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Changes in visuocortical engagement and oscillatory brain activity during associative learning.

Code ↔ Paper

2 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 2 matches
  1. [1] § Data recording and processing › Statistical analyses ↔ Code/ggtone_postpro_final.m, lines 832–839 · score 0.93 · 0–700 ms, 1300–2000 ms, 700–1300 ms
  2. [2] § Data recording and processing › SSVEP ↔ Code/ggtone_postpro_final.m, lines 187–256 · score 0.52 · single trial spectra, attenuates, spectral, noise, Gabor, RESS

Paper

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The authors' code

MATLAB · 845 lines · 29 KB · no license · 2 matches

  1. %% GaborGen Tone post-processing code - Final for repository
  3. %% SSVEP RESS Method
  4. % Set working directory to the RESS data folder
  5. clear
  6. clc
  7. cd ;
  9. % Get trial files for RESS analysis
  10. filemat = getfilesindir(pwd, 'gaborgentone*trls*.pow3.mat');
  11. %
  12. % Loop over subjects to compute RESS spatial filters for each group of 4 conditions.
  13. for filestart = 1:4:88
  14. filemat_actual = filemat(filestart:filestart+3, :);
  15. pause(1)
  16. RESS_filegroups23(filemat_actual, 1:120, 301:1300, 500, 15, 0); %1000 sample points from baseline to sound onset = 2000 ms
  17. end
  19. % Merge RESS power files across conditions
  20. filemat = getfilesindir(pwd, 'gabor*RESSpow*');
  21. mergemulticons(filemat, 4, 'GM22.RESSpow');
  25. cd
  26. filemat = getfilesindir(pwd, 'gabor*RESSpow*');
  28. % Load merged condition files for plotting/statistics
  29. Csplus = ReadAvgFile('GM22.RESSpow.at1');
  30. GS1 = ReadAvgFile('GM22.RESSpow.at2');
  31. GS2 = ReadAvgFile('GM22.RESSpow.at3');
  32. GS3 = ReadAvgFile('GM22.RESSpow.at4');
  34. % Build a 4D matrix for statistics: channels x frequencies x subjects x conditions
  35. [repmatress] = makerepmat(filemat, 22, 4, []);
  36. load repmatress.mat;
  38. % Perform an F test (ANOVA-like contrast) on RESS power over frequency
  39. for frequency = 1:500
  40. temprep = squeeze(repmatress(:, frequency, :, :));
  41. repmatress2 = reshape(temprep, [1, 22, 4]);
  42. [Fcontmat_linear_ress(:, frequency),~,~,~,~] = contrast_rep_sign(repmatress2, [2 1 -1 -2]);
  43. end
  45. SaveAvgFile('Fcontmat_linear_ress.at',Fcontmat_linear_ress,[],[],2000)
  46. % Plot
  48. faxisFFT = 0:.5:250;
  51. figure
  52. plot(faxisFFT(1:40), Csplus(1:40), 'r', LineWidth=3), hold on,
  53. plot(faxisFFT(1:40), GS1(1:40), 'm', LineWidth=3),
  54. plot(faxisFFT(1:40), GS2(1:40), 'b', LineWidth=3),
  55. plot(faxisFFT(1:40), GS3(1:40), 'g', LineWidth=3),
  56. title 'RESS Spectra by Condition', fontsize = 22, legend
  58. %% open emegs and plot ssvep
  59. emegs2d
  61. %% extract the power at 15 Hz for an anova-like spreadsheet in jasp
  62. filemat = getfilesindir(pwd, '*RESSpow.at')
  63. [outmat] = extractstats(filemat, 4, 1, 31, []);
  64. bar(faxisFFT(5:100), GS3(5:100), 'k')
  66. %% RESS Linear effect Bayesian Bootstrapping and Permutation
  67. % uses the repmatress
  68. clear
  69. clc
  70. % add location of RESS files
  71. cd
  72. load repmatress.mat;
  73. linearBootstrap =[];
  74. size(repmatress)
  75. nsubjects = size(repmatress, 3);
  76. % make distributions of effects
  77. lineareffect = [2 1 -1 -2];
  79. % the linear effect distribution
  80. for elec = 1:size(repmatress,1)
  81. for frequency = 1:size(repmatress,2)
  82. for draw = 1:2000
  83. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  84. linearBootstrap(elec, frequency, draw) = mean(squeeze(repmatress(elec, frequency, bootstrapvec, : )), 1) * lineareffect';
  85. end
  86. end
  87. end
  90. % the null distribution permutation
  91. linearBootstrapPerm = []
  92. elec = 1;
  93. for perm = 1:2000
  94. repeatmatperm_Ress = repmatress;
  95. for subject = 1:nsubjects
  96. repeatmatperm_Ress(: , :, subject, 1:4) = repmatress(: , :, subject, randperm(4));
  98. end
  99. for frequency = 1:size(repmatress,2)
  101. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  102. linearBootstrapPerm(elec, frequency, perm) = mean(squeeze(repeatmatperm_Ress(elec, frequency, bootstrapvec, : )), 1) * lineareffect';
  104. end
  105. disp(['draw ', num2str(perm)])
  106. end
  109. % Compare the bootstrapped with permuted data
  110. for elec = 1:size(repmatress,1)
  111. for frequency = 1:size(repmatress,2)
  113. BFmap_RESS_linear(elec, frequency) = bootstrap2BF_z(squeeze(linearBootstrap(elec, frequency, :)),squeeze(linearBootstrapPerm(elec, frequency, :)), 0);
  115. end
  116. end
  118. faxisFFT = 0:.5:250;
  119. faxisFFT(31)
  120. plot(BFmap_RESS_linear)
