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REM sleep prefrontal high-frequency oscillation chains mediate distinct cortical - hippocampal reactivation patterns compared to NREM sleep.

Code ↔ Paper

34 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 34 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Detection of population suppression ↔ Figure4/jds_detectPopulationSuppressionREM.m, lines 1–62 · score 0.87 · population suppression events, 300–500 ms, circularly shifted, population activity, smoothed, 300 ms
  2. [2] § Methods › Spatial information of assembly fields ↔ Figure6/jds_assemblyFieldMetricsShuffle.m, the whole file · a weak match · score 0.82 · assembly field, assembly activation, activation event, bits, tailed, shuffled
  3. [3] § Methods › Intracranial electromyogram ↔ Figure1/jds_pEMG.m, lines 1–21 · score 0.82 · 300–600 Hz, reference tetrode, 275 Hz, 625 Hz, EMG, shoulders
  4. [4] § Methods › Phase slope index ↔ Figure3/jds_phaseSlopeIndexCA1PFC.m, lines 1–89 · score 0.82 · FieldTrip, 6–12 Hz, CA1 leads, CA1 PFC, PSI, slope
  5. [5] § Results › REM theta-phase shifting CA1 neurons are preferentially engaged during PFC REM HFOs ↔ Figure8/jds_thetaPhaseShiftersREM.m, lines 1–63 · score 0.81 · REM phase shifting, Shift magnitude, preferred theta phase, shifting cells, phase locked, REM theta
  6. [6] § Results › Differential engagement of distinct populations of CA1 neurons by PFC REM HFO chains ↔ Figure7/jds_CA1PFCRippleCoactivityREMDeltaFR.m, lines 1–37 · score 0.80 · CA1 firing rates, scored cofiring, NREM bout, Firing rate change, low cofiring, CA1 PFC
  7. [7] § Results › Differential engagement of distinct populations of CA1 neurons by PFC REM HFO chains ↔ Figure7/jds_CA1PFCRippleCoactivityREMDeltaFR.m, lines 1–37 · score 0.80 · CA1 firing rates, NREM bout, Firing rate change, Low cofiring, CA1 PFC, sleep epoch
  8. [8] § Methods › Theta inter-peak intervals during bouts of high and low theta power ↔ Figure3/jds_thetaPowerThetaIEI.m, lines 96–155 · score 0.80 · inter peak intervals, zero crossings, high theta power, tonic, phasic, LFPs
  9. [9] § Methods › Phase locking ↔ Figure8/jds_thetaPhaseShiftersREM.m, lines 1–63 · score 0.79 · phase distribution, REM theta phase, Phase locking, preferred phases, theta periods, U2
  10. [10] § Results › PFC ensemble activity is sequentially organized by REM HFO chains ↔ Figure6/jds_assemblyFieldMetricsShuffle.m, the whole file · a weak match · score 0.77 · scored spatial information, rate maps, assembly maps, PFC assembly, reactivation strength, tailed
  11. [11] § Methods › Assembly sequence detection surrounding HFOs ↔ Figure6/jds_rippleTriggeredAssemblyStrengthREMxVal.m, lines 1–46 · score 0.77 · randomly split, cross validation, assembly reactivation, sequences, halves, Pearson
  12. [12] § Methods › Assembly sequence detection surrounding HFOs ↔ Figure6/jds_rippleTriggeredAssemblyStrengthREMxVal.m, lines 1–46 · score 0.76 · assembly strength, chain onset, assembly reactivation, sequence, halves, slope
  13. [13] § Methods › Event type prediction ↔ Figure4/jds_sleepStateRipplePrediction.m, lines 1–133 · score 0.71 · fold cross validation, predicted, accuracy, fitclinear, training, matrices
  14. [14] § Results › Coupling of theta, gamma, and prefrontal HFOs in REM sleep ↔ Figure3/jds_thetaNestingSurrogate.m, lines 1–107 · score 0.70 · theta nested oscillation, phase preference, Preferred theta phases, theta phase bin, surrogate, gamma
  15. [15] § Results › Differential engagement of distinct populations of CA1 neurons by PFC REM HFO chains ↔ Figure7/jds_remRippleCA1CoactivitySpatialCorr.m, the whole file · a weak match · score 0.69 · spatial correlation, cell pairs, Low cofiring, High cofiring, CA1 cells, map
  16. [16] § Results › Coupling of theta, gamma, and prefrontal HFOs in REM sleep ↔ Figure3/jds_phaseSlopeIndexCA1PFC.m, lines 1–89 · score 0.69 · Phase slope, 6–12 Hz, theta band, CA1 PFC, PSI, coherence
