OSCR

Real-time fMRI-triggered experience sampling: A proof-of-concept study.

Code ↔ Paper

5 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 5 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Results › Exploratory validation of real-time BOLD signal triggers ↔ Code/Figure 3/offlineonlineplots.m, lines 680–758 · score 0.68 · Shaded error bars, PMC activity, PMC Trigger Events, trial onset, BOLD activity, SEM
  2. [2] § Results › Description of final dataset ↔ Code/Figure 2/exclusionbarplot.m, lines 33–61 · score 0.62 · Opposite ROI triggered, Incomplete rating, Head Motion, thoughts, Figure 2
  3. [3] § Results › Exploratory validation of real-time BOLD signal triggers ↔ Code/Figure 3/offlineonlineplots.m, lines 320–397 · score 0.61 · offline PMC, trial onset, BOLD activity, trigger events, alignment, validation
  4. [4] § Data Analyses › Power analysis ↔ Code/Figure 4 + Pre-registered Hypothesis Testing/rtfMRI_HypothesisTesting.R, lines 44–122 · score 0.59 · fitted model, lme4, CIs, coefficient, slope
  5. [5] § Methods › Experimental tasks › GradCPT ↔ gradCPT_mk_Mt_EV.m, the whole file · a weak match · score 0.56 · button presses, commission, city, mountains, stimuli, event