  121. title 'Bayes Factors Linear RESS'
  123. BFmap_RESS_linear(31)
  125. log10(BFmap_RESS_linear(31))
  128. %% RESS Selective effect Bayesian Bootstrapping and Permutation
  129. clear
  130. clc
  132. load repmatress.mat; % 1 electrode, 500 freqs, 22 people, 4 conds
  134. % uses the repmatress
  135. selectBootstrap_csplus =[];
  136. size(repmatress)
  137. nsubjects = size(repmatress, 3);
  139. selecteffect = [3 -1 -1 -1];
  141. % the linear effect distribution
  142. for elec = 1:size(repmatress,1)
  143. for frequency = 1:size(repmatress,2)
  144. for draw = 1:2000
  145. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  146. selectBootstrap_csplus(elec, frequency, draw) = mean(squeeze(repmatress(elec, frequency, bootstrapvec, : )), 1) * selecteffect';
  147. end
  148. end
  149. end
  152. % the null distribution permutation
  153. selectBootstrapPerm_csplus = []
  154. elec = 1;
  155. for perm = 1:2000
  156. repeatmatperm_Ress = repmatress;
  157. for subject = 1:nsubjects
  158. repeatmatperm_Ress(: , :, subject, 1:4) = repmatress(: , :, subject, randperm(4));
  159. end
  160. for frequency = 1:size(repmatress,2)
  162. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  163. selectBootstrapPerm_csplus(elec, frequency, perm) = mean(squeeze(repeatmatperm_Ress(elec, frequency, bootstrapvec, : )), 1) * selecteffect';
  165. end
  166. disp(['draw ', num2str(perm)])
  167. end
  170. % Compare the bootstrapped with permuted data
  171. for elec = 1:size(repmatress,1)
  172. for frequency = 1:size(repmatress,2)
  174. BFmap_RESS_select_cs(elec, frequency) = bootstrap2BF_z(squeeze(selectBootstrap_csplus(elec, frequency, :)), ...
  175. squeeze(selectBootstrapPerm_csplus(elec, frequency, :)), 0);
  177. end
  178. end
  180. faxisFFT = 0:.5:250;
  181. faxisFFT(31) %15 hz
  182. figure, plot(BFmap_RESS_select_cs)
  183. BFmap_RESS_select_cs(31) % result = .484
  184. log10(.484)
  187. %% SSVEP Single-Trial Spectra Method
  188. clear
  189. clc
  190. % power from the FFT3d (normal FFT, no RESS) is an aletrnative to the RESS, we examine this next:
  191. cd ;
  193. % does spectral analysis on each single trial and then
  194. % averages them together within conditions) which gives combo of steady state response from each
  195. % single trial and also all spontaneous (noise) frequencies will be
  196. % retained bc they are not attenuated by any type of averaging; (each trial
  197. % may be in different phase but phase information is not used)
  199. %only want original trls files (not PLI or POW)
  200. filemat = getfilesindir(pwd, 'gabor*trls*.mat'); %dont need to do this now
  201. % Compute FFT on each trial (using the desired time window and frequency resolution)
  202. get_FFT_mat3d(filemat, 301:1300, 500);
  204. % Get FFT spectrum files ('.spec') and merge across conditions
  205. cd
  206. filemat = getfilesindir(pwd, '*.spec');
  207. mergemulticons(filemat, 4, 'GM22.singletrialspec');
  209. [outmat] = extractstats(filemat, 4, [70 71 74 75 76 82 83] , 31, []);
  212. epoch = 301:1300;
  213. sizeEpochInSecs = length(epoch)*2/1000;
  214. fstep = 1/sizeEpochInSecs;
  215. faxis = 0:.5:250;
  217. % Get all matching files first
  218. filemat_all = getfilesindir(pwd, 'gaborgentone_*.trls.*.mat');
  219. % Now filter out the ones that contain '.trls.21.'
  220. exclude_pattern = 'trls.21.mat';
  221. keep_idx = ~contains(string(filemat_all), '.trls.21.');
  222. % Apply logical indexing to keep only desired filenames
  223. filemat = filemat_all(keep_idx, :);
  225. for i = 1:size(filemat, 1)
  226. % Load file and extract matrix
  227. fname = deblank(filemat(i, :));
  228. data = load(fname, '-mat');
  230. % Assumes the variable is called Mat3D
  231. if isfield(data, 'Mat3D')
  232. dat = data.Mat3D;
  233. else
  234. warning(['Mat3D not found in file: ' fname]);
  235. continue
  236. end
  238. % Average over 3rd dimension
  239. avgmat = mean(dat, 3, 'omitnan');
  241. % Generate output filename
  242. [~, name, ~] = fileparts(fname);
  243. outfile = [name '_avg.mat'];
  245. % % Save the averaged matrix
  246. % save(outfile, 'avgmat', '-mat');
  247. % disp(['Saved: ' outfile])
  248. end
  251. filematavg = getfilesindir(pwd, 'gaborgentone_*.trls.*_avg.mat');
  252. ERP = avgmats_mat(filematavg, 'ERP_Oz.mat');
  253. ERP_Oz = load('ERP_Oz.mat');
  254. ERP_Oz = ERP_Oz.avgmat;
  255. figure
  256. plot(taxis, ERP_Oz(75, :));
  258. %% Get FFT spectrum files ('.spec') and merge across conditions
  259. cd
  261. filemat = getfilesindir(pwd, '*.spec');
  262. mergemulticons(filemat, 4, 'GM22.singletrialspec');
