OSCR

Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children.

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 · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Abnormal neural state stability and transition structure in ADHD › Associations between neural dynamics and functional impairment ↔ behaviordata.m, the whole file · a weak match · score 0.56 · attentional deficits, skills, family, impulsive, hyperactive, behavioral
  2. [2] § Abnormal neural state stability and transition structure in ADHD › Associations between neural dynamics and functional impairment ↔ behaviordata.m, the whole file · a weak match · score 0.52 · risky activities, psychosomatic, family, hyperactive, behavioral

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 79 lines · 3.2 KB · no license · 2 matches

  1. clear
  2. close all
  3. load('P300_ctrl.mat')
  4. load('P300_ADHD1.mat')
  5. load('P300_ADHD2.mat')
  6. data{1}=[scale_ctrl.snap;scale_ADHD1.snap;scale_ADHD2.snap];
  7. data{2}=[scale_ctrl.conners;scale_ADHD1.conners;scale_ADHD2.conners];
  8. data{3}=[scale_ctrl.weiss;scale_ADHD1.weiss;scale_ADHD2.weiss];
  9. figure
  10. set(gcf,'unit','centimeters','position',[5 5 6 6])
  11. hold on
  12. Position=[1 2 3];
  13. cdata=[128,128,128;0,153,153;255,0,110;]/255;
  14. cdata=flip(cdata);
  15. for i=1:2
  16. snapdata= padcat(scale_ctrl.snap(:,i),scale_ADHD1.snap(:,i),scale_ADHD2.snap(:,i));
  17. position=Position+3.5*(i-1);
  18. hold on
  19. box1= boxplot(snapdata,'positions',position,'colors',[0,0,0],'width',0.45,'ExtremeMode','compress','notch','on','symbol','');
  20. boxobj = findobj(gca,'Tag','Box');
  21. for j=1:3
  22. patch(get(boxobj(j),'XData'),get(boxobj(j),'YData'),cdata(j,:),'FaceAlpha',0.8);
  23. end
  24. end
  25. xlim([0.5,position(end)+0.5]), xticks(2:3.5:position(2)),
  26. yticks([0:1:10])
  27. % xticklabels({'Attention deficit','Hyperactive'}),
  28. ylabel('Score');
  29. box off
  30. set(gca,'color','none','xcolor',[0 0 0],'ycolor',[0 0 0],'Linewidth',1.5,'FontName','Arial','FontSize',12,'FontWeight','bold','tickdir','out','ticklength',[0.04 0.025])
  33. figure
  34. set(gcf,'unit','centimeters','position',[5 5 15 6])
  35. hold on
  36. Position=[1 2 3];
  37. cdata=[128,128,128;0,153,153;255,0,110;]/255;
  38. cdata=flip(cdata);
  39. for i=1:6
  40. snapdata= padcat(scale_ctrl.weiss(:,i),scale_ADHD1.weiss(:,i),scale_ADHD2.weiss(:,i));
  41. position=Position+3.5*(i-1);
  42. hold on
  43. box1= boxplot(snapdata,'positions',position,'colors',[0,0,0],'width',0.45,'ExtremeMode','compress','notch','on','symbol','');
  44. boxobj = findobj(gca,'Tag','Box');
  45. for j=1:3
  46. patch(get(boxobj(j),'XData'),get(boxobj(j),'YData'),cdata(j,:),'FaceAlpha',0.8);
  47. end
  48. end
  49. xlim([0.5,position(end)+0.5]), xticks(2:3.5:position(2)),
  50. yticks([0:1:10])
  51. % xticklabels({'Family',"School and learning"," Life skills","Child’s self-concept","Social activities","Risky activities"}),
  52. ylabel('Score');
  53. box off
  54. set(gca,'color','none','xcolor',[0 0 0],'ycolor',[0 0 0],'Linewidth',1.5,'FontName','Arial','FontSize',12,'FontWeight','bold','tickdir','out','ticklength',[0.02 0.025])
  57. figure
  58. set(gcf,'unit','centimeters','position',[5 5 15 6])
  59. hold on
  60. Position=[1 2 3];
  61. cdata=[128,128,128;0,153,153;255,0,110;]/255;
  62. cdata=flip(cdata);
  63. for i=1:6
  64. snapdata= padcat(scale_ctrl.conners(:,i),scale_ADHD1.conners(:,i),scale_ADHD2.conners(:,i));
  65. position=Position+3.5*(i-1);
  66. hold on
  67. box1= boxplot(snapdata,'positions',position,'colors',[0,0,0],'width',0.45,'ExtremeMode','compress','notch','on','symbol','');
  68. boxobj = findobj(gca,'Tag','Box');
  69. for j=1:3
  70. patch(get(boxobj(j),'XData'),get(boxobj(j),'YData'),cdata(j,:),'FaceAlpha',0.8);
  71. end
  72. end
  73. xlim([0.5,position(end)+0.5]), xticks(2:3.5:position(2)),
  74. yticks([0:1:10])
  75. ylim([-0.2,4])
  76. % xticklabels({'Conduct Problem','Learning Problem','Psychosomatic','Impulsive-Hyperactive','Anxiety','Hyperactivity Index'}),
  77. ylabel('Score');
  78. box off
  79. set(gca,'color','none','xcolor',[0 0 0],'ycolor',[0 0 0],'Linewidth',1.5,'FontName','Arial','FontSize',12,'FontWeight','bold','tickdir','out','ticklength',[0.02 0.025])

