OSCR

Effects of TMS on the Decoding and Electrophysiology of Priority in Working Memory.

Code ↔ Paper

9 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 9 matches
  1. [1] § Materials and Methods › Decoding ↔ MVPA/MVPAcode/MVPALight_HTC_DSRfullsplit.m, lines 95–166 · score 0.82 · MVPA Light, beta power, theta power, alpha power, FieldTrip, L2
  2. [2] § Materials and Methods › Decoding ↔ MVPA/MVPAcode/MVPALight_HTC_SRfullsplit.m, lines 96–168 · score 0.82 · MVPA Light, beta power, theta power, alpha power, FieldTrip, L2
  3. [3] § Materials and Methods › EEG dynamics › EEG analyses of phase ↔ Phase Analyses/TMSPLV_slidingcombined.m, lines 1–31 · score 0.76 · frequency adaptive, sliding window, circular statistics, PLV changes, phase shifts, spTMS
  4. [4] § Materials and Methods › EEG dynamics › EEG analyses of phase ↔ Phase Analyses/CuePLV_slidingcombined.m, lines 44–47 · score 0.75 · 20–30 Hz, 8–13 Hz, 13–20 Hz, 4–8 Hz, high beta, low beta
  5. [5] § Materials and Methods › EEG dynamics › EEG analyses of phase ↔ Phase Analyses/TMSPLV_slidingcombined.m, lines 33–36 · score 0.74 · 20–30 Hz, 8–13 Hz, 13–20 Hz, 4–8 Hz, high beta, low beta
  6. [6] § Materials and Methods › EEG dynamics › EEG analyses of phase ↔ Phase Analyses/TMSPLV_slidingcombined.m, lines 41–153 · score 0.70 · cycle length, window lengths, candidate, high beta, sliding, optimal
  7. [7] § Materials and Methods › EEG dynamics › EEG analyses of phase ↔ Phase Analyses/CuePLV_slidingcombined.m, lines 1–14 · score 0.67 · frequency adaptive, sliding window, PLV changes, phase shifts, cue, SR
  8. [8] § EEG dynamics › Phase dynamics › Prioritization cues ↔ Phase Analyses/CuePLV_slidingcombined.m, lines 450–474 · score 0.56 · circular boundary, phase angle shifts, PLV, low beta, FDR, cue
  9. [9] § Materials and Methods › EEG dynamics › EEG analyses of phase ↔ Phase Analyses/CuePLV_slidingcombined.m, lines 157–182 · score 0.52 · post cue, absolute, chosen, candidate, cycles, PLV

