OSCR

Human forebrain neural synchronization and entrainment to breathing during wakefulness, sleep, and external mechanical ventilation.

Code ↔ Paper

12 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 12 matches · 6 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Across participants, neural oscillations at numerous forebrain sites and regions synchronized with breathing during wakefulness ↔ reqfunc/roiNameUpdate.m, the whole file · a weak match · score 0.99 · superior parietal lobule, inferior frontal, inferior temporal, superior temporal, superior frontal, posterior cingulate
  2. [2] § Results › Across participants, neural oscillations at numerous forebrain sites and regions synchronized with breathing during wakefulness ↔ reqfunc/sortROI.m, the whole file · a weak match · score 0.78 · AngG, ACC, IFG, ITG, MCC, MFG
  3. [3] § Results › External mechanical ventilation drove forebrain entrainment to breathing ↔ reqfunc/roiNameUpdate.m, the whole file · a weak match · score 0.76 · superior frontal, posterior dorsal, middle temporal, supramarginal, fusiform, orbital
  4. [4] § Methods › Statistical analysis ↔ reqfunc/plotLME.m, the whole file · a weak match · score 0.76 · logit transformation, LME model, linear mixed, fitlme, channel, coherence
  5. [5] § Results › External mechanical ventilation using slow, deep breathing parameters increased the number of forebrain sites entrained to breathing ↔ NeuralbreathingScript.m, lines 412–511 · score 0.69 · deep breathing, sided Wilcoxon, template brain, brain sites, Violin, horizontal
  6. [6] § Results › External mechanical ventilation using slow, deep breathing parameters increased the number of forebrain sites entrained to breathing ↔ NeuralbreathingScript.m, lines 412–511 · score 0.64 · tidal volume, deep breathing, brain sites, Wilcoxon, ventilator, coherence
  7. [7] § Methods › Spectral and coherence analysis ↔ reqfunc/plotPSD.m, the whole file · a weak match · score 0.57 · Power spectral density, pwelch, PSD, overlap, windows
  8. [8] § Methods › Signal processing ↔ dbt.m, lines 1–126 · score 0.57 · demodulated band transform, cutoff, downsampled, filtered, lowpass, DBT
  9. [9] § Methods › Statistical analysis ↔ dbtcoh.m, lines 1–51 · score 0.56 · cross spectral, permutation, efficiency, spectrum, correlation, zero
  10. [10] § Methods › Signal processing ↔ dbtvbw.m, lines 1–118 · score 0.56 · demodulated band transform, cutoff, downsampled, filtered, lowpass, DBT
  11. [11] § Methods › Statistical analysis ↔ reqfunc/plotLME.m, the whole file · a weak match · score 0.54 · coefTest, intercept, predicted, model, coefficients, sleep
  12. [12] § Results › Forebrain synchrony with breathing decreased during sleep ↔ NeuralbreathingScript.m, lines 194–336 · score 0.51 · Wilcoxon rank sum, IQR, Median, sleep, awake, coherence

