OSCR

Midfrontal theta power relates to response speeding following frustrative nonreward.

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] § Method › EEG recording and preprocessing ↔ code/MATLAB/Step1_PREPROCESS.m, lines 91–176 · score 0.95 · eye blinks, clearly identifiable nonbrain, ICLabel, removed channels, preprocessing, EEGLAB
  2. [2] § Method › Time-frequency analysis ↔ code/MATLAB/Step3_GED.m, lines 298–359 · score 0.89 · Gaussian centered, best component, selected component, best theta, R2, selection
  3. [3] § Method › Time-frequency analysis ↔ code/MATLAB/Step3_GED.m, lines 72–184 · score 0.79 · 0–700 ms, theta peak frequency, 4–9 Hz, dB, wavelets, baseline
  4. [4] § Method › Time-frequency analysis ↔ code/MATLAB/Step3_GED.m, lines 190–295 · score 0.72 · narrowband filters, covariance matrices, theta peaks, window, width, channel
  5. [5] § Method › Time-frequency analysis › Single-trial LMM of MFθ and RT adjustment ↔ code/R/analysis_code_AffectivePosner.R, lines 565–634 · score 0.65 · probe_interaction, subsequent RT, intervals, profile, predicted, LMM
  6. [6] § Method › Time-domain analysis ↔ code/MATLAB/Step2_ERP.m, lines 2–45 · score 0.58 · RewP, Reward Positivity, waveforms, amplitude, ERP, feedback
  7. [7] § Method › Time-frequency analysis ↔ code/MATLAB/Step3_GED.m, lines 2–39 · score 0.52 · multivariate source separation, GED, band, neural, filter, power
  8. [8] § Method › Time-frequency analysis ↔ code/MATLAB/Step3_GED.m, lines 362–456 · score 0.52 · 0–600 ms, 4–8 Hz, power, theta, feedback, win
  9. [9] § Method › Time-frequency analysis ↔ code/MATLAB/Step3_GED.m, lines 72–184 · score 0.51 · baseline normalization, convolution, wavelet, window, power, EEG