Paper

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The authors' code

MATLAB · 531 lines · 21 KB · no license · 4 matches

  1. % =================================================================
  2. % Cue-Related Sliding-Window, Frequency-Adaptive Phase Analysis
  3. % DSR vs SR comparisons for PLV change, mean phase shift,
  4. % and inter-subject PLV (pre/post windows matched in length)
  5. % =================================================================
  7. % Fulvio & Postle
  9. clear; close all; clc;
  12. % rng('shuffle'); % sets the seed based on current time
  13. % s = rng; % capture current state
  14. % fprintf('Random seed for this run: %d (RNG type: %s)\n', s.Seed, s.Type);
  16. %% -----------------------------
  17. %% Set random seed for reproducibility
  18. %% -----------------------------
  19. seed_used = 147377794;
  20. rng(seed_used, 'twister');
  21. fprintf('RNG seed set to %d\n', seed_used);
  23. % addpath to Circular Statistics Toolbox here
  25. fprintf('=== LOADING TRIAL DATA ===\n');
  27. %% Load preprocessed baselined DSR and SR trial data
  28. load 'TMSEEG_DSR_NoLures_August2025_100hz_CueERP_indivtrials.mat';
  29. DSR_trials = ERPall;
  30. load 'TMSEEG_SR_NoLures_August2025_100hz_CueERP_indivtrials.mat';
  31. SR_trials = ERPall;
  33. n_subjects = 12;
  34. fs = 100; % Hz
  35. full_time_vector = -2000:(1000/fs):2000; % full time axis
  36. n_timepoints_full = length(full_time_vector);
  38. subset_cols = 901:1301; % these are the indices of the cue-related timepoints in the EEG data
  39. % extracts -2000 to +2000 ms relative to cue onset
  40. % (cue onset is at column 1101 in the original data matrix)
  41. time_vector = full_time_vector;
  42. n_timepoints = length(time_vector);
  44. %% Frequency bands
  45. freq_bands = {[4 8], [8 13], [13 20], [20 30]};
  46. band_names = {'Theta','Alpha','Low Beta','High Beta'};
  47. n_bands = length(freq_bands);
  49. %% Candidate cycles per band
  50. candidate_cycles = {
  51. 2:4, % Theta
  52. 2:4, % Alpha
  53. 3:6, % Low Beta
  54. 3:6 % High Beta
  55. };
  57. pre_end_ms = -1; % pre window must end 1 ms before cue (we go to nearest sample)
  58. post_max = 500; % ms
  59. n_perm_between = 5000; % number of permutations to test
  60. n_boot = 1000; % bootstrap samples for SEM
  62. %% Prepare storage
  63. cue_results = struct();
  65. %% Extract trial data
  66. combined_phase_data = cell(n_subjects,1);
  67. dsr_phase_data = cell(n_subjects,1);
  68. sr_phase_data = cell(n_subjects,1);
  70. for subj = 1:n_subjects
  71. dsr_trials_subj = DSR_trials{subj}(:, subset_cols); % cue window subset
  72. sr_trials_subj = SR_trials{subj}(:, subset_cols);
  73. dsr_phase_data{subj} = dsr_trials_subj;
  74. sr_phase_data{subj} = sr_trials_subj;
  75. combined_phase_data{subj} = [dsr_trials_subj; sr_trials_subj];
  76. end
  78. %% Precompute some time/sample helpers
  79. dt = time_vector(2) - time_vector(1); % ms per sample (should be 1000/fs)
  80. cue_idx_in_subset = find(time_vector == 0, 1, 'first');
  81. if isempty(cue_idx_in_subset)
  82. error('Cue (time 0) not found in the cropped time vector.');
  83. end
  84. pre_end_idx = cue_idx_in_subset - 1; % last sample before cue (pre window MUST end here)
  85. if pre_end_idx < 1
  86. error('pre_end index would be < 1; check time_vector and subset_cols.');
  87. end
  89. %% -----------------------------
  90. %% Sliding-window adaptive analysis
  91. %% -----------------------------
  92. for band_idx = 1:n_bands
  93. band_name = band_names{band_idx};
  94. freq_band = freq_bands{band_idx};
  95. max_freq = max(freq_band);
  96. fprintf('\n--- %s ---\n', band_name);
  98. % Bandpass filter
  99. filter_order = min(4, floor(n_timepoints/6));
  100. [b,a] = butter(filter_order, freq_band/(fs/2), 'bandpass');
