OSCR

Metric validation for detection of delayed and directed coupling.

Code ↔ Paper

7 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 7 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Network reconstruction metrics ↔ idtxl/__init__.py, lines 1–21 · score 0.75 · Information Dynamics Toolkit, IDTxl, multivariate transfer entropy, bivariate
  2. [2] § Methods › Network simulations ↔ gen_model.m, the whole file · a weak match · score 0.65 · coupling strength, coupling matrix, measurement noise, model, delays, connectivity
  3. [3] § Results › Measurement noise ↔ paper_figures.m, lines 553–627 · score 0.61 · Dunn Sidak, zero lag mutual, Granger causality, transfer entropy, Kruskal, mutual information
  4. [4] § Results › Network coverage ↔ paper_figures.m, lines 744–815 · score 0.55 · Dunn Sidak, Granger causality, zero lag, coverage, transfer entropy, Kruskal
  5. [5] § Methods › Network simulations ↔ paper_figures.m, lines 85–125 · score 0.53 · uncoupled nodes, amplitude, Channel, coupling
  6. [6] § Methods › Network simulations ↔ gen_model.m, the whole file · a weak match · score 0.53 · coupling strength, vector, dynamics, neural, models, delays
  7. [7] § Results › Number of nodes ↔ paper_figures.m, lines 553–627 · score 0.53 · Dunn Sidak, Granger causality, zero lag, transfer entropy, Kruskal, mutual information