Paper

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The authors' code

MATLAB · 461 lines · 15 KB · no license · 6 matches

  2. %% STEP 3 — MULTIVARIATE SOURCE SEPARATION GED — AFFECTIVE POSNER TASK
  3. % ------------------------------------------------------------------------
  4. % Paper: Midfrontal theta power relates to response speeding following frustrative nonreward
  5. % Author: Nellia Bellaert
  6. % Version: 2025-10-30
  7. % Contact: [email hidden]
  8. %
  9. % Requires the FilterFGx function for narrow-band filtering
  10. % This works with EEG data structures in the EEGLAB format
  11. % This code is largely based on the scripts from Duprez et al. (2020): https://doi.org/10.1016/j.neuroimage.2019.116340 ,
  12. % and on the equations and scripts accompanying the book 'Analyzing Neural Time Series Data' by Mike X. Cohen : mikexcohen.com
  16. eeglab('nogui')
  18. data_dir = './data/preprocessed/';
  20. % List all preprocessed AFP feedback .set files
  21. set_files = dir(fullfile(data_dir, 'sub-*AFP_fb_ICA_interp.set'));
  23. % Storage
  24. nSubs = length(set_files);
  25. matrices = cell(nSubs, 1);
  26. subject_ids = cell(nSubs, 1);
  28. % Load data
  29. for i = 1:nSubs
  30. % Extract subject ID from filename
  31. filename = set_files(i).name;
  32. subject_id = extractBefore(filename, '_AFP_fb_ICA_interp.set');
  34. EEG = pop_loadset('filename', filename, 'filepath', data_dir);
  36. % Store the matrix and subject number
  37. matrices{i} = EEG;
  38. subject_ids{i} = subject_id;
  39. end
  45. %% Fix event structure for subjects 121 & 122
  46. fields = ["eventlatency", "eventurevent", "eventduration", "eventtype"];
  48. for idx = 21:22
  49. epochs_n = matrices{idx, 1}.epoch;
  51. for i = 1:length(epochs_n)
  52. for f = fields
  53. matrices{idx, 1}.epoch(i).(f) = matrices{idx, 1}.epoch(i).(f){1,1};
  54. end
  55. matrices{idx, 1}.epoch(i).event = matrices{idx, 1}.epoch(i).event(1);
  56. end
  57. end
  61. matrices_eegfiles = matrices;
  63. % select channel FCz
  64. chan = 6;
  66. %% Initialize storage
  67. matrix_power = cell(nSubs,1);
  68. maxtheta = zeros(2,nSubs); % store peak frequency and time
  69. mapsTh_avg = zeros(126, nSubs); % forward model storage
  72. %% Main loop over subjects
  73. for subj_n = 1:size(matrices_eegfiles, 1)
  74. EEG = matrices_eegfiles{subj_n,1};
  76. FB_type = {'F_win', 'F_loss'}; % feedback types in the frustrating cond
  78. epochs = struct2table(EEG.epoch);
  80. % time window for baseline normalization.
  81. baseline_window = [ -150 -50 ];
  82. baseidx = dsearchn(EEG.times',baseline_window'); % convert baseline time into indices
  85. % wavelet parameters
  86. numfrex = 100;
  87. lowfreq = 1; % Hz
  88. highfreq = 20; % Hz
  89. frex = logspace(log10(lowfreq),log10(highfreq),numfrex);
  90. s=logspace(log10(2),log10(10),numfrex)./(2*pi*frex);
  91. time = -2:1/EEG.srate:2;
  92. half_wavelet = (length(time)-1)/2;
  95. % FFT parameters
  96. n_wavelet = length(time);
  97. n_data = EEG.pnts*EEG.trials;
  98. n_conv = n_wavelet+n_data-1;
  101. % initialize output time-frequency data for plotting
  102. power_fb = zeros(1, length(frex), EEG.pnts, length(chan));
  105. % FFT of data
  106. data_fft = fft( reshape(EEG.data(chan,:,:),1,[]) ,n_conv);
  108. chanid = 1;
  109. for fi=1:length(frex) % loop over frequency
  111. % create wavelet and get its FFT
  112. wavelet_fft = fft( exp(2*1i*pi*frex(fi).*time) .* exp(-time.^2./(2*(s(fi)^2))) , n_conv );
  114. % run convolution and get its ifft
  115. EEG_conv = ifft(wavelet_fft.*data_fft);
  116. EEG_conv = EEG_conv(half_wavelet+1:end-half_wavelet);
  117. EEG_conv = reshape(EEG_conv,EEG.pnts,EEG.trials);
  119. % get power by feedback types
  120. for cond=1:2
  121. thisidx = string(table2array(epochs(:,2))) == string(FB_type(cond));
  122. power_fb(cond,fi,:, chanid) = mean(abs(EEG_conv(:, thisidx)).^2,2);