  102. % Hilbert phases for combined trials (trials x time)
  103. combined_phase_hilb = cell(n_subjects,1);
  104. for subj = 1:n_subjects
  105. filt_data = filtfilt(b,a,combined_phase_data{subj}')'; % keep trials x time
  106. combined_phase_hilb{subj} = angle(hilbert(filt_data')'); % trials x time
  107. end
  109. % Candidate post-window start times (ms)
  110. post_start_candidates_ms = 0:post_max; % will convert to sample indices below
  111. tstats = zeros(length(post_start_candidates_ms), length(candidate_cycles{band_idx}));
  112. subj_diffs_all = zeros(n_subjects, length(post_start_candidates_ms), length(candidate_cycles{band_idx}));
  114. %% Loop over candidate cycles
  115. for c = 1:length(candidate_cycles{band_idx})
  116. cycles = candidate_cycles{band_idx}(c);
  117. window_length_ms = cycles / mean(freq_band) * 1000; %max_freq * 1000;
  118. % convert to number of samples (round to nearest sample)
  119. window_samples = max(1, round(window_length_ms / dt));
  121. % Pre window: end exactly 1 sample before cue and have length = window_samples
  122. pre_idx = (pre_end_idx - window_samples + 1) : pre_end_idx;
  123. % if this would go before start, shorten to available samples
  124. if pre_idx(1) < 1
  125. pre_idx = 1:pre_end_idx;
  126. window_samples = length(pre_idx);
  127. end
  129. for w = 1:length(post_start_candidates_ms)
  130. post_start_ms = post_start_candidates_ms(w);
  131. % find the first sample in the cropped time vector >= post_start_ms
  132. post_start_idx = find(time_vector >= post_start_ms, 1, 'first');
  133. if isempty(post_start_idx)
  134. tstats(w,c) = NaN; continue
  135. end
  136. post_end_idx = post_start_idx + window_samples - 1;
  137. % ensure post_end within allowed post_max and within data bounds
  138. if post_end_idx > n_timepoints || time_vector(post_end_idx) > post_max
  139. tstats(w,c) = NaN; continue
  140. end
  141. post_idx = post_start_idx : post_end_idx;
  143. % compute subject-level pre/post PLVs (using combined trials)
  144. for subj = 1:n_subjects
  145. phases = combined_phase_hilb{subj}; % trials x time
  146. % average across trials then across timepoints → use all samples
  147. pre_plv = abs(mean(mean(exp(1i*phases(:,pre_idx)),1),2));
  148. post_plv = abs(mean(mean(exp(1i*phases(:,post_idx)),1),2));
  149. subj_diffs_all(subj,w,c) = post_plv - pre_plv;
  150. end
  151. % t-stat across subjects
  152. [~,~,~,st] = ttest(subj_diffs_all(:,w,c));
  153. tstats(w,c) = st.tstat;
  154. end
  155. end
  157. %% Select best window (by absolute t-stat)
  158. [~, idx] = max(abs(tstats(:)));
  159. [best_w, best_c] = ind2sub(size(tstats), idx);
  160. best_post_start_ms = post_start_candidates_ms(best_w);
  161. best_window_ms = candidate_cycles{band_idx}(best_c) / mean(freq_band) *1000; %max_freq * 1000;
  162. % recompute window_samples for the chosen window (to avoid rounding drift)
  163. window_samples = max(1, round(best_window_ms / dt));
  165. % final post indices (in cropped time vector)
  166. post_start_idx_final = find(time_vector >= best_post_start_ms, 1, 'first');
  167. post_end_idx_final = post_start_idx_final + window_samples - 1;
  168. if post_end_idx_final > n_timepoints || time_vector(post_end_idx_final) > post_max
  169. error('Selected post window exceeds bounds — check candidate ranges.');
  170. end
  171. post_idx_final = post_start_idx_final : post_end_idx_final;
  173. % final pre indices: end at pre_end_idx and match length (window_samples)
  174. pre_end_idx = cue_idx_in_subset - 1; % ensure correct
  175. pre_start_idx = pre_end_idx - window_samples + 1;
  176. if pre_start_idx < 1
  177. pre_start_idx = 1; % shorten if necessary
  178. end
  179. pre_idx_final = pre_start_idx : pre_end_idx;