  17. [17] § Results › Coupling of theta, gamma, and prefrontal HFOs in REM sleep ↔ Figure3/jds_thetaPowerThetaIEI.m, lines 1–55 · score 0.66 · Inter peak intervals, high theta power, tonic REM, signal, bands, Phasic
  18. [18] § Results › Coupling of theta, gamma, and prefrontal HFOs in REM sleep ↔ Figure3/jds_thetaNestingSurrogate.m, lines 1–107 · score 0.65 · theta nesting, preferred theta phase, phasic REM, cycle, oscillations, gamma
  19. [19] § Results › Coupling of theta, gamma, and prefrontal HFOs in REM sleep ↔ Figure3/jds_thetaPowerThetaIEI.m, lines 96–155 · score 0.65 · theta inter peak, low theta power, high theta power, IPI, tonic, intervals
  20. [20] § Methods › Event aligned multi-unit activity ↔ Figure2/jds_normPopulationActivityREM.m, the whole file · a weak match · score 0.65 · power spectral density, population activity, windows, Figure 2, 12 Hz, binned
  21. [21] § Results › High-frequency events in PFC during REM sleep ↔ Figure2/jds_normPopulationActivityREM.m, the whole file · a weak match · score 0.64 · power spectral density, multiunit activity, population activity, PSD, MUA, 100 Hz
  22. [22] § Results › Differential modulation of PFC neuronal activity during NREM prefrontal ripples and REM prefrontal HFOs ↔ Figure4/jds_sleepStateRipplePrediction.m, lines 1–133 · score 0.64 · fold cross validation, linear classifier, NREM ripples, trained, vector, spiking
  23. [23] § Methods › Theta coherence ↔ Figure3/jds_thetaCoherenceREM.m, lines 1–131 · score 0.63 · Theta coherence, Chronux, CA1 PFC, window, Gaussian, 12 Hz
  24. [24] § Methods › Phase locking ↔ Figure8/jds_thetaPhaseLockingPFCRipplesShifters.m, lines 123–182 · score 0.63 · theta phase shifting, Phase locking, Rayleigh, uniformity, U2, Watson
  25. [25] § Results › Differential engagement of distinct populations of CA1 neurons by PFC REM HFO chains ↔ Figure7/jds_compareNREMCA1SuppressionSWRReactivationREMCofiring.m, lines 115–190 · score 0.62 · SWR reactivation, CA1 suppression, high cofiring, cofiring CA1, Figure 7, ripples
  26. [26] § Methods › Cross-frequency phase-amplitude coupling ↔ Figure3/jds_phaseAmpCouplingREMShuffle.m, lines 106–132 · score 0.60 · Hilbert transform, frequency band, Comodulogram, envelope, amplitude, LFP
  27. [27] § Methods › Theta nesting ↔ Figure3/jds_thetaNestingSurrogate.m, lines 126–183 · score 0.59 · theta nesting, phase bin, high theta, LFP, 150 Hz, gamma
  28. [28] § Results › Differential modulation of PFC neuronal activity during NREM prefrontal ripples and REM prefrontal HFOs ↔ Figure4/jds_detectPopulationSuppressionREM.m, lines 1–62 · score 0.59 · population suppression events, population spiking, population activity, peri, histograms, shuffle
  29. [29] § Methods › Sleep state identification ↔ Figure1/jds_sleepStateVelTDComparison.m, lines 1–20 · score 0.58 · delta ratio, transition, TD, box, velocity, REM sleep
  30. [30] § Methods › Change in firing rate across sleep ↔ Figure7/jds_CA1PFCRippleCoactivityREMCA1RipAlignedMUA.m, lines 1–38 · score 0.58 · scored cofiring, CA1 PFC, PFC ripples, coactive, spike, chains
  31. [31] § Results › Coupling of theta, gamma, and prefrontal HFOs in REM sleep ↔ Figure3/jds_rippleThetaPhaseLockingREM.m, lines 1–57 · score 0.56 · phase locking, pairwise phase, theta phase, PPC, isolated, chains
  32. [32] § Methods › Detection of population suppression ↔ Figure4/jds_periSuppressionRippleProbREM.m, the whole file · a weak match · score 0.56 · population suppression events, circularly shifted, probability, shuffled, 500 ms, Figure 4
  33. [33] § Results › REM theta-phase shifting CA1 neurons are preferentially engaged during PFC REM HFOs ↔ Figure8/jds_rippleBurstingREM.m, lines 1–80 · score 0.54 · Shift magnitude, phase shifting, theta phase, cortical ripples, bursting, isolated
  34. [34] § Results › Differential engagement of distinct populations of CA1 neurons by PFC REM HFO chains ↔ Figure7/jds_CA1PFCRippleCoactivityREMDeltaFR.m, lines 108–167 · score 0.54 · low cofiring, high cofiring, Firing rates, Bonferroni, Friedman, PFC ripples