  123. end
  126. end % end of frequency loop
  128. % db conversion by feedback types
  129. for cond=1:2
  130. power_fb(cond,:,:, chanid) = 10*log10( bsxfun(@rdivide, squeeze(power_fb(cond,:,:, chanid)), ...
  131. mean(power_fb(cond,:,baseidx(1):baseidx(2), chanid),3)' ) );
  132. end
  136. % define time-frequency window to look for theta peak
  137. thetatime = dsearchn(EEG.times',[0 700]');
  138. thetafreq = dsearchn(frex',[4 9]');
  140. tf_ave = squeeze(mean(mean(power_fb(:, :, :, :),1),4));
  141. tf_win = squeeze(mean(mean(power_fb(1, :, :, :),1),4));
  142. tf_loss = squeeze(mean(mean(power_fb(2, :, :, :),1),4));
  144. % get the index of the peak frequency and peak time based on the
  145. % average of all trials
  146. [maxfreq_theta,maxtime_theta] = ind2sub(size(tf_ave),find(tf_ave==max(reshape(tf_ave(thetafreq(1):thetafreq(2),thetatime(1):thetatime(2)),1,[]))));
  148. % visually inspect the delta and theta peaks
  149. figure(1);
  150. colormap(jet(256))
  151. subplot(2,2,1)
  152. contourf(EEG.times,frex, tf_win,40,'linecolor','none')
  153. set(gca,'clim',[-3 3], 'ylim', [1 10], 'xlim', [-200 1000])
  154. xlabel('Time (ms)'), ylabel('Frequency (Hz)')
  155. title([ 'F Win trials for ' char(subject_ids(subj_n))])
  156. hold on;
  157. plot(EEG.times(maxtime_theta), frex(maxfreq_theta), 'ko', 'MarkerSize', 10, 'MarkerFaceColor', 'm');
  158. hold off;
  160. subplot(2,2,2)
  161. contourf(EEG.times,frex, tf_loss,40,'linecolor','none')
  162. set(gca,'clim',[-3 3], 'ylim', [1 10], 'xlim', [-200 1000])
  163. xlabel('Time (ms)'), ylabel('Frequency (Hz)')
  164. title([ 'F Loss trials for ' char(subject_ids(subj_n))])
  165. hold on;
  166. plot(EEG.times(maxtime_theta), frex(maxfreq_theta), 'ko', 'MarkerSize', 10, 'MarkerFaceColor', 'm');
  167. hold off;
  169. subplot(2,2,3)
  170. contourf(EEG.times,frex, tf_ave,40,'linecolor','none')
  171. set(gca,'clim',[-3 3], 'ylim', [1 10], 'xlim', [-200 1000])
  172. xlabel('Time (ms)'), ylabel('Frequency (Hz)')
  173. title([ 'Average trials for ' char(subject_ids(subj_n))])
  174. hold on;
  175. plot(EEG.times(maxtime_theta), frex(maxfreq_theta), 'ko', 'MarkerSize', 10, 'MarkerFaceColor', 'm');
  176. hold off;
  179. maxtheta(1,subj_n) = frex(maxfreq_theta); % store max theta power frequency
  180. maxtheta(2,subj_n) = EEG.times(maxtime_theta); % store time
  184. matrix_power{subj_n,:} = power_fb;
  190. %% GED FOR THETA %%
  192. % select time window of 800ms around theta peak
  193. time_window = [EEG.times(maxtime_theta)-400 EEG.times(maxtime_theta)+400];
  194. timeidx = dsearchn(EEG.times',time_window'); % convert to indices
  196. snipn = EEG.pnts; % data points
  198. % initialize output for subject averaged TF maps
  199. tf_theta_avg = zeros(2, numfrex,length(EEG.times));
  201. % select epochs from the frustration condition
  202. n_epochs = size(EEG.data, 3); % Number of epochs
  204. channels = 1:126;
  207. % create R covariance matrix
  208. % full R
  209. R = zeros(n_epochs,length(channels),length(channels));
  210. for segi=1:n_epochs
  211. snipdat = EEG.data(channels,timeidx(1):timeidx(2),segi);
  212. snipdat = bsxfun(@minus,snipdat,mean(snipdat,2)); % mean-center
  213. R(segi,:,:) = snipdat*snipdat'/snipn;
  214. end
  216. % clean R
  217. meanR = squeeze(mean(R));
  218. dists = zeros(1,size(R,1));
  219. for segi=1:size(R,1)
  220. r = R(segi,:,:);
  221. dists(segi) = sqrt( sum((r(:)-meanR(:)).^2) ); % Euclidian distance
  222. end
  223. R = squeeze(mean( R(zscore(dists)<3,:,:) ,1));
  225. % regularized R
  226. gamma = .01;
  227. Rr = R*(1-gamma) + eye(length(channels))*gamma*mean(eig(R));
  231. % create S covariance matrix
  232. % full S
  233. S = zeros(n_epochs,length(channels),length(channels));
  235. % narrowband filter in theta
  236. fdat = filterFGx(EEG.data,EEG.srate,frex(maxfreq_theta),3, 1);
  238. for segi=1:n_epochs
