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Opposite network patterns of integration-segregation in psychedelic and sedated states of consciousness.

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The authors' code

MATLAB · 304 lines · 16 KB · CC-BY-4.0

  1. % 设置基本目录
  2. baseDir = 'E:\DAIRUI\REHO_study\fc_matrix\Data_450ROI_Music\';
  3. groupDir = 'E:\DAIRUI\REHO_study\fc_matrix\Data_450ROI_Music\APFC_valueZero';
  4. subjID = {'P01'; 'P02'; 'P03'; 'P05'; 'P06R'; 'P07'; 'P08'; 'P11'; 'P12'; 'P13'; 'P14'; 'P15'; 'P16'; 'P17'; 'P18'; 'P19'; 'P20'; 'P21'; 'P22'; 'P23'; 'P24'; 'P25'; 'P26'; 'P28'; 'P29R'; 'P30'};
  5. runID = {'REST1';'REST2';};
  6. gsrID = {'noGSR';};
  8. mkdir(groupDir);
  10. % 定义ROI的编号数组
  11. AT = [383, 389, 182, 184, 382, 178, 380, 183, 118, 390, 179, 386, 385, 186, 168, 324, 352, 187, 176, 387, ...
  12. 188, 379, 173, 388, 170, 174, 375, 171, 169, 354, 166, 343, 135, 185, 175, 377, 381, 391, 342, 172, ...
  13. 321, 181, 116, 180, 376, 353, 148, 378, 361, 117, 349, 140, 138, 341, 323, 137, 348, 351, 141, 360, ...
  14. 340, 345, 350, 167, 322, 189, 143, 134, 359, 114, 147, 358, 136, 346, 99, 319, 146, 142, 177, 139, ...
  15. 355, 115, 347, 320, 87, 90, 309, 310, 344, 89, 105, 384, 103, 306];
  16. AU = [106, 313, 293, 101, 318, 91, 303, 102, 291, 107, 311, 314, 292, 88, 113, 111, 308, 304, 307, ...
  17. 305, 97, 110, 100, 109, 112, 98, 301, 61, 312, 65, 266, 302, 104, 317, 261, 86, 108, 234, 55, ...
  18. 68, 269, 316, 268, 54, 64, 34, 260, 56, 247, 250, 57, 245, 315, 249, 290, 263, 254, 264, 256, ...
  19. 42, 270, 63, 236, 62, 242, 49, 47, 233, 248, 36, 40, 243, 35, 235, 41, 246, 255, 43, 239, 240, ...
  20. 244, 241, 39, 251, 50, 252];
  21. PT = [364, 162, 368, 196, 195, 164, 399, 150, 396, 366, 154, 395, 192, 371, 152, 160, 199, 163, ...
  22. 165, 149, 357, 365, 200, 373, 367, 332, 397, 124, 197, 327, 129, 190, 338, 326, 198, 370, ...
  23. 155, 151, 398, 362, 191, 194, 122, 333, 130, 133, 126, 337, 161, 392, 131, 339, 334, 158, ...
  24. 331, 363, 394, 127, 356, 125, 393, 336, 400, 300, 274, 193, 372, 119, 144, 132, 329, 335, ...
  25. 369, 374, 145, 285, 2, 123, 128, 96, 296, 294, 121, 328, 77, 281, 20];
  26. PU = [120, 93, 277, 159, 95, 299, 153, 74, 202, 75, 7, 157, 330, 12, 83, 278, 325, 156, 213, 71, ...
  27. 280, 81, 287, 288, 217, 69, 94, 295, 276, 272, 78, 273, 232, 70, 282, 84, 275, 207, 289, 79, ...
  28. 297, 85, 298, 92, 80, 267, 283, 238, 76, 279, 37, 231, 73, 58, 46, 67, 32, 284, 262, 265, 66, ...
  29. 1, 237, 59, 258, 53, 259, 253, 52, 33, 201, 28, 38, 45, 271, 44, 48, 51, 60, 72, 13, 257, 221, ...
  30. 15, 209, 14, 31, 82, 218, 19, 8, 210, 215, 6, 16, 286, 230, 227, 216, 208, 18, 23, 222, 17, 9, ...
  31. 3, 203, 214, 30, 21, 219, 22, 204, 4, 228, 220, 27, 11, 212, 205, 24, 10, 26, 224, 211, 206, ...
  32. 223, 226, 225, 5, 25, 29, 229];
  35. % 新的顺序数组
  36. new_order = [AT, AU, PT, PU];
  38. % 初始化数据存储
  39. all_FCmat_REST1 = [];
  40. all_FCmat_REST2 = [];
  41. all_within_FC_REST1 = [];
  42. all_within_FC_REST2 = [];