  264. [outmat] = extractstats(filemat, 4, [70 71 74 75 76 82 83] , 31, []);
  266. % we saved the stat matrix to a file called resspower4cons.csv
  268. % to further examine this with method with higher sensitivity, we examined the largest effect visible post-hoc
  269. % Build repmat for statistical analysis: sensors x frequencies x subjects x conditions
  270. [repmatsingleSpec] = makerepmat(filemat, 22, 4, []);
  271. %
  272. [Fcontmat,rcontmat,MScont,MScs, dfcs]=contrast_rep_sign(squeeze(repmatsingleSpec(:, 31, :, :)) ,[1.5 .5 -.5 -1.5]);
  274. % now do the permutation test to see if this survives
  275. for draw = 1:1000
  277. for x = 1:22
  278. repmatsingleSpec(:, :, x, 1:4) = repmatsingleSpec(:, :,x, randperm(4));
  279. end
  281. [Fcontmat,rcontmat,MScont,MScs, dfcs]=contrast_rep_sign(squeeze(repmatsingleSpec(:, 31, :, :)) ,[1.5 .5 -.5 -1.5]);
  283. dist(draw) = max(Fcontmat);
  285. if draw./100 == round(draw./100), fprintf('.'), end
  287. end
  289. hist(dist, 50)
  290. quantile(dist, .95)
  292. % Load merged condition files for plotting/statistics
  293. csplus = ReadAvgFile('GM22.singletrialspec.at1');
  294. GS1 = ReadAvgFile('GM22.singletrialspec.at2');
  295. GS2 = ReadAvgFile('GM22.singletrialspec.at3');
  296. GS3 = ReadAvgFile('GM22.singletrialspec.at4');
  299. %
  300. figure
  301. plot(faxisFFT(1:50),GS1(75, 1:50), LineWidth=2),hold on,
  302. plot(faxisFFT(1:50),GS2(75, 1:50), LineWidth=2),
  303. plot(faxisFFT(1:50),GS3(75, 1:50), LineWidth=2),
  304. plot(faxisFFT(1:50),csplus(70, 1:50), 'r', LineWidth=2)
  305. title('Frequency Spectra by Condition', 'FontSize', 22)
  306. xlabel('Frequency (Hz)', 'FontSize', 20) % Label for x-axis
  307. ylabel('Power', 'FontSize', 20) % Label for y-axis
  308. legend({'GS1', 'GS2', 'GS3', 'CS+'}, 'FontSize', 20) % Adding legend
  314. ssvep21 = csplus(75,31);
  315. ssvep22 = GS1(75,31);
  316. ssvep23 = GS2(75,31);
  317. ssvep24 = GS3(75,31), 2;
  318. % Transpose if needed so rows = subjects, columns = conditions
  319. alpha_gm = [alphagm21; alphagm22; alphagm23; alphagm24]'; % size: [4 x N_conditions]
  320. alpha_mean = mean(alpha_gm, 1); % Mean across participants
  321. alpha_sem = std(alpha_gm, 0, 1) / sqrt(size(alpha_gm, 1)); % Standard Error of Mean (SEM)
  323. % Plot with error bars
  324. figure;
  325. % bar(alpha_mean);
  326. hold on
  327. errorbar(1:length(alpha_mean), alpha_mean, alpha_sem);
  328. xlabel('Condition');
  329. ylabel('Alpha Power (Grand Mean ± SEM)');
  330. xticks(1:length(alpha_mean));
  331. xticklabels({'Cond1','Cond2','Cond3','Cond4'});
  332. title('Alpha Power Across Conditions');
  339. % examine with emegs2d
  340. emegs2d
  342. %% Bayes bootstrapping for single-trial spectra - linear
  343. clear
  344. clc
  345. cd
  346. % uses the repmatsingleSpec: 129 channels, 500 freqs, 22 people, 4 conds
  347. load('repmatsingleSpec.mat')
  348. linearBootstrap =[];
  349. size(repmatsingleSpec)
  350. nsubjects = size(repmatsingleSpec, 3);
  351. % make distributions of effects
  352. lineareffect = [2 1 -1 -2];
  355. % the linear effect distribution
  356. for elec = 1:size(repmatsingleSpec,1)
  357. for frequency = 1:size(repmatsingleSpec,2)
  358. for draw = 1:2000
  359. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  360. linearBootstrap(elec, frequency, draw) = mean(squeeze(repmatsingleSpec(elec, frequency, bootstrapvec, : )), 1) * lineareffect';
  361. end
  362. end
  363. disp(['draw ', num2str(draw)])
  364. end
  365. %%
  366. cd
  367. load('linearBootstrap_singleSpec.mat')
  368. %%
  369. % the null distribution permutation
  370. linearBootstrapPerm = [];
  372. for perm = 1:2000
  373. repeatmatperm_singleSpec = repmatsingleSpec;
  374. for subject = 1:nsubjects
  375. repeatmatperm_singleSpec(: , :, subject, 1:4) = repmatsingleSpec(: , :, subject, randperm(4));
  376. end
  378. for elec = 1:size(repmatsingleSpec,1)
  379. for frequency = 1:size(repmatsingleSpec,2)
  380. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  381. linearBootstrapPerm(elec, frequency, perm) = mean(squeeze(repeatmatperm_singleSpec(elec, frequency, bootstrapvec, : )), 1) * lineareffect';
  383. end
  384. end
  385. disp(['draw ', num2str(perm)])
  387. end
  389. %% load permuted bf
  390. load('single_Spec_linearBootPerm.mat')
  391. %% Compare the bootstrapped with permuted data
  392. for elec = 1:size(repmatsingleSpec,1)
  393. for frequency = 1:size(repmatsingleSpec,2)
  394. BFmap_singleSpec_linear(elec, frequency) = bootstrap2BF_z(squeeze(linearBootstrap(elec, frequency, :)),squeeze(linearBootstrapPerm(elec, frequency, :)), 0);
  395. end
  396. disp(['elec', num2str(elec)])
  397. end