  239. snipdat = fdat(channels, timeidx(1):timeidx(2), segi);
  240. snipdat = bsxfun(@minus,snipdat,mean(snipdat,2)); % mean-center
  241. S(segi,:,:) = snipdat*snipdat'/snipn;
  242. end
  244. % clean S
  245. meanS = squeeze(mean(S));
  246. dists = zeros(1,size(S,1));
  247. for segi=1:size(S,1)
  248. s = S(segi,:,:);
  249. dists(segi) = sqrt( sum((s(:)-meanS(:)).^2) );
  250. end
  251. S = squeeze(mean( S(zscore(dists)<3,:,:) ,1));
  254. % GED
  255. [evecsTh,evals] = eig( S,Rr );
  257. % sort evecs according to evals
  258. evals_extracted = diag(evals);
  259. [evals_sorted,sidx] = sort(evals_extracted, 'descend');
  260. evecsTh = evecsTh(:,sidx);
  262. % Plot eigenvalues
  263. figure(2);
  264. plot(evals_sorted, 'o-', 'LineWidth', 1.5);
  265. xlabel('Component');
  266. ylabel('Eigenvalue');
  267. title('Generalized Eigenvalues');
  268. grid on;
  271. % forward model to see activation patterns
  272. mapsTh = inv(evecsTh');
  274. % force sign of components so that topographical maps always show positive values
  275. for ci=1:126
  276. [~,idx] = max(abs(mapsTh(:,ci))); % find strongest weight
  277. mapsTh(:,ci) = mapsTh(:,ci) * sign(mapsTh(idx,ci)); % force to positive sign
  278. end
  281. % create data
  282. % keep the 15 components with the highest eigenvalues
  283. comps2keep = 1:15;
  284. thetadata = reshape( (reshape(EEG.data(:,:,:),126,[])'*evecsTh(:,comps2keep))' ,[length(comps2keep) EEG.pnts EEG.trials]);
  285. mapsTh = mapsTh(:,comps2keep); % overwrites mapsTh with only the components we want to keep
  288. % Plot forward models to inspect activation patterns
  289. chanlocs = EEG.chanlocs;
  290. figure(3), clf
  291. for topi = 1:size(mapsTh, 2)
  292. subplot(5,3,topi)
  293. topoplot(mapsTh(:,topi),chanlocs(channels),'numcontour', 6, 'gridscale', 100)
  294. title([ 'Comp ' num2str(topi) ])
  295. end
  298. %% Selection of the best theta component
  300. % Construct midfrontal theta template: Gaussian centered on FCz
  301. fczidx = strcmpi('E6',{EEG.chanlocs.labels});
  302. eucdist = zeros(1,EEG.nbchan);
  304. for chani = 1:EEG.nbchan
  305. eucdist(chani) = sqrt( (EEG.chanlocs(chani).X-EEG.chanlocs(fczidx).X)^2 + (EEG.chanlocs(chani).Y-EEG.chanlocs(fczidx).Y)^2 + (EEG.chanlocs(chani).Z-EEG.chanlocs(fczidx).Z)^2 );
  306. end
  308. midf.template = exp(-(eucdist.^2)/(2*50^2) );
  310. figure(4);
  311. colormap(jet(256));
  312. topoplot(midf.template, EEG.chanlocs, 'electrodes', 'on', 'numcontour', 6, 'gridscale', 100);
  313. title('Midfrontal Template - Gaussian Weighting Centered at FCz');
  314. clim([.981 1]);
  317. % compute shared variance between each component and the template
  318. midf.template_r2 = zeros(1, size(mapsTh,2));
  319. midf.ffm_EEG = zeros(EEG.nbchan, size(mapsTh,2));
  320. midf.ewr2 = zeros(1, size(mapsTh,2));
  322. num_figure = 900;
  323. for comp = 1:size(mapsTh,2)
  324. topo = mapsTh(:,comp);
  325. % Flip component by sign of correlation with FCz template
  326. midf.ffm_EEG(:,comp) = topo * sign(corr(topo, midf.template'));
  328. midf.template_r2(comp) = corr(topo, midf.template')^2;
  330. % formula to select the best component
  331. % eigenvalue-weighted correlation
  332. midf.ewr2(comp) = (evals_sorted(comp) / sum(evals_sorted)) * midf.template_r2(comp);
  334. figure(num_figure);
  335. subplot(5,3,comp)
  336. topoplot(midf.ffm_EEG(:,comp), EEG.chanlocs,'numcontour', 6, 'gridscale', 100)
  337. title([ 'Comp ' num2str(comp) ' r:' num2str(midf.template_r2(comp)) ])
  338. end
  339. num_figure = num_figure + 1;
  341. [maxEwr2, maxComp] = max(midf.ewr2);
  342. hold on
  343. subplot(5,3, maxComp)
  344. title([ 'Comp ' num2str(maxComp) ' r:' num2str(midf.template_r2(maxComp)) ], 'Color', 'r')
  345. hold on
  347. thetacompidx = maxComp;
  350. mapsTh_avg(:,subj_n) = mapsTh(:,thetacompidx); % store forward problem in subject averaged matrix
  353. % keep the selected component