Paper

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The authors' code

MATLAB · 312 lines · 10 KB · MIT · 3 matches

  1. function jds_CA1PFCRippleCoactivityREMDeltaFR(animalprefixlist)
  2. %JDS_CA1PFCRIPPLECOACTIVITYREMDELTAFR CA1 firing-rate change vs REM ripple cofiring.
  3. % jds_CA1PFCRippleCoactivityREMDeltaFR(animalprefixlist) computes each CA1
  4. % cell's mean z-scored cofiring with PFC cells in REM chain cortical
  5. % ripples, along with its firing rate in the first NREM bout, REM, and the
  6. % last NREM bout of each sleep epoch. Compares firing rates across sleep
  7. % stages (Friedman test) and relates NREM firing-rate change to REM
  8. % cofiring (rank-sum on high/low cofiring split, robust regression,
  9. % quartile plot).
  10. %
  11. % animalprefixlist - cell array of animal prefix strings
  13. day = 1;
  15. ncRippleCo = [];
  16. cRippleCo = [];
  17. firingRates = [];
  18. firingRatesWithRem = [];
  19. meanFRLow = [];
  20. meanFRHigh = [];
  21. upFR = [];
  22. downFR = [];
  24. for a = 1:length(animalprefixlist)
  26. animalprefix = char(animalprefixlist(a));
  27. dir = sprintf('/Volumes/JUSTIN/SingleDay/%s_direct/',animalprefix);
  29. %Load reactivation strength file for all assemblies and epochs
  30. load(sprintf('%s%sCA1_RTimeStrengthSleepNewSpk_20_%02d.mat',dir,animalprefix,day));
  31. CA1_R = RtimeStrength; clear RtimeStrength
  32. load(sprintf('%s%sPFC_RTimeStrengthSleepNewSpk_20_%02d.mat',dir,animalprefix,day));
  33. PFC_R = RtimeStrength; clear RtimeStrength
  34. load(sprintf('%s%sremeps%02d.mat',dir,animalprefix,day));
  35. load(sprintf('%s%sswsALL%02d.mat',dir,animalprefix,day));
  36. rem = load(sprintf('%s%srem%02d.mat',dir,animalprefix,day));
  37. rem = rem.rem;
  40. %Load ripples
  41. indrips = load(sprintf('%s%sctxrippletime_chainREM%02d.mat',dir,animalprefix,day));
  43. epochs = remeps;
  45. for e = 1:length(epochs)
  47. ep = epochs(e);
  48. if ep == 1
  49. continue
  50. end
  51. firstsws = [sws{day}{ep}.starttime(1) sws{day}{ep}.endtime(1)];
  52. lastsws = [sws{day}{ep}.starttime(end) sws{day}{ep}.endtime(end)];
  53. allrem = [rem{day}{ep}.starttime rem{day}{ep}.endtime];
  54. remdur = rem{day}{ep}.total_duration;
  55. if ((firstsws(2)-firstsws(1) < 30)) || ((lastsws(2)-lastsws(1) < 30))
  56. continue
  57. end
  58. [ctxidx, hpidx] = jds_getallepcells(dir, animalprefix, day, ep, []);
  59. CA1assemblytmp = CA1_R{ep}.reactivationStrength;
  60. CA1num = length(CA1assemblytmp);
  61. PFCassemblytmp = PFC_R{ep}.reactivationStrength;
  62. PFCnum = length(PFCassemblytmp);
  65. ncrips = [indrips.ctxripple{day}{ep}.starttimeC indrips.ctxripple{day}{ep}.endtimeC];
  66. if isempty(ncrips)
  67. continue
  68. end
  69. if length(ncrips(:,1)) > 10
  70. load(sprintf('%s%sspikes%02d.mat',dir,animalprefix,day));
  71. numncrips = length(ncrips(:,1));
  73. CA1idx = CA1_R{ep}.cellidx;
  74. PFCidx = PFC_R{ep}.cellidx;
  76. Chigh = CA1idx;
  77. Phigh = PFCidx;
  78. for c = 1:length(Chigh(:,1))
  79. tmp = [];
  80. cell1spks = spikes{day}{ep}{Chigh(c,1)}{Chigh(c,2)}.data(:,1);
  81. firstnremFR = (sum(isExcluded(cell1spks, firstsws)))/(firstsws(2)-firstsws(1));
  82. lastnremFR = (sum(isExcluded(cell1spks, lastsws)))/(lastsws(2)-lastsws(1));
  83. remFR = (sum(isExcluded(cell1spks, allrem)))/remdur;
  84. FRchange = lastnremFR - firstnremFR;
  85. meanRt = spikes{day}{ep}{Chigh(c,1)}{Chigh(c,2)}.meanrate;
  86. if FRchange < -5
  87. continue
  88. end
  89. firingRates = [firingRates; [firstnremFR lastnremFR]];
  90. firingRatesWithRem = [firingRatesWithRem; [firstnremFR remFR lastnremFR]];
  91. for pp = 1:length(Phigh(:,1))
  92. if (~isempty(spikes{day}{ep}{Chigh(c,1)}{Chigh(c,2)})) &&...
