OSCR

Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis.

Code ↔ Paper

13 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 13 matches
  1. [1] § Methods › Multiverse analyses ↔ scripts/a4_extract_featuresHBN.m, lines 18–60 · score 0.81 · 2–30 Hz, 13–30 Hz, left frontal, right frontal, midline, 4–8 Hz
  2. [2] § Methods › Multiverse analyses ↔ scripts/a5_extract_features_validation.m, lines 18–61 · score 0.81 · 2–30 Hz, 13–30 Hz, left frontal, right frontal, midline, 4–8 Hz
  3. [3] § Methods › Feature extraction ↔ scripts/a2_tf_analysisHBN.m, lines 184–214 · score 0.79 · 1–40 Hz, cfg.output, power spectra, FieldTrip, Hanning, periodic
  4. [4] § Methods › Feature extraction ↔ scripts/a3_tf_analysis_validation.m, lines 154–196 · score 0.79 · 1–40 Hz, cfg.output, power spectra, FieldTrip, Hanning, periodic
  5. [5] § Methods › Feature extraction ↔ scripts/a4_extract_featuresHBN.m, lines 18–60 · score 0.77 · 7–14 Hz, E62, E70, E71, E72, E75
  6. [6] § Methods › EEG preprocessing ↔ scripts/a2_tf_analysisHBN.m, lines 110–182 · score 0.74 · pop_eegfiltnew, EEGLAB, noise, amplitudes, preprocessed, window
  7. [7] § Methods › Multiverse analyses › Proportions plots and possibility space ↔ scripts/a13_Proportions_SWAN.R, lines 172–234 · score 0.62 · binom.test, H0, H1, binomial, aperiodic signal, uncorrected
  8. [8] § Methods › Feature extraction ↔ scripts/a5_extract_features_validation.m, lines 18–61 · score 0.62 · 2–30 Hz, 13–30 Hz, Klimesch, sub, 4–8 Hz, 13 Hz
  9. [9] § Methods › Multiverse analyses › Proportions plots and possibility space ↔ scripts/a17_Proportions_validation.R, lines 177–240 · score 0.61 · binom.test, H0, H1, binomial, aperiodic signal, uncorrected
  10. [10] § Methods › EEG preprocessing ↔ scripts/a2_tf_analysisHBN.m, lines 110–182 · score 0.59 · amplitude threshold, linked mastoid, segments, preprocessing, EEG, HBN
  11. [11] § Methods › EEG preprocessing ↔ scripts/a3_tf_analysis_validation.m, lines 86–152 · score 0.59 · amplitude threshold, linked mastoid, segments, preprocessing, EEG, validation
  12. [12] § Methods › Feature extraction ↔ scripts/a2_tf_analysisHBN.m, lines 32–107 · score 0.56 · E62, E70, E71, E72, E75, E76
  13. [13] § Methods › EEG preprocessing ↔ scripts/a3_tf_analysis_validation.m, lines 86–152 · score 0.55 · EEGLAB, pop, amplitudes, preprocessed, window, segments