  354. thetadata = thetadata(thetacompidx,:,:);
  355. EEG.thetadata = thetadata; % store in EEG struct
  358. % keep theta peak frequency for further analysis
  359. EEG.th_maxfreq = frex(maxfreq_theta);
  362. %% THETA: TF DECOMPOSITION TO INSPECT TIME-FREQUENCY POWER OF THE COMPONENT
  364. % recreate s because it gets overwritten
  365. s=logspace(log10(2),log10(10),numfrex)./(2*pi*frex);
  367. % fft of data
  368. eegX = fft( reshape(thetadata,1,[]) ,n_conv);
  370. power_trial = zeros(length(fi),EEG.pnts,EEG.trials);
  372. % loop over frequencies
  373. for fi=1:numfrex
  374. wavelet_fft = fft( exp(2*1i*pi*frex(fi).*time) .* exp(-time.^2./(2*(s(fi)^2))) , n_conv );
  375. as = ifft(wavelet_fft.*eegX);
  376. as = as(half_wavelet+1:end-half_wavelet);
  377. as = reshape(as,EEG.pnts,EEG.trials);
  380. % get the power per single-trial
  381. power_trial(fi,:,:) = abs(as(:, :)).^2;
  384. % concatenate the baseline
  385. baselines(fi,:,:) = power_trial(fi,baseidx(1):baseidx(2),:);
  387. %size_fused_baseline = size(baselines, 2) * size(baselines, 3);
  388. fused_baseline(fi,:) = reshape(baselines(fi,:,:), 1, []);
  390. for trial=1:EEG.trials
  391. power_trial(fi,:, trial) = ( power_trial(fi,:,trial) - mean(fused_baseline(fi,:),2) ) / std(fused_baseline(fi,:));
  392. end
  395. end % end frequencies loop
  399. thetafreq_extract = [4 8];
  400. thetatime_extract = [0 600];
  402. figure(5);
  403. colormap(jet(256))
  404. contourf(EEG.times,frex, mean(power_trial(:,:,:),3),40,'linecolor','none')
  405. set(gca, 'ylim', [.9 10], 'xlim', [-200 1000])
  406. rectangle('Position',[thetatime_extract(1) thetafreq_extract(1) diff(thetatime_extract) diff(thetafreq_extract)],'linew',3)
  407. text(thetatime_extract(1),thetafreq_extract(2),'ROI','VerticalAlignment','bottom','FontSize',10)
  408. xlabel('Time (ms)'), ylabel('Frequency (Hz)')
  413. % separate trials by feedback type for plotting
  414. power_trial_cond = zeros(2,length(frex), EEG.pnts);
  415. for cond=1:2
  416. thisidx = string(table2array(epochs(:,2))) == string(FB_type(cond));
  417. power_trial_cond(cond,:,:) = mean(power_trial(:,:,thisidx),3);
  418. end
  421. figure(6);
  422. colormap(jet(256))
  423. subplot(1,2,1)
  424. contourf(EEG.times,frex, squeeze(mean(power_trial_cond(1,:,:),1)),40,'linecolor','none')
  425. set(gca, 'ylim', [1 10], 'xlim', [-200 1000])
  426. rectangle('Position',[thetatime_extract(1) thetafreq_extract(1) diff(thetatime_extract) diff(thetafreq_extract)],'linew',3)
  427. text(thetatime_extract(1),thetafreq_extract(2),'ROI','VerticalAlignment','bottom','FontSize',10)
  428. xlabel('Time (ms)'), ylabel('Frequency (Hz)')
  429. title([ '\theta component TF power for F win ' char(subject_ids(subj_n))])
  430. subplot(1,2,2)
  431. contourf(EEG.times,frex, squeeze(mean(power_trial_cond(2,:,:),1)),40,'linecolor','none')
  432. set(gca, 'ylim', [1 10], 'xlim', [-200 1000])
  433. rectangle('Position',[thetatime_extract(1) thetafreq_extract(1) diff(thetatime_extract) diff(thetafreq_extract)],'linew',3)
  434. text(thetatime_extract(1),thetafreq_extract(2),'ROI','VerticalAlignment','bottom','FontSize',10)
  435. xlabel('Time (ms)'), ylabel('Frequency (Hz)')
  436. title([ '\theta component TF power for F loss ' char(subject_ids(subj_n))])
  438. save(fullfile(['GED_theta_AFP_fb_', char(subject_ids(subj_n)), '.mat']), 'EEG', 'mapsTh', 'thetacompidx', 'power_trial', 'power_trial_cond',"mapsTh_avg", "evals_sorted");
  442. file_name = fullfile(['GED_theta_' char(subject_ids(subj_n)) '.pdf']);
  444. % Find all open figures
  445. figHandles = findall(0, 'Type', 'figure');
  447. % Loop over figures and append them to the PDF
  448. for i = 1:length(figHandles)
  449. exportgraphics(figHandles(i), file_name, 'Append', true);
  450. end
  452. close all
  453. clearvars -except matrices_eegfiles subject_ids channel_FCz subject_ids_to_redo_idx
  456. end

