OSCR

Awake cortex stabilizes traveling waves for global and reliable information routing.

Code ↔ Paper

10 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 10 matches
  1. [1] § STAR★Methods › Quantification and statistical analysis › Simulation of neural signal propagation ↔ figure2.m, lines 15–35 · score 0.90 · latency jitter, standard deviation, trial variability, Brown noise, oscillation, duration
  2. [2] § STAR★Methods › Quantification and statistical analysis › PCI calculation ↔ PCIst/pci_st.py, lines 150–262 · score 0.84 · PCIst, state transitions, principal component, distance matrix, perturbational, NST
  3. [3] § Results › In vivo, awake state enhances the consistency of visually evoked traveling waves ↔ figure3.m, lines 7–61 · score 0.68 · 20–450 ms, 25–267 ms, iTWC, 25 ms, 20 ms, Figure 3
  4. [4] § STAR★Methods › Quantification and statistical analysis › Simulation of neural signal propagation ↔ figure2.m, lines 15–35 · score 0.65 · coupling strength, brown noise, model, delay, propagation, Simulation
  5. [5] § STAR★Methods › Quantification and statistical analysis › Statistical analysis ↔ figure2.m, lines 314–374 · score 0.61 · generated surrogate, trial shuffling, simulation, Figure 2, signals
  6. [6] § STAR★Methods › Quantification and statistical analysis › GP calculation ↔ figure2.m, lines 216–304 · score 0.59 · generalized phase vector, 4–40 Hz, GP
  7. [7] § Results › Enhanced selectivity of informational tuning for wave motifs in awake state ↔ figureS7.m, lines 1–15 · score 0.59 · TE magnitude, signal power, tuning selectivity, awake, anesthetized
  8. [8] § STAR★Methods › Quantification and statistical analysis › Confound-control analysis for TE tuning selectivity › Linear mixed-effects models ↔ figureS7.m, lines 1–15 · score 0.59 · TE tuning selectivity, signal power, LMMs, SNR, anesthesia, awake
  9. [9] § STAR★Methods › Quantification and statistical analysis › Theta and gamma band power analysis ↔ figureS9.m, lines 1–19 · score 0.56 · gamma bands, scored, theta, power, dynamics
  10. [10] § Results › Enhanced selectivity of informational tuning for wave motifs in awake state ↔ figureS8.m, lines 1–13 · score 0.56 · tuning selectivity, tuning curves, S8, lag, motif, TE

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 397 lines · 15 KB · CC-BY-4.0 · 4 matches