Paper

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

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The authors' code

MATLAB · 758 lines · 33 KB · no license · 2 matches

  1. %% Offline Online Comparison
  2. %Written July 25th 2025 by Tiara Bounyarith
  5. %% Group level and / or subject level analysis
  7. group_level = 1;
  8. subject_level = 0;
  10. %%
  11. data_dir = '/Volumes/LaCie/R21-rtfMRI';
  13. %Load in rtfMRI master table
  14. load([data_dir filesep 'analyses' filesep 'rtfMRI_master_table.mat']);
  16. %Exclude trials
  17. master = master(master.keep, :);
  19. %Exclude subjects
  20. excludedsubs = ["sub-01", "sub-02", "sub-28", "sub-29", "sub-30", "sub-44", "sub-70"];
  21. master = master(~ismember(master.subj, excludedsubs), :);
  23. %Get list of remaining subjects
  24. subs = unique(master.subj');
  26. %epoch range
  27. epoch_range = 8;
  29. %make plot folder for group
  30. if group_level == 1
  31. mkdir([data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'group_level_plots']);
  32. savepath_group=[data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'group_level_plots'];
  33. end
  35. %make plot folder for subj
  36. if subject_level == 1
  37. mkdir([data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'subject_level_plots']);
  38. savepath_sub=[data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'subject_level_plots'];
  39. end
  40. %% Graph activity around when daIC trials were triggered
  42. if group_level == 1
  43. group_online_target_daIC_epochs = []; group_z_online_target_daIC_epochs = [];
  44. group_online_nontarget_PMC_epochs = []; group_z_online_nontarget_PMC_epochs = [];
  45. group_offline_target_daIC_epochs = []; group_z_offline_target_daIC_epochs = [];
  46. group_offline_nontarget_PMC_epochs = []; group_z_offline_nontarget_PMC_epochs = [];
  47. end
  50. for i=1:length(subs)
  52. runs = length(unique(master.run(master.subj == subs(i))));
  54. subj_num = str2double(extractAfter(subs(i), "sub-"));
  56. if subject_level == 1
  57. sub_online_target_daIC_epochs = []; sub_z_online_target_daIC_epochs = [];
  59. if subj_num > 20
  60. sub_online_nontarget_PMC_epochs = [];
  61. sub_z_online_nontarget_PMC_epochs = [];
  62. end
  63. sub_offline_target_daIC_epochs = []; sub_z_offline_target_daIC_epochs = [];
  65. sub_offline_nontarget_PMC_epochs = []; sub_z_offline_nontarget_PMC_epochs = [];
  66. end
  68. for j=1:runs
  70. target_ROI = unique(master.ROI(master.subj == subs(i) & master.run == j));
  72. % ensure we are only looking at runs that are triggering from the daIC
  73. if target_ROI == "daIC"
  74. nontarget_ROI = "PMC";
  76. %load in online target daIC data
  77. load([data_dir filesep 'subject_opennft_data' filesep char(subs(i)) filesep 'mainLoopData' filesep char(subs(i)) '_' num2str(j) '_mainLoopData.mat'], 'scalProcTimeSeries');
  78. online_target_daIC = scalProcTimeSeries(1,:);
  81. %load in online nontarget PMC data for subjects post-algorithm fix
  83. if subj_num > 20
  84. online_nontarget_PMC = scalProcTimeSeries(2,:);
  85. end
  87. %load in offline target daIC data
  88. subj_padded = sprintf('sub-%03d', subj_num);
  89. offline_target_daIC = (load([data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'OfflineTargetROITimeSeries' filesep subj_padded filesep 'run' num2str(j) filesep subj_padded '_run' num2str(j) '_preproc_meants.txt']))';
  91. %load in offline nontarget PMC data
  92. offline_nontarget_PMC = (load([data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'OfflineNontargetROITimeSeries' filesep subj_padded filesep 'run' num2str(j) filesep subj_padded '_run' num2str(j) '_preproc_meants.txt']))';
  94. trials = master.trial_num(master.subj == subs(i) & master.run == j);
  96. for k=1:length(trials)
  98. % ensure we're only looking at daIC-triggered trials
  99. if master.trigger_type(master.subj == subs(i) & master.run == j & master.trial_num == trials(k)) == "ROI"
  101. % epoch around ROI-triggered trials for this run - online
  102. % target daIC
  103. curr_trial = master.trial_onset_TR(master.subj == subs(i) & master.run == j & master.trial_num == trials(k));
  105. run_length = size(online_target_daIC,2);
  107. start_epoch = max(1, curr_trial - epoch_range);
  108. end_epoch = min(run_length, curr_trial + epoch_range);
  110. curr_online_tar_daIC_epoch = online_target_daIC(1, start_epoch:end_epoch);
  112. if group_level == 1
  113. group_online_target_daIC_epochs = [group_online_target_daIC_epochs; curr_online_tar_daIC_epoch];
  114. end
  116. if subject_level == 1
  117. sub_online_target_daIC_epochs = [sub_online_target_daIC_epochs; curr_online_tar_daIC_epoch];
  118. end
  120. curr_z_online_tar_daIC = (curr_online_tar_daIC_epoch - mean(curr_online_tar_daIC_epoch)) / std(curr_online_tar_daIC_epoch);
  122. if group_level == 1
  123. group_z_online_target_daIC_epochs = [group_z_online_target_daIC_epochs; curr_z_online_tar_daIC];
  124. end
  126. if subject_level == 1
  127. sub_z_online_target_daIC_epochs = [sub_z_online_target_daIC_epochs; curr_z_online_tar_daIC];