Step3_GED.m, no license · at the source

Overview

Authors: Nellia Bellaert1,2, Federico Cassioli3,4, Michael J. Crowley2, Hilary P. Blumberg2,5, Mandy Rossignol1, Christen M. Deveney6, Wan-Ling Tseng2
ORCID iDs: Nellia Bellaert, Federico Cassioli, Christen M. Deveney, Wan-Ling Tseng
  1. Department of Cognitive Psychology and Neuropsychology, University of Mons, Mons, Belgium
  2. Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
  3. Department of Experimental Psychology, Ghent University, Ghent, Belgium
  4. Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
  5. Departments of Psychiatry and Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
  6. Department of Psychology, Wellesley College, MA, USA
Institutions: University of Mons (Belgium); Yale University (United States); Ghent University (Belgium); Wellesley College (United States)
Journal: NeuroImage, volume 338, article 122115
Dates: published online 8 July 2026; in print September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.neuroimage.2026.122115 · PMID 42419656 · PMCID PMC13500859 · OpenAlex W4416182346
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.31234/osf.io/gshfw_v1
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Spectral & time-frequency, Evoked potentials, Physiology & signal measures
Keywords: Frustrative nonreward, Midfrontal theta, Cognitive control, Irritability, Frustration
MeSH: Adaptation, Psychological*, Executive Function*, Feedback, Psychological*, Frontal Lobe*, Frustration*, Irritable Mood*, Reaction Time*, Theta Rhythm*, Adolescent, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Mindfulness and Compassion Interventions (Clinical Psychology, Psychology), according to OpenAlex
Funding: Belgian American Educational Foundation Inc; Charles H Hood Foundation Inc; Fund for Scientific Research; Yale Child Study Center; NIMH NIH HHS (DP2 MH140132, R00 MH110570); National Institute of Mental Health; Yale Center for Clinical Investigation
Citations: not cited yet (Europe PMC); 106 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.