  1. %% figure2
  4. % --------------------------------------------------------------------------
  5. % This script generates simulated data and calculates Transfer Entropy (TE)
  6. % to the scenarios presented in Figure 2 of the paper.
  7. %
  8. % Two scenarios are simulated:
  9. % 1. 'Connected' condition: Signal Y is generated directly from a delayed
  10. % version of signal X, representing a direct connection.
  11. % 2. 'Disconnected' condition: Both signals are driven by an external input
  12. % with different latencies, but without a direct connection between them.
  13. % --------------------------------------------------------------------------
  15. %% --- Simulation Parameters ---
  17. % General parameters
  18. fs = 1000; % Sampling frequency (Hz)
  19. duration = 2.4; % Duration of each trial (s)
  20. nTrials = 100; % Number of trials per session
  21. nSessions = 45; % Number of simulated sessions
  23. % Parameters for the synthetic Event-Related Potential (ERP) waveform
  24. erp_peak_time = 1.2; % Time of the ERP peak (s)
  25. erp_freq = 10; % Frequency of the ERP oscillation (Hz)
  26. erp_std_dev = 0.05; % Standard deviation of the Gaussian window for the ERP
  28. % Parameters for trial-to-trial variability
  29. amplitude_variability = 0.03; % Variability in ERP amplitude
  30. latency_jitter_std = 0.003; % Standard deviation of latency jitter (s)
  32. % Model-specific parameters
  33. tau_ms = 30; % Time delay for signal propagation (ms)
  34. coupling_strength = 0.8;% Coupling strength between signals
  35. noise_level = 1.2; % Amplitude of the brown noise
  37. %% --- Initialization ---
  39. t = 0:1/fs:duration-1/fs;
  40. nSamples = length(t);
  41. tau_samples = round(tau_ms / 1000 * fs);
  43. % Pre-allocate matrices to store results from all sessions
  44. TE_simu_con_all = nan(nSessions, nSamples - tau_ms, 2, 2);
  45. TE_simu_dis_all = nan(nSessions, nSamples - tau_ms, 2, 2);
  46. MI_simu_con_all = nan(nSessions, nSamples - tau_ms, 2, 2);
  47. MI_simu_dis_all = nan(nSessions, nSamples - tau_ms, 2, 2);
  49. % Pre-allocate matrices to store the raw simulated data
  50. Connected_data_all = nan(nSessions, nTrials, 2, nSamples);
  51. Disconnected_data_all = nan(nSessions, nTrials, 2, nSamples);
  53. %% --- Main Simulation Loop ---
  58. for isess = 1:nSessions
  60. brownNoiseGenerator = dsp.ColoredNoise('InverseFrequencyPower', 2, ...
  61. 'SamplesPerFrame', nSamples);
  63. X_connected = zeros(nTrials, nSamples);
  64. Y_connected = zeros(nTrials, nSamples);
  65. X_disconnected = zeros(nTrials, nSamples);
  66. Y_disconnected = zeros(nTrials, nSamples);
  68. for i = 1:nTrials
  70. % Generate trial-specific variability
  71. amp_mod = 1 + amplitude_variability * randn();
  72. jitter_samples = round(latency_jitter_std * randn() * fs);
  74. % Generate the base ERP waveform for this trial
  75. gauss_win = exp(-(t - erp_peak_time).^2 / (2 * erp_std_dev^2));
  76. sin_wave = sin(2 * pi * erp_freq * (t - erp_peak_time));
  77. base_erp_waveform = amp_mod * gauss_win .* sin_wave;
  78. base_erp_waveform = circshift(base_erp_waveform, jitter_samples);
  80. % Generate independent brown noise sources
  81. % release(brownNoiseGenerator);
  82. noise_x_source = noise_level * zscore(brownNoiseGenerator()');
  83. % release(brownNoiseGenerator);
  84. noise_y_connected = noise_level * zscore(brownNoiseGenerator()');
  85. % release(brownNoiseGenerator);
  86. noise_y_disconnected = noise_level * zscore(brownNoiseGenerator()');
  88. % Generate signal X (common for both scenarios)
  89. X_trial = base_erp_waveform + noise_x_source;
  90. X_connected(i, :) = X_trial;
  91. X_disconnected(i, :) = X_trial;
  93. % Scenario 1: 'Connected' condition
  94. X_lagged = [zeros(1, tau_samples), X_trial(1:end-tau_samples)];
  95. Y_connected(i, :) = coupling_strength * X_lagged + noise_y_connected;
  97. % Scenario 2: 'Disconnected' condition
  98. Z_trial = base_erp_waveform;
  99. Z_lagged = [zeros(1, tau_samples), Z_trial(1:end-tau_samples)];