  128. end
  130. % epoch around ROI-triggered trials for this run - online
  131. % nontarget PMC
  133. if subj_num > 20
  135. run_length = size(online_nontarget_PMC,2);
  137. start_epoch = max(1, curr_trial - epoch_range);
  138. end_epoch = min(run_length, curr_trial + epoch_range);
  140. curr_online_nontar_PMC_epoch = online_nontarget_PMC(1, start_epoch:end_epoch);
  142. if group_level == 1
  143. group_online_nontarget_PMC_epochs = [group_online_nontarget_PMC_epochs; curr_online_nontar_PMC_epoch];
  144. end
  146. if subject_level == 1
  147. sub_online_nontarget_PMC_epochs = [sub_online_nontarget_PMC_epochs; curr_online_nontar_PMC_epoch];
  148. end
  150. curr_z_online_nontar_PMC = (curr_online_nontar_PMC_epoch - mean(curr_online_nontar_PMC_epoch)) / std(curr_online_nontar_PMC_epoch);
  152. if group_level == 1
  153. group_z_online_nontarget_PMC_epochs = [group_z_online_nontarget_PMC_epochs; curr_z_online_nontar_PMC];
  154. end
  156. if subject_level == 1
  157. sub_z_online_nontarget_PMC_epochs = [sub_z_online_nontarget_PMC_epochs; curr_z_online_nontar_PMC];
  158. end
  159. end
  162. % offline target daIC
  163. run_length = size(offline_target_daIC,2);
  165. start_epoch = max(1, curr_trial - epoch_range);
  166. end_epoch = min(run_length, curr_trial + epoch_range);
  168. curr_offline_tar_daIC_epoch = offline_target_daIC(1, start_epoch:end_epoch);
  170. if group_level == 1
  171. group_offline_target_daIC_epochs = [group_offline_target_daIC_epochs; curr_offline_tar_daIC_epoch];
  172. end
  174. if subject_level == 1
  175. sub_offline_target_daIC_epochs = [sub_offline_target_daIC_epochs; curr_offline_tar_daIC_epoch];
  176. end
  178. curr_z_offline_tar_daIC = (curr_offline_tar_daIC_epoch - mean(curr_offline_tar_daIC_epoch)) / std(curr_offline_tar_daIC_epoch);
  180. if group_level == 1
  181. group_z_offline_target_daIC_epochs = [group_z_offline_target_daIC_epochs; curr_z_offline_tar_daIC];
  182. end
  184. if subject_level == 1
  185. sub_z_offline_target_daIC_epochs = [sub_z_offline_target_daIC_epochs; curr_z_offline_tar_daIC];
  186. end
  188. % offline nontarget PMC
  189. run_length = size(offline_nontarget_PMC,2);
  191. start_epoch = max(1, curr_trial - epoch_range);
  192. end_epoch = min(run_length, curr_trial + epoch_range);
  194. curr_offline_nontar_PMC_epoch = offline_nontarget_PMC(1, start_epoch:end_epoch);
  196. if group_level == 1
  197. group_offline_nontarget_PMC_epochs = [group_offline_nontarget_PMC_epochs; curr_offline_nontar_PMC_epoch];
  198. end
  200. if subject_level == 1
  201. sub_offline_nontarget_PMC_epochs = [sub_offline_nontarget_PMC_epochs; curr_offline_nontar_PMC_epoch];
  202. end
  204. curr_z_offline_nontar_PMC = (curr_offline_nontar_PMC_epoch - mean(curr_offline_nontar_PMC_epoch)) / std(curr_offline_nontar_PMC_epoch);
  206. if group_level == 1
  207. group_z_offline_nontarget_PMC_epochs = [group_z_offline_nontarget_PMC_epochs; curr_z_offline_nontar_PMC];
  208. end
  210. if subject_level == 1
  211. sub_z_offline_nontarget_PMC_epochs = [sub_z_offline_nontarget_PMC_epochs; curr_z_offline_nontar_PMC];
  212. end
  214. end
  217. end
  219. elseif target_ROI == "PMC"
  220. nontarget_ROI = "daIC";
  221. end
  223. end
  225. if subject_level == 1
  226. % find grand mean & SEM of z-scored online target daIC activity
  227. sub_avg_online_tar_daIC_epoch = mean(sub_z_online_target_daIC_epochs,1);
  228. sub_sem_online_tar_daIC_epoch = std(sub_z_online_target_daIC_epochs, 0, 1) / sqrt(size(sub_z_online_target_daIC_epochs,1));
  230. % mean & SEM of z-scored online nontarget PMC
  231. if subj_num > 20
  232. sub_avg_online_nontar_PMC_epoch = mean(sub_z_online_nontarget_PMC_epochs,1);
  233. sub_sem_online_nontar_PMC_epoch = std(sub_z_online_nontarget_PMC_epochs, 0, 1) / sqrt(size(sub_z_online_nontarget_PMC_epochs,1));
  234. end
  236. % mean & SEM z-scored offline daIC
  237. sub_avg_offline_tar_daIC_epoch = mean(sub_z_offline_target_daIC_epochs,1);
  238. sub_sem_offline_tar_daIC_epoch = std(sub_z_offline_target_daIC_epochs, 0, 1) / sqrt(size(sub_z_offline_target_daIC_epochs, 1));
  240. % mean & SEM z-scoredoffline PMC
  241. sub_avg_offline_nontar_PMC_epoch = mean(sub_z_offline_nontarget_PMC_epochs,1);
  242. sub_sem_offline_nontar_PMC_epoch = std(sub_z_offline_nontarget_PMC_epochs, 0, 1) / sqrt(size(sub_z_offline_nontarget_PMC_epochs,1));
  244. % Graph
  246. figure('Position', [500 500 1200 500])
  247. %figure('Position', [900 500 1500 500])
  249. a = shadedErrorBar([], sub_avg_online_tar_daIC_epoch, sub_sem_online_tar_daIC_epoch, 'lineprops', {'color', [1, 0, 0]}, 'patchSaturation', 0.08);
  250. hold on
  251. if subj_num > 20
  252. b = shadedErrorBar([], sub_avg_online_nontar_PMC_epoch, sub_sem_online_nontar_PMC_epoch, 'lineprops', {'color',[0, 0, 1]}, 'patchSaturation', 0.08);
  253. c = shadedErrorBar([], sub_avg_offline_tar_daIC_epoch, sub_sem_offline_tar_daIC_epoch, 'lineprops', {'color',[0.6, 0, 0]}, 'patchSaturation', 0.08);