  43. all_between_FC_REST1 = [];
  44. all_between_FC_REST2 = [];
  46. for subj = 1:length(subjID)
  47. subj_dir = fullfile(baseDir, subjID{subj});
  48. results_dir = fullfile(subj_dir, 'APFC_valueZero');
  49. if ~exist(results_dir, 'dir')
  50. mkdir(results_dir);
  51. end
  53. for GSR = 1:length(gsrID)
  54. for run = 1:length(runID)
  55. disp(['Processing ' runID{run} ' ' gsrID{GSR} ' for subject ' subjID{subj}])
  56. data_file = fullfile(subj_dir, [runID{run}, '_', gsrID{GSR}, '_LowFreq_', 'CortexSub450.1D']);
  57. whole_data = dlmread(data_file);
  59. % 按照新的顺序调整数据
  60. reordered_data = whole_data(:, new_order);
  62. % 计算FC矩阵
  63. FCmat = corr(reordered_data);
  65. % 去掉对角线并进行Fisher Z-transform
  66. FCmat(eye(size(FCmat))==1) = NaN;
  67. FCmat = atanh(FCmat);
  69. % 保存FC矩阵
  70. save(fullfile(results_dir, sprintf('%s_%s_FCmat.mat', runID{run}, gsrID{GSR})), 'FCmat');
  72. % 提取并保存各个网络的within network和between network矩阵
  73. withinAT = FCmat(1:94, 1:94);
  74. withinAU = FCmat(95:180, 95:180);
  75. withinPT = FCmat(181:267, 181:267);
  76. withinPU = FCmat(268:400, 268:400);
  77. betweenATAU = FCmat(1:94, 95:180);
  78. betweenATPT = FCmat(1:94, 181:267);
  79. betweenATPU = FCmat(1:94, 268:400);
  80. betweenAUPT = FCmat(95:180, 181:267);
  81. betweenAUPU = FCmat(95:180, 268:400);
  82. betweenPTPU = FCmat(181:267, 268:400);
  83. withinA = FCmat(1:180, 1:180);
  84. withinP = FCmat(181:400, 181:400);
  85. betweenAP = FCmat(1:180, 181:400);
  88. % 保存每个矩阵
  89. save(fullfile(results_dir, sprintf('%s_%s_withinAT.mat', runID{run}, gsrID{GSR})), 'withinAT');
  90. save(fullfile(results_dir, sprintf('%s_%s_withinAU.mat', runID{run}, gsrID{GSR})), 'withinAU');
  91. save(fullfile(results_dir, sprintf('%s_%s_withinPT.mat', runID{run}, gsrID{GSR})), 'withinPT');
  92. save(fullfile(results_dir, sprintf('%s_%s_withinPU.mat', runID{run}, gsrID{GSR})), 'withinPU');
  93. save(fullfile(results_dir, sprintf('%s_%s_betweenATPT.mat', runID{run}, gsrID{GSR})), 'betweenATPT');
  94. save(fullfile(results_dir, sprintf('%s_%s_betweenATPU.mat', runID{run}, gsrID{GSR})), 'betweenATPU');
  95. save(fullfile(results_dir, sprintf('%s_%s_betweenAUPT.mat', runID{run}, gsrID{GSR})), 'betweenAUPT');
  96. save(fullfile(results_dir, sprintf('%s_%s_betweenAUPU.mat', runID{run}, gsrID{GSR})), 'betweenAUPU');
  97. save(fullfile(results_dir, sprintf('%s_%s_betweenPTPU.mat', runID{run}, gsrID{GSR})), 'betweenPTPU');
  98. save(fullfile(results_dir, sprintf('%s_%s_withinA.mat', runID{run}, gsrID{GSR})), 'withinA');
  99. save(fullfile(results_dir, sprintf('%s_%s_withinP.mat', runID{run}, gsrID{GSR})), 'withinP');
  100. save(fullfile(results_dir, sprintf('%s_%s_betweenAP.mat', runID{run}, gsrID{GSR})), 'betweenAP');
  103. % 保存所有被试的FC矩阵和相关的within/between值
  104. % 保存所有被试的FC矩阵和相关的within/between值
  105. if strcmp(runID{run}, 'REST1')
  106. all_FCmat_REST1 = cat(3, all_FCmat_REST1, FCmat);
  107. all_within_FC_REST1 = [all_within_FC_REST1; nanmean(withinAT(:)), nanmean(withinAU(:)), nanmean(withinPT(:)), nanmean(withinPU(:)), nanmean(withinA(:)), nanmean(withinP(:))];