  100. Y_disconnected(i, :) = coupling_strength * Z_lagged + noise_y_disconnected;
  101. end
  103. % --- Calculate TE and MI for the generated data ---
  105. % Reshape data into the format: [trial, channel, time]
  106. Connected_data = cat(2, reshape(X_connected, [nTrials, 1, nSamples]), ...
  107. reshape(Y_connected, [nTrials, 1, nSamples]));
  108. Disconnected_data = cat(2, reshape(X_disconnected, [nTrials, 1, nSamples]), ...
  109. reshape(Y_disconnected, [nTrials, 1, nSamples]));
  111. target_time = (tau_ms + 1):nSamples;
  112. past_tau = tau_ms;
  114. [TE_all_xy_con, ~, ~, ~, ~, ~, ~, MI_all_con] = ...
  115. calc_PhiID_trial_based2(Connected_data, target_time, past_tau);
  117. [TE_all_xy_dis, ~, ~, ~, ~, ~, ~, MI_all_dis] = ...
  118. calc_PhiID_trial_based2(Disconnected_data, target_time, past_tau);
  120. % --- Store results for the current session ---
  121. Connected_data_all(isess, :, :, :) = Connected_data;
  122. Disconnected_data_all(isess, :, :, :) = Disconnected_data;
  123. TE_simu_con_all(isess, :, :, :) = TE_all_xy_con;
  124. TE_simu_dis_all(isess, :, :, :) = TE_all_xy_dis;
  125. MI_simu_con_all(isess, :, :, :) = MI_all_con;
  126. MI_simu_dis_all(isess, :, :, :) = MI_all_dis;
  128. fprintf('Session %d/%d completed.\n', isess, nSessions);
  129. end
  133. %%
  135. %% Averaged ERP plot
  139. tar_time = 901:1500;
  140. post_time = 1001:1400;
  142. vep_sess_x = squeeze(mean(Connected_data_all(:,:,1,tar_time),[2]));
  143. vep_sess_y_con = squeeze(mean(Connected_data_all(:,:,2,tar_time),[2]));
  144. vep_sess_y_dis = squeeze(mean(Disconnected_data_all(:,:,2,tar_time),[2]));
  146. y_x = mean(vep_sess_x);
  147. sem_x = sqrt(var(vep_sess_x) / length(size(Connected_data_all,1)));
  148. y_y_con = mean(vep_sess_y_con);
  149. sem_y_con = sqrt(var(vep_sess_y_con) / length(size(Connected_data_all,1)));
  150. y_y_dis = mean(vep_sess_y_dis);
  151. sem_y_dis = sqrt(var(vep_sess_y_dis) / length(size(Connected_data_all,1)));
  152. x = 1:length(y_x);
  154. % Create figure with appropriate size
  155. figure('Position', [100, 100, 800, 500], 'Color', 'white')
  157. % Plot data with shaded error bars
  158. hold on
  160. h2 = shadedErrorBar(x, y_x, sem_x, 'lineProps', {'Color', [0.5 0 0.5 0.85], 'LineWidth', 2, 'LineStyle', '-'}, 'patchSaturation', 0.08);
  161. set(h2.edge, 'LineWidth', 1);
  162. set(h2.edge, 'Color', [0.5 0 0.5, 0.4]);
  166. h0 = shadedErrorBar(x, y_y_dis, sem_y_dis, 'lineProps', {'Color', [0 0.5 0 0.93], 'LineWidth', 2, 'LineStyle', '-.'}, 'patchSaturation', 0.1);
  167. set(h0.edge, 'LineWidth', 1);
  168. set(h0.edge, 'Color', [0 0.5 0, 0.4]);
  171. h1 = shadedErrorBar(x, y_y_con, sem_y_con, 'lineProps', {'Color', [1 0.5 0 0.93], 'LineWidth', 2, 'LineStyle', '-'}, 'patchSaturation', 0.1);
  172. set(h1.edge, 'LineWidth', 1);
  173. set(h1.edge, 'Color', [1 0.5 0, 0.4]);
  177. % Set axis limits and ticks
  178. % xlim([850 1500])
  179. % xticks([900 1000 : 100: 1500])
  180. % xticklabels({'-200', '-100', '0', '100', '200', '300', '400', '500', '600', '700', '800'})
  181. ylim([-1.2 1.2])
  182. % xticklabels({'-100', '0', '100', '200', '300', '400', '500'})
  183. % Remove axes ticks and labels
  184. set(gca, 'XTick', [], 'YTick', [])
  185. set(gca, 'XTickLabel', [], 'YTickLabel', [])
  187. % Set axis properties for clean appearance
  188. set(gca, 'Box', 'off')
  189. set(gca, 'XColor', 'none', 'YColor', 'none')
  190. set(gca, 'Color', 'white')
  192. % % Add scale bar (100 units) in bottom left
  193. ylim_current = ylim;
  194. xlim_current = xlim;
  195. y_pos = ylim_current(1) + 0.1 * (ylim_current(2) - ylim_current(1)); % 10% from bottom
  196. x_start = xlim_current(1) + 0.05 * (xlim_current(2) - xlim_current(1)); % 5% from left
  197. x_end = x_start + 100; % 100 units long
  199. % % Draw scale bar
  200. % line([x_start, x_end], [y_pos, y_pos], 'Color', 'black', 'LineWidth', 2)
  201. % % Add vertical lines at ends