behaviordata.m at commit d229f21, no license · at the source

Overview

Authors: Yanan Zhang1,2, Tongxia Li3,4, Jun Jiang5, Hao Li6, Ruidi Sun5, Yunjie Li2,4, Wenqi Chen2,4
ORCID iDs: Wenqi Chen
  1. School of Medicine, Jianghan University, Wuhan, China
  2. Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  3. Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  4. Brain-Computer Interface Research Institute, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China
  5. Department of Electrophysiology, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  6. Innovation Center for Brain Medical Sciences, The Ministry of Education of the People's Republic of China, Huazhong University of Science and Technology, Wuhan, China
Institutions: Jianghan University (China); Tongji Hospital (China); Huazhong University of Science and Technology (China); Wuhan Children's Hospital (China); Ministry of Education of the People's Republic of China (China)
Journal: NeuroImage. Clinical, volume 50, article 104011
Dates: received 1 February 2026; accepted 19 May 2026; published online 23 May 2026; in print 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.nicl.2026.104011 · PMID 42184469 · PMCID PMC13226792 · OpenAlex W7162213094
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), ADHD (population)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Evoked potentials
Keywords: Attention-deficit/hyperactivity disorder, EEG neural dynamics, Temporal organization, Neural trajectory analysis, Data-driven neural states, ADHD subtypes
MeSH: Attention*, Attention Deficit Disorder with Hyperactivity*, Brain*, Acoustic Stimulation, Adolescent, Brain Mapping, Child, Electroencephalography, Evoked Potentials, Female, Humans, Male (* major topic)
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Science and Technology Major Project; National Natural Science Foundation of China (32500905); National Major Science and Technology Projects of China (2025ZD0217900)
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) shows marked heterogeneity, and conventional event-related potential (ERP) measures have limited sensitivity to subtype differences. This study examined whether alterations in the temporal organization of neural responses during attentional processing differentiate ADHD subtypes.

Methods: Children with predominantly inattentive ADHD (ADHD-I), combined-type ADHD (ADHD-C), and typically developing (TD) controls completed an auditory oddball task during electroencephalography. Neural responses were analyzed using time-resolved scalp topographies, low-dimensional neural trajectory analysis, and data-driven neural state modeling. Associations with clinical symptoms were examined.

Results: Both ADHD subtypes showed altered temporal alignment of neural responses relative to TD children, particularly during target processing. Neural trajectories exhibited reduced differentiation between standard and target stimuli, with ADHD-I showing reduced trajectory separation and ADHD-C showing exaggerated but inefficient state excursions. Data-driven analyses further revealed subtype-specific alterations in neural state stability and transitions, which showed exploratory associations with attentional and behavioral impairment.

Conclusions: ADHD is characterized by disrupted temporal organization of neural responses that is not captured by conventional ERP measures. Subtype-specific neural dynamics provide a mechanistic account of ADHD heterogeneity.

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 2 matches between paragraphs and lines of code.

ChenWQpublish/ADHD_EEG

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d229f21ca35ba5da684968ab0c6b07be9bb49712, 26 May 2026
Languages: MATLAB (15)
Size: 16 files, 15 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (14 files), Signal Processing Toolbox (6 files), EEGLAB (5 files), fdr_bh (Benjamini-Hochberg FDR) (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
16 files
At the source: github.com/ChenWQpublish/ADHD_EEG

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;
  • 15 scripts, each with its path and the digest of its content;
  • 2 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

The source code and sample data is publicly available online (https://github.com/ChenWQpublish/ADHD_EEG). The data supporting the findings of this study are available from the corresponding author upon reasonable request. Due to ethical and privacy considerations related to participant data, the datasets are not publicly available.

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 2, 28 September 2026

  • Authors: added Wenqi Chen (0000-0002-7905-0689); removed Wenqi Chen

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 6 keywords, 12 MeSH terms, 3 funders, 39 references.