  254. d = shadedErrorBar([], sub_avg_offline_nontar_PMC_epoch, sub_sem_offline_nontar_PMC_epoch, 'lineprops', {'color',[0, 0, 0.6]}, 'patchSaturation', 0.08);
  255. else
  256. b = shadedErrorBar([], sub_avg_offline_tar_daIC_epoch, sub_sem_offline_tar_daIC_epoch, 'lineprops', {'color',[0.6, 0, 0]}, 'patchSaturation', 0.08);
  257. c = shadedErrorBar([], sub_avg_offline_nontar_PMC_epoch, sub_sem_offline_nontar_PMC_epoch, 'lineprops', {'color',[0, 0, 0.6]}, 'patchSaturation', 0.08);
  258. end
  260. if subj_num > 20
  261. set(a.edge, 'LineWidth', 2, 'LineStyle', ':')
  262. a.mainLine.LineWidth = 4;
  264. set(b.edge, 'LineWidth', 2, 'LineStyle', ':')
  265. b.mainLine.LineWidth = 4;
  267. set(c.edge, 'LineWidth', 2, 'LineStyle', ':')
  268. c.mainLine.LineWidth = 4;
  270. set(d.edge, 'LineWidth', 2, 'LineStyle', ':')
  271. d.mainLine.LineWidth = 4;
  272. else
  273. set(a.edge, 'LineWidth', 2, 'LineStyle', ':')
  274. a.mainLine.LineWidth = 4;
  276. set(b.edge, 'LineWidth', 2, 'LineStyle', ':')
  277. b.mainLine.LineWidth = 4;
  279. set(c.edge, 'LineWidth', 2, 'LineStyle', ':')
  280. c.mainLine.LineWidth = 4;
  282. end
  284. xline(epoch_range+1, '_', {'Trial Onset'}, 'LabelVerticalAlignment','bottom','LabelHorizontalAlignment','center','FontSize', 18, 'LineWidth', 2)
  286. xlabel('TR', 'FontSize', 20)
  289. xticks([1:17])
  290. xticklabels({'-8','-7','-6','-5', '-4', '-3', '-2', '-1', '0', '1', '2', '3', '4', '5', '6','7','8'})
  291. ax=gca;
  292. ax.XAxis.FontSize = 25;
  295. % ylim([-1.5 2.0]);
  296. ylabel('BOLD Activity (z)', 'FontSize', 20)
  297. ax.YAxis.FontSize = 20;
  299. if subj_num > 20
  300. legend({'Real-time daIC Activity (Target)', 'Real-time PMC Activity (Non-target)', 'Offline daIC Activity (Target)', 'Offline PMC Activity (Non-target)'}, 'FontSize', 18, 'Location', 'best');
  301. else
  302. legend({'Real-time daIC Activity (Target)', 'Offline daIC Activity (Target)', 'Offline PMC Activity (Non-target)'}, 'FontSize', 18, 'Location', 'best');
  303. end
  305. title({char(subs(i)),'Real-time vs Offline Estimates of Average Region Activity During daIC-triggered Trials'}, 'FontSize', 23);
  306. if subj_num <20
  307. subtitle('*Online PMC Activity Estimates Unavailable');
  308. end
  310. print('-opengl', '-dpng','-r350',[savepath_sub filesep char(subs(i)) '_daICtrial_offline-online_plot.png']);
  312. hold off
  315. pause; close;
  317. end
  319. end
  320. %%
  321. if group_level == 1
  322. % find grand mean & SEM of z-scored online target daIC activity
  323. group_avg_online_tar_daIC_epoch = mean(group_z_online_target_daIC_epochs,1);
  324. group_sem_online_tar_daIC_epoch = std(group_z_online_target_daIC_epochs, 0, 1) / sqrt(size(group_z_online_target_daIC_epochs,1));
  326. % mean & SEM of z-scored online nontarget PMC
  327. group_avg_online_nontar_PMC_epoch = mean(group_z_online_nontarget_PMC_epochs,1);
  328. group_sem_online_nontar_PMC_epoch = std(group_z_online_nontarget_PMC_epochs, 0, 1) / sqrt(size(group_z_online_nontarget_PMC_epochs,1));
  331. % mean & SEM z-scored offline daIC
  332. group_avg_offline_tar_daIC_epoch = mean(group_z_offline_target_daIC_epochs,1);
  333. group_sem_offline_tar_daIC_epoch = std(group_z_offline_target_daIC_epochs, 0, 1) / sqrt(size(group_z_offline_target_daIC_epochs, 1));
  335. % mean & SEM z-scoredoffline PMC
  336. group_avg_offline_nontar_PMC_epoch = mean(group_z_offline_nontarget_PMC_epochs,1);
  337. group_sem_offline_nontar_PMC_epoch = std(group_z_offline_nontarget_PMC_epochs, 0, 1) / sqrt(size(group_z_offline_nontarget_PMC_epochs,1));
  339. % Graph
  341. figure('Position', [500 500 1200 500])
  342. %figure('Position', [900 500 1500 500])
  344. a = shadedErrorBar([], group_avg_online_tar_daIC_epoch, group_sem_online_tar_daIC_epoch, 'lineprops', {'color', [1, 0, 0]}, 'patchSaturation', 0.08);
  345. hold on
  346. b = shadedErrorBar([], group_avg_online_nontar_PMC_epoch, group_sem_online_nontar_PMC_epoch, 'lineprops', {'color',[0, 0, 1]}, 'patchSaturation', 0.08);
  347. c = shadedErrorBar([], group_avg_offline_tar_daIC_epoch, group_sem_offline_tar_daIC_epoch, 'lineprops', {'color',[0.6, 0, 0]}, 'patchSaturation', 0.08);
  348. d = shadedErrorBar([], group_avg_offline_nontar_PMC_epoch, group_sem_offline_nontar_PMC_epoch, 'lineprops', {'color',[0, 0, 0.6]}, 'patchSaturation', 0.08);
  351. set(a.edge, 'LineWidth', 2, 'LineStyle', ':')
  352. a.mainLine.LineWidth = 4;
  354. set(b.edge, 'LineWidth', 2, 'LineStyle', ':')
  355. b.mainLine.LineWidth = 4;
  357. set(c.edge, 'LineWidth', 2, 'LineStyle', ':')
  358. c.mainLine.LineWidth = 4;
  360. set(d.edge, 'LineWidth', 2, 'LineStyle', ':')
  361. d.mainLine.LineWidth = 4;
  363. xline(epoch_range+1, '_', {'Trial Onset'},'LabelVerticalAlignment','bottom','LabelHorizontalAlignment','center', 'FontSize', 18, 'LineWidth', 2)
  365. xlabel('Time (seconds)', 'FontSize', 32)
  368. xticks([1:17])
  369. xticklabels({'-16','-14','-12','-10', '-8', '-6', '-4', '-2', '0', '2', '4', '6', '8', '10', '12','14','16'})
  370. ax=gca;
  371. ax.XAxis.FontSize = 28;
  374. % ylim([-1.5 2.0]);