  181. fprintf('Selected window: %.1f - %.1f ms post-cue (length %.1f ms, %d samples) %f %f\n', ...
  182. time_vector(post_idx_final(1)), time_vector(post_idx_final(end)), best_window_ms, window_samples, length(pre_idx_final), length(post_idx_final));
  184. %% -----------------------------
  185. %% Compute PLV change and phase shifts per subject (DSR and SR separately) within the selected window
  186. %% -----------------------------
  187. dsr_diffs = zeros(n_subjects,1);
  188. sr_diffs = zeros(n_subjects,1);
  189. dsr_phase_shift = zeros(n_subjects,1);
  190. sr_phase_shift = zeros(n_subjects,1);
  192. for subj = 1:n_subjects
  193. % DSR
  194. filt_dsr = filtfilt(b,a,dsr_phase_data{subj}')';
  195. phase_dsr = angle(hilbert(filt_dsr')'); % trials x time
  196. % mean across trials then across timepoints in window
  197. dsr_pre_plv = abs(mean(mean(exp(1i*phase_dsr(:,pre_idx_final)),1),2));
  198. dsr_post_plv = abs(mean(mean(exp(1i*phase_dsr(:,post_idx_final)),1),2));
  199. dsr_diffs(subj) = dsr_post_plv - dsr_pre_plv;
  200. % phase (mean over trials then over timepoints)
  201. pre_phase = angle(mean(mean(exp(1i*phase_dsr(:,pre_idx_final)),1),2));
  202. post_phase = angle(mean(mean(exp(1i*phase_dsr(:,post_idx_final)),1),2));
  203. dsr_phase_shift(subj) = circ_dist(post_phase, pre_phase);
  205. % SR
  206. filt_sr = filtfilt(b,a,sr_phase_data{subj}')';
  207. phase_sr = angle(hilbert(filt_sr')');
  208. sr_pre_plv = abs(mean(mean(exp(1i*phase_sr(:,pre_idx_final)),1),2));
  209. sr_post_plv = abs(mean(mean(exp(1i*phase_sr(:,post_idx_final)),1),2));
  210. sr_diffs(subj) = sr_post_plv - sr_pre_plv;
  211. pre_phase = angle(mean(mean(exp(1i*phase_sr(:,pre_idx_final)),1),2));
  212. post_phase = angle(mean(mean(exp(1i*phase_sr(:,post_idx_final)),1),2));
  213. sr_phase_shift(subj) = circ_dist(post_phase, pre_phase);
  214. end
  216. %% -----------------------------
  217. %% Permutation tests DSR vs SR (paired across subjects)
  218. %% -----------------------------
  219. observed_diff_plv = mean(dsr_diffs) - mean(sr_diffs);
  220. observed_diff_phase = circ_mean(dsr_phase_shift) - circ_mean(sr_phase_shift);
  222. perm_diffs_plv = zeros(n_perm_between,1);
  223. perm_diffs_phase = zeros(n_perm_between,1);
  224. for p = 1:n_perm_between
  225. flip_signs = randi([0 1], n_subjects, 1) * 2 - 1;
  226. perm_diffs_plv(p) = mean(flip_signs .* (dsr_diffs - sr_diffs));
  227. perm_diffs_phase(p) = mean(flip_signs .* (dsr_phase_shift - sr_phase_shift));
  228. end
  229. p_perm_plv = 2*min(mean(perm_diffs_plv >= observed_diff_plv), mean(perm_diffs_plv <= observed_diff_plv));
  230. p_perm_phase = 2*min(mean(perm_diffs_phase >= observed_diff_phase), mean(perm_diffs_phase <= observed_diff_phase));
  232. %% -----------------------------
  233. %% Inter-subject PLV for DSR and SR (consistency of subject-level shifts)
  234. %% -----------------------------
  235. plv_dsr = abs(mean(exp(1i*dsr_phase_shift)));
  236. plv_sr = abs(mean(exp(1i*sr_phase_shift)));
  238. % Bootstrap SEM for each condition
  239. boot_plv_dsr = zeros(n_boot,1);
  240. boot_plv_sr = zeros(n_boot,1);
  241. for bb = 1:n_boot
  242. idxs = randsample(n_subjects, n_subjects, true);
  243. boot_plv_dsr(bb) = abs(mean(exp(1i*dsr_phase_shift(idxs))));
  244. boot_plv_sr(bb) = abs(mean(exp(1i*sr_phase_shift(idxs))));
  245. end
  246. sem_dsr = std(boot_plv_dsr);
  247. sem_sr = std(boot_plv_sr);
  249. % Paired permutation test on inter-subject PLV (swap subject labels)
  250. observed_diff_inter_plv = plv_dsr - plv_sr;
  251. perm_inter_plv = zeros(n_perm_between,1);
  252. for p = 1:n_perm_between
  253. flip = randi([0 1], n_subjects, 1) * 2 - 1;
  254. dsr_perm = dsr_phase_shift;
  255. sr_perm = sr_phase_shift;
  256. idx_flip = flip == -1;