  93. (~isempty(spikes{day}{ep}{Phigh(pp,1)}{Phigh(pp,2)}))
  94. cell1spks = spikes{day}{ep}{Chigh(c,1)}{Chigh(c,2)}.data(:,1);
  95. cell2spks = spikes{day}{ep}{Phigh(pp,1)}{Phigh(pp,2)}.data(:,1);
  97. spkbins1_c = periodAssign(cell1spks, ncrips);
  98. spkbins2_p = periodAssign(cell2spks, ncrips);
  100. ripnum1_c = unique(spkbins1_c);
  101. activeinrip1_c = ripnum1_c(find(ripnum1_c ~= 0));
  103. ripnum2_p = unique(spkbins2_p);
  104. activeinrip2_p = ripnum2_p(find(ripnum2_p ~= 0));
  106. common_hprip = length(find(ismember(activeinrip1_c, activeinrip2_p)));
  108. %calculate zscored ripple coactivity
  110. nAB_hp = common_hprip;
  111. nA_hp = length(activeinrip1_c);
  112. nB_hp = length(activeinrip2_p);
  114. coact_hprip = (nAB_hp - (nA_hp*nB_hp/numncrips))/...
  115. sqrt(nA_hp*nB_hp*(numncrips - nA_hp)*(numncrips - nB_hp)/...
  116. (numncrips^2*(numncrips-1)));
  118. tmp = [tmp; coact_hprip];
  119. end
  120. end
  121. ncRippleCo = [ncRippleCo; [nanmean(tmp) FRchange meanRt remFR]];
  122. if FRchange > -0.0447% mean = -0.0363 %median = -0.0447
  123. upFR = [upFR; nanmean(tmp)];
  124. elseif FRchange < -0.0447
  125. downFR = [downFR; nanmean(tmp)];
  126. end
  127. end
  128. end
  129. end
  130. end
  131. p = friedman(firingRatesWithRem,1,'on')
  132. [p, tbl, stats] = friedman(firingRatesWithRem, 1, 'off');
  133. [COMPARISON,MEANS,H,GNAMES] = multcompare(stats, 'CType', 'bonferroni');
  134. meanFrs = mean(firingRatesWithRem);
  135. semFrs = std(firingRatesWithRem)./sqrt(size(firingRatesWithRem,1));
  136. figure
  137. errorbar(1:3,meanFrs,semFrs,'-k')
  138. xlim([0.5 3.5])
  139. xticks([1:3])
  140. xticklabels({'First NREM','REM','Last NREM'})
  141. set(gcf, 'renderer', 'painters')
  143. datacombinedEpFrs = [firingRatesWithRem(:,1); firingRatesWithRem(:,2); firingRatesWithRem(:,3)];
  144. g1 = repmat({'First NREM'},length(firingRatesWithRem(:,1)),1);
  145. g2 = repmat({['REM p=' num2str(COMPARISON(1,6))]},length(firingRatesWithRem(:,2)),1);
  146. g3 = repmat({['Last NREM p=' num2str(COMPARISON(2,6))]},length(firingRatesWithRem(:,3)),1);
  147. g = [g1;g2;g3];
  149. figure;
  150. h = boxplot(datacombinedEpFrs,g,'OutlierSize',7,'Symbol','k+'); set(h(7,:),'Visible','off');
  151. title(['CA1 FR eps-p = ' num2str(COMPARISON(3,6)) ' compare 2-3'])
  152. ylim([-0.4 1.6])
  153. yticks([-0.4:0.4:1.6])
  154. ylabel('Firing Rate (Hz)')
  155. set(gcf, 'renderer', 'painters')
  157. meanCo = 0; %split cells into cofiring > 0 vs < 0
  159. highCo = ncRippleCo(find(ncRippleCo(:,1)>meanCo),:);
  160. lowCo = ncRippleCo(find(ncRippleCo(:,1)<meanCo),:);
  162. [p1 h1] = ranksum(highCo(:,2),lowCo(:,2))
  164. datacombinedFRchange = [highCo(:,2); lowCo(:,2)];
  165. g1 = repmat({'HighCofiring'},length(highCo(:,2)),1);
  166. g2 = repmat({'LowCofiring'},length(lowCo(:,2)),1);
  167. g = [g1;g2];
  169. figure;
  170. h = boxplot(datacombinedFRchange,g,'OutlierSize',7,'Symbol','k+'); set(h(7,:),'Visible','off');
  171. title(['CA1 FR change-p = ' num2str(p1)])
  172. set(gcf, 'renderer', 'painters')
  174. [pFR hFR] = ranksum(highCo(:,3),lowCo(:,3))
  176. datacombinedFR = [highCo(:,3); lowCo(:,3)];
  177. g1 = repmat({'HighCofiring'},length(highCo(:,3)),1);
  178. g2 = repmat({'LowCofiring'},length(lowCo(:,3)),1);
  179. g = [g1;g2];
  181. figure;
  182. h = boxplot(datacombinedFR,g,'OutlierSize',7,'Symbol','k+'); set(h(7,:),'Visible','off');
  183. ylim([-1 1])
  184. title(['CA1 mean FR-p = ' num2str(pFR)])
  185. set(gcf, 'renderer', 'painters')
  187. figure;
  188. bar([mean(highCo(:,2)) mean(lowCo(:,2))],'k');
  189. hold on
  190. errorbar([1 2], [mean(highCo(:,2)) mean(lowCo(:,2))],...
  191. [std(highCo(:,2))./sqrt(length(highCo(:,2))) std(lowCo(:,2))./sqrt(length(lowCo(:,2)))],...