  108. all_between_FC_REST1 = [all_between_FC_REST1; nanmean(betweenATPT(:)), nanmean(betweenATPU(:)), nanmean(betweenATAU(:)), nanmean(betweenAUPT(:)), nanmean(betweenAUPU(:)), nanmean(betweenPTPU(:)), nanmean(betweenAP(:))];
  109. elseif strcmp(runID{run}, 'REST2')
  110. all_FCmat_REST2 = cat(3, all_FCmat_REST2, FCmat);
  111. all_within_FC_REST2 = [all_within_FC_REST2; nanmean(withinAT(:)), nanmean(withinAU(:)), nanmean(withinPT(:)), nanmean(withinPU(:)), nanmean(withinA(:)), nanmean(withinP(:))];
  112. all_between_FC_REST2 = [all_between_FC_REST2; nanmean(betweenATPT(:)), nanmean(betweenATPU(:)), nanmean(betweenATAU(:)), nanmean(betweenAUPT(:)), nanmean(betweenAUPU(:)), nanmean(betweenPTPU(:)), nanmean(betweenAP(:))];
  113. end
  115. % 绘制并保存FC矩阵图，统一Color Bar范围，标记出不同区域
  116. figure;
  117. imagesc(FCmat, [-1, 1]); % Color Bar范围固定为 [-1, 1]
  118. colorbar;
  119. hold on;
  121. % 用红色方框标记 within AT 区域
  122. rectangle('Position', [1, 1, 94, 94], 'EdgeColor', 'r', 'LineWidth', 1.5);
  123. text(47.5, 47.5, 'AT', 'Color', 'r', 'FontSize', 12, 'HorizontalAlignment', 'center');
  125. % 用绿色方框标记 within AU 区域
  126. rectangle('Position', [95, 95, 86, 86], 'EdgeColor', 'g', 'LineWidth', 1.5);
  127. text(138, 138, 'AU', 'Color', 'g', 'FontSize', 12, 'HorizontalAlignment', 'center');
  129. % 用蓝色方框标记 within PT 区域
  130. rectangle('Position', [181, 181, 87, 87], 'EdgeColor', 'b', 'LineWidth', 1.5);
  131. text(224.5, 224.5, 'PT', 'Color', 'b', 'FontSize', 12, 'HorizontalAlignment', 'center');
  133. % 用黄色方框标记 within PU 区域
  134. rectangle('Position', [268, 268, 133, 133], 'EdgeColor', 'y', 'LineWidth', 1.5);
  135. text(334.5, 334.5, 'PU', 'Color', 'y', 'FontSize', 12, 'HorizontalAlignment', 'center');
  137. % 标记 AT-PT
  138. rectangle('Position', [1, 181, 94, 87], 'EdgeColor', 'k', 'LineWidth', 1.5);
  139. rectangle('Position', [181, 1, 87, 94], 'EdgeColor', 'k', 'LineWidth', 1.5);
  140. text(47.5, 224.5, 'AT-PT', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  141. text(224.5, 47.5, 'AT-PT', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  143. % 标记 AT-PU
  144. rectangle('Position', [1, 268, 94, 133], 'EdgeColor', 'k', 'LineWidth', 1.5);
  145. rectangle('Position', [268, 1, 133, 94], 'EdgeColor', 'k', 'LineWidth', 1.5);
  146. text(47.5, 334.5, 'AT-PU', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  147. text(334.5, 47.5, 'AT-PU', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  149. % 标记 AU-PT
  150. rectangle('Position', [95, 181, 86, 87], 'EdgeColor', 'k', 'LineWidth', 1.5);
  151. rectangle('Position', [181, 95, 87, 86], 'EdgeColor', 'k', 'LineWidth', 1.5);
  152. text(138, 224.5, 'AU-PT', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  153. text(224.5, 138, 'AU-PT', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  155. % 标记 AU-PU
  156. rectangle('Position', [95, 268, 86, 133], 'EdgeColor', 'k', 'LineWidth', 1.5);
  157. rectangle('Position', [268, 95, 133, 86], 'EdgeColor', 'k', 'LineWidth', 1.5);