  399. faxisFFT = 0:.5:250;
  400. faxisFFT(31) %15 hz
  402. figure, plot(BFmap_singleSpec_linear(:, 31)) %select frequency of interest for all channels (15 hz which is the 31st bin)
  404. cd
  405. % use SaveAvgFile to create file to use in emegs for heads
  406. AvgMat = BFmap_singleSpec_linear;
  407. SaveAvgFile('BF_singleSpec_linear.at',AvgMat,[],[], 1,[],[],[],[],1)
  409. log10_BFssVEP_linear = log10(BFmap_singleSpec_linear);
  410. SaveAvgFile('log10_BFssVEP_linear.at', log10_BFssVEP_linear,[],[], 1,[],[],[],[],1)
  412. log10_BFssVEP_linear(75, 31)
  414. figure, plot(BFmap_singleSpec_linear(:, 31)) %select frequency of interest for all channels (15 hz which is the 31st bin)
  415. size(repmatsingleSpec)
  417. plot(squeeze(repmatsingleSpec(83, 31, :, :)))
  418. plot(squeeze(repmatsingleSpec(83, 31, :, :))')
  419. plot(squeeze(repmatsingleSpec(83, 31, :, 1))-squeeze(repmatsingleSpec(83, 31, :, 4)))
  420. bar(squeeze(repmatsingleSpec(83, 31, :, 1))-squeeze(repmatsingleSpec(83, 31, :, 4)))
  421. bar(mean(squeeze(repmatsingleSpec(75, 31, :, :)))')
  423. %% selective ssvep single trial spectra, bayes bootstrapping for single-trial spectra
  424. clear
  425. clc
  426. cd
  427. % uses the repmatsingleSpec: 129 channels, 500 freqs, 22 people, 4 conds
  428. load('repmatsingleSpec.mat')
  429. selectBootstrap =[];
  430. size(repmatsingleSpec)
  431. nsubjects = size(repmatsingleSpec, 3);
  432. % make distributions of effects
  433. selecteffect = [3 -1 -1 -1];
  436. % the all or nothing effect distribution
  437. for elec = 1:size(repmatsingleSpec,1)
  438. for frequency = 1:size(repmatsingleSpec,2)
  439. for draw = 1:2000
  440. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  441. selectBootstrap(elec, frequency, draw) = mean(squeeze(repmatsingleSpec(elec, frequency, bootstrapvec, : )), 1) * selecteffect';
  442. end
  443. end
  444. disp(['elec ', num2str(elec)])
  445. end
  448. %% selective pattern
  449. % the null distribution permutation
  450. selectBootstrapPerm = [];
  452. for perm = 1:2000
  453. repeatmatperm_singleSpec = repmatsingleSpec;
  454. for subject = 1:nsubjects
  455. repeatmatperm_singleSpec(: , :, subject, 1:4) = repmatsingleSpec(: , :, subject, randperm(4));
  456. end
  458. for elec = 1:size(repmatsingleSpec,1)
  459. for frequency = 1:size(repmatsingleSpec,2)
  460. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  461. selectBootstrapPerm(elec, frequency, perm) = mean(squeeze(repeatmatperm_singleSpec(elec, frequency, bootstrapvec, : )), 1) * lineareffect';
  463. end
  464. end
  465. disp(['draw ', num2str(perm)])
  467. end
  469. %saved as selectBootstrapPerm_singleSpec.mat
  471. %% Compare the bootstrapped with permuted data
  472. for elec = 1:size(repmatsingleSpec,1)
  473. for frequency = 1:size(repmatsingleSpec,2)
  474. BFmap_singleSpec_select(elec, frequency) = bootstrap2BF_z(squeeze(selectBootstrap(elec, frequency, :)),squeeze(selectBootstrapPerm(elec, frequency, :)), 0);
  475. end
  476. disp(['elec', num2str(elec)])
  477. end
  479. faxisFFT = 0:.5:250;
  480. faxisFFT(31) %15 hz
  482. figure, plot(BFmap_singleSpec_select(:, 31)) %select frequency of interest for all channels (15 hz which is the 31st bin)
  484. %
  485. SaveAvgFile('BF_singleSpec_select.at',BFmap_singleSpec_select,[],[], 1,[],[],[],[],1)
  486. %need to make heads for single trial spec;
  487. emegs2d
  489. log10_BFssVEP_select = log10(BFmap_singleSpec_select);
  490. SaveAvgFile('log10_BFssVEP_select.at', log10_BFssVEP_select,[],[], 1,[],[],[],[],1)
  492. log10_BFssVEP_select(75, 31)
  494. %% Alpha Wavelet Analysis
  495. clear
  496. clc
  498. % Set working directory
  499. cd
  500. % Define frequency and time axes for wavelet analysis
  501. faxisall = 0:1000/3600:250; % full frequency axis for wavelets
  502. faxis = faxisall(11:4:110); % frequency axis used for plotting
  503. taxis = (-598:2:3000); % time axis in ms
  505. % Run wavelet analysis on trial files (returns power, phase-locking index, etc.)
  506. filemat = getfilesindir(pwd, 'gabor*trls.2*.mat');
  507. [WaPower, PLI, PLIdiff] = wavelet_app_matfiles(filemat, 500, 11, 110, 4, 200:300, []); %baseline corr but not an issue bc its before waveletting
  509. % Process power files and group by condition:
  510. filematpow = getfilesindir(pwd, 'gabor*pow3.mat');
  511. filematpow21 = filematpow(1:4:end, :);
  512. filematpow22 = filematpow(2:4:end, :);
  513. filematpow23 = filematpow(3:4:end, :);
  514. filematpow24 = filematpow(4:4:end, :);
  516. % Compute grand means for each condition
  517. GM22pow3_21 = avgmats_mat(filematpow21, 'GM22.at21.pow3.mat');