Paper

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The authors' code

MATLAB · 815 lines · 25 KB · no license · 4 matches

  1. %% ========================================================================
  3. % Name: paper_figures.m
  5. % Author: Kate Dembny
  6. % Date: 1/20/23
  7. % Updated: 6/16/23
  9. % Syntax:
  10. % Arguments:
  12. % Description: figures for paper
  13. % Requirements: matlab
  14. % Notes:
  16. %% ========================================================================
  18. clearvars
  20. % DIRECTORIES
  22. scr_dir = pwd;
  23. pdir = fileparts(cd);
  24. data_dir = [pdir filesep() 'data'];
  25. fig_dir = [pdir filesep() 'figures'];
  27. addpath("/home/kdembny/MATLAB/toolboxes/violinplot/Violinplot-Matlab-master")
  28. addpath("/home/kdembny/MATLAB/toolboxes/sigstar")
  29. addpath('/home/kdembny/MATLAB/toolboxes/cprintf/cprintf/')
  31. %% ========================================================================
  33. % Figure 1 - appearance of data
  34. cwd = [data_dir filesep() 'nodes' filesep() '10' filesep() '004' ];
  36. load([cwd filesep() 'orig' filesep() 'orig_data.mat'])
  38. % establish figure
  39. f = figure;
  40. f.Position = [100 100 1200 700];
  41. set(gcf,'renderer','Painters')
  43. % third tile - adjacency matrix - 1/0 connection
  44. subplot(2,3,1)
  45. imagesc(tcoup)
  46. colormap(flipud(bone))
  47. xlabel("Reciever Node")
  48. ylabel("Source Node")
  49. title('True Network Connections')
  50. yticks(1:10)
  51. yticklabels(1:10)
  52. xticks(1:10)
  53. xticklabels(1:10)
  54. text(-1.2,0, ['A)'], 'FontSize', 20)
  56. % second tile - adjacency matrix - node to node
  57. subplot(2,3,4)
  58. dg = digraph(tcoup);
  59. plot(dg, 'Layout', 'force', 'EdgeColor', "k", 'NodeColor', 'k')
  60. ax = gca;
  61. ax.FontSize=16;
  62. text(-3.5,3, ['B)'], 'FontSize', 20)
  64. % first set of tiles - timeseries data
  65. subplot(2,3,[2:3, 5:6])
  66. hold on
  67. for i = 1:size(ts, 1)
  68. plot(ts(i,1:1000) - 0.25*i, 'Color', "k")
  69. end
  70. ax = gca;
  71. ax.FontSize=16;
  73. ylim([-0.25*(i+1),0])
  74. yticks(-0.25*(i):0.25:-0.25)
  75. yticklabels(size(ts,1):-1:1)
  76. ylabel('Node Number')
  77. xlabel('Time (samples)')
  78. ax = gca;
  79. ax.FontSize=16;
  80. text(-70,.07, ['C)'], 'FontSize', 20)
  82. saveas(gcf, [fig_dir filesep() '01_methods.jpg'])
  83. exportgraphics(gcf, [fig_dir filesep() '01_methods.eps'], 'ContentType', 'vector')
  85. %% ========================================================================
  87. % Figure 2 - Coupled vs uncoupled channels
  89. % assign colors
  90. colors = [136 34 85; 102 17 0; 17 119 51; 68 170 153; 102 153 204; 51 34 136; 204 102 119; 170 68 153; 153 153 51; 148 148 148]/255;
  92. % establish figure
  93. f = figure;
  94. f.Position = [100 100 1600 1000];
  95. set(gcf,'renderer','Painters')
  97. lw = 2;
  98. nd_end = 100;
  100. subplot(211)
  101. hold on
  102. plot(ts(1,1:nd_end), 'LineWidth', lw, 'Color', [colors(3,:)])
  103. plot(ts(6,1:nd_end), 'LineWidth', lw, 'Color', [colors(7,:)])
  104. legend(["Node 1", "Node 6"])
  105. xlabel('Time (samples)')
  106. ylabel('Amplitude')
  107. ylim([-0.12, 0.12])
  108. title('Coupled Nodes')
  109. ax = gca;
  110. ax.FontSize=16;
  112. subplot(212)
  113. hold on
  114. plot(ts(1,1:nd_end), 'Linewidth',lw, 'Color', [colors(3,:)])
  115. plot(ts(7,1:nd_end), 'Linewidth',lw, 'Color', [colors(5,:)])
  116. legend(["Node 1", "Node 7"])
  117. xlabel('Time (samples)')
  118. ylim([-0.12, 0.12])
  119. ylabel('Amplitude')
  120. title('Uncoupled Nodes')
  121. ax = gca;
  122. ax.FontSize=16;
  124. saveas(gcf, [fig_dir filesep() '02.coupled_noncoupled.jpg'])
  125. exportgraphics(gcf, [fig_dir filesep() '02.coupled_noncoupled.eps'], 'ContentType', 'vector')
  127. %% ========================================================================
  129. % Figure 3 - Network Reconstructions
  131. % load data
  132. pt_file = [data_dir filesep() 'nodes' filesep() '10' filesep() '004'];
  133. load([pt_file filesep() 'orig' filesep() 'orig_data.mat'])
  134. load([pt_file filesep() 'ml_fc' filesep() 'xc_biv.mat'])
  135. load([pt_file filesep() 'ml_fc' filesep() 'xc_pt.mat'])
  136. load([pt_file filesep() 'ml_fc' filesep() 'gc.mat'])
  137. load([pt_file filesep() 'bvte' filesep() 'te_bivar.mat'])
  138. load([pt_file filesep() 'mvte' filesep() 'te_multivar.mat'])
  139. load([pt_file filesep() 'mi_lagged' filesep() 'mi_lagged.mat'])
  140. load([pt_file filesep() 'mi_zerolag' filesep() 'mi_zerolag.mat'])
  141. load([pt_file filesep() 'ml_fc' filesep() 'xc_zerolag_biv.mat'])
  142. load([pt_file filesep() 'ml_fc' filesep() 'xc_zerolag_pt.mat'])
  143. calc_mi = calc_mi_lag;
  144. load([pt_file filesep() 'shuff' filesep() 'shuff.mat'])
  146. % plot
  147. labs = {{"True Network Connections"},{"Biv. Cross-Corr"}, {"Pt. Cross-Corr"}, {"Mv. Granger Causality"}, ...
  148. {"Mutual Information"}, {"Biv. Transfer Entropy"},{"Mv. Transfer Entropy"},...
  149. {"Zero-Lag Biv.","Cross-Corr"}, {"Zero-Lag Pt.","Cross-Corr"}, {"Zero-Lag Mutual","Information"}, {"Shuffled"}};
  151. arrs = zeros(size(labs,2), size(gc,1), size(gc,2));
  152. arrs(1,:,:) = tcoup;
  154. arrs(8,:,:) = xc_zerolag_biv;
  155. arrs(9,:,:) = xc_zerolag_partial;
  157. arrs(10,:,:) = calc_mi_zerolag;