  375. ylabel('BOLD Activity (z)', 'FontSize', 32)
  376. ax.YAxis.FontSize = 28;
  378. subj_nums = [];
  379. for i=1:length(subs)
  380. n = str2double(extractAfter(subs(i), "sub-"));
  381. subj_nums= [subj_nums; n];
  382. end
  384. legend({'Real-time daIC Activity (Target)',...
  385. ['Real-time PMC Activity (Non-target; n=' num2str(sum(~(subj_nums<20))) ')'],...
  386. 'Offline daIC Activity (Target)',...
  387. 'Offline PMC Activity (Non-target)'}, ...
  388. 'FontSize', 20, 'Location', 'best');
  391. title('Validation of daIC Trigger Events', 'FontSize', 30);
  393. print('-opengl', '-dpng','-r350',[savepath_group filesep 'group_n' num2str(length(subj_nums)) '_daICtrial_offline-online_plot.png']);
  395. hold off
  397. end
  401. %% Graph activity around when PMC trials were triggered
  403. if group_level == 1
  404. group_online_target_PMC_epochs = []; group_z_online_target_PMC_epochs = [];
  405. group_online_nontarget_daIC_epochs = []; group_z_online_nontarget_daIC_epochs = [];
  406. group_offline_target_PMC_epochs = []; group_z_offline_target_PMC_epochs = [];
  407. group_offline_nontarget_daIC_epochs = []; group_z_offline_nontarget_daIC_epochs = [];
  408. end
  411. for i=1:length(subs)
  413. runs = length(unique(master.run(master.subj == subs(i))));
  415. subj_num = str2double(extractAfter(subs(i), "sub-"));
  417. if subject_level == 1
  418. sub_online_target_PMC_epochs = []; sub_z_online_target_PMC_epochs = [];
  420. if subj_num > 20
  421. sub_online_nontarget_daIC_epochs = [];
  422. sub_z_online_nontarget_daIC_epochs = [];
  423. end
  424. sub_offline_target_PMC_epochs = []; sub_z_offline_target_PMC_epochs = [];
  426. sub_offline_nontarget_daIC_epochs = []; sub_z_offline_nontarget_daIC_epochs = [];
  427. end
  429. for j=1:runs
  431. target_ROI = unique(master.ROI(master.subj == subs(i) & master.run == j));
  433. % ensure we are only looking at runs that are triggering from the PMC
  434. if target_ROI == "PMC"
  435. nontarget_ROI = "daIC";
  437. %load in online target PMC data
  438. load([data_dir filesep 'subject_opennft_data' filesep char(subs(i)) filesep 'mainLoopData' filesep char(subs(i)) '_' num2str(j) '_mainLoopData.mat'], 'scalProcTimeSeries');
  439. online_target_PMC = scalProcTimeSeries(1,:);
  442. %load in online nontarget daIC data for subjects post-algorithm fix
  444. if subj_num > 20
  445. online_nontarget_daIC = scalProcTimeSeries(2,:);
  446. end
  448. %load in offline target PMC data
  449. subj_padded = sprintf('sub-%03d', subj_num);
  450. offline_target_PMC = (load([data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'OfflineTargetROITimeSeries' filesep subj_padded filesep 'run' num2str(j) filesep subj_padded '_run' num2str(j) '_preproc_meants.txt']))';
  452. %load in offline nontarget daIC data
  453. offline_nontarget_daIC = (load([data_dir filesep 'analyses' filesep 'OfflineOnlineCompare' filesep 'OfflineNontargetROITimeSeries' filesep subj_padded filesep 'run' num2str(j) filesep subj_padded '_run' num2str(j) '_preproc_meants.txt']))';
  455. trials = master.trial_num(master.subj == subs(i) & master.run == j);
  457. for k=1:length(trials)
  459. % ensure we're only looking at PMC-triggered trials
  460. if master.trigger_type(master.subj == subs(i) & master.run == j & master.trial_num == trials(k)) == "ROI"
  462. % epoch around ROI-triggered trials for this run - online
  463. % target PMC
  464. curr_trial = master.trial_onset_TR(master.subj == subs(i) & master.run == j & master.trial_num == trials(k));
  466. run_length = size(online_target_PMC,2);
  468. start_epoch = max(1, curr_trial - epoch_range);
  469. end_epoch = min(run_length, curr_trial + epoch_range);
  471. curr_online_tar_PMC_epoch = online_target_PMC(1, start_epoch:end_epoch);
  473. if group_level == 1
  474. group_online_target_PMC_epochs = [group_online_target_PMC_epochs; curr_online_tar_PMC_epoch];
  475. end
  477. if subject_level == 1
  478. sub_online_target_PMC_epochs = [sub_online_target_PMC_epochs; curr_online_tar_PMC_epoch];
  479. end
  481. curr_z_online_tar_PMC = (curr_online_tar_PMC_epoch - mean(curr_online_tar_PMC_epoch)) / std(curr_online_tar_PMC_epoch);
  483. if group_level == 1
  484. group_z_online_target_PMC_epochs = [group_z_online_target_PMC_epochs; curr_z_online_tar_PMC];
  485. end
  487. if subject_level == 1
  488. sub_z_online_target_PMC_epochs = [sub_z_online_target_PMC_epochs; curr_z_online_tar_PMC];
  489. end
  491. % epoch around ROI-triggered trials for this run - online
  492. % nontarget daIC
  494. if subj_num > 20
  496. run_length = size(online_nontarget_daIC,2);
  498. start_epoch = max(1, curr_trial - epoch_range);
  499. end_epoch = min(run_length, curr_trial + epoch_range);
  501. curr_online_nontar_daIC_epoch = online_nontarget_daIC(1, start_epoch:end_epoch);
  503. if group_level == 1
  504. group_online_nontarget_daIC_epochs = [group_online_nontarget_daIC_epochs; curr_online_nontar_daIC_epoch];
  505. end
  507. if subject_level == 1
  508. sub_online_nontarget_daIC_epochs = [sub_online_nontarget_daIC_epochs; curr_online_nontar_daIC_epoch];