  202. % line([x_start, x_start], [y_pos-0.02*(ylim_current(2)-ylim_current(1)), y_pos+0.02*(ylim_current(2)-ylim_current(1))], 'Color', 'black', 'LineWidth', 2)
  203. % line([x_end, x_end], [y_pos-0.02*(ylim_current(2)-ylim_current(1)), y_pos+0.02*(ylim_current(2)-ylim_current(1))], 'Color', 'black', 'LineWidth', 2)
  206. % Draw scale bar (simple horizontal line)
  207. line([x_start, x_end], [y_pos, y_pos], 'Color', 'black', 'LineWidth', 2)
  208. % Set figure background to white
  209. set(gcf, 'Color', 'white')
  211. % Optional: adjust margins
  212. set(gca, 'Position', [0.1 0.15 0.85 0.8])
  214. hold off
  216. %% ISPC
  217. % Generalized Phase toolbox is required.
  220. fs = 1000;
  221. [b, a] = butter(2, [4 40]/(fs/2), 'bandpass');
  224. Connected_ispc_all = nan(45,length(t));
  225. Disconnected_ispc_all = nan(45,length(t));
  228. for isess = 1 : 45
  230. Connected_data = squeeze(Connected_data_all(isess,:,:,:,:));
  231. Disconnected_data = squeeze(Disconnected_data_all(isess,:,:,:,:));
  234. gp_direc = zeros(size(Connected_data));
  235. gp_comm = zeros(size(Disconnected_data));
  238. parfor itri = 1 : nTrials
  239. hako1 = nan(2,size(Connected_data,3));
  240. hako2 = nan(2,size(Disconnected_data,3));
  242. [hako1(1,:), ~, ~] = generalized_phase_vector( filtfilt(b, a, squeeze(Connected_data(itri,1,:))), fs, 0 );
  243. [hako1(2,:), ~, ~] = generalized_phase_vector( filtfilt(b, a, squeeze(Connected_data(itri,2,:))), fs, 0 );
  244. [hako2(1,:), ~, ~] = generalized_phase_vector( filtfilt(b, a, squeeze(Disconnected_data(itri,1,:))), fs, 0 );
  245. [hako2(2,:), ~, ~] = generalized_phase_vector( filtfilt(b, a, squeeze(Disconnected_data(itri,2,:))), fs, 0 );
  247. gp_direc(itri,:,:) = hako1;
  248. gp_comm(itri,:,:) = hako2;
  249. end
  251. Connec_ispc = abs(sum(exp(1i*(angle(squeeze(gp_direc(:,1,:)./conj(gp_direc(:,2,:))))))))/nTrials;
  252. Dsiconnec_ispc = abs(sum(exp(1i*(angle(squeeze(gp_comm(:,1,:)./conj(gp_comm(:,2,:))))))))/nTrials;
  254. Connected_ispc_all(isess,:) = Connec_ispc;
  255. Disconnected_ispc_all(isess,:) = Dsiconnec_ispc;
  257. end
  260. tar_time = 901:1500;
  262. ispc_sess_y_con = Connected_ispc_all(:,tar_time);
  263. ispc_sess_y_dis = Disconnected_ispc_all(:,tar_time);
  265. % y_x = mean(vep_sess_x);
  266. % sem_x = sqrt(var(vep_sess_x) / length(size(Connected_data_all,1)));
  267. y_ispc_con = mean(ispc_sess_y_con);
  268. sem_ispc_con = sqrt(var(ispc_sess_y_con) / length(size(Connected_data_all,1)));
  269. y_ispc_dis = mean(ispc_sess_y_dis);
  270. sem_ispc_dis = sqrt(var(ispc_sess_y_dis) / length(size(Connected_data_all,1)));
  271. x = 1:length(y_ispc_con);
  274. figure('Position', [100, 100, 720, 500], 'Color', 'white')
  275. % Plot data with shaded error bars
  276. hold on
  277. h0 = shadedErrorBar(x, y_ispc_dis, sem_ispc_dis, 'lineProps', {'Color', [0 0.5 0 0.93], 'LineWidth', 2, 'LineStyle', '-.'}, 'patchSaturation', 0.1);
  278. set(h0.edge, 'LineWidth', 1);
  279. set(h0.edge, 'Color', [0 0.5 0, 0.4]);
  280. h1 = shadedErrorBar(x, y_ispc_con, sem_ispc_con, 'lineProps', {'Color', [1 0.5 0 0.93], 'LineWidth', 2, 'LineStyle', '-'}, 'patchSaturation', 0.1);
  281. set(h1.edge, 'LineWidth', 1);
  282. set(h1.edge, 'Color', [1 0.5 0, 0.4]);
  283. % Set axis properties for publication
  284. set(gca, 'Box', 'off')
  285. set(gca, 'FontSize', 20)
  286. set(gca, 'FontName', 'Arial')
  287. set(gca, 'LineWidth', 1.5)
  288. % Set axis colors to black
  289. set(gca, 'XColor', 'black', 'YColor', 'black')
  290. ylim([0 1])
  291. yticks(0:0.2:1)
  292. % % % Add axis labels
  293. % xlabel('Time (ms)', 'FontSize', 16, 'FontWeight', 'bold')
  294. % ylabel('ISPC', 'FontSize', 16, 'FontWeight', 'bold')
  295. % Set figure background to white
  296. set(gcf, 'Color', 'white')
  297. % Adjust margins for proper label display
  298. set(gca, 'Position', [0.15 0.2 0.8 0.75])
  299. hold off
  303. % print('fig2_ispc', '-dpng', '-r300');
  304. % fprintf('fig2_ispc.png (300 DPI)\n');
  308. %%
  314. %% --- Statistical Analysis using Surrogate Data (Example on Last Session) ---