  518. GM22pow3_22 = avgmats_mat(filematpow22, 'GM22.at22.pow3.mat');
  519. GM22pow3_23 = avgmats_mat(filematpow23, 'GM22.at23.pow3.mat');
  520. GM22pow3_24 = avgmats_mat(filematpow24, 'GM22.at24.pow3.mat');
  522. gm22 = getfilesindir(pwd, 'GM22.at2*'); %all except CS+ condition
  523. GM22_all = avgmats_mat(gm22, 'GM22_all.mat');
  524. % GM22_all = permute(GM22_all,[2 1 3]);
  525. SaveAvgFile('GM22_all.at',GM22_all,[],[], 1,[],[],[],[],1)
  528. %% time-freq plot for paper for channel 75 avg over all except cs+
  529. % filematGM = getfilesindir(pwd, 'GM22.at2*.pow3.mat')
  530. % GM22_all = avgmats_mat(filematGM, 'GM22_all.mat');
  531. load('GM22_all.mat')
  532. GM22_all_bsl = bslcorrWAMat_percent(GM22_all, 125:240);
  535. % Define frequency and time axes for wavelet analysis
  536. faxisall = 0:1000/3600:250; % full frequency axis for wavelets
  537. faxis = faxisall(11:4:110); % frequency axis used for plotting
  538. taxis = -598:2:3000; % time axis in ms
  541. % Plot baseline-corrected contours
  542. figure
  543. subplot(2,1,1), contourf(taxis, faxis, squeeze(GM22_all(sensor,:,:))'), colorbar
  544. subplot(2,1,2), contourf(taxis, faxis, squeeze(GM22_all_bsl(sensor,:,:))'), colorbar
  546. %%
  548. %load grand means for each condition
  549. GM22pow3_21 = importdata('GM22.at21.pow3.mat', 'avgmat');
  550. GM22pow3_22 = importdata('GM22.at22.pow3.mat', 'avgmat');
  551. GM22pow3_23 = importdata('GM22.at23.pow3.mat', 'avgmat');
  552. GM22pow3_24 = importdata('GM22.at24.pow3.mat', 'avgmat');
  554. GM22pow3_21 = GM22pow3_21(:, :, 8);
  555. GM22pow3_22 = GM22pow3_22(:, :, 8);
  556. GM22pow3_23 = GM22pow3_23(:, :, 8);
  557. GM22pow3_24 = GM22pow3_24(:, :, 8);
  558. %save as at files for emegs
  559. SaveAvgFile('GM22powAlpha_21_alpha.at',GM22pow3_21,[],[], 1,[],[],[],[],1)
  560. SaveAvgFile('GM22powAlpha_22_alpha.at',GM22pow3_22,[],[], 1,[],[],[],[],1)
  561. SaveAvgFile('GM22powAlpha_23_alpha.at',GM22pow3_23,[],[], 1,[],[],[],[],1)
  562. SaveAvgFile('GM22powAlpha_24_alpha.at',GM22pow3_24,[],[], 1,[],[],[],[],1)
  563. emegs2d
  566. % Plot contour plots for a selected sensor
  567. sensor = 75;
  568. faxisindex = 4:20;
  569. figure
  570. subplot(4,1,1), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_21(sensor,50:end-50, faxisindex))', 15)%, caxis([.4 5]),colorbar
  571. subplot(4,1,2), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_22(sensor,50:end-50, faxisindex))', 15)%, caxis([.4 5]),colorbar
  572. subplot(4,1,3), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_23(sensor,50:end-50, faxisindex))', 15)%, caxis([.4 5]),colorbar
  573. subplot(4,1,4), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_24(sensor,50:end-50, faxisindex))', 15)%, caxis([.4 5]),colorbar
  574. sgtitle(['Average Power by Condition: Sensor ' num2str(sensor)])%
  577. alphagm21 = GM22pow3_21(75,450:800);
  578. alphagm22 = GM22pow3_22(75,450:800);
  579. alphagm23 = GM22pow3_23(75,450:800);
  580. alphagm24 = GM22pow3_24(75,450:800);
  581. alpha_gm = [alphagm21; alphagm22; alphagm23; alphagm24]';
  582. alpha_mean = mean(alpha_gm, 1); % Mean across participants
  583. alpha_sem = std(alpha_gm, 0, 1) / sqrt(size(alpha_gm, 1)); % Standard Error of Mean (SEM)
  585. % Plot with error bars
  586. figure;
  587. bar(alpha_mean);
  588. hold on
  589. errorbar(1:length(alpha_mean), alpha_mean, alpha_sem);
  590. xlabel('Condition');
  591. ylabel('Alpha Power (Grand Mean ± SEM)');
  592. xticks(1:length(alpha_mean));
  593. xticklabels({'Cond1','Cond2','Cond3','Cond4'});
  594. title('Alpha Power Across Conditions');
  598. %% Baseline-correct the grand means (using baseline indices 125:240)
  599. GM22pow3_21_bsl = bslcorrWAMat_percent(GM22pow3_21, 75:225);
  600. GM22pow3_22_bsl = bslcorrWAMat_percent(GM22pow3_22, 75:225);
  601. GM22pow3_23_bsl = bslcorrWAMat_percent(GM22pow3_23, 75:225);
  602. GM22pow3_24_bsl = bslcorrWAMat_percent(GM22pow3_24, 75:225);
  604. % %save as at files for emegs
  605. % SaveAvgFile('GM22powAlpha_21_alpha_bsl.at',GM22pow3_21,[],[], 1,[],[],[],[],1)
  606. % SaveAvgFile('GM22powAlpha_22_alpha_bsl.at',GM22pow3_22,[],[], 1,[],[],[],[],1)
  607. % SaveAvgFile('GM22powAlpha_23_alpha_bsl.at',GM22pow3_23,[],[], 1,[],[],[],[],1)
  608. % SaveAvgFile('GM22powAlpha_24_alpha_bsl.at',GM22pow3_24,[],[], 1,[],[],[],[],1)
  611. % Plot baseline-corrected contoursfor alpha frequencies
  612. figure
  613. % faxisindex = 6:18;
  614. faxisindex = 8;
  615. sensor = 75;%
  617. subplot(4,1,1), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_21_bsl(sensor,50:end-50,faxisindex))')%, colorbar%, caxis([-15 100])