Paper

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The authors' code

MATLAB · 604 lines · 22 KB · MIT · 3 matches

  1. coh_results_Path = '.\saved_results\';
  2. addpath(genpath('.\reqfunc\'));
  3. % load saved coherence results
  4. dataAw = load([coh_results_Path,'CohAwakeBiPolar.mat']);
  5. dataAnes = load([coh_results_Path,'CohAnesBiPolar.mat']);
  6. dataVS = load([coh_results_Path,'CohVentBiPolar.mat']);
  7. dataSLP = load([coh_results_Path,'CohSleepBiPolar.mat']);
  9. %% define colors
  10. color_aw = [0.1 0.7 0];
  11. colorSleep = [0.9 0.4 0.1];
  12. color_sp = [0 0.6 0.8];
  13. color_vs = [0.6 0.12 0.47];
  14. colorS = [color_aw;colorSleep;color_sp;color_vs];
  15. Fs = 500; % sampling rate all blocks except sleep
  16. Fs_slp = 250; % sampling rate for sleep data
  18. % some plotting variables to define
  19. pThr = 0.05;
  20. Msize = 80;
  21. Csize = [50,180];
  22. templateAlpha = 0.09;
  23. Malpha = 0.9;
  24. BrHem = 1;
  25. % find significant indexes
  26. % get significant contacts indexes
  27. awakeIndx = dataAw.significance_v;
  28. anesIndx = dataAnes.significance_v;
  29. ventsIndx = dataVS.significance_v;
  30. sleepIndx = dataSLP.significance_v;
  31. %% Figure 1: Plot coverage and Coherence for each patient
  32. Msize = 35;
  33. Csize = [50,180]; BrainAlpha = 0.3; BrainColor = [1 0.98 0.98];
  34. unqPt = unique(dataAw.ptID);
  35. for j = 1:length(unqPt)
  36. close all;
  37. plotPatientBrain(unqPt(j),BrHem,BrainAlpha,BrainColor,[]); % plot the template brain LH on right, RH on left hemReverse=true
  38. h = gca; h.ZAxis.Visible = 'off'; light('position', [-30000 0 0]);
  39. set(gcf, 'Position', [100, 100, 590, 600]); % for lateral view
  40. AwCord = dataAw.ChnCoordinates(dataAw.ptID==unqPt(j),:); % locIndx_aw from later section to plot only GM contacts
  41. h1=scatter3(AwCord(:,1), AwCord(:,2),AwCord(:,3), Msize.*ones(size(AwCord,1),1),...
  42. MarkerFaceColor = [0.4 0.4 0.4],MarkerFaceAlpha = Malpha,MarkerEdgeColor=[0.1 0.1 0.1]);
  43. axx = gca;
  44. camlight('headlight');
  45. zlim([-60 95]);
  46. view([0 0 90]); % Axial
  47. %view([0 -90 0]); % coronal view
  48. %view([-90 0 0]); % lateral view
  49. tmpVal = dataAw.CohValue(dataAw.ptID==unqPt(j));
  50. tmpcoord = dataAw.ChnCoordinates(dataAw.ptID==unqPt(j),:);
  51. tmpindx = awakeIndx(dataAw.ptID==unqPt(j));
  52. plotPatientscatter(unqPt(j),tmpcoord, tmpindx,tmpVal,BrHem,BrainAlpha,Csize,color_aw,Malpha,BrainColor,false);
  53. camlight('headlight');
  54. h = gca; h.ZAxis.Visible = 'off';
  55. zlim([-60 95]);
  56. view([0 0 90]); % Axial
  57. % plot patient brain surf
  58. plotPatientBrain(unqPt(j),BrHem,0.92,[0.92 0.92 0.92],[],false);
  59. %camlight('headlight','infinite');
  60. delete(findall(gcf,'Type','light'));
  61. light('position', [0 0 900000]); %light('position', [0 -300000 0])
  62. material dull;
  63. h = gca; h.YAxis.Visible = 'off';
  64. fig = gcf; % Get the current figure handle
  65. set(fig, 'Name',num2str(unqPt(j)) , 'NumberTitle', 'off');
  66. fprintf('Patient: %d\n',unqPt(j))
  67. % plot respiration data
  68. tt = (1:length(dataAw.tsData(j).belt))./Fs;
  69. figure('Position',[200 200 750 280],'Color','w');
  70. plot(tt,zscore(dataAw.tsData(j).belt),'color',[0.4,0.4,0.4],'LineWidth',1.6);
  71. xlim([50 30+55]);
  72. ylim([-2.9 2.9]);
  73. yticks([-2,-1,0,1,2]);
  74. set(gca,'FontSize',25);
  75. [Pbb,pF] = plotPSD(dataAw.tsData(j).belt,Fs,50,0,2^19,'n');
  76. [~,mxIn] = max(Pbb);
  77. Resfreq(j) = pF(mxIn);
  78. box off; title(sprintf(['%d: f = %0.2fHz'], unqPt(j),Resfreq(j) ));
  80. h = gca; h.XAxis.Visible = 'off';
  81. end
  82. % Plot sagittal Left and Right
  83. fprintf('Plotting: Lateral views........\n');
  84. Msize = 35; Csize = [50,180];
  85. unqPt = unique(dataAw.ptID);
  86. for j = 1:length(unqPt)
  87. %close all;
  88. tmpVal = dataAw.CohValue(dataAw.ptID==unqPt(j));
  89. tmpcoord = dataAw.ChnCoordinates(dataAw.ptID==unqPt(j),:);
  90. tmpindx = awakeIndx(dataAw.ptID==unqPt(j));
  91. plotPatientSagittal(unqPt(j),tmpcoord, tmpindx,tmpVal,BrainAlpha,Csize,color_aw,Malpha,BrainColor,0);
  92. fprintf('Patient: %d\n',unqPt(j));
  93. end
  95. %% plot coverage during Awake States and Coherence on template Brain
  96. % plot Coverage
  97. hemRev = false; % plot the template brain LH on right, RH on left
  98. plotBrainPatch(BrHem,templateAlpha,[],[], hemRev);
  99. % plot Significant contacts in Awake breathing
  100. awCord = dataAw.ChnCoordinates;
  101. h1=scatter3(awCord(:,1),awCord(:,2),awCord(:,3), Msize.*ones(size(awCord,1),1)./2, [0.3 0.3 0.3],'fill');
  102. h1.MarkerFaceAlpha = Malpha;
  103. axx = gca;
  104. % Create line
  105. view([0 0 90]);
  106. material metal;
  107. camlight(axx, 'headlight');
  108. set(gcf,'Name','Coverage Map: Awake Fig3B, Anesthetized Fig5B, Ventilated Fig6B','NumberTitle', 'off');
  109. %BrainColor = [0.78 0.7 0.7];
  111. %