  257. dsr_perm(idx_flip) = sr_phase_shift(idx_flip);
  258. sr_perm(idx_flip) = dsr_phase_shift(idx_flip);
  259. perm_inter_plv(p) = abs(mean(exp(1i*dsr_perm))) - abs(mean(exp(1i*sr_perm)));
  260. end
  261. p_perm_inter_plv = 2 * min(mean(perm_inter_plv >= observed_diff_inter_plv), mean(perm_inter_plv <= observed_diff_inter_plv));
  263. %% -----------------------------
  264. %% Store results
  265. %% -----------------------------
  266. cue_results(band_idx).band_name = band_name;
  267. cue_results(band_idx).freq_band = freq_band;
  268. cue_results(band_idx).pre_idx = pre_idx_final;
  269. cue_results(band_idx).post_idx = post_idx_final;
  271. % PLV change
  272. cue_results(band_idx).dsr_diffs = dsr_diffs;
  273. cue_results(band_idx).sr_diffs = sr_diffs;
  274. cue_results(band_idx).observed_diff_plv = observed_diff_plv;
  275. cue_results(band_idx).p_perm_plv = p_perm_plv;
  277. % Mean phase shift
  278. cue_results(band_idx).dsr_phase_shift = dsr_phase_shift;
  279. cue_results(band_idx).sr_phase_shift = sr_phase_shift;
  280. cue_results(band_idx).observed_diff_phase = observed_diff_phase;
  281. cue_results(band_idx).p_perm_phase = p_perm_phase;
  283. % Inter-subject PLV
  284. cue_results(band_idx).plv_dsr = plv_dsr;
  285. cue_results(band_idx).plv_sr = plv_sr;
  286. cue_results(band_idx).sem_dsr = sem_dsr;
  287. cue_results(band_idx).sem_sr = sem_sr;
  288. cue_results(band_idx).observed_diff_inter_plv = observed_diff_inter_plv;
  289. cue_results(band_idx).p_perm_inter_plv = p_perm_inter_plv;
  290. end
  292. %% FDR correction step
  294. % Collect raw p-values
  295. p_raw = [];
  296. for b = 1:n_bands
  297. p_raw = [p_raw; ...
  298. cue_results(b).p_perm_plv; ...
  299. cue_results(b).p_perm_phase; ...
  300. cue_results(b).p_perm_inter_plv];
  301. end
  303. % Apply FDR
  304. p_fdr = fdr_bh(p_raw);
  306. % Assign back to structure
  307. idx = 1;
  308. for b = 1:n_bands
  309. cue_results(b).p_perm_plv_fdr = p_fdr(idx); idx = idx+1;
  310. cue_results(b).p_perm_phase_fdr = p_fdr(idx); idx = idx+1;
  311. cue_results(b).p_perm_inter_plv_fdr = p_fdr(idx); idx = idx+1;
  312. end
  315. %% -----------------------------
  316. %% Report statistics
  317. %% -----------------------------
  319. fprintf('\n=== Statistics: DSR vs SR (FDR-corrected) ===\n');
  320. for b = 1:n_bands
  321. fprintf('\n%s:\n', cue_results(b).band_name);
  322. fprintf(' PLV change: p=%.4f (FDR=%.4f), Mean diff=%.4f\n', ...
  323. cue_results(b).p_perm_plv, cue_results(b).p_perm_plv_fdr, cue_results(b).observed_diff_plv);
  325. fprintf(' Mean phase shift: p=%.4f (FDR=%.4f), Mean diff=%.2f deg\n', ...
  326. cue_results(b).p_perm_phase, cue_results(b).p_perm_phase_fdr, mean(cue_results(b).dsr_phase_shift - cue_results(b).sr_phase_shift)*180/pi);
  328. fprintf(' Inter-subject PLV: p=%.4f (FDR=%.4f) DSR=%.3f ± %.3f, SR=%.3f ± %.3f\n', ...
  329. cue_results(b).p_perm_inter_plv, cue_results(b).p_perm_inter_plv_fdr, cue_results(b).plv_dsr, cue_results(b).sem_dsr, ...
  330. cue_results(b).plv_sr, cue_results(b).sem_sr);
  331. end
  334. %% -----------------------------
  335. %% Plot bar graphs: 2 bars per frequency band
  336. %% -----------------------------
  337. % PLV change
  338. plv_means = [];
  339. plv_sems = [];
  340. for b = 1:n_bands
  341. plv_means = [plv_means, mean(cue_results(b).dsr_diffs), mean(cue_results(b).sr_diffs)];
  342. plv_sems = [plv_sems, std(cue_results(b).dsr_diffs)/sqrt(n_subjects), std(cue_results(b).sr_diffs)/sqrt(n_subjects)];
  343. end
  345. figure; hold on;
  346. b_plot = bar(reshape(plv_means,2,[])','grouped');
  347. b_plot(1).FaceColor = [75 0 130]/255; % DSR
  348. b_plot(2).FaceColor = [53 94 59]/255; % SR
  350. % Error bars
  351. for i = 1:2*n_bands
  352. x = b_plot(mod(i-1,2)+1).XEndPoints(ceil(i/2));
