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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

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It can be read at the source: scripts/a2_tf_analysisHBN.m.

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, to read at the source

This repository has no license: its authors keep all rights. Read it at the source.

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

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