Paper

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

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The authors' code

MATLAB · 385 lines · 13 KB · no license · 4 matches

  1. %% all analysis performed on Matlab 2023b
  3. % Requirements:
  4. % eeglab2025.0.0
  5. % eye-eeg-master
  6. % pop_epoch_methlab
  8. clc
  9. clear
  10. restoredefaultpath
  11. %% merge EEG, BD and ET data, epoch and remove trials with bad ET
  13. %% start parpool
  14. % try
  15. % delete(gcp('nocreate'))
  16. % catch
  17. % end
  18. % c = parcluster;
  19. % parpool(c.NumWorkers)
  21. %% init paths
  22. a0_initPaths
  24. %% folder with functions
  25. addpath(functions)
  26. initTools
  28. %% prepare to load EEG data
  29. dataDir = automagicdataHBN;
  30. pathEEG = dir([dataDir, filesep, '*p*EEG.mat']);
  32. %% compute power
  33. noisy_chan = [1 8 14 17 21 25 32 48 49 56 63 68 73 81 88 94 99 107 113 119 125 126 127 128];
  35. % chans of interest
  36. chan_of_interest = unique({'Cz', 'E11', 'E22', 'E24', 'E33', 'E9', 'E124', 'E122', 'E11', 'E62', 'E36', 'E104', 'E62', 'E75', 'E70', 'E83', 'E72', 'E71', 'E76'});
  39. for sub = 1 : size(pathEEG, 1)
  41. % dont process bad ratings
  42. if startsWith(pathEEG(sub).name, 'b')
  43. continue;
  44. end
  46. % subject id
  47. xxx = strsplit(pathEEG(sub).folder, '/');
  48. subjectID = xxx{end};
  50. % load data
  51. load(fullfile(pathEEG(sub).folder, pathEEG(sub).name));
  53. % EEG.times(19904) - EEG.times(14904) % 20s for eo
  54. % EEG.times(2.9904e+04) - EEG.times(19904) % 40s for ec
  55. % trim first and last 2 s as well
  56. % Copy the original events
  57. newEvents = EEG.event;
  58. cnt = length(newEvents);
  59. interval = 500; % 2 seconds in samples at 250 Hz
  60. % Get event latencies and types
  61. latencies = [EEG.event.latency];
  62. types = {EEG.event.type};
  64. if not(any(ismember(types, '20'))) | not(any(ismember(types, '30')))
  65. continue;
  66. end
  68. % Loop through all events
  69. for i = 1:length(types) - 1
  70. curr_type = types{i};
  71. next_type = types{i+1};
  73. % Check for '20' -> '30' transition
  74. if strcmp(curr_type, '20') && strcmp(next_type, '30')
  75. start_latency = latencies(i);
  76. end_latency = latencies(i+1);
  78. % Insert '21' events every 500 samples
  79. insert_points = start_latency + interval : interval : end_latency - interval;
  80. for l = 1:length(insert_points)-1 % remove last 2 seconds
  81. cnt = cnt + 1;
  82. newEvents(cnt).type = '21';
  83. newEvents(cnt).latency = insert_points(l);
  84. newEvents(cnt).duration = 0;
  85. end
  87. % Check for '30' -> '20' transition
  88. elseif strcmp(curr_type, '30') && strcmp(next_type, '20')
  89. start_latency = latencies(i);
  90. end_latency = latencies(i+1);
  92. % Insert '31' events every 500 samples
  93. insert_points = start_latency + interval : interval : end_latency - interval;
  94. for l = 1:length(insert_points)-1 % remove last 2 seconds
  95. cnt = cnt + 1;
  96. newEvents(cnt).type = '31';
  97. newEvents(cnt).latency = insert_points(l);
  98. newEvents(cnt).duration = 0;
  99. end
  100. end
  101. end
  103. % Update EEG.event and sort by latency
  104. EEG.event = newEvents;