  353. errorbar(x, plv_means(i), plv_sems(i),'k','LineStyle','none','LineWidth',2);
  354. end
  355. xticks(1:n_bands)
  356. set(gca,'XTickLabel',band_names,'TickDir','out','LineWidth',1.5)
  357. ylabel('PLV Change (Post - Pre)')
  358. title('DSR vs SR: PLV Change')
  359. set(gcf,'Color','white')
  361. % Mean phase shift
  362. phase_means = [];
  363. phase_sems = [];
  364. for b = 1:n_bands
  365. phase_means = [phase_means, circ_mean(cue_results(b).dsr_phase_shift), circ_mean(cue_results(b).sr_phase_shift)];
  366. phase_sems = [phase_sems, circ_std(cue_results(b).dsr_phase_shift)/sqrt(n_subjects), circ_std(cue_results(b).sr_phase_shift)/sqrt(n_subjects)];
  367. end
  368. phase_means_deg = phase_means*180/pi;
  369. phase_sems_deg = phase_sems*180/pi;
  371. figure; hold on;
  372. b_plot = bar(reshape(phase_means_deg,2,[])','grouped');
  373. b_plot(1).FaceColor = [75 0 130]/255; % DSR
  374. b_plot(2).FaceColor = [53 94 59]/255; % SR
  376. for i = 1:2*n_bands
  377. x = b_plot(mod(i-1,2)+1).XEndPoints(ceil(i/2));
  378. errorbar(x, phase_means_deg(i), phase_sems_deg(i),'k','LineStyle','none','LineWidth',2);
  379. end
  380. xticks(1:n_bands)
  381. set(gca,'XTickLabel',band_names,'TickDir','out','LineWidth',1.5)
  382. ylabel('Mean Phase Shift (deg)')
  383. title('DSR vs SR: Mean Phase Shift')
  384. set(gcf,'Color','white')
  386. % Inter-subject PLV
  387. plv_vals = [];
  388. plv_sems = [];
  389. for b = 1:n_bands
  390. plv_vals = [plv_vals, cue_results(b).plv_dsr, cue_results(b).plv_sr];
  391. plv_sems = [plv_sems, cue_results(b).sem_dsr, cue_results(b).sem_sr];
  392. end
  394. figure; hold on;
  395. b_plot = bar(reshape(plv_vals,2,[])','grouped');
  396. b_plot(1).FaceColor = [75 0 130]/255; % DSR
  397. b_plot(2).FaceColor = [53 94 59]/255; % SR
  399. for i = 1:2*n_bands
  400. x = b_plot(mod(i-1,2)+1).XEndPoints(ceil(i/2));
  401. errorbar(x, plv_vals(i), plv_sems(i),'k','LineStyle','none','LineWidth',2);
  402. end
  403. xticks(1:n_bands)
  404. set(gca,'XTickLabel',band_names,'TickDir','out','LineWidth',1.5)
  405. ylabel('Inter-Subject PLV')
  406. title('DSR vs SR: Inter-Subject Phase Alignment')
  407. ylim([0 1])
  408. set(gcf,'Color','white')
  410. %% Single-subject direction counts for cue-related phase effects
  411. %% Run this after the cue phase analysis script (cue_results must be in workspace)
  412. %% For each frequency band showing a significant effect, reports N participants
  413. %% showing the DSR vs SR difference in the same direction as the group-level effect
  415. fprintf('\n=== SINGLE-SUBJECT DIRECTION COUNTS: CUE-RELATED EFFECTS ===\n');
  416. fprintf('(Run after cue phase analysis script)\n\n');
  417. fprintf('For each contrast, N = participants whose individual difference\n');
  418. fprintf('score shares the sign of the group-level mean difference.\n\n');
  419. fprintf('Note: sign-based counts are only interpretable when the group-level\n');
  420. fprintf('mean difference is well away from the circular boundary (+-180 deg).\n\n');
  422. band_names = {'Theta','Alpha','Low Beta','High Beta'};
  423. n_bands = length(band_names);
  424. n_subjects = 12;
  426. %% -----------------------------------------------------------------------
  427. %% 1. Within-participant PLV change: Low Beta (significant effect)
  428. %% DSR vs SR
  429. %% -----------------------------------------------------------------------
  430. fprintf('=== Within-participant PLV change: All bands ===\n');
  431. fprintf('(Reporting all bands for completeness; key significant effect in Low Beta)\n\n');
  433. for band_idx = 1:n_bands
  434. diff_vals = cue_results(band_idx).dsr_diffs - cue_results(band_idx).sr_diffs;
  435. group_mean = mean(diff_vals);
  436. n_same_dir = sum(sign(diff_vals) == sign(group_mean));