  158. arrs(6,:,:) = te_bivar;
  160. arrs(2,:,:) = xc_biv(:,:,1);
  161. arrs(3,:,:) = xc_partial(:,:,1);
  162. arrs(4,:,:) = gc;
  163. arrs(5,:,:) = calc_mi;
  165. arrs(7,:,:) = te_multivar;
  166. arrs(11,:,:) = shuff;
  168. num_cols = round((size(arrs,1))/2) + 1;
  169. figLabs = 'A':'K';
  171. f = figure;
  172. clf
  173. f.Position = [100 100 2000 500];
  174. set(gcf,'renderer','Painters')
  176. subplot(2,num_cols,[1,2,num_cols + 1,num_cols + 2])
  177. imagesc(squeeze(arrs(1,:,:)))
  178. title(labs{1})
  179. axis square
  180. ylabel("Source Node")
  181. xlabel("Receiver Node")
  182. c = colorbar();
  183. c.Ticks = [0, 1];
  184. c.TickLabels = ["2%", "98%"];
  186. text(-1,.7,[figLabs(1) ')'], 'FontSize', 20)
  188. for i = 2:size(arrs,1)
  190. if i < num_cols
  191. loc = i+1;
  192. else
  193. loc = i+3;
  194. end
  196. subplot(2,num_cols,loc)
  197. imagesc(squeeze(arrs(i,:,:)))
  198. title(labs{i})
  199. axis square
  201. pct2 = prctile(arrs(i,:,:), 2, 'all');
  202. pct98 = prctile(arrs(i,:,:), 98, 'all');
  203. if i<11
  204. clim([pct2 pct98])
  205. else
  206. clim([0 1])
  207. end
  209. text(-2.2,0.7,[figLabs(i) ')'], 'FontSize', 20)
  211. end
  213. saveas(gcf, [fig_dir filesep() '03.samp_recons.jpg'])
  214. exportgraphics(gcf, [fig_dir filesep() '03.samp_recons.eps'], 'ContentType', 'vector')
  216. %% ========================================================================
  218. % Figure 4 - number of nodes by metric
  220. cwd = [data_dir filesep() 'agg'];
  221. load([cwd filesep() 'nodes_all_dist.mat'])
  222. nodes_alpha = 0.05/15;
  224. makeFig_byMetric(nodes_cos_dist, nodes_opts, nodes_alpha, 'Number of Nodes', 'Cosine Distance')
  225. saveas(gcf, [fig_dir filesep() '04.1_nodes_by_metrics.jpg'])
  226. exportgraphics(gcf, [fig_dir filesep() '04.1_nodes_by_metrics.eps'], 'ContentType', 'vector')
  227. %
  228. % Alt Figure 4 - number of nodes by bin of nodes
  229. makeFig_byParam(nodes_cos_dist, nodes_opts, nodes_alpha, 'Metrics', 'Cosine Distance', 'Nodes')
  230. saveas(gcf, [fig_dir filesep() '04.2_methods_by_nodes.jpg'])
  231. exportgraphics(gcf, [fig_dir filesep() '04.2_methods_by_nodes.eps'], 'ContentType', 'vector')
  233. %%
  234. % STATS
  236. clc
  237. cprintf('red', 'Nodes by Cosine Distance \n')
  238. doStats(nodes_cos_dist, nodes_opts, nodes_alpha, 'Nodes')
  240. %% ========================================================================
  242. % Figure 5 - number of time points by metric
  244. cwd = [data_dir filesep() 'agg'];
  245. load([cwd filesep() 'tps_all_dist.mat'])
  246. tps_alpha = 0.05/14;
  248. makeFig_byMetric(tps_cos_dist,tps_opts, tps_alpha, 'Number of Time Points', 'Cosine Distance')
  249. saveas(gcf, [fig_dir filesep() '05.1_tps_by_methods.jpg'])
  250. exportgraphics(gcf, [fig_dir filesep() '05.1_tps_by_methods.eps'], 'ContentType', 'vector')
  252. % Alt Figure 5 - number of time points by time points bin
  253. makeFig_byParam(tps_cos_dist, tps_opts, tps_alpha, 'Metrics', 'Cosine Distance', 'Time Points')
  254. saveas(gcf, [fig_dir filesep() '05.2_methods_by_tps.jpg'])
  255. exportgraphics(gcf, [fig_dir filesep() '05.2_methods_by_tps.eps'], 'ContentType', 'vector')
  257. %% STATS
  259. clc
  260. cprintf('red', 'Time Points by Cosine Distance \n')
  261. doStats(tps_cos_dist, tps_opts, tps_alpha, 'Time Points')
  263. %% ========================================================================
  265. % Figure 6 - noise by metric
  267. cwd = [data_dir filesep() 'agg'];
  268. load([cwd filesep() 'noise_all_dist.mat'])
  269. noise_alpha = 0.05/18;
  271. makeFig_byMetric(noise_cos_dist,noise_opts, noise_alpha, 'SNR', 'Cosine Distance')
  272. saveas(gcf, [fig_dir filesep() '06.1_noise_by_methods.jpg'])
  273. exportgraphics(gcf, [fig_dir filesep() '06.1_noise_by_methods.eps'], 'ContentType', 'vector')
  275. % Alt Figure 6 - noise by SNR bin
  276. makeFig_byParam(noise_cos_dist, noise_opts, noise_alpha, 'Metrics', 'Cosine Distance', 'SNR')
  277. saveas(gcf, [fig_dir filesep() '06.2_methods_by_noise.jpg'])
  278. exportgraphics(gcf, [fig_dir filesep() '06.2_methods_by_noise.eps'], 'ContentType', 'vector')
  280. %% STATS
  282. clc
  283. cprintf('red', 'Noise by Cosine Distance \n')
  284. doStats(noise_cos_dist, noise_opts, noise_alpha, 'SNR')
  286. %% ========================================================================
  288. % Figure 7 - nodes node dropping
  290. cwd = [data_dir filesep() 'agg'];
  292. load([cwd filesep() 'nodes_all_dist.mat'])
  293. ntwkCov_alpha = 0.05/20;
  295. pcts = ["100%","90%","80%","70%","60%","50%","40%","30%","20%","10%"];
  296. makeFig_nodeDrop(nodes_cos_dist, pcts, ntwkCov_alpha, '% of Network Covered', 'Cosine Distance')
  297. saveas(gcf, [fig_dir filesep() '07.1_node_dropping.jpg'])
  298. exportgraphics(gcf, [fig_dir filesep() '07.1_node_dropping.eps'], 'ContentType', 'vector')
  300. pcts = ["100%","90%","80%","70%","60%","50%","40%","30%","20%","10%"];
  301. makeFig_nodeDrop_param(nodes_cos_dist, pcts, ntwkCov_alpha, 'Metrics', 'Cosine Distance')
  302. saveas(gcf, [fig_dir filesep() '07.2_node_dropping_params.jpg'])
  303. exportgraphics(gcf, [fig_dir filesep() '07.2_node_dropping_params.eps'], 'ContentType', 'vector')
  305. %%
  306. cwd = [data_dir filesep() 'roc'];