  105. [~, sort_idx] = sort([EEG.event.latency]);
  106. EEG.event = EEG.event(sort_idx);
  107. EEG = eeg_checkset(EEG, 'eventconsistency');
  110. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  111. % filter with low pass to further remove line noise (60 Hz)
  112. EEG = pop_eegfiltnew(EEG, [], 50);
  114. % duplicate
  115. EEGorig = EEG;
  117. % redo everything for average reference and linked mastoids
  118. for ref = 1 : 2
  120. % re-referencing, [] is average reference
  121. % M1 (left mastoid) - Channel 57
  122. % M2 (right mastoid) - Channel 100
  123. if ref == 1
  124. % average
  125. EEG = pop_reref(EEGorig, [], 'keepref', 'on');
  126. elseif ref == 2
  127. el_m1 = find(strcmp({EEGorig.chanlocs.labels}, 'E57')); % M1
  128. el_m2 = find(strcmp({EEGorig.chanlocs.labels}, 'E100')); % M2
  129. EEG = pop_reref(EEGorig, [el_m1 el_m2], 'keepref', 'on');
  130. end
  132. % reduce channels number to 105
  133. EEG = pop_select(EEG, 'nochannel', noisy_chan);
  135. % Segmentation
  136. EEG = pop_epoch(EEG, {21, 31}, [0 2]);
  138. % remove bad segments if any channel of interest exceeds ±90uV
  139. % threshold
  140. win_size = 3; % 3 samples
  141. amp_thresh = 90; % uV threshold
  142. min_channels = 1;
  143. bad_epochs = zeros(1, size(EEG.data, 3));
  144. ch_of_interest = find(ismember({EEG.chanlocs.labels}, chan_of_interest));
  145. for e = 1:size(EEG.data, 3)
  146. epoch_data = EEG.data(ch_of_interest,:,e);
  147. % Compute moving average with window size 3 along time (dim=2)
  148. mov_avg = movmean(epoch_data, win_size, 2);
  149. % Check for each channel if any 3-sample average exceeds threshold
  150. above_thresh = any(abs(mov_avg) > amp_thresh, 2);
  151. if sum(above_thresh) > min_channels
  152. bad_epochs(e) = 1;
  153. end
  154. end
  155. bad_epochs = find(bad_epochs);
  157. %figure;
  158. %plot(EEG.times, squeeze(EEG.data(8,:, :)))
  159. bad_epochs = find(bad_epochs);
  160. bad_types = {EEG.event(bad_epochs).type};
  161. if not(isempty(bad_types))
  162. [bad_nums, ~, ~, bad_labels] = crosstab(bad_types);
  163. else
  164. bad_nums = [0 ; 0];
  165. end
  166. total_trials = size(EEG.data, 3);
  167. ratio_rejected = length(bad_epochs) / total_trials;
  169. % if all trials are bad, leave some of them so that we can comopute
  170. % the rejection rate later. this subject will be rejected anyway
  171. if ratio_rejected > 0.6
  172. bad_epochs(1:30) = [];
  173. end
  175. % remove bad trials
  176. EEG = pop_select(EEG, 'rmtrial', bad_epochs);
  178. %figure;
  179. %plot(EEG.times, squeeze(EEG.data(8,:, :)))
  181. % convert to fieldtrip
  182. ftdata = eeglab2fieldtrip(EEG, 'preprocessing');
  184. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  185. % do the fooofing
  186. cfg = [];
  187. cfg.method = 'mtmfft';
  188. cfg.taper = 'hanning'; %
  189. cfg.tapsmofrq = 1; % needed overlap for continuese data
  190. %cfg.foi = [1 : 1/5 : 40];
  191. cfg.pad = 4;
  192. cfg.foilim = [1 40];
  193. cfg.output = 'fooof'; % returns a smooth power-spectrum, based on a parametrization of a mixture of aperiodic and periodic components
  195. % eyes open
  196. cfg.trials = find(ismember(ftdata.trialinfo.type, '21')); % eyes open