  192. 'LineStyle','none')
  193. xticklabels({'High cofiring','Low'})
  194. title(['CA1 FR change-p = ' num2str(p1)])
  195. ylabel('FR change first-last NREM')
  196. set(gcf, 'renderer', 'painters')
  198. first = firingRates(:,1);
  199. last = firingRates(:,2);
  201. [p2 h2] = signrank(firingRates(:,1),firingRates(:,2))
  203. datacombinedFRchange = [first; last];
  204. g1 = repmat({'first'},length(first),1);
  205. g2 = repmat({'last'},length(last),1);
  206. g = [g1;g2];
  208. figure; hold on
  209. h = boxplot(datacombinedFRchange,g,'OutlierSize',7,'Symbol','k+'); set(h(7,:),'Visible','off');
  210. xlim([0.5 2.5])
  211. set(gca, 'YScale', 'log')
  212. title(['CA1 FR -p = ' num2str(p2)])
  213. set(gcf, 'renderer', 'painters')
  215. [p3 h3] = ranksum(highCo(:,4),lowCo(:,4))
  217. datacombinedREMFR = [highCo(:,4); lowCo(:,4)];
  218. g1 = repmat({'HighCofiring'},length(highCo(:,4)),1);
  219. g2 = repmat({'LowCofiring'},length(lowCo(:,4)),1);
  220. g = [g1;g2];
  222. figure;
  223. h = boxplot(datacombinedREMFR,g,'OutlierSize',7,'Symbol','k+'); set(h(7,:),'Visible','off');
  224. ylim([-0.25 2.5])
  225. title(['CA1 REM FR -p = ' num2str(p3)])
  226. set(gcf, 'renderer', 'painters')
  228. [p4 h4] = ranksum(downFR,upFR)
  230. datacombinedFRchangeCofiring = [downFR; upFR];
  231. g1 = repmat({'FR change lower than mean'},length(downFR),1);
  232. g2 = repmat({'FR change higher than mean'},length(upFR),1);
  233. g = [g1;g2];
  235. figure;
  236. h = boxplot(datacombinedFRchangeCofiring,g,'OutlierSize',7,'Symbol','k+'); set(h(7,:),'Visible','off');
  237. ylabel('Cofiring with PFC')
  238. title(['CA1 Cofiring -p = ' num2str(p4)])
  239. set(gcf, 'renderer', 'painters')
  241. set(gca, 'YScale', 'log')
  242. xlim([0.5 2.5])
  243. for c = 1:length(first)
  244. x = [1 2];
  245. tmp = firingRates(c,:);
  246. if tmp(2) > tmp(1)
  247. plot(x,tmp,'-r')
  248. elseif tmp(2) < tmp(1)
  249. plot(x,tmp,'-k')
  250. elseif tmp(2) == tmp(1)
  251. plot(x,tmp,'-m')
  252. end
  253. end
  254. ylim([0.003 10])
  256. meanFr = mean(firingRates);
  257. semFr = std(firingRates)./sqrt(size(firingRates,1))
  258. figure
  259. errorbar([1:2], meanFr, semFr)
  260. xlim([0.5 2.5])
  261. ylim([0.46 0.6])
  262. title(['CA1 FR -p = ' num2str(p2)])
  264. figure
  265. scatter(ncRippleCo(:,1),ncRippleCo(:,2))
  266. mdlr = fitlm(ncRippleCo(:,1),ncRippleCo(:,2),'RobustOpts','on')
  267. pVal = mdlr.Coefficients.pValue(2);
  268. r = sqrt(mdlr.Rsquared.Ordinary);
  269. hold on
  270. lsline
  271. title(['CA1 FR change vs cofiring - p=' num2str(pVal) ' r=' num2str(r) ' Robust LR'])
  272. set(gcf, 'renderer', 'painters')
  275. figure
  276. idx = find(~isnan(ncRippleCo(:,1)));
  277. tt = ncRippleCo(idx,:);
  278. quartile_sep = floor(length(tt(:,1))/4);
  279. spklatquar = sortrows(tt,1);
  280. vals = [];
  281. cnt = 1;
  282. for s = 1:4
  283. if s < 4
  284. tmp = spklatquar(cnt:quartile_sep*s,1:2);
  285. tmp(:,3) = s;
  286. else
  287. tmp = spklatquar(cnt:end,1:2);
  288. tmp(:,3) = s;
  289. end
  290. vals{s} = tmp;
  291. cnt = cnt + quartile_sep;
  292. clear tmp
  293. end
  295. v = cellfun(@mean,vals,'UniformOutput',false);
  297. v2 = cellfun((@(x) std(x)./sqrt(length(x(:,1)))),vals,'UniformOutput',false);
  299. data_sems = vertcat(v2{:});
  301. data_means = vertcat(v{:});
  303. X = [1:4];
  305. errorbar(X, data_means(:,2), data_sems(:,2),'b','LineWidth',3);
  306. hold on
  307. xlim([0.5 4.5])
  308. ylabel('Firing Rate change')
  309. xlabel('Cofiring Quartile')
  310. title(['CA1 FR change vs cofiring - p=' num2str(pVal) ' r=' num2str(r) ' Robust LR'])
  311. set(gcf, 'renderer', 'painters')