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Found in: the end of the paper
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (4 files), Statistics and Machine Learning Toolbox (2 files), car (1 file), easystats (1 file), emmeans (1 file), ggplot2 (1 file), ggpubr (1 file), ICLabel (1 file), lme4 (1 file), lmerTest (1 file), Signal Processing Toolbox (1 file), patchwork (1 file), psych (1 file), reshape2 (1 file), rstatix (1 file), tidyverse (1 file)
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  • 7 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);
  • 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.

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

  • Publisher: n/a → Elsevier BV

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 5 keywords, 15 MeSH terms, 7 funders, 105 references.

Cite

This paper

Bellaert, N., Cassioli, F., Crowley, M. J., Blumberg, H. P., Rossignol, M., Deveney, C. M., & Tseng, W.-L. (2026). Midfrontal theta power relates to response speeding following frustrative nonreward. NeuroImage, 338, 122115. https://doi.org/10.1016/j.neuroimage.2026.122115

BibTeX

@article{bellaert2026midfrontal,
author = {Bellaert, Nellia and Cassioli, Federico and Crowley, Michael J. and Blumberg, Hilary P. and Rossignol, Mandy and Deveney, Christen M. and Tseng, Wan-Ling},
title = {{Midfrontal theta power relates to response speeding following frustrative nonreward}},
journal = {NeuroImage},
year = {2026},
month = jul,
volume = {338},
pages = {122115},
publisher = {Elsevier BV},
issn = {1053-8119},
doi = {10.1016/j.neuroimage.2026.122115},
url = {https://doi.org/10.1016/j.neuroimage.2026.122115},
pmid = {42419656},
pmcid = {PMC13500859}
}

RIS

TY - JOUR
AU - Bellaert, Nellia
AU - Cassioli, Federico
AU - Crowley, Michael J.
AU - Blumberg, Hilary P.
AU - Rossignol, Mandy
AU - Deveney, Christen M.
AU - Tseng, Wan-Ling
TI - Midfrontal theta power relates to response speeding following frustrative nonreward
T2 - NeuroImage
J2 - Neuroimage
PY - 2026
DA - 2026/07/08
VL - 338
SP - 122115
SN - 1053-8119
PB - Elsevier BV
DO - 10.1016/j.neuroimage.2026.122115
UR - https://doi.org/10.1016/j.neuroimage.2026.122115
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.neuroimage.2026.122115",
"type": "article-journal",
"title": "Midfrontal theta power relates to response speeding following frustrative nonreward",
"container-title": "NeuroImage",
"author": [
{
"family": "Bellaert",
"given": "Nellia"
},
{
"family": "Cassioli",
"given": "Federico"
},
{
"family": "Crowley",
"given": "Michael J."
},
{
"family": "Blumberg",
"given": "Hilary P."
},
{
"family": "Rossignol",
"given": "Mandy"
},
{
"family": "Deveney",
"given": "Christen M."
},
{
"family": "Tseng",
"given": "Wan-Ling"
}
],
"container-title-short": "Neuroimage",
"volume": "338",
"page": "122115",
"DOI": "10.1016/j.neuroimage.2026.122115",
"PMID": "42419656",
"PMCID": "PMC13500859",
"ISSN": "1053-8119",
"publisher": "Elsevier BV",
"URL": "https://doi.org/10.1016/j.neuroimage.2026.122115",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
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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