  307. load([cwd filesep() 'nodes_roc.mat'])
  309. makeFig_nodeDrop(nodes_roc_aucs(:,:,1:9,:), pcts(1:9), ntwkCov_alpha, '% of Network Covered', 'AUC of ROC')
  310. saveas(gcf, [fig_dir filesep() '07.2_roc_node_dropping.jpg'])
  311. exportgraphics(gcf, [fig_dir filesep() '07.2_roc_node_dropping.eps'], 'ContentType', 'vector')
  313. %% compare methods within metric
  314. for i = 1:10
  315. [p, tbl, stats] = kruskalwallis(squeeze(nodes_cos_dist(4,:,:,i))); %, pcts);
  316. disp(labels(i))
  318. if p < alpha
  319. results = multcompare(stats);
  320. tbl2 = array2table(results,"VariableNames", ...
  321. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  322. close all
  323. disp(tbl2)
  324. else
  325. disp('no significant differences')
  326. end
  327. disp(' ')
  328. end
  330. %% compare methods by percent of nodes dropped
  331. for i = 1:10
  332. [p, tbl, stats] = kruskalwallis(squeeze(nodes_cos_dist(4,:,i,:))); %, pcts);
  333. disp([num2str(100 - (i-1)*10) ' pct covered'])
  335. if p < alpha
  336. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', ntwkCov_alpha);
  337. tbl2 = array2table(results,"VariableNames", ...
  338. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  339. close all
  340. disp(tbl2)
  341. else
  342. disp('no significant differences')
  343. end
  344. disp(' ')
  345. end
  347. %% compare for roc - by metric
  348. for i = 1:10
  349. [p, tbl, stats] = kruskalwallis(squeeze(nodes_roc_aucs(4,:,:,i))); %, pcts);
  350. disp(labels(i))
  351. disp([num2str(100 - (i-1)*10) ' pct covered'])
  353. if p < alpha
  354. results = multcompare(stats);
  355. tbl2 = array2table(results,"VariableNames", ...
  356. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  357. close all
  358. disp(tbl2)
  359. else
  360. disp('no significant differences')
  361. end
  362. disp(' ')
  363. end
  365. %% compare for roc - by percent covered
  366. for i = 1:10
  367. [p, tbl, stats] = kruskalwallis(squeeze(nodes_roc_aucs(4,:,i,:))); %, pcts);
  368. disp([num2str(100 - (i-1)*10) ' pct covered'])
  370. if p < alpha
  371. results = multcompare(stats);
  372. tbl2 = array2table(results,"VariableNames", ...
  373. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  374. close all
  375. disp(tbl2)
  376. else
  377. disp('no significant differences')
  378. end
  379. disp(' ')
  380. end
  382. %% compare methods by node dropping - variance
  384. for i = 1:10
  385. [p, stats] = vartestn(squeeze(nodes_cos_dist(4,:,:,i)), 'TestType', 'LeveneQuadratic'); %, pcts);
  386. close all
  387. disp(labels(i))
  388. disp(p)
  389. disp(stats)
  390. disp(' ')
  391. end
  393. %% ========================================================================
  395. % Figure 8 - runtimes
  397. cwd = [data_dir filesep() 'agg'];
  398. load([cwd filesep() 'nodes_all_rts.mat'])
  399. labels = ["Biv. Cross-Corr", "Pt. Cross-Corr", "Mv. Granger Causality", "Mutual Information",...
  400. "Biv. Transfer Entropy", "Mv. Transfer Entropy", "Zero-Lag Biv. Cross-Corr", ...
  401. "Zero-Lag Pt. Cross-Corr", "Zero-Lag Mutual Information" ];
  403. colors = [136 34 85; 102 17 0; 17 119 51; 68 170 153; 102 153 204; 51 34 136; 204 102 119; 170 68 153; 153 153 51; 148 148 148]/255;
  405. f = figure;
  406. f.Position = [100 100 1500 400];
  408. for j = 1:size(nodes_opts,2)
  409. subplot(1,5,j)
  410. for i = 1:6%size(nodes_runtimes,4)
  411. vp = Violin({squeeze(nodes_runtimes(j,:,1,i))'}, i, 'MarkerSize', 10, 'ViolinColor', {colors(i,:)});
  412. end
  414. xticks(1:6)%size(nodes_runtimes,4))
  415. xticklabels(labels (1:6))
  416. ylabel('Runtime (s)')
  417. xlabel('Metrics')
  418. title([num2str(nodes_opts(j)) ' Nodes'])
  419. ax = gca;
  420. ax.FontSize=12;
  421. set(gca, 'Yscale', 'log')
  422. ylim([10^0, 10^6])
  423. text(-2.3,10^6.5,[figLabs(j) ')'], 'FontSize', 20)
  424. end
  426. saveas(gcf, [fig_dir filesep() '08.1_runtimes.jpg'])
  427. exportgraphics(gcf, [fig_dir filesep() '08.1_runtimes.eps'], 'ContentType', 'vector')
  429. %% ========================================================================
  431. % Figure 8 - runtimes - no TE
  433. cwd = [data_dir filesep() 'agg'];
  434. load([cwd filesep() 'nodes_all_rts.mat'])
  436. f = figure;
  437. f.Position = [100 100 2000 400];
  439. for j = 2:size(nodes_opts,2)
  440. subplot(1,5,j)
  441. for i = 1:4
  442. vp = Violin({squeeze(nodes_runtimes(j,:,2,i))'}, i, 'MarkerSize', 10, 'ViolinColor', {colors(i,:)});
  443. end
  445. xlabel('Method')
  446. xticks(1:6)
  447. xticklabels(labels)
  448. ylabel('Runtime (s)')
  449. title([num2str(nodes_opts(j)) ' Nodes'])
  450. ax = gca;
  451. ax.FontSize=20;
  452. end
  454. sgtitle('Reconstruction Time by Method')
  455. saveas(gcf, [fig_dir filesep() '08.2_runtimes_noTE.jpg'])
  458. %% ========================================================================
  460. % Figure 6 - runtimes
  462. cwd = [data_dir filesep() 'agg'];
  463. load([cwd filesep() 'tps_all_rts.mat'])
  466. f = figure;
  467. f.Position = [100 100 2000 400];
  469. for j = 1:size(tps_opts,2)
  470. subplot(1,5,j)
  471. for i = 1:6
  472. vp = Violin({squeeze(tps_runtimes(j,:,1,i))'}, i, 'MarkerSize', 10);
  473. end
  475. xlabel('Method')
  476. xticks(1:6)
  477. xticklabels(labels)
  478. ylabel('Runtime (s)')
  479. title([num2str(tps_opts(j)) ' Time Points'])