  112. BrainAlpha = 0.3; BrainColor = [1 0.98 0.98];
  113. plotbrainscatter(dataAw,awakeIndx,dataAw.CohValue,BrHem,BrainAlpha,Csize,color_aw,Malpha,BrainColor)
  114. set(gcf,'Name','Coherence Map: Awake Fig3C','NumberTitle', 'off');
  115. plotbrainscatter(dataAw,awakeIndx,dataAw.CohValue,BrHem,BrainAlpha,Csize,color_aw,Malpha,BrainColor,true)
  116. set(gcf,'Name','Coherence Map: Awake Fig3C','NumberTitle', 'off');
  117. % plot coverage during Sleep States and Coherence on template Brain
  118. plotBrainPatch(BrHem,templateAlpha,[],[]); % plot the template brain LH on right, RH on left
  119. % plot Significant contacts in Awake breathing
  120. slpCord = dataSLP.ChnCoordinates; % locIndx_aw from later section to plot only GM contacts
  121. h1=scatter3(-slpCord(:,1), slpCord(:,2),slpCord(:,3), Msize.*ones(size(slpCord,1),1)./2,[0.3 0.3 0.3],'fill');
  122. h1.MarkerFaceAlpha = Malpha;
  123. axx = gca;
  124. % Create line
  125. view([0 0 90]);
  126. camlight(axx, 'headlight');
  127. set(gcf,'Name','Coverage Map: Sleep Fig4B','NumberTitle', 'off');
  128. % plot coherence
  129. plotbrainscatter(dataSLP,sleepIndx,dataSLP.CohValue,BrHem,BrainAlpha,Csize,colorSleep,Malpha,BrainColor)
  130. set(gcf,'Name','Coherence Map: Sleep Fig4C','NumberTitle', 'off');
  132. % plot Coherence on template Brain: Anesthesia and mechanical ventilation
  133. plotbrainscatter(dataAnes,anesIndx,dataAnes.CohValue,BrHem,BrainAlpha,Csize,color_sp,Malpha,BrainColor);
  134. set(gcf,'Name','Coherence Map: Anesthetized Fig5C','NumberTitle', 'off');
  135. plotbrainscatter(dataAnes,anesIndx,dataAnes.CohValue,BrHem,BrainAlpha,Csize,color_sp,Malpha,BrainColor,true);
  136. set(gcf,'Name','Coherence Map: Anesthetized Fig5C','NumberTitle', 'off');
  137. % mechanical ventilation
  138. plotbrainscatter(dataVS,ventsIndx,dataVS.CohValue,BrHem,BrainAlpha,Csize,color_vs,Malpha,BrainColor);
  139. set(gcf,'Name','Coherence Map: Ventilated Fig6C','NumberTitle', 'off');
  140. plotbrainscatter(dataVS,ventsIndx,dataVS.CohValue,BrHem,BrainAlpha,Csize,color_vs,Malpha,BrainColor,true);
  141. set(gcf,'Name','Coherence Map: Ventilated Fig6C','NumberTitle', 'off');
  142. %% ROIbased scatter plots
  143. roiscatterplot(dataAw.CohValue,dataAw.LocationsKN,color_aw,[0,1]);
  144. set(gcf,'Name','Fig3H Awake Breathing-LFP coherence','NumberTitle', 'off');
  146. roiscatterplot(dataSLP.CohValue,dataSLP.LocationsKN,colorSleep,[0,0.8]);
  147. set(gcf,'Name','Fig4D Sleep Breathing-LFP coherence','NumberTitle', 'off');
  149. roiscatterplot(dataAnes.CohValue,dataAnes.LocationsKN,color_sp,[0,1]);
  150. set(gcf,'Name','Fig5D Anesthetized automatic Breathing-LFP coherence','NumberTitle', 'off');
  152. roiscatterplot(dataVS.CohValue,dataVS.LocationsKN,color_vs,[0,0.8]);
  153. set(gcf,'Name','Fig5D Anesthetized ventilated Breathing-LFP coherence','NumberTitle', 'off');
  155. %% summary stats
  156. dataList = {dataAw, dataSLP, dataAnes, dataVS};
  157. stateNames = {'Awake', 'Sleep', 'Anesthetized','Ventilation'};
  159. for d = 1:length(dataList)
  160. data = dataList{d};
  161. state = stateNames{d};
  163. sigIndx = data.significance_v;
  164. ptIDs = unique(data.ptID);
  166. nSite = zeros(length(ptIDs), 1);
  167. nSigf = zeros(length(ptIDs), 1);
  169. for ii = 1:length(ptIDs)
  170. nSite(ii) = sum(data.ptID == ptIDs(ii));
  171. nSigf(ii) = sum(sigIndx(data.ptID == ptIDs(ii)));
  172. end
  174. totalSites = sum(nSite);
  175. totalSigf = sum(nSigf);
  177. meanSitesPerPt = mean(nSite);
  178. stdSitesPerPt = std(nSite);
  180. meanSigfPerPt = mean(nSigf);
  181. stdSigfPerPt = std(nSigf);
  183. percentSigf = (totalSigf / totalSites) * 100;
  185. % Print for this state
  186. fprintf('\n=== %s ===\n', state);
  187. fprintf('Total sites: %d\n', totalSites);
  188. fprintf('Total significant sites: %d\n', totalSigf);
  189. fprintf('Sites per participant: %.1f ± %.1f\n', meanSitesPerPt, stdSitesPerPt);
  190. fprintf('Significant sites per participant: %.1f ± %.1f\n', meanSigfPerPt, stdSigfPerPt);
  191. fprintf('Overall percentage of significant sites: %.1f%%\n', percentSigf);
  192. end
  194. %% Coherence in each ROI bar plot
  195. awValues = dataAw.CohValue; slpValues = dataSLP.CohValue;
  196. spValues = dataAnes.CohValue; vsValues = dataVS.CohValue;
  198. LocOR = roiNameUpdate(dataAw.LocationsKN);
  199. LocSLP = roiNameUpdate(dataSLP.LocationsKN);
  201. % unique ROIs
  202. unqROI = unique([LocOR;LocSLP]);
  203. Nroi = length(unqROI);
  204. CountsROI = zeros(Nroi, 2); countsS = zeros(Nroi, 4);
  206. vioData = cell(Nroi,4);
  207. for kk = 1:Nroi
  208. indxAW = strcmp(LocOR, unqROI{kk});
  209. indxSLP = strcmp(LocSLP, unqROI{kk});
  210. CountsROI(kk,1) = sum(indxAW);%sum(cell2mat(strfind(Loc, uniqLoc{kk})));
  211. CountsROI(kk,2) = sum(indxSLP);
  212. countsS(kk,1) = sum(strcmp(LocOR(awakeIndx), unqROI{kk}));
  213. countsS(kk,2) = sum(strcmp(LocSLP(sleepIndx), unqROI{kk}));