  618. subplot(4,1,2), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_22_bsl(sensor,50:end-50,faxisindex))')%, colorbar%, caxis([-15 100])
  619. subplot(4,1,3), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_23_bsl(sensor,50:end-50,faxisindex))')%, colorbar%, caxis([-15 100])
  620. subplot(4,1,4), contourf(taxis(50:end-50), faxis(faxisindex), squeeze(GM22pow3_24_bsl(sensor,50:end-50,faxisindex))')%, colorbar%, caxis([-15 100])
  621. sgtitle(['Average Power by Condition: Sensor ' num2str(sensor)])
  623. time = 1050:1125;
  624. %plot alpha differences by condition over time
  625. figure
  626. plot(taxis(time), squeeze(GM22pow3_21_bsl(75, time)))
  627. hold on
  628. plot(taxis(time), squeeze(GM22pow3_22_bsl(75, time)))
  629. plot(taxis(time), squeeze(GM22pow3_23_bsl(75, time)))
  630. plot(taxis(time), squeeze(GM22pow3_24_bsl(75, time)))
  635. %% we used ttest3d to easily obtain 4-D arrays
  636. % [ttestmat21_22, ~, mat4d22] = ttest3d(filematpow21, filematpow22, 1, []);
  637. % [ttestmat21_23, ~, mat4d23] = ttest3d(filematpow21, filematpow23, 1, []);
  638. % [ttestmat21_24, mat4d21, mat4d24] = ttest3d(filematpow21, filematpow24, 1, []);
  641. %load in 4-d arrays
  642. load('mat4d21_csplus.mat')
  643. load('mat4d22_gs1.mat')
  644. load('mat4d23_gs2.mat')
  645. load('mat4d24_gs3.mat')
  647. repeatmat_alpha = cat(5, mat4d21(:, 1:10:end, :, :), mat4d22(:, 1:10:end, :, :), mat4d23(:, 1:10:end, :, :), mat4d24(:, 1:10:end, :, :));
  649. [ttestmat21_22, ~, mat4d22_bsl] = ttest3d(filematpow21, filematpow22, 1, [75:225]);
  650. [ttestmat21_23, ~, mat4d23_bsl] = ttest3d(filematpow21, filematpow23, 1, [75:225]);
  651. [ttestmat21_24, mat4d21_bsl, mat4d24_bsl] = ttest3d(filematpow21, filematpow24, 1, [75:225]);
  653. repeatmat_alpha_bsl = cat(5, mat4d21_bsl(:, 1:10:end, :, :), mat4d22_bsl(:, 1:10:end, :, :), mat4d23_bsl(:, 1:10:end, :, :), mat4d24_bsl(:, 1:10:end, :, :));
  655. % F test across time for the alpha band (averaging across 25 frequencies)
  656. for time = 1:180
  657. for frequency = 1:25
  658. [Fcontmat_linearWavelet(:, time, frequency),rcontmat,~,MScs, dfcs]=contrast_rep_sign(squeeze(repeatmat_alpha(:, time, frequency, :, :)),[-2 -1 1 2]);
  659. [Fcontmat_CSselectWavelet(:, time, frequency),rcontmat,MScont,MScs, dfcs]=contrast_rep_sign(squeeze(repeatmat_alpha(:, time, frequency, :, :)),[-3 1 1 1]);
  660. end
  661. if mod(time,100)==0, fprintf('.'); end
  662. end
  664. % Plot F-test results for alpha at selected sensors
  665. figure
  666. contourf(taxis(1:10:end), faxis, squeeze(Fcontmat_CSselectWavelet(72,:,:))'), colorbar
  667. title('F tests for alpha - Sensor 72')
  669. figure
  670. plot(taxis(1:10:end), Fcontmat_CSselectWavelet(72,:,8))
  671. title('F tests for alpha - Sensor 72')
  673. % Decimate and average data for further alpha statistics
  674. % first, we out the data into an array to have an easier time
  675. % to make this happen, we decimate the time dimension and the frequemcy
  676. % dimension
  677. mat4d4stats = squeeze(cat(5, mat4d21(:,1:10:1800,8,:), ...
  678. mat4d22(:,1:10:1800,8,:), ...
  679. mat4d23(:,1:10:1800,8,:), ...
  680. mat4d24(:,1:10:1800,8,:)));
  684. % from 550- to 1300 sample points is 500 post stimulus to 2000 ms post
  685. % stimulus
  686. % in decimated points that is 55 to 130
  687. outmat4statsalpha = squeeze(mean(mean(mat4d4stats([75 62 55 81 72],55:130,:,:))));
  690. %% do the bayesian bootstrap for Alpha Power;
  691. % uses the repeatmat
  692. linearBootstrap =[];
  693. size(repeatmat_alpha)
  694. nsubjects = size(repeatmat_alpha, 4);
  695. % make distributions of effects
  696. lineareffect = [-2 -1 1 2]; %expect this direction of effect
  698. % the linear effect distribution
  699. for draw = 1:2000
  700. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  701. for elec = 1:size(repeatmat_alpha,1)
  702. for timepoint = 1:size(repeatmat_alpha,2)
  703. for frequency = 1:size(repeatmat_alpha,3)
  706. linearBootstrap(elec, timepoint, frequency, draw) = ...
  707. mean(squeeze(repeatmat_alpha(elec, timepoint, frequency, bootstrapvec, : ))) * lineareffect';
  708. end
  709. end
  710. end
  711. disp(['draw ', num2str(draw)])
  712. end
  714. %%
  715. % the null distribution permutation
  716. linearBootstrapPerm = [];
  717. for perm = 1:2000
  718. repeatmat_alphaperm = repeatmat_alpha;
  719. for subject = 1:nsubjects
  720. repeatmat_alphaperm(:, :, :, subject, :) = repeatmat_alphaperm(:, :, :, subject, randperm(4));
  721. end
  722. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  723. for elec = 1:size(repeatmat_alpha,1)
  724. for timepoint = 1:size(repeatmat_alpha,2)
  725. for frequency = 1:size(repeatmat_alpha,3)