  437. sig_str = '';
  438. if cue_results(band_idx).p_perm_plv_fdr < 0.05
  439. sig_str = ' *';
  440. end
  441. fprintf(' %s: group mean diff = %.4f, %d/%d participants in same direction%s\n', ...
  442. band_names{band_idx}, group_mean, n_same_dir, n_subjects, sig_str);
  443. end
  445. fprintf('\n');
  447. %% -----------------------------------------------------------------------
  448. %% 2. Mean phase angle shift: Theta, Alpha, Low Beta (significant effects)
  449. %% DSR vs SR — using circ_dist for circular measure
  450. %% -----------------------------------------------------------------------
  451. fprintf('=== Mean phase angle shift: All bands ===\n');
  452. fprintf('(Reporting all bands for completeness; significant effects in Theta, Alpha, Low Beta)\n\n');
  454. for band_idx = 1:n_bands
  455. diff_vals = circ_dist(cue_results(band_idx).dsr_phase_shift, cue_results(band_idx).sr_phase_shift);
  456. group_mean = circ_mean(diff_vals);
  457. group_mean_deg = rad2deg(group_mean);
  458. n_same_dir = sum(sign(diff_vals) == sign(group_mean));
  459. sig_str = '';
  460. if cue_results(band_idx).p_perm_phase_fdr < 0.05
  461. sig_str = ' *';
  462. end
  464. % Flag if near circular boundary
  465. boundary_warning = '';
  466. if abs(group_mean_deg) > 150
  467. boundary_warning = ' [WARNING: near +/-180 deg boundary - count may not be interpretable]';
  468. end
  470. fprintf(' %s: group mean diff = %.2f deg, %d/%d participants in same direction%s%s\n', ...
  471. band_names{band_idx}, group_mean_deg, n_same_dir, n_subjects, sig_str, boundary_warning);
  472. end
  474. fprintf('\n');
  476. %% -----------------------------------------------------------------------
  477. %% 3. Inter-subject PLV: report for completeness
  478. %% -----------------------------------------------------------------------
  479. fprintf('=== Inter-subject PLV: All bands ===\n');
  480. fprintf('(No significant effects expected; reported for completeness)\n\n');
  482. for band_idx = 1:n_bands
  483. % Inter-subject PLV is a scalar (not per-subject difference),
  484. % so we report the individual subject phase shifts going in the
  485. % same direction as the group mean for each condition separately
  486. dsr_group_mean = circ_mean(cue_results(band_idx).dsr_phase_shift);
  487. sr_group_mean = circ_mean(cue_results(band_idx).sr_phase_shift);
  489. n_dsr_same = sum(sign(cue_results(band_idx).dsr_phase_shift) == sign(dsr_group_mean));
  490. n_sr_same = sum(sign(cue_results(band_idx).sr_phase_shift) == sign(sr_group_mean));
  492. sig_str = '';
  493. if cue_results(band_idx).p_perm_inter_plv_fdr < 0.05
  494. sig_str = ' *';
  495. end
  496. fprintf(' %s: DSR inter-subject PLV = %.3f (%d/%d in group direction), SR = %.3f (%d/%d in group direction)%s\n', ...
  497. band_names{band_idx}, ...
  498. cue_results(band_idx).plv_dsr, n_dsr_same, n_subjects, ...
  499. cue_results(band_idx).plv_sr, n_sr_same, n_subjects, sig_str);
  500. end
  502. fprintf('\n');
  503. fprintf('* = FDR-corrected p < 0.05\n');
  504. fprintf('circ_dist used for all phase shift contrasts to handle circular wrapping\n');
  505. fprintf('Group mean differences near +-180 deg are flagged as potentially uninterpretable\n');
  509. %% Helper function
  511. function p_fdr = fdr_bh(pvals)
  512. % Benjamini-Hochberg FDR correction
  513. p = pvals(:);
  514. [p_sorted, idx] = sort(p);
  515. m = length(p);
  516. q = zeros(m,1);
  518. % Compute BH threshold-adjusted values
  519. for i = 1:m
  520. q(i) = p_sorted(i) * m / i;
  521. end
  523. % Ensure monotonicity
  524. q = cummin(flipud(q));
  525. q = flipud(q);
  526. q(q>1) = 1;
  528. % Return p-values in original order
  529. p_fdr = zeros(m,1);
  530. p_fdr(idx) = q;
  531. end