  214. countsS(kk,3) = sum(strcmp(LocOR(anesIndx), unqROI{kk}));
  215. countsS(kk,4) = sum(strcmp(LocOR(ventsIndx), unqROI{kk}));
  216. vioData{kk,1} = awValues(indxAW)';
  217. vioData{kk,2} = slpValues(indxSLP)';
  218. vioData{kk,3} = spValues(indxAW)';
  219. vioData{kk,4} = vsValues(indxAW)';
  220. end
  221. % remove ROIs less than 5 total contacts
  222. rmvIndx = find(CountsROI(:,1)<=5);
  223. CountsROI(rmvIndx,:)=[];
  224. countsS(rmvIndx,:)=[];
  225. unqROI(rmvIndx)=[];
  226. vioData(rmvIndx,:) = [];
  228. roiInx = sortROI(unqROI);
  230. CountsROI = CountsROI(roiInx,:);
  231. countsS = countsS(roiInx,:);
  232. unqROI = unqROI(roiInx);
  233. vioData = vioData(roiInx,:);
  234. roiPercentage = 100*([countsS(:,1)./CountsROI(:,1),countsS(:,2)./CountsROI(:,2),countsS(:,3:4)./CountsROI(:,1)]);
  235. % find empty cells in vioData
  236. isEmptyCell = cellfun(@isempty, vioData);
  237. [vioData{isEmptyCell}] = deal(NaN); % filling NaN values on empty cells
  239. meanCorr = cell2mat(cellfun(@mean, vioData, 'UniformOutput', false));
  240. stdCorr = cell2mat(cellfun(@std, vioData, 'UniformOutput', false));
  243. % Compare coherence during Awake vs Sleep across participants
  244. AwakeVal = [];
  245. SleepVal = [];
  246. nPt = unique(dataAw.ptID);
  248. for i = 1:length(nPt)
  249. IndxAwake = dataAw.ptID == nPt(i);
  250. IndxSleep = dataSLP.ptID == nPt(i);
  252. % Mean coherence per participant in each state
  253. if ~isnan(mean(dataSLP.CohValue(IndxSleep)))
  254. SleepVal = [SleepVal; mean(dataSLP.CohValue(IndxSleep))];
  255. end
  256. if ~isnan(mean(dataAw.CohValue(IndxAwake)))
  257. AwakeVal = [AwakeVal; mean(dataAw.CohValue(IndxAwake))];
  258. end
  259. end
  261. % Statistical Comparison
  262. % Wilcoxon Rank-Sum test
  263. [p_slp_awake, ~, stats] = ranksum(AwakeVal, SleepVal);
  265. % Rank-biserial correlation effect size
  266. ranksumU = stats.ranksum;
  267. nAw = numel(AwakeVal);
  268. nSlp = numel(SleepVal);
  269. U = stats.ranksum - nAw*(nAw+1)/2; % convert from W to U
  270. rankBiserial = (2*U)/(nAw * nSlp) - 1; % effect size
  271. % Medians and IQRs
  272. AwakeMedian = median(AwakeVal);
  273. AwakeIQR = iqr(AwakeVal);
  274. SleepMedian = median(SleepVal);
  275. SleepIQR = iqr(SleepVal);
  277. fprintf('\n--- Statistical Summary (Awake vs Sleep) ---\n');
  278. fprintf('Wilcoxon rank-sum test: p = %.4f\n', p_slp_awake);
  279. fprintf('Rank-biserial correlation: %.2f\n', rankBiserial);
  280. fprintf('Awake: median = %.3f, IQR = %.3f\n', AwakeMedian, AwakeIQR);
  281. fprintf('Sleep: median = %.3f, IQR = %.3f\n', SleepMedian, SleepIQR);
  283. % Violin Plot
  284. mxLen = max([length(AwakeVal), length(SleepVal)]);
  285. vio_aw = [AwakeVal; NaN(mxLen-length(AwakeVal),1)];
  286. vio_slp = [SleepVal; NaN(mxLen-length(SleepVal),1)];
  287. violinData = [vio_aw, vio_slp];
  289. figure("Position", [200, 100, 400, 350]);
  290. vp = violinplot(violinData, {'Awake', 'Sleep'}, ...
  291. 'ViolinColor', brighten(colorS, 0.4), 'Width', 0.2, 'ShowMean', true);
  293. for ii = 1:length(vp)
  294. mmColor = brighten(colorS(ii,:), -0.6);
  295. vp(ii).MeanPlot.Color = mmColor;
  296. vp(ii).MeanPlot.LineWidth = 2;
  297. vp(ii).MedianColor = [1, 1, 1];
  298. vp(ii).MedianPlot.SizeData = 100;
  299. end
  301. ylim([0 0.45]);
  302. ylabel('Coherence (R)')
  303. box off;
  304. xlim([0.5 2.5]);
  305. set(gcf, 'color', 'w');
  306. axx = gca;
  307. axx.YTick = [0:0.2:1];
  308. set(axx, 'fontSize', 16);
  309. set(gcf,'Name','Awake vs. Sleep: Fig4E');
  310. % compute where to place the text
  311. ymax = max([AwakeVal; SleepVal]);
  312. % display the exact p-value
  313. text(1.5, ymax*1.3, sprintf('p = %.4f', p_slp_awake), ...
  314. 'HorizontalAlignment','center', 'FontSize',14);
  316. % plot awake vs sleep
  317. % unique ROIs
  318. sleepROI = unique([LocOR;LocSLP]);
  319. Nroi = length(sleepROI);
  320. sleepROIcount = zeros(Nroi, 1);
  321. for kk = 1:Nroi
  322. indxSLP = strcmp(LocSLP, sleepROI{kk});
  323. sleepROIcount(kk) = sum(indxSLP);
  324. end
  325. % remove ROIs less than 5 total contacts
  326. rmvIndx = find(sleepROIcount(:,1)<=5);
  327. sleepROIcount(rmvIndx,:)=[];
  328. sleepROI(rmvIndx)=[];
  330. roiInx = sortROI(sleepROI);
  331. sleepROIcount = sleepROIcount(roiInx);
  332. sleepROI = sleepROI(roiInx);
  334. awake_sleep_LME = plotLME(sleepROI,dataAw,dataSLP,awakeIndx,sleepIndx,color_aw,colorSleep,{'Awake','Sleep'});
  335. ylim([0 0.38]);
  336. set(gcf,'Name','Awake vs. Sleep LME: Fig4F');
  338. %% compare states Spont vs vents
  339. spontVal = [];
  340. ventVal = [];
  341. nPt = unique(dataAnes.ptID);
  342. for i = 1:length(nPt)
  343. IndxSpont = dataAnes.ptID ==nPt(i);
  344. IndxVent = dataVS.ptID ==nPt(i);
  345. if ~isnan(mean(dataAnes.CohValue(IndxSpont)))
  346. spontVal = [spontVal;mean(dataAnes.CohValue(IndxSpont))];
  347. end
  348. if ~isnan(mean(dataAw.CohValue(IndxVent)))