  509. end
  511. curr_z_online_nontar_daIC = (curr_online_nontar_daIC_epoch - mean(curr_online_nontar_daIC_epoch)) / std(curr_online_nontar_daIC_epoch);
  513. if group_level == 1
  514. group_z_online_nontarget_daIC_epochs = [group_z_online_nontarget_daIC_epochs; curr_z_online_nontar_daIC];
  515. end
  517. if subject_level == 1
  518. sub_z_online_nontarget_daIC_epochs = [sub_z_online_nontarget_daIC_epochs; curr_z_online_nontar_daIC];
  519. end
  520. end
  523. % offline target PMC
  524. run_length = size(offline_target_PMC,2);
  526. start_epoch = max(1, curr_trial - epoch_range);
  527. end_epoch = min(run_length, curr_trial + epoch_range);
  529. curr_offline_tar_PMC_epoch = offline_target_PMC(1, start_epoch:end_epoch);
  531. if group_level == 1
  532. group_offline_target_PMC_epochs = [group_offline_target_PMC_epochs; curr_offline_tar_PMC_epoch];
  533. end
  535. if subject_level == 1
  536. sub_offline_target_PMC_epochs = [sub_offline_target_PMC_epochs; curr_offline_tar_PMC_epoch];
  537. end
  539. curr_z_offline_tar_PMC = (curr_offline_tar_PMC_epoch - mean(curr_offline_tar_PMC_epoch)) / std(curr_offline_tar_PMC_epoch);
  541. if group_level == 1
  542. group_z_offline_target_PMC_epochs = [group_z_offline_target_PMC_epochs; curr_z_offline_tar_PMC];
  543. end
  545. if subject_level == 1
  546. sub_z_offline_target_PMC_epochs = [sub_z_offline_target_PMC_epochs; curr_z_offline_tar_PMC];
  547. end
  549. % offline nontarget daIC
  550. run_length = size(offline_nontarget_daIC,2);
  552. start_epoch = max(1, curr_trial - epoch_range);
  553. end_epoch = min(run_length, curr_trial + epoch_range);
  555. curr_offline_nontar_daIC_epoch = offline_nontarget_daIC(1, start_epoch:end_epoch);
  557. if group_level == 1
  558. group_offline_nontarget_daIC_epochs = [group_offline_nontarget_daIC_epochs; curr_offline_nontar_daIC_epoch];
  559. end
  561. if subject_level == 1
  562. sub_offline_nontarget_daIC_epochs = [sub_offline_nontarget_daIC_epochs; curr_offline_nontar_daIC_epoch];
  563. end
  565. curr_z_offline_nontar_daIC = (curr_offline_nontar_daIC_epoch - mean(curr_offline_nontar_daIC_epoch)) / std(curr_offline_nontar_daIC_epoch);
  567. if group_level == 1
  568. group_z_offline_nontarget_daIC_epochs = [group_z_offline_nontarget_daIC_epochs; curr_z_offline_nontar_daIC];
  569. end
  571. if subject_level == 1
  572. sub_z_offline_nontarget_daIC_epochs = [sub_z_offline_nontarget_daIC_epochs; curr_z_offline_nontar_daIC];
  573. end
  575. end
  578. end
  580. elseif target_ROI == "daIC"
  581. nontarget_ROI = "PMC";
  582. end
  584. end
  586. if subject_level == 1
  587. % find grand mean & SEM of z-scored online target PMC activity
  588. sub_avg_online_tar_PMC_epoch = mean(sub_z_online_target_PMC_epochs,1);
  589. sub_sem_online_tar_PMC_epoch = std(sub_z_online_target_PMC_epochs, 0, 1) / sqrt(size(sub_z_online_target_PMC_epochs,1));
  591. % mean & SEM of z-scored online nontarget daIC
  592. if subj_num > 20
  593. sub_avg_online_nontar_daIC_epoch = mean(sub_z_online_nontarget_daIC_epochs,1);
  594. sub_sem_online_nontar_daIC_epoch = std(sub_z_online_nontarget_daIC_epochs, 0, 1) / sqrt(size(sub_z_online_nontarget_daIC_epochs,1));
  595. end
  597. % mean & SEM z-scored offline PMC
  598. sub_avg_offline_tar_PMC_epoch = mean(sub_z_offline_target_PMC_epochs,1);
  599. sub_sem_offline_tar_PMC_epoch = std(sub_z_offline_target_PMC_epochs, 0, 1) / sqrt(size(sub_z_offline_target_PMC_epochs, 1));
  601. % mean & SEM z-scoredoffline daIC
  602. sub_avg_offline_nontar_daIC_epoch = mean(sub_z_offline_nontarget_daIC_epochs,1);
  603. sub_sem_offline_nontar_daIC_epoch = std(sub_z_offline_nontarget_daIC_epochs, 0, 1) / sqrt(size(sub_z_offline_nontarget_daIC_epochs,1));
  605. % Graph
  607. figure('Position', [900 500 1500 500])
  609. a = shadedErrorBar([], sub_avg_online_tar_PMC_epoch, sub_sem_online_tar_PMC_epoch, 'lineprops', {'color', [0, 0, 1]}, 'patchSaturation', 0.08);
  610. hold on
  611. if subj_num > 20
  612. b = shadedErrorBar([], sub_avg_online_nontar_daIC_epoch, sub_sem_online_nontar_daIC_epoch, 'lineprops', {'color',[1, 0, 0]}, 'patchSaturation', 0.08);
  613. c = shadedErrorBar([], sub_avg_offline_tar_PMC_epoch, sub_sem_offline_tar_PMC_epoch, 'lineprops', {'color',[0, 0, 0.6]}, 'patchSaturation', 0.08);
  614. d = shadedErrorBar([], sub_avg_offline_nontar_daIC_epoch, sub_sem_offline_nontar_daIC_epoch, 'lineprops', {'color',[0.6, 0, 0]}, 'patchSaturation', 0.08);
  615. else
  616. b = shadedErrorBar([], sub_avg_offline_tar_PMC_epoch, sub_sem_offline_tar_PMC_epoch, 'lineprops', {'color',[0, 0, 0.6]}, 'patchSaturation', 0.08);
  617. c = shadedErrorBar([], sub_avg_offline_nontar_daIC_epoch, sub_sem_offline_nontar_daIC_epoch, 'lineprops', {'color',[0.6, 0, 0]}, 'patchSaturation', 0.08);
  618. end
  620. if subj_num > 20
  621. set(a.edge, 'LineWidth', 2, 'LineStyle', ':')
  622. a.mainLine.LineWidth = 4;
  624. set(b.edge, 'LineWidth', 2, 'LineStyle', ':')
  625. b.mainLine.LineWidth = 4;
  627. set(c.edge, 'LineWidth', 2, 'LineStyle', ':')