  480. ax = gca;
  481. ax.FontSize=14;
  482. end
  484. %sgtitle('Reconstruction Time by Method')
  485. saveas(gcf, [fig_dir filesep() '08.1_runtimes.jpg'])
  487. %% ========================================================================
  489. % FUNCTIONS
  491. function makeFig_byMetric(data,opts, alpha, xAxLabel, yAxLabel)
  493. labels = {{"Biv. Cross-Corr"}, {"Pt. Cross-Corr"}, {"Mv. Granger","Causality"}, {"Mutual","Information"},...
  494. {"Biv. Transfer","Entropy"}, {"Mv. Transfer","Entropy"}, {"Zero-Lag Biv.","Cross-Corr"}, ...
  495. {"Zero-Lag Pt.","Cross-Corr"}, {"Zero-Lag Mutual","Information"}, {"Shuffled"}};
  497. colors = [136 34 85; 102 17 0; 17 119 51; 68 170 153; 102 153 204; 51 34 136; 204 102 119; 170 68 153; 153 153 51; 148 148 148]/255;
  499. set(gcf,'renderer','Painters')
  500. f = figure(1);
  501. clf
  502. f.Position = [10 10 1800 650];
  504. figLabs = 'A':'J';
  506. for i = 1:size(data,4)
  508. [~, ~, stats] = kruskalwallis(squeeze(data(:,:,1,i))', opts);
  509. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', alpha);
  510. tbl2 = array2table(results,"VariableNames", ...
  511. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  512. disp(tbl2)
  514. figure(1)
  515. for j = 1:size(opts,2)
  516. subplot(2,5,i)
  517. vp = Violin({round(squeeze(data(j,:,1,i)),4)'}, j, 'MarkerSize', 10, 'ViolinColor', {colors(i,:)});
  518. end
  520. grps = [];
  521. ps = [];
  522. for ct_p = 1:size(tbl2,1)
  523. if tbl2.("P-value")(ct_p) < alpha
  524. grps = [grps; {[tbl2.("Group A")(ct_p), tbl2.("Group B")(ct_p)]}];
  525. ps = [ps; tbl2.("P-value")(ct_p)];
  526. end
  527. end
  529. mxCompars = size(data,1) * (size(data,1)-1)/2;
  530. if size(grps,1) == mxCompars
  531. %text(0.2, 1.15, '*all comparisons significant')
  532. elseif size(grps,1) == 0
  533. text(0.2, 0.1, '*no comparisons significant')
  534. end
  536. if i > 5
  537. xlabel(xAxLabel)
  538. end
  539. xticks(1:size(opts,2))
  540. xticklabels(opts)
  541. if mod(i-1,5) == 0
  542. ylabel(yAxLabel)
  543. end
  545. title(labels{i})
  546. ylim([0, 1.2])
  547. text(-1,1.3,[figLabs(i) ')'], 'FontSize', 20)
  548. ax = gca;
  549. ax.FontSize=14;
  550. end
  551. end
  553. function makeFig_byParam(data, opts, alpha, xAxLabel, yAxLabel, paramName)
  555. labels = ["Biv. Cross-Corr", "Pt. Cross-Corr", "Mv. Granger Causality", "Mutual Information", ...
  556. "Biv. Transfer Entropy", "Mv. Transfer Entropy", "Zero-Lag Biv. Cross-Corr", ...
  557. "Zero-Lag Pt. Cross-Corr", "Zero-Lag Mutual Information", "Shuffled"];
  559. figLabs = 'A':'J';
  561. colors = [136 34 85; 102 17 0; 17 119 51; 68 170 153; 102 153 204; 51 34 136; 204 102 119; 170 68 153; 153 153 51; 148 148 148]/255;
  563. set(gcf,'renderer','Painters')
  564. f = figure(2);
  565. clf
  566. if size(opts,2) < 8
  567. f.Position = [100 100 500*size(opts,2) 500];
  568. else
  569. f.Position = [100 100 500*size(opts,2)/2 2*500];
  570. end
  572. for j = 1:size(opts,2)
  574. [p, tbl, stats] = kruskalwallis(squeeze(data(j,:,1,:)), labels);
  575. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', alpha);
  577. tbl2 = array2table(results,"VariableNames", ...
  578. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  580. figure(2)
  581. if size(opts,2) < 8
  582. subplot(1,size(opts,2),j)
  583. else
  584. subplot(2,size(opts,2)/2,j)
  585. end
  587. for i = 1:6 %size(nodes_cos_dist,4)
  588. vp = Violin({round(squeeze(data(j,:,1,i)),4)'}, i, 'MarkerSize', 10, 'ViolinColor', {colors(i,:)});
  589. end
  591. grps = [];
  592. ps = [];
  593. for ct_p = 1:size(tbl2,1)
  594. if tbl2.("P-value")(ct_p) < alpha && tbl2.("Group A")(ct_p) < 7 && tbl2.("Group B")(ct_p) < 7
  595. grps = [grps; {[tbl2.("Group A")(ct_p), tbl2.("Group B")(ct_p)]}];
  596. ps = [ps; tbl2.("P-value")(ct_p)];
  597. end
  598. end
  600. mxCompars = (6*5)/2;%size(data,4) * (size(data,4)-1)/2;
  601. if size(grps,1) == mxCompars
  602. %text(0.2, 1.15, '*all comparisons significant')
  603. elseif size(grps,1) == 0
  604. text(0.2, 0.1, '*no comparisons significant')
  605. end
  607. % if size(grps,1) > 0
  608. % sigstar(grps, ps)
  609. % end
  610. xticks(1:size(data,4))
  611. xticklabels(labels)
  612. if j == 1
  613. ylabel(yAxLabel)
  614. end
  615. yticks([0, 0.5, 1])
  617. if size(opts,2) < 8 || j > size(opts,2)/2
  618. xlabel(xAxLabel)
  619. end
  620. title([num2str(opts(j)) ' ' paramName])
  621. ylim([0, 1.2])
  622. ax = gca;
  623. ax.FontSize=14;
  624. axis square
  625. text(-1.2,1.2,[figLabs(j) ')'], 'FontSize', 20)
  626. end
  627. end
  629. function doStats(data, opts, alpha, paramName)
  631. labels = ["Biv. Cross-Corr", "Pt. Cross-Corr", "Mv. Granger Causality", "Mutual Information",...
  632. "Biv. Transfer Entropy", "Mv. Transfer Entropy", "Zero-Lag Biv. Cross-Corr", ...
  633. "Zero-Lag Pt. Cross-Corr", "Zero-Lag Mutual Info.", "Shuffled" ];
  635. % compare metric by parameter
  636. for i = 1:size(data,1)
  637. [p, tbl, stats] = kruskalwallis(squeeze(data(i,:,1,:)), labels);
  638. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', alpha);
  639. disp([num2str(opts(i)) ' ' paramName])
  641. if p < alpha
  642. tbl2 = array2table(results,"VariableNames", ...
  643. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  644. close all