  349. ventVal = [ventVal;mean(dataVS.CohValue(IndxVent))];
  350. end
  351. end
  353. % Wilcoxon signed-rank test for paired samples
  354. [p_sp_vs, ~, stats] = signrank(spontVal, ventVal);
  356. % Rank-biserial correlation (paired samples)
  357. % r = (number of positive - number of negative differences) / total pairs
  358. diffSigns = sign(spontVal - ventVal);
  359. rankBiserial = sum(diffSigns) / numel(diffSigns);
  361. % Medians and IQRs
  362. SpontMedian = median(spontVal);
  363. SpontIQR = iqr(spontVal);
  364. VentMedian = median(ventVal);
  365. VentIQR = iqr(ventVal);
  367. fprintf('\n--- Statistical Summary (Spontaneous vs Ventilation, Paired) ---\n');
  368. fprintf('Wilcoxon signed-rank test: p = %.4f\n', p_sp_vs);
  369. fprintf('Rank-biserial correlation: %.2f\n', rankBiserial);
  370. fprintf('Spontaneous: median = %.3f, IQR = %.3f\n', SpontMedian, SpontIQR);
  371. fprintf('Ventilation: median = %.3f, IQR = %.3f\n', VentMedian, VentIQR);
  373. mxLen = max([length(spontVal),length(ventVal)]);
  374. vio_sp = [spontVal;NaN(mxLen-length(spontVal),1)];
  375. vio_vs = [ventVal;NaN(mxLen-length(ventVal),1)];
  377. %violinData = [vio_aw,vio_slp, vio_sp,vio_vs];
  378. violinData = [vio_sp, vio_vs];
  380. figure("Position",[200,100,700,550]);
  381. vp = violinplot(violinData,{'Anesthetized','Ventilated'},...
  382. 'ViolinColor',brighten(colorS(3:4,:),0.4),'Width',0.2,'ShowMean',true); %'ShowBox',false,'ShowMedian',false);
  383. for ii = 1:length(vp)
  384. mmColor = brighten(colorS(2+ii,:),-0.6);
  385. vp(ii).MeanPlot.Color = mmColor;
  386. vp(ii).MeanPlot.LineWidth = 2;
  387. vp(ii).MedianColor = [1,1,1];
  388. vp(ii).MedianPlot.SizeData = 100;
  389. end
  391. ylim([0 0.45]);
  392. ylabel('Coherence (R)')
  393. box off;
  394. xlim([0.5 2.5]);
  395. set(gcf, 'color', 'w');
  396. axx = gca;
  397. axx.YTick = [0:0.2:1];
  398. set(axx, 'fontSize', 16);
  400. % hh = sigstar({[1 2]}, p_sp_vs, 0);
  401. % set(hh(:,2), 'FontSize', 20);
  402. % compute where to place the text
  403. ymax = max([spontVal; ventVal]);
  404. % display the exact p-value
  405. text(1.5, ymax*1.3, sprintf('p = %.3f', p_sp_vs), ...
  406. 'HorizontalAlignment','center', 'FontSize',14);
  407. % plot spont vs vents
  408. lme_anes_vent = plotLME(unqROI,dataAnes,dataVS,anesIndx,ventsIndx,color_sp,color_vs,{'Anesthetized','Ventilated'});
  409. ylim([0 0.38]);
  410. set(gcf,'Name','Anethetized vs. Ventilated LME: Fig6E');
  412. %% slow-deep breathing
  413. clear
  414. addpath(genpath('.\reqfunc\'));
  415. roundM = @(x,m) round(x*10^m)./10^m;
  416. dataVHi = load('.\saved_results\CohVentHi.mat'); % load saved results for high-tidal volume ventilation
  417. dataVS = load('.\saved_results\CohVent_matchedHiTV.mat'); % load saved results for mathed ventilation (5 Part)
  418. % define colors
  419. color_vs = [0.6 0.12 0.47];
  420. color_vHi = [0 0.5 0.3]; %[0.2 0.9 0.8];
  421. colorS = [color_vs;color_vHi];
  422. Fs = 500;
  423. % some used variables to define
  424. pThr = 0.05;
  425. Msize = 70;
  426. Csize = [50,180];
  427. templateAlpha = 0.09;
  428. BrainAlpha = 0.3;
  429. Malpha = 0.9;
  430. BrHem = 1;
  431. % significant contact indexes
  432. ventsIndx = dataVS.significance_v;
  433. ventHiIndx = dataVHi.significance_v;
  434. %plot coverage during Awake States and Correlations on template Brain
  435. % plot Coverage
  436. plotBrainPatch(BrHem,templateAlpha,[],[],true); % plot the template brain LH on right, RH on left
  437. % plot Significant contacts in Awake breathing
  438. vHiCord = dataVHi.ChnCoordinates; % Brain sites MNI coordinates
  439. h1=scatter3(-vHiCord(:,1),vHiCord(:,2),vHiCord(:,3),Msize.*ones(size(vHiCord,1),1)./2,...
  440. MarkerFaceColor = [0.4 0.4 0.4],MarkerFaceAlpha = Malpha,MarkerEdgeColor=[0.1 0.1 0.1]);
  441. axx = gca;
  442. % Create line
  443. view([0 0 90]);
  444. camlight(axx, 'headlight');
  445. set(gcf,'Name','Coverage Map: Slow-deep Fig7A','NumberTitle', 'off');
  447. % plot coherence on template brain
  448. dataVS.ChnCoordinates = dataVHi.ChnCoordinates;
  449. ventsIndx(dataVS.CohValue< 0.1)=0;
  450. BrainColor = [1 0.98 0.98];
  452. plotbrainscatter(dataVS,ventsIndx,dataVS.CohValue,BrHem,BrainAlpha,Csize,color_vs,Malpha,BrainColor);
  453. set(gcf,'Name','Coherence: Standard Fig7B','NumberTitle', 'off');
  455. plotbrainscatter(dataVHi,ventHiIndx,dataVHi.CohValue,BrHem,BrainAlpha,Csize,color_vHi,Malpha,BrainColor)
  456. set(gcf,'Name','Coherence: Slow-deep Fig7C','NumberTitle', 'off');
  457. %
  458. conds = {"VentS","VentHi"};
  459. % Common participants present in both conditions
  460. commonPt = sort(intersect(unique(dataVS.ptID(:)), unique(dataVHi.ptID(:))));
  462. ratioVS = nan(numel(commonPt),1);
  463. ratioVHi = nan(numel(commonPt),1);
  465. for i = 1:numel(commonPt)
  466. p = commonPt(i);
  467. ratioVS(i) = mean(logical(dataVS.significance_v(dataVS.ptID==p)));