  727. linearBootstrapPerm(elec, timepoint, frequency, perm) = ...
  728. mean(squeeze(repeatmat_alphaperm(elec,timepoint, frequency, bootstrapvec, : ))) * lineareffect';
  729. end
  730. end
  732. end
  733. disp(['permutation ', num2str(perm)])
  734. end
  738. %% comparison between wavelet and permuted values
  739. % BFmap_alpha results in 129 channels x 180 timepoints x 25 frequencies
  740. for elec = 1:size(repeatmat_alpha,1)
  741. for timepoint = 1:size(repeatmat_alpha,2) %180 timepoints, decimated earlier
  742. for frequency = 1:size(repeatmat_alpha,3) %25 frequencies, decimated earlier (8th is alpha)
  743. BFmap_alpha(elec, timepoint, frequency) = bootstrap2BF_z(squeeze(linearBootstrap(elec,timepoint, frequency, :)), ...
  744. squeeze(linearBootstrapPerm(elec,timepoint, frequency, :)), 0);
  745. end
  746. end
  747. disp(['elec', num2str(elec)])
  748. end
  749. %%
  750. plot(BFmap_alpha(75,:,8)) %faxis shows alpha is now 8th in 3rd dimension
  751. %[75 62 55 81 72]
  753. %%
  754. alphaBF = BFmap_alpha(:,:,8);
  755. SaveAvgFile('alphaBF.at', alphaBF, [], [],1,[],[],[],[],1)
  756. emegs2d
  760. %% do the bayesian bootstrap for Alpha Power - selective pattern;
  761. % uses the repeatmat
  762. selectBootstrap_alpha =[];
  763. size(repeatmat_alpha)
  764. nsubjects = size(repeatmat_alpha, 4);
  765. % make distributions of effects
  766. selecteffect = [-3 1 1 1]; %expect this direction of effect
  768. % the linear effect distribution
  769. for draw = 1:2000
  770. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  771. for elec = 1:size(repeatmat_alpha,1)
  772. for timepoint = 1:size(repeatmat_alpha,2)
  773. for frequency = 1:size(repeatmat_alpha,3)
  774. selectBootstrap_alpha(elec, timepoint, frequency, draw) = ...
  775. mean(squeeze(repeatmat_alpha(elec, timepoint, frequency, bootstrapvec, : ))) * selecteffect';
  776. end
  777. end
  778. end
  779. disp(['draw ', num2str(draw)])
  780. end
  782. %% the null distribution permutation
  783. selectBootstrapPerm_alpha = [];
  784. for perm = 1:2000
  785. repeatmat_alphaperm = repeatmat_alpha;
  786. for subject = 1:nsubjects
  787. repeatmat_alphaperm(:, :, :, subject, :) = repeatmat_alphaperm(:, :, :, subject, randperm(4));
  788. end
  789. bootstrapvec = randi(nsubjects, 1,nsubjects)';
  790. for elec = 1:size(repeatmat_alpha,1)
  791. for timepoint = 1:size(repeatmat_alpha,2)
  792. for frequency = 1:size(repeatmat_alpha,3)
  794. selectBootstrapPerm_alpha(elec, timepoint, frequency, perm) = ...
  795. mean(squeeze(repeatmat_alphaperm(elec,timepoint, frequency, bootstrapvec, : ))) * selecteffect';
  796. end
  797. end
  799. end
  800. disp(['permutation ', num2str(perm)])
  801. end
  805. %% comparison between wavelet and permuted values
  806. % BFmap_alpha results in 129 channels x 180 timepoints x 25 frequencies
  807. for elec = 1:size(repeatmat_alpha,1)
  808. for timepoint = 1:size(repeatmat_alpha,2) %180 timepoints, decimated earlier
  809. for frequency = 1:size(repeatmat_alpha,3) %25 frequencies, decimated earlier (8th is alpha)
  810. BFmap_alpha_select(elec, timepoint, frequency) = bootstrap2BF_z(squeeze(selectBootstrap(elec,timepoint, frequency, :)), ...
  811. squeeze(selectBootstrapPerm_alpha(elec,timepoint, frequency, :)), 0);
  812. end
  813. end
  814. disp(['elec', num2str(elec)])
  815. end
  818. linear = ReadAvgFile('Log10TypicalLinearBFs.at');
  819. antilinear = ReadAvgFile('Log10AntiLinearBFs.at');
  820. antiallnothing = ReadAvgFile('Log10allnothingBFs.at');
  822. allnothing = antiallnothing* -1;
  823. SaveAvgFile('Log10allnothingBF_typical.at',allnothing,[],[], ...
  824. [],[],[],[],[],[],[],[],[],[],[])
  826. early_bfmap_linear = squeeze(mean(linear (75, 30:65), 2))
  827. middle_bfmap_linear = squeeze(mean(linear(75, 65:95), 2))
  828. late_bfmap_linear = squeeze(mean(linear (75, 95:130), 2))
  829. % antilinear : [129 × 180] matrix of log10 Bayes factors
  830. % (129 electrodes, 180 decimated time-samples)
  832. %% Channel 70/75, three time windows; BFs
  833. % early_bfmap_linear = mean(linear(75, 31:63)) % 0–660 ms
  834. % middle_bfmap_linear = mean(linear(75, 64:97)) % 670–1330 ms
  835. % late_bfmap_linear = mean(linear(75, 98:130)) % 1340–2000 ms
  837. early_bfmap_allnothing = mean(allnothing(70, 30:65)) % ~0–700 ms
  838. middle_bfmap_allnothing = mean(allnothing(70, 65:95)) % ~700–1300 ms
  839. late_bfmap_allnothing = mean(allnothing(70, 95:130)) % ~1300–2000 ms
  841. %%
  842. alphaBF_select = BFmap_alpha_select(:,:,8);
  843. SaveAvgFile('alphaBF_select.at', alphaBF_select, [], [],1,[],[],[],[],1)
  844. emegs2d