  645. disp(tbl2)
  646. else
  647. disp('no significant differences')
  648. end
  649. disp(' ')
  650. end
  652. %% compare parameter by metric
  653. for i = 1:size(data,4)
  654. [p, tbl, stats] = kruskalwallis(squeeze(data(:,:,1,i))', opts);
  655. disp(labels(i))
  657. if p < alpha
  658. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', alpha);
  659. tbl2 = array2table(results,"VariableNames", ...
  660. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  661. close all
  662. disp(tbl2)
  663. else
  664. disp('no significant differences')
  665. end
  666. disp(' ')
  667. end
  669. end
  671. function makeFig_nodeDrop(data, opts, alpha, xAxLabel, yAxLabel)
  673. labels = {{"Biv. Cross-Corr"}, {"Pt. Cross-Corr"}, {"Mv. Granger","Causality"}, {"Mutual","Information"},...
  674. {"Biv. Transfer","Entropy"}, {"Mv. Transfer","Entropy"}, {"Zero-Lag Biv.","Cross-Corr"}, ...
  675. {"Zero-Lag Pt.","Cross-Corr"}, {"Zero-Lag Mutual","Information"}, {"Shuffled"}};
  677. colors = [136 34 85; 102 17 0; 17 119 51; 68 170 153; 102 153 204; 51 34 136; 204 102 119; 170 68 153; 153 153 51; 148 148 148]/255;
  678. figLabs = 'A':'J';
  680. f = figure(3);
  681. clf
  682. f.Position = [10 10 1800 650];
  683. set(gcf,'renderer','Painters')
  685. for i = 1:size(data,4)
  686. [p, tbl, stats] = kruskalwallis(squeeze(data(4,:,:,i)), opts);
  687. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', alpha);
  688. tbl2 = array2table(results,"VariableNames", ...
  689. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  691. figure(3)
  692. subplot(2,5,i)
  693. for j = 1:size(data,3)
  694. vp = Violin({round(squeeze(data(4,:,j,i)),4)'}, j, 'MarkerSize', 10, 'ViolinColor', {colors(i,:)});
  695. end
  697. grps = [];
  698. ps = [];
  699. for ct_p = 1:size(tbl2,1)
  700. if tbl2.("P-value")(ct_p) < alpha
  701. grps = [grps; {[tbl2.("Group A")(ct_p), tbl2.("Group B")(ct_p)]}];
  702. ps = [ps; tbl2.("P-value")(ct_p)];
  703. end
  704. end
  706. mxCompars = size(data,3) * (size(data,1)-3)/2;
  707. if size(grps,1) == mxCompars
  708. if mean(data(4,:,j,i), 'omitnan') < 0.6
  709. %text(0.2, 1.15, '*all comparisons significant')
  710. else
  711. %text(0.2, 0.1, '*all comparisons significant')
  712. end
  713. elseif size(grps,1) == 0
  714. if mean(data(4,:,j,i), 'omitnan') < 0.6
  715. text(0.2, 1.15, '*no comparisons significant')
  716. % if i < 7
  717. % text(0.2, 1.15, '*no comparisons significant')
  718. % else
  719. else
  720. text(0.2, 0.1, '*no comparisons significant')
  721. end
  722. end
  724. if i > 5
  725. xlabel(xAxLabel)
  726. end
  727. xticks(1:10)
  728. xticklabels(opts)
  729. xtickangle(45)
  730. if mod(i,5) == 1
  731. ylabel(yAxLabel)
  732. end
  733. title(labels{i})
  734. ylim([0, 1.2])
  735. yticks([0 0.5 1])
  736. ax = gca;
  737. ax.FontSize=13;
  738. xlim([0 size(data,3)+1])
  739. text(-1,1.3,[figLabs(i) ')'], 'FontSize', 18)
  741. end
  742. end
  744. function makeFig_nodeDrop_param(data, opts, alpha, xAxLabel, yAxLabel)
  746. labels = ["Biv. Cross-Corr", "Pt. Cross-Corr", "Mv. Granger Causality", "Mutual Information",...
  747. "Biv. Transfer Entropy", "Mv. Transfer Entropy", "Zero-Lag Biv. Cross-Corr", ...
  748. "Zero-Lag Pt. Cross-Corr", "Zero-Lag Mutual Information", "Shuffled"];
  750. colors = [136 34 85; 102 17 0; 17 119 51; 68 170 153; 102 153 204; 51 34 136; 204 102 119; 170 68 153; 153 153 51; 148 148 148]/255;
  751. figLabs = 'A':'J';
  753. f = figure(3);
  754. clf
  755. f.Position = [100 100 1800 900];
  756. set(gcf,'renderer','Painters')
  758. for i = 1:size(data,3)
  759. [p, tbl, stats] = kruskalwallis(squeeze(data(4,:,i,:)), labels);
  760. results = multcompare(stats, 'CriticalValueType', 'dunn-sidak', 'Alpha', alpha);
  761. tbl2 = array2table(results,"VariableNames", ...
  762. ["Group A","Group B","Lower Limit","A-B","Upper Limit","P-value"]);
  764. figure(3)
  765. subplot(2,5,i)
  766. for j = 1:6%size(data,3)
  767. vp = Violin({round(squeeze(data(4,:,i,j)),4)'}, j, 'MarkerSize', 10, 'ViolinColor', {colors(j,:)});
  768. end
  770. grps = [];
  771. ps = [];
  772. for ct_p = 1:size(tbl2,1)
  773. if tbl2.("P-value")(ct_p) < alpha
  774. grps = [grps; {[tbl2.("Group A")(ct_p), tbl2.("Group B")(ct_p)]}];
  775. ps = [ps; tbl2.("P-value")(ct_p)];
  776. end
  777. end
  779. mxCompars = size(data,3) * (size(data,1)-3)/2;
  780. if size(grps,1) == mxCompars
  781. if mean(data(4,:,j,i), 'omitnan') < 0.6
  782. %text(0.2, 1.15, '*all comparisons significant')
  783. else
  784. %text(0.2, 0.1, '*all comparisons significant')
  785. end
  786. elseif size(grps,1) == 0
  787. if mean(data(4,:,j,i), 'omitnan') < 0.6
  788. text(0.2, 1.15, '*no comparisons significant')
  789. % if i < 7
  790. % text(0.2, 1.15, '*no comparisons significant')
  791. % else
  792. else
  793. text(0.2, 0.1, '*no comparisons significant')
  794. end
  795. end
  797. if i > 5
  798. xlabel(xAxLabel)
  799. end
  800. xticks(1:6)
  801. xticklabels(labels(1:6))
  802. xtickangle(45)
  803. if mod(i,5) == 1
  804. ylabel(yAxLabel)
  805. end
  806. title([convertStringsToChars(opts(i)), ' Coverage'])
  807. ylim([0, 1.2])
  808. yticks([0 0.5 1])
  809. ax = gca;
  810. ax.FontSize=13;
  811. xlim([0 7])
  812. text(-1,1.3,[figLabs(i) ')'], 'FontSize', 18)
  814. end
  815. end