jds_CA1PFCRippleCoactivityREMDeltaFR.m at commit 3b4245a, under MIT · at the source

Overview

Authors: Justin D Shin1, Michael Satchell1, Paul Miller1, Shantanu P Jadhav1
ORCID iDs: Justin D Shin, Michael Satchell, Paul Miller, Shantanu P Jadhav
  1. Neuroscience Program, Department of Psychology, and Volen National Center for Complex Systems, Brandeis University, Waltham, United States
Institutions: Brandeis University (United States)
Journal: eLife, volume 15, article RP110795
Dates: published online 7 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.110795 · PMID 42704658 · PMCID PMC13549623 · OpenAlex W7154120262
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.7554/elife.110795.2
Status: code verified
Categories: rat (organism)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Preprocessing, Evoked potentials, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Rat
MeSH: CA1 Region, Hippocampal*, Hippocampus*, Prefrontal Cortex*, Sleep, REM*, Sleep, Slow-Wave*, Animals, Male, Memory Consolidation, Rats, Theta Rhythm (* major topic)
Topic: Sleep and Wakefulness Research (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: NIMH NIH HHS (R01 MH112661, R01MH112661)
Citations: not cited yet (Europe PMC); 121 references in the paper
Research resources: Rat RRID:RGD_2308852, MATLAB 2022a RRID:SCR_001622, Chronux RRID:SCR_005547, RRID:SCR_021623