  197. tfr_ff_eo = ft_freqanalysis_methlab(cfg, ftdata); % ADAPTED! Check line 1018 or ctrl f for 'dawid'
  198. % eyes closed
  199. cfg.trials = find(ismember(ftdata.trialinfo.type, '31')); % eyes closed
  200. tfr_ff_ec = ft_freqanalysis_methlab(cfg, ftdata); % ADAPTED! Check line 1018 or ctrl f for 'dawid'
  202. % extract raw power: eyes open
  203. tfr_ff_eo.power_spectrum = [];
  204. tmp_pwr_spec = {tfr_ff_eo.fooofparams.power_spectrum};
  205. for e = 1 : size(tfr_ff_eo.label, 2)
  206. tfr_ff_eo.power_spectrum(e, :) = tmp_pwr_spec{e};
  207. end
  209. % extract raw power: eyes closed
  210. tfr_ff_ec.power_spectrum = [];
  211. tmp_pwr_spec = {tfr_ff_ec.fooofparams.power_spectrum};
  212. for e = 1 : size(tfr_ff_ec.label, 2)
  213. tfr_ff_ec.power_spectrum(e, :) = tmp_pwr_spec{e};
  214. end
  216. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  217. % compute relative power: eyes open
  218. pow = 10.^(tfr_ff_eo.power_spectrum); % convert log to pow
  219. tfr_ff_eo.relative_power = pow ./ nanmean(pow, 2);
  222. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  223. % compute relative power: eyes closed
  224. pow = 10.^(tfr_ff_ec.power_spectrum); % convert log to pow
  225. tfr_ff_ec.relative_power = pow ./ nanmean(pow, 2);
  228. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  229. % compute aperiodic adjusted signal: eyes open
  230. % first, convert to power
  231. pow = 10.^(tfr_ff_eo.power_spectrum);
  232. % find offset and exponent
  233. aperiodic = vertcat(tfr_ff_eo.fooofparams.aperiodic_params);
  234. off = aperiodic(:, 1);
  235. exp = aperiodic(:, 2);
  236. % compute aperiodic signal
  237. y = 10.^off .* (1./(tfr_ff_eo.freq .^ exp));
  238. % save aperiodic slope
  239. tfr_ff_eo.aperiodic_slope = y;
  240. % compute aperiodic adjusted signal
  241. tfr_ff_eo.aperiodic_adjusted = 10.^(log10(pow)-log10(y));
  242. % fooof peaks
  243. pow = tfr_ff_eo.powspctrm; % fooof peaks
  244. tfr_ff_eo.fooof_peaks = pow - y;
  246. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  247. % compute aperiodic adjusted signal: eyes closed
  248. % first, convert to power
  249. pow = 10.^(tfr_ff_ec.power_spectrum);
  250. % find offset and exponent
  251. aperiodic = vertcat(tfr_ff_ec.fooofparams.aperiodic_params);
  252. off = aperiodic(:, 1);
  253. exp = aperiodic(:, 2);
  254. % compute aperiodic signal
  255. y = 10.^off .* (1./(tfr_ff_ec.freq .^ exp));
  256. % save aperiodic slope
  257. tfr_ff_ec.aperiodic_slope = y;
  258. % compute aperiodic adjusted signal
  259. tfr_ff_ec.aperiodic_adjusted = 10.^(log10(pow)-log10(y));
  260. % fooof peaks
  261. pow = tfr_ff_ec.powspctrm; % fooof peaks
  262. tfr_ff_ec.fooof_peaks = pow - y;
  264. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  265. % save bad trial information
  266. tfr_ff_eo.badTrial = struct;
  267. tfr_ff_eo.badTrial.total_trials = total_trials;
  268. tfr_ff_eo.badTrial.bad_epochs = bad_epochs;
  269. tfr_ff_eo.badTrial.bad_nums = bad_nums;
  270. tfr_ff_eo.badTrial.bad_labels = bad_labels;
  271. tfr_ff_eo.badTrial.ratio_rejected = ratio_rejected;
  273. % save results
  274. if ref == 1
  275. tfr_eo_avg = tfr_ff_eo;
  276. tfr_ec_avg = tfr_ff_ec;
  277. elseif ref == 2
  278. tfr_eo_mast = tfr_ff_eo;