paper_figures.m at commit f2d6020, no license · at the source

Overview

Authors: Kate Dembny1,2, Hafsa Farooqi1, Alexander B Herman3,4, Theoden I Netoff1, David P Darrow3,5
ORCID iDs: Kate Dembny, Hafsa Farooqi, Alexander B Herman, Theoden I Netoff, David P Darrow
  1. Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
  2. University of Minnesota, Medical Scientist Training Program, Minneapolis, MN, United States of America
  3. Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States of America
  4. University of Minnesota, Medical Discovery Team on Addiction, Minneapolis, MN, United States of America
  5. Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States of America
Institutions: University of Minnesota (United States)
Journal: Journal of neural engineering, volume 23, issue 3, article 036012
Dates: received 3 July 2025; accepted 15 April 2026; published online 14 May 2026; in print 1 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1088/1741-2552/ae5fd7 · PMID 41985540 · PMCID PMC13172737 · OpenAlex W4409260304
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.04.02.646886
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism)
Methods: Statistics, Machine learning, Connectivity, Graphs, Spectral & time-frequency
Keywords: network neuroscience, effective connectivity, transfer entropy, delayed coupling, Granger causality, mutual information, cross-correlation
MeSH: Brain*, Models, Neurological*, Nerve Net*, Algorithms, Computer Simulation, Humans, Reproducibility of Results, Time Factors (* major topic)
Topic: Advanced Chemical Sensor Technologies (Biomedical Engineering, Engineering), according to OpenAlex
Funding: Brain and Behavior Research Foundation (Young Investigator Award #28426); National Institute on Drug Abuse (K23 DA050909); University of Minnesota Medical Discovery Team on Addiction; MnDrive Data Science Initiative (Graduate Assistantship Program); National Institute of Neurological Disorders and Stroke (P50 NS123109 and R01 NS094206)
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

Objective. The brain functions as a complex network of billions of interconnected neurons, coordinating processes from basic reflexes to high-level cognition. Dysfunction in these networks contribute to neurological and psychiatric disorders, including epilepsy, depression, and Parkinson’s disease. Understanding these network alterations is essential for developing effective therapies. However, reconstructing network topology from human electrophysiology data is challenging due to sparse spatial sampling, measurement noise, and variable time delays in interregional communication. Effective connectivity (EC) metrics have been developed to infer directed neural interactions, but their accuracy under real-world data constraints remain unclear. This study empirically compares the ability of common EC metrics to reconstruct relationships between simulated time series with known temporal relationships and network topologies in the presence of data limitations common to human electrophysiology data. By utilizing networks and temporal relationships that are mathematically simple, this framework provides broad conceptual backing to understand the reliability of EC metrics and establishes groundwork upon which more complex spatial and temporal relationships between time series can be evaluated. Approach. We generated Erdős–Rényi networks and simulated time series using a time-delayed vector autoregressive model. We systematically varied network size, data length, measurement noise, and network coverage. Variations of four commonly used EC metrics, cross-correlation, Granger causality (GC), mutual information (MI), and transfer entropy, were evaluated for reconstruction accuracy using cosine distance, as well as receiver operating characteristic (ROC) curves, to compare estimated and true coupling matrices. Main Results. Multivariate transfer entropy demonstrated the highest accuracy across various conditions but required significantly longer computation times. For small networks (<30 nodes), MI and GC rapidly and accurately reconstructed networks. For larger networks, partial cross-correlation performed well with good computational efficiency. Notably, zero-lag metrics perform no better than chance for time-lagged time series relationships in nearly all conditions. Significance. The choice of an EC metric should consider specific data constraints. While multivariate transfer entropy is the most reliable across conditions, its long runtime limits its practical application. For large networks, partial cross-correlation offers a faster and reasonably accurate alternative. GC and MI are effective for small networks. Critically, time-lagged metrics are essential for accurate network reconstructions, as failing to account for time delays leads to reconstructions no more accurate than random network models.