  628. c.mainLine.LineWidth = 4;
  630. set(d.edge, 'LineWidth', 2, 'LineStyle', ':')
  631. d.mainLine.LineWidth = 4;
  632. else
  633. set(a.edge, 'LineWidth', 2, 'LineStyle', ':')
  634. a.mainLine.LineWidth = 4;
  636. set(b.edge, 'LineWidth', 2, 'LineStyle', ':')
  637. b.mainLine.LineWidth = 4;
  639. set(c.edge, 'LineWidth', 2, 'LineStyle', ':')
  640. c.mainLine.LineWidth = 4;
  642. end
  644. xline(epoch_range+1, '_', {'Trial Onset'}, 'FontSize', 18, 'LineWidth', 2)
  646. xlabel('TR', 'FontSize', 20)
  649. xticks([1:17])
  650. xticklabels({'-8','-7','-6','-5', '-4', '-3', '-2', '-1', '0', '1', '2', '3', '4', '5', '6','7','8'})
  651. ax=gca;
  652. ax.XAxis.FontSize = 25;
  655. % ylim([-1.5 2.0]);
  656. ylabel('BOLD Activity (z)', 'FontSize', 20)
  657. ax.YAxis.FontSize = 20;
  659. if subj_num > 20
  660. legend({'Real-time PMC Activity (Target)', 'Real-time daIC Activity (Non-target)', 'Offline PMC Activity (Target)', 'Offline daIC Activity (Non-target)'}, 'FontSize', 18, 'Location', 'best');
  661. else
  662. legend({'Real-time PMC Activity (Target)', 'Offline PMC Activity (Target)', 'Offline daIC Activity (Non-target)'}, 'FontSize', 18, 'Location', 'best');
  663. end
  665. title({char(subs(i)),'Real-time vs Offline Estimates of Average Region Activity During PMC-triggered Trials'}, 'FontSize', 23);
  666. if subj_num <20
  667. subtitle('*Online daIC Activity Estimates Unavailable');
  668. end
  670. print('-opengl', '-dpng',[savepath_sub filesep char(subs(i)) '_PMCtrial_offline-online_plot.png']);
  672. hold off
  675. pause; close;
  677. end
  679. end
  680. %%
  681. if group_level == 1
  682. % find grand mean & SEM of z-scored online target PMC activity
  683. group_avg_online_tar_PMC_epoch = mean(group_z_online_target_PMC_epochs,1);
  684. group_sem_online_tar_PMC_epoch = std(group_z_online_target_PMC_epochs, 0, 1) / sqrt(size(group_z_online_target_PMC_epochs,1));
  686. % mean & SEM of z-scored online nontarget daIC
  687. group_avg_online_nontar_daIC_epoch = mean(group_z_online_nontarget_daIC_epochs,1);
  688. group_sem_online_nontar_daIC_epoch = std(group_z_online_nontarget_daIC_epochs, 0, 1) / sqrt(size(group_z_online_nontarget_daIC_epochs,1));
  691. % mean & SEM z-scored offline PMC
  692. group_avg_offline_tar_PMC_epoch = mean(group_z_offline_target_PMC_epochs,1);
  693. group_sem_offline_tar_PMC_epoch = std(group_z_offline_target_PMC_epochs, 0, 1) / sqrt(size(group_z_offline_target_PMC_epochs, 1));
  695. % mean & SEM z-scoredoffline daIC
  696. group_avg_offline_nontar_daIC_epoch = mean(group_z_offline_nontarget_daIC_epochs,1);
  697. group_sem_offline_nontar_daIC_epoch = std(group_z_offline_nontarget_daIC_epochs, 0, 1) / sqrt(size(group_z_offline_nontarget_daIC_epochs,1));
  699. % Graph
  701. figure('Position', [500 500 1200 500])
  702. %figure('Position', [900 500 1500 500])
  704. a = shadedErrorBar([], group_avg_online_tar_PMC_epoch, group_sem_online_tar_PMC_epoch, 'lineprops', {'color', [0, 0, 1]}, 'patchSaturation', 0.08);
  705. hold on
  706. b = shadedErrorBar([], group_avg_online_nontar_daIC_epoch, group_sem_online_nontar_daIC_epoch, 'lineprops', {'color',[1, 0, 0]}, 'patchSaturation', 0.08);
  707. c = shadedErrorBar([], group_avg_offline_tar_PMC_epoch, group_sem_offline_tar_PMC_epoch, 'lineprops', {'color',[0, 0, 0.6]}, 'patchSaturation', 0.08);
  708. d = shadedErrorBar([], group_avg_offline_nontar_daIC_epoch, group_sem_offline_nontar_daIC_epoch, 'lineprops', {'color',[0.6, 0, 0]}, 'patchSaturation', 0.08);
  711. set(a.edge, 'LineWidth', 2, 'LineStyle', ':')
  712. a.mainLine.LineWidth = 4;
  714. set(b.edge, 'LineWidth', 2, 'LineStyle', ':')
  715. b.mainLine.LineWidth = 4;
  717. set(c.edge, 'LineWidth', 2, 'LineStyle', ':')
  718. c.mainLine.LineWidth = 4;
  720. set(d.edge, 'LineWidth', 2, 'LineStyle', ':')
  721. d.mainLine.LineWidth = 4;
  723. xline(epoch_range+1, '_', {'Trial Onset'}, 'LabelVerticalAlignment', 'bottom', 'LabelHorizontalAlignment','center','FontSize', 18, 'LineWidth', 2)
  725. xlabel('Time (seconds)', 'FontSize', 32)
  728. xticks([1:17])
  729. xticklabels({'-16','-14','-12','-10', '-8', '-6', '-4', '-2', '0', '2', '4', '6', '8', '10', '12','14','16'})
  730. ax=gca;
  731. ax.XAxis.FontSize = 28;
  734. ylim([-1 1]);
  735. yticks(-1:0.5:1)
  736. ylabel('BOLD Activity (z)', 'FontSize', 32)
  737. ax.YAxis.FontSize = 28;
  739. subj_nums = [];
  740. for i=1:length(subs)
  741. n = str2double(extractAfter(subs(i), "sub-"));
  742. subj_nums= [subj_nums; n];
  743. end
  745. legend({'Real-time PMC Activity (Target)',...
  746. ['Real-time daIC Activity (Non-target; n=' num2str(sum(~(subj_nums<20))) ')'],...
  747. 'Offline PMC Activity (Target)',...
  748. 'Offline daIC Activity (Non-target)'}, ...
  749. 'FontSize', 20, 'Location', 'best');
  752. title('Validation of PMC Trigger Events', 'FontSize', 30);
  754. print('-opengl', '-dpng','-r350',[savepath_group filesep 'group_n' num2str(length(subj_nums)) '_PMCtrial_offline-online_plot.png']);
  756. hold off
  758. end