CuePLV_slidingcombined.m, no license · at the source

Overview

Authors: Jacqueline M Fulvio1, Bradley R Postle1,2
ORCID iDs: Jacqueline M Fulvio
  1. Departments of Psychology, University of Wisconsin–Madison, Madison, Wisconsin 53706-1611
  2. Psychiatry, University of Wisconsin–Madison, Madison, Wisconsin 53706-1611
Institutions: University of Wisconsin–Madison (United States)
Journal: eNeuro, volume 13, issue 4, pages ENEURO.0346-25.2026
Dates: received 15 September 2025; accepted 1 April 2026; published online 21 April 2026; in print April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1523/eneuro.0346-25.2026 · PMID 41956898 · PMCID PMC13120838 · OpenAlex W7152534516
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Physiology & signal measures
Keywords: EEG, low-beta, oscillations, prioritization, TMS, working memory
MeSH: Brain*, Memory, Short-Term*, Parietal Lobe*, Transcranial Magnetic Stimulation*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: HHS | National Institutes of Health (MH131678)
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

The flexible control of working memory (WM) requires prioritizing immediately task-relevant information while maintaining information with potential future relevance in a deprioritized state. Using double-serial retrocuing (DSR) with simultaneous EEG recording, we investigated how single pulses of transcranial magnetic stimulation (spTMS) to right intraparietal sulcus impacts neural representations of unprioritized memory items (UMI), relative to irrelevant memory items (IMI) that are no longer needed for the trial. Twelve human participants (8 female) performed DSR plus a single-retrocue task, while spTMS was delivered during delay periods. Multivariate pattern analysis revealed that spTMS restored decodability of the UMI concurrent with stimulation and that of the IMI several timesteps later, after the evoked effects of spTMS were no longer present in the EEG signal. This effect was carried by the alpha (8–13 Hz) and low-beta (13–20 Hz) frequency bands. Analyses of the raw EEG signal showed two effects selective to the epoch containing the UMI: the retrocue and spTMS each produced phase shifts in the low-beta band. These findings demonstrate that deprioritization involves active neural mechanisms distinct from the processing of the IMI and that these are supported by low-beta oscillatory dynamics in parietal cortex. We hypothesize that the mechanism underlying spTMS-triggered involuntary retrieval of the UMI is the disruption of the encoding of priority status, which may depend on oscillatory dynamics in the low-beta band.

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

Repository

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OSF wnt6q

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (12)
Size: 19 files, 12 scripts
Software Heritage: not checked
Found in: “Code accessibility”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (6 files), CircStat (2 files), Signal Processing Toolbox (2 files), Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
12 files
At the source: osf.io/wnt6q

Code accessibility

The code/software described in the paper is freely available online at https://osf.io/wnt6q.

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • 9 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 10 MeSH terms, 1 funder, 46 references.

Cite

This paper

Fulvio, J. M., & Postle, B. R. (2026). Effects of TMS on the Decoding and Electrophysiology of Priority in Working Memory. eNeuro, 13(4), ENEURO.0346-25.2026. https://doi.org/10.1523/eneuro.0346-25.2026

BibTeX

@article{fulvio2026effects,
author = {Fulvio, Jacqueline M and Postle, Bradley R},
title = {{Effects of TMS on the Decoding and Electrophysiology of Priority in Working Memory}},
journal = {eNeuro},
year = {2026},
month = apr,
volume = {13},
number = {4},
pages = {ENEURO.0346--25.2026},
publisher = {Society for Neuroscience},
issn = {2373-2822},
doi = {10.1523/eneuro.0346-25.2026},
url = {https://doi.org/10.1523/eneuro.0346-25.2026},
pmid = {41956898},
pmcid = {PMC13120838}
}

RIS

TY - JOUR
AU - Fulvio, Jacqueline M
AU - Postle, Bradley R
TI - Effects of TMS on the Decoding and Electrophysiology of Priority in Working Memory
T2 - eNeuro
J2 - eNeuro
PY - 2026
DA - 2026/04/27
VL - 13
IS - 4
SP - ENEURO.0346
EP - 25.2026
SN - 2373-2822
PB - Society for Neuroscience
DO - 10.1523/eneuro.0346-25.2026
UR - https://doi.org/10.1523/eneuro.0346-25.2026
LA - en
ER -

CSL-JSON

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"id": "10.1523/eneuro.0346-25.2026",
"type": "article-journal",
"title": "Effects of TMS on the Decoding and Electrophysiology of Priority in Working Memory",
"container-title": "eNeuro",
"author": [
{
"family": "Fulvio",
"given": "Jacqueline M"
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{
"family": "Postle",
"given": "Bradley R"
}
],
"container-title-short": "eNeuro",
"volume": "13",
"issue": "4",
"page": "ENEURO.0346-25.2026",
"DOI": "10.1523/eneuro.0346-25.2026",
"PMID": "41956898",
"PMCID": "PMC13120838",
"ISSN": "2373-2822",
"publisher": "Society for Neuroscience",
"URL": "https://doi.org/10.1523/eneuro.0346-25.2026",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
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]
]
}
}

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