  468. ratioVHi(i) = mean(logical(dataVHi.significance_v(dataVHi.ptID==p)));
  469. end
  471. % Print per-participant ratios
  472. fprintf('Per-participant proportion of significant contacts (all sites):\n');
  473. for i = 1:numel(commonPt)
  474. fprintf(' %d: VentS=%.3f, VentHi=%.3f\n', commonPt(i), ratioVS(i), ratioVHi(i));
  475. end
  477. % One-sided paired Wilcoxon: VentHi > VentS
  478. [p_one,~,~] = signrank(ratioVS, ratioVHi, 'tail','left'); % tests median(VentS - VentHi) < 0
  479. fprintf('One-sided Wilcoxon (VentHi > VentS): N=%d, p=%.4g\n', numel(commonPt), p_one);
  482. violinData = [ratioVS, ratioVHi];
  484. figure("Position",[200,100,400,450]);
  485. vp = violinplot(violinData,{'Standard','Slow-deep'},...
  486. 'ViolinColor',brighten(colorS(1:2,:),0.5),'Width',0.2,'ShowMean',true); %'ShowBox',false,'ShowMedian',false);
  488. for ii = 1:length(vp)
  489. mmColor = brighten(colorS(ii,:),-0.7);
  490. vp(ii).MeanPlot.Color = mmColor;
  491. vp(ii).MeanPlot.LineWidth = 2;
  492. vp(ii).MedianColor = [1,1,1];
  493. vp(ii).MedianPlot.SizeData = 100;
  494. end
  496. ylim([0 0.8]);
  497. ylabel('Proportion of significant sites')
  498. box off;
  499. xlim([0.5 2.5]);
  500. set(gcf, 'color', 'w');
  501. axx = gca;
  502. axx.YTick = 0:0.2:1;
  503. set(axx, 'fontSize', 16);
  505. % hh = sigstar({[1 2]}, p_one, 0);
  506. % set(hh(:,2), 'FontSize', 20);
  508. % display the exact p-value
  509. ymax = max(violinData(:), [], 'omitnan');
  510. text(1.5, ymax*1.15, sprintf('p = %.3g', p_one), ...
  511. 'HorizontalAlignment','center', 'FontSize',14);
  514. %% summary stats
  515. dataList = {dataVS,dataVHi};
  516. stateNames = {'Ventilation','Slow-deep'};
  518. for d = 1:length(dataList)
  519. data = dataList{d};
  520. state = stateNames{d};
  522. sigIndx = data.significance_v;
  523. ptIDs = unique(data.ptID);
  525. nSite = zeros(length(ptIDs), 1);
  526. nSigf = zeros(length(ptIDs), 1);
  528. for ii = 1:length(ptIDs)
  529. nSite(ii) = sum(data.ptID == ptIDs(ii));
  530. nSigf(ii) = sum(sigIndx(data.ptID == ptIDs(ii)));
  531. end
  533. totalSites = sum(nSite);
  534. totalSigf = sum(nSigf);
  536. meanSitesPerPt = mean(nSite);
  537. stdSitesPerPt = std(nSite);
  539. meanSigfPerPt = mean(nSigf);
  540. stdSigfPerPt = std(nSigf);
  542. percentSigf = (totalSigf / totalSites) * 100;
  544. % Print for this state
  545. fprintf('\n=== %s ===\n', state);
  546. fprintf('Total sites: %d\n', totalSites);
  547. fprintf('Total significant sites: %d\n', totalSigf);
  548. fprintf('Sites per participant: %.1f ± %.1f\n', meanSitesPerPt, stdSitesPerPt);
  549. fprintf('Significant sites per participant: %.1f ± %.1f\n', meanSigfPerPt, stdSigfPerPt);
  550. fprintf('Overall percentage of significant sites: %.1f%%\n', percentSigf);
  551. end
  552. %scatter plot
  553. roiscatter2states(dataVS.CohValue,dataVHi.LocationsKN,dataVHi.CohValue,dataVHi.LocationsKN,colorS);
  554. title('Breathing-LFP coherence in ventilator driven breathing: standard and slow, deep ventilation');
  555. set(gcf,'Name','Coherence: Fig7D','NumberTitle', 'off');
  557. % LME for standard vs slow-deep
  558. vsValues = dataVS.CohValue;
  559. vHiValues = dataVHi.CohValue;
  560. LocVent = roiNameUpdate(dataVHi.LocationsKN);
  562. % unique ROIs
  563. unqROI = unique(LocVent);
  564. Nroi = length(unqROI);
  565. CountsROI = zeros(Nroi, 1); countsS = zeros(Nroi, 2);
  567. vioData = cell(Nroi,2); % select number of columns (columns represent states)
  568. for kk = 1:Nroi
  569. indxL = strcmp(LocVent, unqROI{kk});
  570. CountsROI(kk,1) = sum(indxL);%sum(cell2mat(strfind(Loc, uniqLoc{kk})));
  571. countsS(kk,1) = sum(strcmp(LocVent(ventsIndx), unqROI{kk}));
  572. countsS(kk,2) = sum(strcmp(LocVent(ventHiIndx), unqROI{kk}));
  573. vioData{kk,1} = vsValues(indxL)';
  574. vioData{kk,2} = vHiValues(indxL)';
  575. end
  576. % remove ROIs less than 5 total contacts
  577. rmvIndx = find(CountsROI<=5);
  578. CountsROI(rmvIndx,:)=[];
  579. countsS(rmvIndx,:)=[];
  580. unqROI(rmvIndx)=[];
  581. vioData(rmvIndx,:) = [];
  583. roiInx = sortROI(unqROI);
  585. CountsROI = CountsROI(roiInx,:);
  586. countsS = countsS(roiInx,:);
  587. unqROI = unqROI(roiInx);
  588. vioData = vioData(roiInx,:);
  589. roiPercentage = 100*([countsS(:,1),countsS(:,2)]./CountsROI);
  590. % find empty cells in vioData
  591. isEmptyCell = cellfun(@isempty, vioData);
  592. [vioData{isEmptyCell}] = deal(NaN); % filling NaN values on empty cells
  594. meanCorr = cell2mat(cellfun(@mean, vioData, 'UniformOutput', false));
  595. stdCorr = cell2mat(cellfun(@std, vioData, 'UniformOutput', false));
  598. lme_ventHi = plotLME(unqROI,dataVS,dataVHi,ventsIndx,ventHiIndx,color_vs,color_vHi,{'standard ventilation','slow-deep ventilation'});
  599. ylim([0 0.49]);
  600. yticks([0:0.15:1]);
  601. set(gcf,'Name','Coherence: Fig7F','NumberTitle', 'off');