offlineonlineplots.m, no license · at the source

Overview

Authors: Tiara Bounyarith1, David Braun1, Aaron Kucyi1
ORCID iDs: Tiara Bounyarith, Aaron Kucyi
  1. Department of Psychological & Brain Sciences, Drexel University, Philadelphia, PA, United States
Institutions: Drexel University (United States)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1268
Dates: received 8 May 2026; accepted 9 May 2026; published online 8 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1268 · PMID 42272744 · PMCID PMC13248897 · OpenAlex W7161825659
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Statistics, fMRI & imaging, Smoothing, state filtering, decompositions
Keywords: real-time fMRI, experience sampling, spontaneous thought, mental health, default mode network, salience network
Topic: Mind wandering and attention (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (R21 MH129630); National Science Foundation Graduate Research Fellowship Program
Citations: not cited yet (Europe PMC); 93 references in the paper

Abstract

Much of a typical individual’s mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional magnetic resonance imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience sampling (rt-fMRI-ES). We analyzed blood-oxygenation level-dependent signals in real time during a wakeful resting state (n = 60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external attention time locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation than other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendation: https://osf.io/sd4hu (Date of in-principle acceptance: July 24, 2024).

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 5 matches between paragraphs and lines of code.

DynamicBrainMind/rtfMRI_prep

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0cbac2e31b941c916ef587cabd015d5a101ccfa0, 18 June 2025
Languages: Python (3), MATLAB (1)
Size: 1,431 files, 4 scripts
Software Heritage: not archived
Found in: the text, “Functional localizer analysis (rtfMRI-prep)”
Holds: README, tests
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (1 file), NumPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
5 files
At the source: github.com/DynamicBrainMind/rtfMRI_prep

OSF as2q9

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (3), R (2)
Size: 15 files, 5 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: emmeans (1 file), ggplot2 (1 file), lme4 (1 file), lmerTest (1 file), patchwork (1 file), shadedErrorBar (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
5 files
At the source: osf.io/as2q9/overview

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;
  • 9 scripts, each with its path and the digest of its content;
  • 5 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 and Code Availability

In accordance with requirements set by the project sponsor, all study raw data will be available through the online National Institutes of Mental Health Data Archive (NDA), DOI: https://dx.doi.org/10.15154/5aka-y257. Code and data used for analysis and visualization are available on OSF, https://osf.io/as2q9/overview.

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

  • Funding: added National Institute of Mental Health: R21 MH129630; National Science Foundation Graduate Research Fellowship Program

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 6 keywords, 91 references.

Cite

This paper

Bounyarith, T., Braun, D., & Kucyi, A. (2026). Real-time fMRI-triggered experience sampling: A proof-of-concept study. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1268. https://doi.org/10.1162/imag.a.1268

BibTeX

@article{bounyarith2026real,
author = {Bounyarith, Tiara and Braun, David and Kucyi, Aaron},
title = {{Real-time fMRI-triggered experience sampling: A proof-of-concept study}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = jun,
volume = {4},
pages = {IMAG.a.1268},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1268},
url = {https://doi.org/10.1162/imag.a.1268},
pmid = {42272744},
pmcid = {PMC13248897}
}

RIS

TY - JOUR
AU - Bounyarith, Tiara
AU - Braun, David
AU - Kucyi, Aaron
TI - Real-time fMRI-triggered experience sampling: A proof-of-concept study
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/06/08
VL - 4
SP - IMAG.a.1268
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1268
UR - https://doi.org/10.1162/imag.a.1268
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1268",
"type": "article-journal",
"title": "Real-time fMRI-triggered experience sampling: A proof-of-concept study",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Bounyarith",
"given": "Tiara"
},
{
"family": "Braun",
"given": "David"
},
{
"family": "Kucyi",
"given": "Aaron"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1268",
"DOI": "10.1162/imag.a.1268",
"PMID": "42272744",
"PMCID": "PMC13248897",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1268",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
8
]
]
}
}

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

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[5] doi:10.1073/pnas.2603114123 [code]
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