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 7 matches between paragraphs and lines of code.

hermandarrowlab

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: the text, “Network simulations”
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: github.com/hermandarrowlab

pwollstadt/IDTxl

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: c7eacfd9ce5ca6cdb371e19a52ea286db0d33f3c, 23 April 2026
Languages: Python (139), JavaScript (13), CUDA (7), Shell (2), C (1), MATLAB (1)
Size: 403 files, 163 scripts
Software Heritage: not archived
Found in: the text, “Network reconstruction metrics”
Holds: README, license file, environment (setup.py), tests, documentation
Not found: CITATION.cff, continuous integration
Tools: NumPy (114 files), SciPy (15 files), Matplotlib (9 files), h5py (2 files), NetworkX (2 files), scikit-learn (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
165 files
At the source: github.com/pwollstadt/IDTxl

hermandarrowlab/ec-metrics-validation

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: f2d6020d991f4a40cdb3810d17c7828fb1575a61, 3 April 2025
Languages: MATLAB (13), Shell (10), Python (2)
Size: 25 files, 25 scripts
Software Heritage: not archived
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (3 files), Statistics and Machine Learning Toolbox (2 files), NetworkX (2 files), NumPy (2 files), SciPy (2 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
25 files
At the source: github.com/hermandarrowlab/ec-metrics-validation

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;
  • 188 scripts, each with its path and the digest of its content;
  • 7 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

External Software:

Functional Connectivity Toolbox: https://sites.google.com/site/functionalconnectivitytoolbox/.

Multivariate Granger Causality Toolbox: www.mathworks.com/matlabcentral/fileexchange/78727-the-multivariate-granger-causality-mvgc-toolbox.

Fieldtrip Mutual Information: https://github.com/fieldtrip/fieldtrip/blob/master/connectivity/ft_connectivity_mutualinformation.m.

Information Dynamics Toolbox XL (IDTxl): https://github.com/pwollstadt/IDTxl.

The data that support the findings of this study are openly available at the following URL/DOI: https://github.com/hermandarrowlab/ec-metrics-validation (Dembny et al 2026).

Supplementary Data 1 available at https://doi.org/10.1088/1741-2552/ae5fd7/data1.

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

Versions

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

Version 2, 28 September 2026

  • Publisher: n/a → IOP Publishing

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 8 MeSH terms, 5 funders, 60 references.

Cite

This paper

Dembny, K., Farooqi, H., Herman, A. B., Netoff, T. I., & Darrow, D. P. (2026). Metric validation for detection of delayed and directed coupling. Journal of neural engineering, 23(3), 036012. https://doi.org/10.1088/1741-2552/ae5fd7

BibTeX

@article{dembny2026metric,
author = {Dembny, Kate and Farooqi, Hafsa and Herman, Alexander B and Netoff, Theoden I and Darrow, David P},
title = {{Metric validation for detection of delayed and directed coupling}},
journal = {Journal of neural engineering},
year = {2026},
month = may,
volume = {23},
number = {3},
pages = {036012},
publisher = {IOP Publishing},
issn = {1741-2560},
doi = {10.1088/1741-2552/ae5fd7},
url = {https://doi.org/10.1088/1741-2552/ae5fd7},
pmid = {41985540},
pmcid = {PMC13172737}
}

RIS

TY - JOUR
AU - Dembny, Kate
AU - Farooqi, Hafsa
AU - Herman, Alexander B
AU - Netoff, Theoden I
AU - Darrow, David P
TI - Metric validation for detection of delayed and directed coupling
T2 - Journal of neural engineering
J2 - J Neural Eng
PY - 2026
DA - 2026/05/14
VL - 23
IS - 3
SP - 036012
SN - 1741-2560
PB - IOP Publishing
DO - 10.1088/1741-2552/ae5fd7
UR - https://doi.org/10.1088/1741-2552/ae5fd7
LA - en
ER -

CSL-JSON

{
"id": "10.1088/1741-2552/ae5fd7",
"type": "article-journal",
"title": "Metric validation for detection of delayed and directed coupling",
"container-title": "Journal of neural engineering",
"author": [
{
"family": "Dembny",
"given": "Kate"
},
{
"family": "Farooqi",
"given": "Hafsa"
},
{
"family": "Herman",
"given": "Alexander B"
},
{
"family": "Netoff",
"given": "Theoden I"
},
{
"family": "Darrow",
"given": "David P"
}
],
"container-title-short": "J Neural Eng",
"volume": "23",
"issue": "3",
"page": "036012",
"DOI": "10.1088/1741-2552/ae5fd7",
"PMID": "41985540",
"PMCID": "PMC13172737",
"ISSN": "1741-2560",
"publisher": "IOP Publishing",
"URL": "https://doi.org/10.1088/1741-2552/ae5fd7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
14
]
]
}
}

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

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