NeuralbreathingScript.m at commit 5eb6972, under MIT · at the source

Overview

Authors: Md Rakibul Mowla1, Ariane E Rhone1, Sukhbinder Kumar1, Christopher K Kovach1,2, Junjie V Liu3, Aubrey C Chan4,5, Hiroto Kawasaki1, Rashmi N Mueller1,6, Justin D Kuhn7, Ryan T Frede8, Michael A Ciliberto9, Theresa M Czech9, Sreenath Thati Ganganna9, James W Owens9, Ania K Dabrowski9, Brittany N Sprigg9, Mark A Granner3, Kristina Simonyan10,11,12, Kirill V Nourski1,13, Bryan M Krause14
and 7 other authorsMatthew I Banks14, Matthew A Howard III1,3,13, Paul W Davenport15, Kyle T S Pattinson16,17, George B Richerson1,3,13, John A Wemmie1,4,5,13, Brian J Dlouhy1,9,13
ORCID iDs: Md Rakibul Mowla, Ariane E Rhone, Christopher K Kovach, Aubrey C Chan, Hiroto Kawasaki, Rashmi N Mueller, Ryan T Frede, Kristina Simonyan, Kirill V Nourski, Bryan M Krause, Matthew I Banks, George B Richerson, Brian J Dlouhy
17 affiliations
  1. Department of Neurosurgery, University of Iowa, Iowa City, IA USA
  2. Department of Neurosurgery, University of Nebraska Medical Center, Omaha, NE USA
  3. Department of Neurology, University of Iowa, Iowa City, IA USA
  4. Department of Psychiatry, University of Iowa, Iowa City, IA USA
  5. Department of Veterans Affairs Medical Center, Iowa City, IA USA
  6. Department of Anesthesia, University of Iowa, Iowa City, IA USA
  7. Institute for Clinical and Translational Science, University of Iowa, Iowa City, IA USA
  8. Department of Respiratory Care, University of Iowa, Iowa City, IA USA
  9. Department of Pediatrics, University of Iowa, Iowa City, IA USA
  10. Department of Otolaryngology, Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA USA
  11. Department of Neurology, Massachusetts General Hospital, Boston, MA USA
  12. Harvard Medical School, Boston, MA USA
  13. Iowa Neuroscience Institute, University of Iowa, Iowa City, IA USA
  14. Department of Anesthesiology, University of Wisconsin, Madison, WI USA
  15. Department of Physiology, University of Florida, Gainesville, FL USA
  16. Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
  17. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
Institutions: University of Iowa (United States); University of Nebraska Medical Center (United States); Iowa City VA Medical Center (United States); University of Iowa Health Care (United States); Massachusetts Eye and Ear Infirmary (United States); Harvard University (United States); Massachusetts General Hospital (United States); University of Wisconsin–Madison (United States); University of Florida (United States); University of Oxford (United Kingdom); Wellcome Centre for Integrative Neuroimaging (United Kingdom); University of Iowa Hospitals and Clinics (United States)
Journal: Nature communications, volume 17, issue 1, article 6949
Dates: received 30 April 2025; accepted 19 May 2026; published online 28 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-73828-0 · PMID 42209532 · PMCID PMC13388959 · OpenAlex W7162687326
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism)
Methods: Connectivity, Spectral & time-frequency, Statistics, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: Respiration, Neural circuits, Sensory processing
MeSH: Prosencephalon*, Respiration*, Respiration, Artificial*, Sleep*, Wakefulness*, Adult, Female, Humans, Interoception, Male, Young Adult (* major topic)
Topic: Neuroscience of respiration and sleep (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH) (R01MH113325); U.S. Department of Health & Human Services | NIH | National Institute on Drug Abuse (NIDA) (R01DA052953); NIMH NIH HHS (T32 MH019113, R01 MH113325); U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (K08 NS112573-01); U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD) (P50DC01990); NIDCD NIH HHS (P50 DC019900); NINDS NIH HHS (K08 NS112573, R01 NS113764); NIDA NIH HHS (R01 DA052953)
Citations: cited by 1 paper (Europe PMC); 105 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 12 matches between paragraphs and lines of code.

Zenodo 19502572

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source:

rakib05/NeuralBreathing

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 5eb697294ca4adc3f0974daaccc8564f48efdaf7, 10 April 2026
Languages: MATLAB (13)
Size: 17 files, 13 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files), Signal Processing Toolbox (1 file), Violinplot-Matlab (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/rakib05/NeuralBreathing

ckovach/DBT

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 82751b4b360f400f16cc6d91d4b26fdaf07102c4, 2 March 2018
Languages: MATLAB (19)
Size: 21 files, 19 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (5 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
21 files
At the source: github.com/ckovach/DBT

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41467-026-73828-0.