  158. text(138, 334.5, 'AU-PU', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  159. text(334.5, 138, 'AU-PU', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  161. % 标记 PT-PU
  162. rectangle('Position', [181, 268, 87, 133], 'EdgeColor', 'k', 'LineWidth', 1.5);
  163. rectangle('Position', [268, 181, 133, 87], 'EdgeColor', 'k', 'LineWidth', 1.5);
  164. text(224.5, 334.5, 'PT-PU', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  165. text(334.5, 224.5, 'PT-PU', 'Color', 'k', 'FontSize', 12, 'HorizontalAlignment', 'center');
  168. % 保存图像
  169. saveas(gcf, fullfile(results_dir, sprintf('%s_%s_FCmat_Annotated.png', runID{run}, gsrID{GSR})));
  170. close(gcf);
  171. end
  172. end
  173. end
  175. % 计算 REST2 - REST1 配对t检验
  176. [~, p_within, ~, stats_within] = ttest(all_within_FC_REST2, all_within_FC_REST1);
  177. [~, p_between, ~, stats_between] = ttest(all_between_FC_REST2, all_between_FC_REST1);
  179. % 获取t值
  180. t_within = stats_within.tstat;
  181. t_between = stats_between.tstat;
  183. % 绘制组水平的结果图（带散点和柱状图）
  184. figure;
  186. % 绘制within network的组水平结果
  187. subplot(1,2,1);
  188. hold on;
  189. b = bar(1:6, [mean(all_within_FC_REST1); mean(all_within_FC_REST2)]', 'FaceColor', 'flat');
  191. % 设置柱图的颜色，REST1为黑色，REST2为红色
  192. b(1).CData = repmat([0 0 0], 6, 1); % 黑色
  193. b(2).CData = repmat([1 0 0], 6, 1); % 红色
  195. % 添加散点，颜色与柱图颜色匹配
  196. for i = 1:6
  197. scatter(i * ones(size(all_within_FC_REST1, 1), 1), all_within_FC_REST1(:, i), 50, [0 0 0], 'filled', 'jitter','on', 'jitterAmount', 0.15);
  198. scatter(i * ones(size(all_within_FC_REST2, 1), 1), all_within_FC_REST2(:, i), 50, [1 0 0], 'filled', 'jitter','on', 'jitterAmount', 0.15);
  199. end
  201. % 绘制误差条
  202. errorbar(1:6, mean(all_within_FC_REST1), std(all_within_FC_REST1)/sqrt(size(all_within_FC_REST1,1)), 'k', 'LineStyle', 'none', 'LineWidth', 2);
  203. errorbar(1:6, mean(all_within_FC_REST2), std(all_within_FC_REST2)/sqrt(size(all_within_FC_REST2,1)), 'r', 'LineStyle', 'none', 'LineWidth', 2);
  205. % 标注显著性
  206. for i = 1:6
  207. if p_within(i) < 0.05
  208. % 在柱状图上方添加星号
  209. max_y = max([mean(all_within_FC_REST1(:, i)) + std(all_within_FC_REST1(:, i))/sqrt(size(all_within_FC_REST1,1)), ...
  210. mean(all_within_FC_REST2(:, i)) + std(all_within_FC_REST2(:, i))/sqrt(size(all_within_FC_REST2,1))]);
  211. text(i, max_y + 0.02, '*', 'FontSize', 16, 'HorizontalAlignment', 'center', 'Color', 'black');
  212. end
  213. end
  215. set(gca, 'XTick', 1:6, 'XTickLabel', {'AT', 'AU', 'PT', 'PU', 'A', 'P'});
  216. xlabel('Network');
  217. ylabel('Mean FC');
  218. title('Within Network FC');
  219. legend({'REST1', 'REST2'}, 'Location', 'Best');
  220. hold off;
  222. % 绘制between network的组水平结果
  223. subplot(1,2,2);
  224. hold on;
  226. % 将 1:6 修改为 1:7
  227. b = bar(1:7, [mean(all_between_FC_REST1); mean(all_between_FC_REST2)]', 'FaceColor', 'flat');
  229. % 设置柱图的颜色，REST1为黑色，REST2为红色
  230. b(1).CData = repmat([0 0 0], 7, 1); % 黑色
