in Environments
T2 - The Journal of neuroscience : the official journal of the Society for Neuroscience
J2 - J Neurosci
PY - 2026
DA - 2026/08/12
VL - 46
IS - 32
SP - e2001252026
SN - 0270-6474
PB - Society for Neuroscience
DO - 10.1523/jneurosci.2001-25.2026
UR - https://doi.org/10.1523/jneurosci.2001-25.2026
LA - en
ER -

CSL-JSON

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"id": "10.1523/jneurosci.2001-25.2026",
"type": "article-journal",
"title": "Dynamics of Dentate Gyrus Place Cells and Dentate Spikes during Spatial and Nonspatial Changes in Environments",
"container-title": "The Journal of neuroscience : the official journal of the Society for Neuroscience",
"author": [
{
"family": "Demetrovich",
"given": "Peyton G"
},
{
"family": "Colgin",
"given": "Laura Lee"
}
],
"container-title-short": "J Neurosci",
"volume": "46",
"issue": "32",
"page": "e2001252026",
"DOI": "10.1523/jneurosci.2001-25.2026",
"PMID": "42469025",
"PMCID": "PMC13470782",
"ISSN": "0270-6474",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/jneurosci.2001-25.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
12
]
]
}
}

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

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