  316. % This section demonstrates how to create a null distribution for TE
  317. % using trial-shuffled surrogates. The analysis is performed on the
  318. % data from the final simulated session.
  320. % --- Calculate Normalized TE for the original data ---
  321. norm_TE_xy_con = squeeze(TE_all_xy_con(:,1,2) ./ MI_all_con(:,1,2));
  322. norm_TE_yx_con = squeeze(TE_all_xy_con(:,2,1) ./ MI_all_con(:,2,1));
  323. norm_TE_xy_dis = squeeze(TE_all_xy_dis(:,1,2) ./ MI_all_dis(:,1,2));
  324. norm_TE_yx_dis = squeeze(TE_all_xy_dis(:,2,1) ./ MI_all_dis(:,2,1));
  326. % --- Generate surrogate data and compute TE to create a null distribution ---
  327. nSurrogates = 1000;
  328. fprintf('Generating %d surrogates to compute the null distribution of TE...\n', nSurrogates);
  330. % Pre-allocate matrices to store surrogate results
  331. surrogate_TE_xy_con = nan(length(target_time), nSurrogates);
  332. surrogate_TE_yx_con = nan(length(target_time), nSurrogates);
  333. surrogate_TE_xy_dis = nan(length(target_time), nSurrogates);
  334. surrogate_TE_yx_dis = nan(length(target_time), nSurrogates);
  335. %
  336. % Note: Requires the Parallel Computing Toolbox.
  337. parfor i = 1:nSurrogates
  338. % Create surrogate data by shuffling trials of the source signal (X)
  339. shuffled_indices = randperm(nTrials);
  341. % Surrogate for 'Connected' condition
  342. Surrogate_data_con = Connected_data;
  343. Surrogate_data_con(:, 1, :) = Connected_data(shuffled_indices, 1, :); % Shuffle X
  345. % Surrogate for 'Disconnected' condition
  346. Surrogate_data_dis = Disconnected_data;
  347. Surrogate_data_dis(:, 1, :) = Disconnected_data(shuffled_indices, 1, :); % Shuffle X
  349. % Calculate TE for the surrogate data
  350. [TE_surr_xy_c, ~, ~, ~, ~, ~, ~, MI_surr_c] = calc_PhiID_trial_based2(Surrogate_data_con, target_time, past_tau);
  351. [TE_surr_xy_d, ~, ~, ~, ~, ~, ~, MI_surr_d] = calc_PhiID_trial_based2(Surrogate_data_dis, target_time, past_tau);
  353. % Normalize and store the results
  354. surrogate_TE_xy_con(:, i) = squeeze(TE_surr_xy_c(:,1,2) ./ MI_surr_c(:,1,2));
  355. surrogate_TE_yx_con(:, i) = squeeze(TE_surr_xy_c(:,2,1) ./ MI_surr_c(:,2,1));
  356. surrogate_TE_xy_dis(:, i) = squeeze(TE_surr_xy_d(:,1,2) ./ MI_surr_d(:,1,2));
  357. surrogate_TE_yx_dis(:, i) = squeeze(TE_surr_xy_d(:,2,1) ./ MI_surr_d(:,2,1));
  359. % Display progress
  360. if mod(i, 200) == 0
  361. fprintf('Surrogate progress: %d / %d\n', i, nSurrogates);
  362. end
  363. end
  364. disp('Surrogate TE calculation completed.');
  366. % --- Determine Significance Threshold ---
  367. % Calculate the 99th percentile of the null distribution to use as a
  368. % significance threshold (p < 0.01).
  369. p_threshold = 99;
  371. TE_threshold_xy_con = prctile(surrogate_TE_xy_con, p_threshold, 2);
  372. TE_threshold_yx_con = prctile(surrogate_TE_yx_con, p_threshold, 2);
  373. TE_threshold_xy_dis = prctile(surrogate_TE_xy_dis, p_threshold, 2);
  374. TE_threshold_yx_dis = prctile(surrogate_TE_yx_dis, p_threshold, 2);
  381. %% plot
  384. post_con_x_y = squeeze(mean(TE_simu_con_all(:,post_time,1,2)./MI_simu_con_all(:,post_time,1,2),2,'omitnan'));
  385. post_con_y_x = squeeze(mean(TE_simu_con_all(:,post_time,2,1)./MI_simu_con_all(:,post_time,2,1),2,'omitnan'));
  386. post_dis_x_y = squeeze(mean(TE_simu_dis_all(:,post_time,1,2)./MI_simu_dis_all(:,post_time,1,2),2,'omitnan'));
  387. post_dis_y_x = squeeze(mean(TE_simu_dis_all(:,post_time,2,1)./MI_simu_dis_all(:,post_time,2,1),2,'omitnan'));
  390. vio = [0.5 0 0.5];
  391. ore = [1 0.5 0];
  393. % Connected
  394. [f, pp] = make_box_plot2_swarm_title(post_con_x_y, post_con_y_x, ore, vio, 'fig_2E_Connected',150, 0.85, 0.45, mean(TE_threshold_xy_con(post_time)), mean(TE_threshold_yx_con(post_time)))
  395. % Disconnected
  396. [f, pp] = make_box_plot2_swarm_title(post_dis_x_y, post_dis_y_x, [0 0.5 0], vio, 'fig_2F_Disonnected',100, 0.85, 0.5, mean(TE_threshold_xy_dis(post_time)), mean(TE_threshold_yx_dis(post_time)))