  231. b(2).CData = repmat([1 0 0], 7, 1); % 红色
  233. % 添加散点，颜色与柱图颜色匹配
  234. for i = 1:7
  235. scatter(i * ones(size(all_between_FC_REST1, 1), 1), all_between_FC_REST1(:, i), 50, [0 0 0], 'filled', 'jitter','on', 'jitterAmount', 0.15);
  236. scatter(i * ones(size(all_between_FC_REST2, 1), 1), all_between_FC_REST2(:, i), 50, [1 0 0], 'filled', 'jitter','on', 'jitterAmount', 0.15);
  237. end
  239. % 绘制误差条
  240. errorbar(1:7, mean(all_between_FC_REST1), std(all_between_FC_REST1)/sqrt(size(all_between_FC_REST1,1)), 'k', 'LineStyle', 'none', 'LineWidth', 2);
  241. errorbar(1:7, mean(all_between_FC_REST2), std(all_between_FC_REST2)/sqrt(size(all_within_FC_REST2,1)), 'r', 'LineStyle', 'none', 'LineWidth', 2);
  243. % 标注显著性
  244. for i = 1:7
  245. if p_between(i) < 0.05
  246. % 在柱状图上方添加星号
  247. max_y = max([mean(all_between_FC_REST1(:, i)) + std(all_between_FC_REST1(:, i))/sqrt(size(all_between_FC_REST1,1)), ...
  248. mean(all_between_FC_REST2(:, i)) + std(all_between_FC_REST2(:, i))/sqrt(size(all_within_FC_REST2,1))]);
  249. text(i, max_y + 0.02, '*', 'FontSize', 16, 'HorizontalAlignment', 'center', 'Color', 'black');
  250. end
  251. end
  253. % 更新 XTick 标签
  254. set(gca, 'XTick', 1:7, 'XTickLabel', {'AT-PT', 'AT-PU', 'AT-AU', 'AU-PT', 'AU-PU', 'PT-PU', 'A-P'});
  255. xlabel('Network Pairs');
  256. ylabel('Mean FC');
  257. title('Between Network FC');
  258. legend({'REST1', 'REST2'}, 'Location', 'Best');
  259. hold off;
  261. % 保存组水平结果图
  262. saveas(gcf, fullfile(groupDir, 'Group_Level_FC_Results.png'));
  264. % 打印配对t检验结果并标出显著的网络或网络对
  265. disp('Paired t-test results for within network FC:');
  266. for i = 1:6
  267. fprintf('Network %d (%s): t = %.3f, p = %.3f', i, getNetworkLabel(i), t_within(i), p_within(i));
  268. if p_within(i) < 0.05
  269. fprintf(' *\n'); % 添加星号表示显著性
  270. else
  271. fprintf('\n');
  272. end
  273. end
  275. disp('Paired t-test results for between network FC:');
  276. for i = 1:6
  277. fprintf('Network Pair %d (%s): t = %.3f, p = %.3f', i, getNetworkPairLabel(i), t_between(i), p_between(i));
  278. if p_between(i) < 0.05
  279. fprintf(' *\n'); % 添加星号表示显著性
  280. else
  281. fprintf('\n');
  282. end
  283. end
  285. % 保存变量到 CSV 文件
  286. csvwrite(fullfile(groupDir, 'all_within_FC_REST1.csv'), all_within_FC_REST1);
  287. csvwrite(fullfile(groupDir, 'all_within_FC_REST2.csv'), all_within_FC_REST2);
  288. csvwrite(fullfile(groupDir, 'all_between_FC_REST1.csv'), all_between_FC_REST1);
  289. csvwrite(fullfile(groupDir, 'all_between_FC_REST2.csv'), all_between_FC_REST2);
  290. csvwrite(fullfile(groupDir, 'p_within.csv'), p_within);
  291. csvwrite(fullfile(groupDir, 'p_between.csv'), p_between);
  293. % 辅助函数，用于获取网络标签
  294. function label = getNetworkLabel(index)
  295. labels = {'AT', 'AU', 'PT', 'PU', 'A', 'P'};
  296. label = labels{index};
  297. end
  299. % 辅助函数，用于获取网络对标签
  300. function label = getNetworkPairLabel(index)
  301. labels = {'AT-PT', 'AT-PU', 'AU-PT', 'AU-PU', 'PT-PU', 'A-P'};
  302. label = labels{index};
  303. end