Abstract

REM (rapid eye movement) and non-REM (NREM) sleep stages contribute to systems memory consolidation in hippocampal–cortical circuits. However, the physiological mechanisms underlying REM memory processes remain relatively unclear compared to NREM memory reactivation. Here we report, in rodents, the existence of prefrontal cortical (PFC) high-frequency oscillation (HFO) chains in REM sleep during the consolidation of recently acquired spatial memory. High-density tetrode recordings in hippocampal area CA1 and PFC reveal that REM cortical HFOs occur in characteristic chains that are phase-modulated by theta oscillations, corresponding to increased CA1-PFC theta coherence and delineating periods of enhanced hippocampal–cortical communication. REM HFO chains sequentially organize sparse PFC ensemble reactivation of behavioral activity during periods of local suppression, distinct from widespread reactivation bursts during NREM ripple oscillations. REM HFO chains also preferentially engage CA1 neuronal populations that demonstrate a shift in their preferred theta-phase from behavior to REM sleep. CA1 neuronal activation during REM HFO chains was correlated with CA1 activity suppression during NREM PFC ripples, and linked to differential changes in CA1 firing rates in sleep, suggesting REM-driven regulation of hippocampal excitability. A cortical network model incorporating the effects of acetylcholine can reproduce the distinct REM and NREM activity patterns, providing a mechanistic basis for widespread coactivity during NREM cortical ripples, compared to sparse, temporally extended reactivation on a background of local suppression during REM HFO chains. Overall, these findings establish a role for PFC HFOs in regulating distinct dual sleep stage reactivation patterns.

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 34 matches between paragraphs and lines of code.

JadhavLab/REMHFOs

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 3b4245accfa27af9ca9be6bf2538791fd948ab96, 7 September 2026
Languages: MATLAB (39)
Size: 41 files, 39 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (35 files), boundedline (13 files), Signal Processing Toolbox (4 files), CircStat (3 files), shadedErrorBar (3 files), Image Processing Toolbox (2 files), Chronux (1 file), EEGLAB (1 file), FieldTrip (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
41 files
At the source: github.com/JadhavLab/REMHFOs

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;
  • 39 scripts, each with its path and the digest of its content;
  • 34 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.

Data availability

Data underlying these results are available in the NWB (Neurodata Without Borders) format on DANDI Archive (ID#000978). Code to replicate these results are available on our lab GitHub (https://github.com/JadhavLab/REMHFOs; copy archived at Shin, 2026).

The following previously published dataset was used:

Shin J, Jadhav S. 2024. Single Day W-Track Learning. DANDI Archive.

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 1 keyword, 10 MeSH terms, 1 funder, 120 references, 4 RRIDs.

Cite

This paper

Shin, J. D., Satchell, M., Miller, P., & Jadhav, S. P. (2026). REM sleep prefrontal high-frequency oscillation chains mediate distinct cortical - hippocampal reactivation patterns compared to NREM sleep. eLife, 15, RP110795. https://doi.org/10.7554/elife.110795

BibTeX

@article{shin2026rem,
author = {Shin, Justin D and Satchell, Michael and Miller, Paul and Jadhav, Shantanu P},
title = {{REM sleep prefrontal high-frequency oscillation chains mediate distinct cortical - hippocampal reactivation patterns compared to NREM sleep}},
journal = {eLife},
year = {2026},
month = sep,
volume = {15},
pages = {RP110795},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.110795},
url = {https://doi.org/10.7554/elife.110795},
pmid = {42704658},
pmcid = {PMC13549623}
}

RIS

TY - JOUR
AU - Shin, Justin D
AU - Satchell, Michael
AU - Miller, Paul
AU - Jadhav, Shantanu P
TI - REM sleep prefrontal high-frequency oscillation chains mediate distinct cortical - hippocampal reactivation patterns compared to NREM sleep
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/09/07
VL - 15
SP - RP110795
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.110795
UR - https://doi.org/10.7554/elife.110795
LA - en
ER -

CSL-JSON

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

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