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;
  • 32 scripts, each with its path and the digest of its content;
  • 12 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 statement

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  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41467-026-73828-0.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 27 authors, 3 keywords, 11 MeSH terms, 8 funders, 96 references.

Cite

This paper

Mowla, M. R., Rhone, A. E., Kumar, S., Kovach, C. K., Liu, J. V., Chan, A. C., Kawasaki, H., Mueller, R. N., Kuhn, J. D., Frede, R. T., Ciliberto, M. A., Czech, T. M., Ganganna, S. T., Owens, J. W., Dabrowski, A. K., Sprigg, B. N., Granner, M. A., Simonyan, K., Nourski, K. V., . . . Dlouhy, B. J. (2026). Human forebrain neural synchronization and entrainment to breathing during wakefulness, sleep, and external mechanical ventilation. Nature communications, 17(1), 6949. https://doi.org/10.1038/s41467-026-73828-0

BibTeX

@article{mowla2026human,
author = {Mowla, Md Rakibul and Rhone, Ariane E and Kumar, Sukhbinder and Kovach, Christopher K and Liu, Junjie V and Chan, Aubrey C and Kawasaki, Hiroto and Mueller, Rashmi N and Kuhn, Justin D and Frede, Ryan T and Ciliberto, Michael A and Czech, Theresa M and Ganganna, Sreenath Thati and Owens, James W and Dabrowski, Ania K and Sprigg, Brittany N and Granner, Mark A and Simonyan, Kristina and Nourski, Kirill V and Krause, Bryan M and Banks, Matthew I and Howard, Matthew A and Davenport, Paul W and Pattinson, Kyle T S and Richerson, George B and Wemmie, John A and Dlouhy, Brian J},
title = {{Human forebrain neural synchronization and entrainment to breathing during wakefulness, sleep, and external mechanical ventilation}},
journal = {Nature communications},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {6949},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-73828-0},
url = {https://doi.org/10.1038/s41467-026-73828-0},
pmid = {42209532},
pmcid = {PMC13388959}
}

RIS

TY - JOUR
AU - Mowla, Md Rakibul
AU - Rhone, Ariane E
AU - Kumar, Sukhbinder
AU - Kovach, Christopher K
AU - Liu, Junjie V
AU - Chan, Aubrey C
AU - Kawasaki, Hiroto
AU - Mueller, Rashmi N
AU - Kuhn, Justin D
AU - Frede, Ryan T
AU - Ciliberto, Michael A
AU - Czech, Theresa M
AU - Ganganna, Sreenath Thati
AU - Owens, James W
AU - Dabrowski, Ania K
AU - Sprigg, Brittany N
AU - Granner, Mark A
AU - Simonyan, Kristina
AU - Nourski, Kirill V
AU - Krause, Bryan M
AU - Banks, Matthew I
AU - Howard, Matthew A
AU - Davenport, Paul W
AU - Pattinson, Kyle T S
AU - Richerson, George B
AU - Wemmie, John A
AU - Dlouhy, Brian J
TI - Human forebrain neural synchronization and entrainment to breathing during wakefulness, sleep, and external mechanical ventilation
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/05/28
VL - 17
IS - 1
SP - 6949
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-73828-0
UR - https://doi.org/10.1038/s41467-026-73828-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-73828-0",
"type": "article-journal",
"title": "Human forebrain neural synchronization and entrainment to breathing during wakefulness, sleep, and external mechanical ventilation",
"container-title": "Nature communications",
"author": [
{
"family": "Mowla",
"given": "Md Rakibul"
},
{
"family": "Rhone",
"given": "Ariane E"
},
{
"family": "Kumar",
"given": "Sukhbinder"
},
{
"family": "Kovach",
"given": "Christopher K"
},
{
"family": "Liu",
"given": "Junjie V"
},
{
"family": "Chan",
"given": "Aubrey C"
},
{
"family": "Kawasaki",
"given": "Hiroto"
},
{
"family": "Mueller",
"given": "Rashmi N"
},
{
"family": "Kuhn",
"given": "Justin D"
},
{
"family": "Frede",
"given": "Ryan T"
},
{
"family": "Ciliberto",
"given": "Michael A"
},
{
"family": "Czech",
"given": "Theresa M"
},
{
"family": "Ganganna",
"given": "Sreenath Thati"
},
{
"family": "Owens",
"given": "James W"
},
{
"family": "Dabrowski",
"given": "Ania K"
},
{
"family": "Sprigg",
"given": "Brittany N"
},
{
"family": "Granner",
"given": "Mark A"
},
{
"family": "Simonyan",
"given": "Kristina"
},
{
"family": "Nourski",
"given": "Kirill V"
},
{
"family": "Krause",
"given": "Bryan M"
},
{
"family": "Banks",
"given": "Matthew I"
},
{
"family": "Howard",
"given": "Matthew A"
},
{
"family": "Davenport",
"given": "Paul W"
},
{
"family": "Pattinson",
"given": "Kyle T S"
},
{
"family": "Richerson",
"given": "George B"
},
{
"family": "Wemmie",
"given": "John A"
},
{
"family": "Dlouhy",
"given": "Brian J"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "6949",
"DOI": "10.1038/s41467-026-73828-0",
"PMID": "42209532",
"PMCID": "PMC13388959",
"ISSN": "2041-1723",
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"URL": "https://doi.org/10.1038/s41467-026-73828-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
28
]
]
}
}

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[9] doi:10.1126/sciadv.aee1002 [code]
Theta oscillations are an organizational unit of odor processing in the olfactory bulb.
Journal: Science advances
In common: Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 3 references
[10] doi:10.1371/journal.pcbi.1014672 [code]
Robust circular cluster-based statistics for respiration-brain coupling.
Journal: PLoS computational biology
In common: Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 3 references

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