Cite

This paper

Zhang, Y., Li, T., Jiang, J., Li, H., Sun, R., Li, Y., & Chen, W. (2026). Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children. NeuroImage. Clinical, 50, 104011. https://doi.org/10.1016/j.nicl.2026.104011

BibTeX

@article{zhang2026altered,
author = {Zhang, Yanan and Li, Tongxia and Jiang, Jun and Li, Hao and Sun, Ruidi and Li, Yunjie and Chen, Wenqi},
title = {{Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children}},
journal = {NeuroImage. Clinical},
year = {2026},
month = may,
volume = {50},
pages = {104011},
publisher = {Elsevier},
issn = {2213-1582},
doi = {10.1016/j.nicl.2026.104011},
url = {https://doi.org/10.1016/j.nicl.2026.104011},
pmid = {42184469},
pmcid = {PMC13226792}
}

RIS

TY - JOUR
AU - Zhang, Yanan
AU - Li, Tongxia
AU - Jiang, Jun
AU - Li, Hao
AU - Sun, Ruidi
AU - Li, Yunjie
AU - Chen, Wenqi
TI - Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children
T2 - NeuroImage. Clinical
J2 - Neuroimage Clin
PY - 2026
DA - 2026/05/23
VL - 50
SP - 104011
SN - 2213-1582
PB - Elsevier
DO - 10.1016/j.nicl.2026.104011
UR - https://doi.org/10.1016/j.nicl.2026.104011
LA - en
ER -

CSL-JSON

{
"id": "10.1016/j.nicl.2026.104011",
"type": "article-journal",
"title": "Altered temporal organization of neural response dynamics during attention processing differentiates ADHD subtypes in children",
"container-title": "NeuroImage. Clinical",
"author": [
{
"family": "Zhang",
"given": "Yanan"
},
{
"family": "Li",
"given": "Tongxia"
},
{
"family": "Jiang",
"given": "Jun"
},
{
"family": "Li",
"given": "Hao"
},
{
"family": "Sun",
"given": "Ruidi"
},
{
"family": "Li",
"given": "Yunjie"
},
{
"family": "Chen",
"given": "Wenqi"
}
],
"container-title-short": "Neuroimage Clin",
"volume": "50",
"page": "104011",
"DOI": "10.1016/j.nicl.2026.104011",
"PMID": "42184469",
"PMCID": "PMC13226792",
"ISSN": "2213-1582",
"publisher": "Elsevier",
"URL": "https://doi.org/10.1016/j.nicl.2026.104011",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
23
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41398-026-04080-9 [code]
Altered flow of information in attention-deficit/hyperactivity disorder.
Journal: Translational psychiatry
In common: Statistics and Machine Learning Toolbox, ADHD, 3 references
[2] doi:10.1371/journal.pbio.3003938 [code]
Theta oscillations tag episodic memories for sleep-dependent consolidation.
Journal: PLoS biology
In common: fdr_bh (Benjamini-Hochberg FDR), EEGLAB, Signal Processing Toolbox, 1 other tool, EEG
[3] doi:10.1162/imag.a.1229 [code]
40 Hz audiovisual stimulation improves sustained attention and related brain oscillations.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: fdr_bh (Benjamini-Hochberg FDR), EEGLAB, Signal Processing Toolbox, 1 other tool, EEG
[4] doi:10.1111/psyp.70285 [code]
Spontaneous Modulation of Alpha Power During a Neurofeedback Session Without Instructions.
Journal: Psychophysiology
In common: EEGLAB, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, EEG, 2 references
[5] doi:10.1038/s41467-026-72917-4 [code]
OFC-induced network modularity improves positive symptoms and attentional alertness in schizophrenia: a combined rTMS-fMRI study.
Journal: Nature communications
In common: fdr_bh (Benjamini-Hochberg FDR), Signal Processing Toolbox, Statistics and Machine Learning Toolbox, 1 reference
[6] doi:10.1038/s41467-026-73878-4 [code]
Neural Response to Familiar Names Predicts Outcome of Comatose ICU Patients: A Prospective Observational Cohort Study.
Journal: Nature communications
In common: fdr_bh (Benjamini-Hochberg FDR), EEGLAB, Statistics and Machine Learning Toolbox, EEG, 1 reference
[7] doi:10.1038/s41593-026-02357-2 [code]
Experience reorganizes content-specific memory traces in macaques.
Journal: Nature neuroscience
In common: fdr_bh (Benjamini-Hochberg FDR), EEGLAB, Signal Processing Toolbox, 1 other tool
[8] doi:10.7554/elife.111114 [code]
Theta beta ratio in attention deficit hyperactivity disorder using a multiverse analysis.
Journal: eLife
In common: EEGLAB, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, ADHD, EEG
[9] doi:10.1111/cogs.70220 [code]
Shared Neural Computations for Syntactic and Morphological Structures: Evidence From Mandarin Chinese.
Journal: Cognitive science
In common: fdr_bh (Benjamini-Hochberg FDR), EEGLAB, Statistics and Machine Learning Toolbox, EEG
[10] doi:10.1016/j.neuroimage.2026.122115 [code]
Midfrontal theta power relates to response speeding following frustrative nonreward.
Journal: NeuroImage
In common: EEGLAB, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, EEG, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.