FC_analysis.m, under CC-BY-4.0 · at the source

Overview

Authors: Rui Dai1,2,3, Hyunwoo Jang1,2, Anthony G. Hudetz1,2,3,4, Zirui Huang1,2,3,4, George A. Mashour1,2,3,4,5,6
ORCID iDs: Rui Dai, Zirui Huang
  1. Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
  2. Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI 48109, USA
  3. Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI 48109, USA
  4. Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
  5. Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
  6. Lead contact
Institutions: University of Michigan (United States); Michigan Medicine (United States)
Journal: Cell reports, volume 45, issue 8, article 117830
Dates: published online 12 August 2026; in print August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.celrep.2026.117830 · PMID 42585017 · PMCID PMC13584462 · OpenAlex W7202251238
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Graphs, fMRI & imaging
Keywords: Complexity, Integration, Sleep, fMRI, Segregation, Sedation, Anesthesia, Consciousness, Functional Connectivity, Psychedelics, Cp: Neuroscience
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Funding: National Institutes of Health (T32-GM103730, R01-GM103894, R01-GM111293); NIGMS NIH HHS (R01 GM103894, T32 GM103730, R01 GM111293)
Citations: not cited yet (Europe PMC); 52 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

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Zenodo 14029241

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 2 files
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source:

biorender.com

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source: BioRender.com

The paper's code and data availability statement is in the Data section.

Tracing map

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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and data availability statement

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

  • it points to a dataset: OpenNeuro ds006072
  • it points to the authors' code: biorender.com, Zenodo 14029241
  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1016/j.celrep.2026.117830.

Versions

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Version 3, 28 September 2026

  • Publisher: — → Cell Press

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 11 keywords, 2 funders, 50 references.

Cite

This paper

Dai, R., Jang, H., Hudetz, A. G., Huang, Z., & Mashour, G. A. (2026). Opposite network patterns of integration-segregation in psychedelic and sedated states of consciousness. Cell reports, 45(8), 117830. https://doi.org/10.1016/j.celrep.2026.117830

BibTeX

@article{dai2026opposite,
author = {Dai, Rui and Jang, Hyunwoo and Hudetz, Anthony G. and Huang, Zirui and Mashour, George A.},
title = {{Opposite network patterns of integration-segregation in psychedelic and sedated states of consciousness}},
journal = {Cell reports},
year = {2026},
month = aug,
volume = {45},
number = {8},
pages = {117830},
publisher = {Cell Press},
issn = {2211-1247},
doi = {10.1016/j.celrep.2026.117830},
url = {https://doi.org/10.1016/j.celrep.2026.117830},
pmid = {42585017},
pmcid = {PMC13584462}
}

RIS

TY - JOUR
AU - Dai, Rui
AU - Jang, Hyunwoo
AU - Hudetz, Anthony G.
AU - Huang, Zirui
AU - Mashour, George A.
TI - Opposite network patterns of integration-segregation in psychedelic and sedated states of consciousness
T2 - Cell reports
J2 - Cell Rep
PY - 2026
DA - 2026/08/12
VL - 45
IS - 8
SP - 117830
SN - 2211-1247
PB - Cell Press
DO - 10.1016/j.celrep.2026.117830
UR - https://doi.org/10.1016/j.celrep.2026.117830
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.celrep.2026.117830",
"type": "article-journal",
"title": "Opposite network patterns of integration-segregation in psychedelic and sedated states of consciousness",
"container-title": "Cell reports",
"author": [
{
"family": "Dai",
"given": "Rui"
},
{
"family": "Jang",
"given": "Hyunwoo"
},
{
"family": "Hudetz",
"given": "Anthony G."
},
{
"family": "Huang",
"given": "Zirui"
},
{
"family": "Mashour",
"given": "George A."
}
],
"container-title-short": "Cell Rep",
"volume": "45",
"issue": "8",
"page": "117830",
"DOI": "10.1016/j.celrep.2026.117830",
"PMID": "42585017",
"PMCID": "PMC13584462",
"ISSN": "2211-1247",
"publisher": "Cell Press",
"URL": "https://doi.org/10.1016/j.celrep.2026.117830",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
12
]
]
}
}

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

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