figure2.m, under CC-BY-4.0 · at the source

Overview

Authors: Kaio Misawa1, Koji Chinen1, Akira Kawabata1,2, Taro Kaiju3,4, Takafumi Suzuki3, Yutaka Komura1,4
ORCID iDs: Akira Kawabata
  1. Graduate School of Human and Environmental Studies, Kyoto University, Kyoto, Japan
  2. Japan Society for the Promotion of Science (JSPS), Tokyo, Japan
  3. Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Osaka, Japan
  4. Human Informatics and Interaction Research Institute, National Institute of Advanced Industrial Science and Technology, Ibaraki, Japan
Institutions: Kyoto University (Japan); Japan Society for the Promotion of Science (Japan); National Institute of Advanced Industrial Science and Technology (Japan); National Institute of Information and Communications Technology (Japan); The University of Osaka (Japan)
Journal: iScience, volume 29, issue 8, article 116728
Dates: received 9 January 2026; accepted 23 June 2026; published online 10 July 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.116728 · PMID 42472099 · PMCID PMC13380430 · OpenAlex W7167911060
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Preprocessing, Evoked potentials
Keywords: bioinformatics of (un)consciousness, superiority of broadband waves, global and diverse motifs, selectivity of informational tuning, stability and efficiency
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: MEXT | Japan Science and Technology Agency (JST); Naito Foundation (21H04423); Ministry of Education, Culture, Sports, Science and Technology
Citations: not cited yet (Europe PMC); 67 references in the paper
Research resources: MATLAB R2025a RRID:SCR_001622, Psychophysics Toolbox v3 RRID:SCR_002881, Python RRID:SCR_008394

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 10 matches between paragraphs and lines of code.

Zenodo 20759618

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data and code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: shadedErrorBar (6 files), Statistics and Machine Learning Toolbox (5 files), Image Processing Toolbox (1 file), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
33 files
At the source: doi.org/10.5281/zenodo.20759618

renzocom/PCIst

License: GPL-3.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ed7d85389c559cc6b65bf20c10f619dfe0acf5e6, 31 August 2023
Languages: Python (4), MATLAB (1)
Size: 8 files, 5 scripts
Software Heritage: not archived
Found in: the text, “PCI calculation”
Holds: README, license file, environment (setup.py)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/renzocom/PCIst

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 37 scripts, each with its path and the digest of its content;
  • 10 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.

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 the authors' code: Zenodo 20759618
  • it says that the data are available on request

Read it in the paper: doi.org/10.1016/j.isci.2026.116728.

Versions

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

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 5 keywords, 3 funders, 67 references, 3 RRIDs.