  279. tfr_ec_mast = tfr_ff_ec;
  280. end
  281. clear tfr_ff_eo tfr_ff_ec EEG ftdata
  283. end
  285. % save to a file
  286. mkdir(fullfile(result_folderHBN, subjectID))
  287. save(fullfile(result_folderHBN, subjectID, ['tfr.mat']), 'tfr_eo_avg', ...
  288. 'tfr_ec_avg', ...
  289. 'tfr_eo_mast', ...
  290. 'tfr_ec_mast', ...
  291. '-v7.3')
  293. clear tfr_eo_avg tfr_ec_avg tfr_eo_mast tfr_ec_mast
  294. end
  298. % end
  299. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  300. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  301. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  302. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
  303. %% sanity check
  304. %
  305. cfg = [];
  306. cfg.layout = lay129_head;
  307. cfg.colormap = unicolor_map_red; % '*RdBu', 'Blues', 'Oranges', 'OrRd'
  308. cfg.parameter = 'power_spectrum'; % 'power_spectrum' 'powspctrm' 'aperiodic_adjusted' 'fooof_peaks'
  309. figure;
  310. ft_multiplotER(cfg, tfr_eo_avg)
  312. figure;
  313. ft_multiplotER(cfg, tfr_ff_ec)
  317. %%
  319. figure;
  320. plot(tfr_eo_avg.freq, tfr_eo_avg.aperiodic_slope(1, :)) % elecs x freqs
  321. hold on
  322. plot(tfr_ff_ec.freq, tfr_ff_ec.aperiodic_slope(1, :)) % elecs x freqs
  323. legend('open', 'closed')
  326. %%
  328. figure;
  329. plot(tfr_eo_avg.freq, tfr_eo_avg.powspctrm(105, :))
  330. hold on
  331. plot(tfr_eo_avg.freq, tfr_eo_avg.power_spectrum(105, :))
  333. plot(tfr_eo_avg.freq, tfr_eo_avg.aperiodic_adjusted(105, :))
  334. plot(tfr_eo_avg.freq, tfr_eo_avg.aperiodic_slope(105, :))
  335. plot(tfr_eo_avg.freq, tfr_eo_avg.relative_power(105, :))
  337. % plot(tfr_eo_avg.freq, 10.^(tfr_ff_ec.power_spectrum(105, :)) )
  338. plot(tfr_eo_avg.freq, tfr_eo_avg.fooof_peaks(105, :))
  339. legend('pwr', 'power', 'adj', 'slope', 'relative', 'fp')
  342. figure;
  343. plot(tfr_eo_avg.freq, tfr_eo_avg.relative_power(105, :))
  345. %%
  346. figure;
  347. hold on
  348. plot(tfr_eo_avg.freq, tfr_eo_avg.power_spectrum(105, :))
  349. plot(tfr_eo_avg.freq, log10(10.^(tfr_ff_eo.power_spectrum(105, :))))
  351. figure;
  352. hold on
  353. plot(tfr_eo_avg.freq, tfr_eo_avg.power_spectrum(105, :)) % log10(pow)
  354. plot(tfr_eo_avg.freq, 10.^(tfr_ff_eo.power_spectrum(105, :))) % pow
  355. legend('db', 'power')
  357. %%
  359. cfg = [];
  360. cfg.output = 'pow';
  361. cfg.method = 'mtmfft';
  362. cfg.taper = 'hanning';
  363. cfg.pad = 4;
  364. cfg.foilim = [1 40];
  365. cfg.trials = find(ismember(ftdata.trialinfo.type, '21')); % eyes open
  366. pow = ft_freqanalysis_methlab(cfg, ftdata);
  367. relative_power = pow.powspctrm ./ nanmean(pow.powspctrm, 2);
  370. %%
  372. figure;
  373. hold on
  374. plot(tfr_eo_avg.freq, tfr_eo_avg.power_spectrum(105, :)) % log10(pow)
  375. plot(tfr_eo_avg.freq, 10.^(tfr_ff_eo.power_spectrum(105, :))) % pow
  376. plot(tfr_eo_avg.freq, pow.powspctrm(105, :)) % pow => identical to 10.^(tfr_ff_eo.power_spectrum(105, :))
  377. plot(tfr_eo_avg.freq, relative_power(105, :))
  378. legend('db', 'power', 'pow', 'relative')
  381. %%
  382. figure;
  383. hold on
  384. plot(tfr_ec_avg.freq, tfr_ec_avg.fooof_peaks(102, :))
  385. plot(tfr_ec_avg.freq, tfr_ec_avg.fooof_peaks(87, :))