ggtone_postpro_final.m, no license · at the source

Overview

Authors: Sarah M. Gardy1,2, Christian Panitz3, Hannah Engle1, Faith Gilbert1, Andreas Keil1
  1. Department of Psychology, University of Florida,Gainesville, FL USA
  2. Laboratory for Brain, Body, and Behavior, Department of Psychology, University of Florida,Gainesville, USA
  3. Department of Psychology, University of Bremen,Bremen, Germany
Institutions: University of Florida (United States); University of Bremen (Germany)
Journal: Scientific reports, volume 16, issue 1, article 16766
Dates: received 30 July 2025; accepted 23 March 2026; published online 9 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-45966-4 · PMID 41957439 · PMCID PMC13223303 · OpenAlex W7152421799
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Connectivity, Preprocessing, Statistics, Physiology & signal measures
Keywords: Pavlovian Conditioning, Association formation, Alpha, ssVEP, EEG, Neuroscience, Psychology
MeSH: Association Learning*, Evoked Potentials, Visual*, Visual Cortex*, Adult, Conditioning, Classical, Electroencephalography, Female, Humans, Male, Photic Stimulation, Young Adult (* major topic)
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institute of Mental Health (#R01MH125615)
Citations: not cited yet (Europe PMC); 56 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

OSF qr37b

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State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (2)
Size: 19 files, 2 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Psychtoolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
2 files
At the source: osf.io/qr37b/overview

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 7 keywords, 11 MeSH terms, 1 funder, 55 references.

Cite

This paper

Gardy, S. M., Panitz, C., Engle, H., Gilbert, F., & Keil, A. (2026). Changes in visuocortical engagement and oscillatory brain activity during associative learning. Scientific reports, 16(1), 16766. https://doi.org/10.1038/s41598-026-45966-4

BibTeX

@article{gardy2026changes,
author = {Gardy, Sarah M. and Panitz, Christian and Engle, Hannah and Gilbert, Faith and Keil, Andreas},
title = {{Changes in visuocortical engagement and oscillatory brain activity during associative learning}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {16766},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-45966-4},
url = {https://doi.org/10.1038/s41598-026-45966-4},
pmid = {41957439},
pmcid = {PMC13223303}
}

RIS

TY - JOUR
AU - Gardy, Sarah M.
AU - Panitz, Christian
AU - Engle, Hannah
AU - Gilbert, Faith
AU - Keil, Andreas
TI - Changes in visuocortical engagement and oscillatory brain activity during associative learning
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/09
VL - 16
IS - 1
SP - 16766
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-45966-4
UR - https://doi.org/10.1038/s41598-026-45966-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-45966-4",
"type": "article-journal",
"title": "Changes in visuocortical engagement and oscillatory brain activity during associative learning",
"container-title": "Scientific reports",
"author": [
{
"family": "Gardy",
"given": "Sarah M."
},
{
"family": "Panitz",
"given": "Christian"
},
{
"family": "Engle",
"given": "Hannah"
},
{
"family": "Gilbert",
"given": "Faith"
},
{
"family": "Keil",
"given": "Andreas"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "16766",
"DOI": "10.1038/s41598-026-45966-4",
"PMID": "41957439",
"PMCID": "PMC13223303",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-45966-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
9
]
]
}
}

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