Cite

This paper

Misawa, K., Chinen, K., Kawabata, A., Kaiju, T., Suzuki, T., & Komura, Y. (2026). Awake cortex stabilizes traveling waves for global and reliable information routing. iScience, 29(8), 116728. https://doi.org/10.1016/j.isci.2026.116728

BibTeX

@article{misawa2026awake,
author = {Misawa, Kaio and Chinen, Koji and Kawabata, Akira and Kaiju, Taro and Suzuki, Takafumi and Komura, Yutaka},
title = {{Awake cortex stabilizes traveling waves for global and reliable information routing}},
journal = {iScience},
year = {2026},
month = jul,
volume = {29},
number = {8},
pages = {116728},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.116728},
url = {https://doi.org/10.1016/j.isci.2026.116728},
pmid = {42472099},
pmcid = {PMC13380430}
}

RIS

TY - JOUR
AU - Misawa, Kaio
AU - Chinen, Koji
AU - Kawabata, Akira
AU - Kaiju, Taro
AU - Suzuki, Takafumi
AU - Komura, Yutaka
TI - Awake cortex stabilizes traveling waves for global and reliable information routing
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/07/10
VL - 29
IS - 8
SP - 116728
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.116728
UR - https://doi.org/10.1016/j.isci.2026.116728
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.isci.2026.116728",
"type": "article-journal",
"title": "Awake cortex stabilizes traveling waves for global and reliable information routing",
"container-title": "iScience",
"author": [
{
"family": "Misawa",
"given": "Kaio"
},
{
"family": "Chinen",
"given": "Koji"
},
{
"family": "Kawabata",
"given": "Akira"
},
{
"family": "Kaiju",
"given": "Taro"
},
{
"family": "Suzuki",
"given": "Takafumi"
},
{
"family": "Komura",
"given": "Yutaka"
}
],
"container-title-short": "iScience",
"volume": "29",
"issue": "8",
"page": "116728",
"DOI": "10.1016/j.isci.2026.116728",
"PMID": "42472099",
"PMCID": "PMC13380430",
"ISSN": "2589-0042",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.isci.2026.116728",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
10
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.7554/elife.108208 [code]
Realistic coupling enables flexible macroscopic traveling waves in the mouse cortex.
Journal: eLife
In common: NumPy, 9 references
[2] doi:10.1038/s41467-026-71386-z [code]
Planar, spiral, and concentric traveling waves distinguish behavioral states in human memory.
Journal: Nature communications
In common: SciPy, NumPy, 7 references
[3] doi:10.3389/fncom.2026.1844662
Quantifying cortex-wide traveling brain waves of complex patterns with a graph-based algorithm.
Journal: Frontiers in computational neuroscience
In common: 8 references
[4] doi:10.1016/j.celrep.2026.117782 [code]
Thermodynamics of consciousness: Non-equilibrium brain dynamics track conscious states.
Journal: Cell reports
In common: Signal Processing Toolbox, Statistics and Machine Learning Toolbox, SciPy, 1 other tool, 5 references
[5] doi:10.1016/j.crmeth.2026.101481 [code]
A hybrid micro-ECoG for functionally targeted multi-site and multi-scale investigation.
Journal: Cell reports methods
In common: shadedErrorBar, Image Processing Toolbox, Signal Processing Toolbox, 1 other tool, 4 references
[6] doi:10.1038/s41586-026-10448-0 [code]
Plasticity and language in the anaesthetized human hippocampus.
Journal: Nature
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 2 other tools, 3 references
[7] doi:10.1186/s40708-026-00312-2 [code]
Synergistic and redundant information dynamics exhibit dissociable alterations across schizophrenia and neurodevelopmental conditions.
Journal: Brain informatics
In common: Statistics and Machine Learning Toolbox, NumPy, 5 references
[8] doi:10.1038/s41467-026-76581-6 [code]
Thalamocortical bursts encode reward contingencies and drive associative learning.
Journal: Nature communications
In common: Image Processing Toolbox, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 2 other tools, 2 references
[9] doi:10.1073/pnas.2527296123
Traveling-wave transcranial alternating current stimulation (twtACS) causally links neural timing to cognitive function.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: 6 references
[10] doi:10.1073/pnas.2532072123 [code]
Spatially structured heterogeneity shapes large-scale cortical dynamics in a model of the human cortex.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: SciPy, NumPy, 5 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.