a2_tf_analysisHBN.m, no license · at the source

Overview

Authors: Dawid Strzelczyk1,2, Andrea Vetsch1, Nicolas Langer1,2
ORCID iDs: Dawid Strzelczyk, Nicolas Langer
  1. Methods of Plasticity Research, Department of Psychology, University of Zurich Zürich Switzerland
  2. Neuroscience Center Zurich (ZNZ) Zurich Switzerland
Institutions: University of Zurich (Switzerland)
Journal: eLife, volume 15, article RP111114
Dates: published online 9 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.111114 · PMID 42423458 · PMCID PMC13349384 · OpenAlex W7160536992
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), ADHD (population)
Methods: Connectivity, Smoothing, state filtering, decompositions, Preprocessing, Spectral & time-frequency, Statistics, fMRI & imaging
Keywords: ADHD, EEG, multiverse, TBR, Human
MeSH: Attention Deficit Disorder with Hyperactivity*, Beta Rhythm*, Brain*, Theta Rhythm*, Child, Electroencephalography, Female, Humans, Male (* major topic)
Journal subjects: Neuroscience
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (100014_175875, 40B2-0_187132)
Citations: not cited yet (Europe PMC); 118 references in the paper

Abstract

Attention deficit hyperactivity disorder (ADHD) affects 5–7% of children worldwide, yet diagnosis continues to rely on clinical-behavioral assessments. The theta/beta ratio (TBR) derived from electroencephalography (EEG) has long been proposed as a complementary neurobiological marker of ADHD based on reports of elevated TBR in affected children. However, accumulating evidence has raised concerns about the robustness and generalizability of these findings, pointing to a strong sensitivity to methodological choices. Here, we used multiverse analyses to systematically quantify how researcher degrees of freedom shape conclusions about associations between TBR and ADHD. Across two large, independent datasets (Healthy brain network: N=1499; validation sample: N=381), we evaluated 576 theoretically plausible analytical specifications, varying recording conditions, reference scheme, frequency band definitions, treatment of aperiodic (1/f) activity, regions of interest, sample inclusion criteria, and covariate specifications. Across the multiverse, we found that group differences in TBR were highly contingent on analytical choices, with no evidence for robust main effects of diagnosis, indicating no reliable differences between healthy controls, ADHD-inattentive, and ADHD-combined subtypes. Instead, significant effects emerged primarily as interactions with age and individual alpha frequency (IAF), particularly when TBR was derived from aperiodic-uncorrected power or from the aperiodic signal itself. These interaction patterns replicated across both independent samples and were observed using both categorical and dimensional definitions of ADHD. Together, these findings indicate that previously reported TBR effects are largely driven by variability in aperiodic activity and IAF rather than genuine differences in oscillatory theta-beta dynamics. Our results challenge the interpretation of TBR as a reliable standalone biomarker for ADHD and underscore the importance of multiverse approaches for evaluating candidate neurobiological markers in heterogeneous clinical populations.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 13 matches between paragraphs and lines of code.

OSF u5yxv

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: R (13), MATLAB (12)
Size: 109 files, 25 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (12 files), cowplot (9 files), Statistics and Machine Learning Toolbox (7 files), FieldTrip (6 files), broom (4 files), ggplot2 (4 files), EEGLAB (3 files), patchwork (3 files), Signal Processing Toolbox (2 files), shadedErrorBar (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
24 files
At the source: osf.io/u5yxv

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 24 scripts, each with its path and the digest of its content;
  • 13 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

All data can be downloaded from https://fcon_1000.projects.nitrc.org/indi/cmi_healthy_brain_network and are publicly available. The code for the analyses presented in this paper is openly accessible at https://osf.io/u5yxv.

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

Versions

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

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 9 MeSH terms, 1 funder, 113 references.

Cite

This paper

Strzelczyk, D., Vetsch, A., & Langer, N. (2026). Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis. eLife, 15, RP111114. https://doi.org/10.7554/elife.111114

BibTeX

@article{strzelczyk2026theta,
author = {Strzelczyk, Dawid and Vetsch, Andrea and Langer, Nicolas},
title = {{Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis}},
journal = {eLife},
year = {2026},
month = jul,
volume = {15},
pages = {RP111114},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.111114},
url = {https://doi.org/10.7554/elife.111114},
pmid = {42423458},
pmcid = {PMC13349384}
}

RIS

TY - JOUR
AU - Strzelczyk, Dawid
AU - Vetsch, Andrea
AU - Langer, Nicolas
TI - Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis
T2 - eLife
J2 - Elife
PY - 2026
DA - 2026/07/09
VL - 15
SP - RP111114
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.111114
UR - https://doi.org/10.7554/elife.111114
LA - en
ER -

CSL-JSON

{
"id": "10.7554/elife.111114",
"type": "article-journal",
"title": "Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis",
"container-title": "eLife",
"author": [
{
"family": "Strzelczyk",
"given": "Dawid"
},
{
"family": "Vetsch",
"given": "Andrea"
},
{
"family": "Langer",
"given": "Nicolas"
}
],
"container-title-short": "Elife",
"volume": "15",
"page": "RP111114",
"DOI": "10.7554/elife.111114",
"PMID": "42423458",
"PMCID": "PMC13349384",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.111114",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
9
]
]
}
}

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