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Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging.

A correction to this paper has been published: the notice, 42719402, from Europe PMC.

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Paper

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

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The authors' code

MATLAB · 42 lines · 1.7 KB · GPL-2.0

  1. % Toolbox for batch processing ASL perfusion based fMRI data.
  2. % All rights reserved.
  3. % Ze Wang @ TRC, CFN, Upenn 2004
  4. par;
  5. % get the subdirectories in the main directory
  6. for sb = 1:length(PAR.subjects) % for each subject
  7. sprintf('uBEN calc. subject %u .........',sb)
  8. %go get the sessions
  9. for ses=1:PAR.nsess(sb)
  10. %now get all images to smooth!
  11. %prepare directory
  12. %P=[];
  13. for c=1:PAR.ncond
  14. if isempty(PAR.condirs{sb,ses,c}) continue; end
  15. P=[];
  16. % Ptmp=spm_select('FPList', PAR.condirs{sb,ses,c}, ['^r' PAR.subjects{sb} '.*' PAR.confilters{c} '.*\.img$']);
  17. P=spm_select('FPList', PAR.condirs{sb,ses,c}, ['^sflt.*\.nii$']);
  18. if isempty(P)
  19. fprintf('!!!!!! No images found for %s session %d and condition %d\n',PAR.subjects{sb},ses,c);
  20. continue;
  21. end
  22. v=spm_vol(P);
  23. if size(v,1)<120, continue; end
  24. cd(PAR.condirs{sb,ses,c})
  25. pmask=spm_select('FPList',PAR.condirs{sb,ses,c},['^brainmask\.nii$']);
  26. oimg=fullfile(PAR.condirs{sb,ses,c}, ['uBEN' PAR.subjects{sb} '.nii']);
  27. % str=['!' PAR.benexe ' -d ' num2str(PAR.dim) ' -r ' num2str(PAR.r) ...
  28. % '-ndummies 2 -tlen_2use 136 -otype 2 -s 1 -c 30 -i ' P ' -m ' pmask ' -o ' oimg];
  29. str=['!' PAR.benexe ' -d ' num2str(PAR.dim) ' -r ' num2str(PAR.r) ...
  30. ' -num_dummies 4 -c 30 -i ' P ' -m ' pmask ' -o ' oimg];
  31. if isunix
  32. eval(str);
  33. else
  34. system(str);
  35. end
  36. end
  37. end
  38. end

batch_calc_uBEN.m at commit f396951, under GPL-2.0 · at the source

Overview

Authors: Ruoxi Lu1,2,3,4, Jianyu Li1,3,4, Yiran Li2, Xinglin Zeng2, Yan Guo1, Danian Li5, Ying Cui6, Xinyu Liang1, Hanyue Zhang7, Jing Wang1,4, Baohua Cheng1,4, Yujie Liu3, Ze Wang2, Shijun Qiu3,4
ORCID iDs: Ze Wang
  1. T he First School of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510400, China
  2. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
  3. Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510400, China
  4. State Key Laboratory of Traditional Chinese Medicine Syndrome, Guangzhou 510006, China
  5. Cerebropathy Center, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510400, China
  6. Cerebropathy Center, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
  7. Department of Radiology, Foshan first People’s Hospital, Foshan 528000, China
Journal: Psychoradiology, volume 6, article kkag009
Dates: received 1 September 2025; accepted 1 February 2026; published online 6 March 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/psyrad/kkag009 · PMID 42017053 · PMCID PMC13092983 · OpenAlex W7134137972
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), depression (population), developmental (subfield)
Methods: Connectivity, Statistics, Machine learning, Complexity, Preprocessing, Graphs, fMRI & imaging
Keywords: major depressive disorder, brain entropy, resting-state fMRI, adolescent, young adult, machine learning
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: cited by 3 papers (Europe PMC); 61 references in the paper
Notices: A correction to this paper has been published (42719402, from Europe PMC)

Abstract

Background: Major depressive disorder (MDD) in adolescents and young adults is increasingly prevalent, yet accurate diagnosis remains challenging due to the limitations of conventional neuroimaging metrics. Traditional resting-state functional magnetic resonance imaging (rs-fMRI) measures such as amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity density (FCD) primarily capture static aspects of brain activity and may overlook critical neural dynamics. Brain entropy (BEN), which quantifies temporal irregularity in rs-fMRI signals, may offer a complementary approach to better characterize neural alterations in MDD.

Methods: We analyzed multimodal rs-fMRI data from 204 individuals aged 12–24 years (119 with MDD and 85 healthy controls). BEN was computed alongside ALFF, ReHo, and FCD to extract region-wise features across the brain. A support vector machine with recursive feature elimination (SVM-RFE) was used to classify MDD and healthy controls based on various feature combinations. Classification performance was evaluated using repeated cross-validation and permutation testing. Additionally, partial Spearman correlations were performed between selected brain features and clinical measures including depression severity, childhood trauma, sleep quality, and cognitive control.

Results: Models incorporating BEN consistently outperformed those using traditional rs-fMRI features alone. The combination of BEN, ALFF, and FCD achieved the highest classification accuracy (AUC = 0.877, permutation test P < 0.001). The most frequently selected brain regions contributing to MDD classification included the putamen, paracentral lobule, cuneus, middle frontal gyrus, and rectus. BEN features also showed preliminary correlations with clinical variables such as childhood trauma and sleep quality, suggesting functional relevance.

Conclusions: This study demonstrates that BEN provides complementary diagnostic information to traditional rs-fMRI features in classifying adolescent and young adult MDD. BEN-related alterations in brain activity may reflect underlying neurobiological disruptions and show potential as a functional neuroimaging biomarker for depression during a critical stage of brain development.

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

Repository

Its files are read in the Code ↔ Paper reader above.

zewangnew/BENtbx

License: GPL-2.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: f39695187951f59ce2db85db876b0582c6180c1b, 31 January 2023
Languages: MATLAB (1)
Size: 9 files, 1 script
Software Heritage: not archived
Found in: the text, “BEN”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: SPM (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
4 files

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;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 14 authors, 6 keywords, 60 references, 1 integrity notice.

Cite

This paper

Lu, R., Li, J., Li, Y., Zeng, X., Guo, Y., Li, D., Cui, Y., Liang, X., Zhang, H., Wang, J., Cheng, B., Liu, Y., Wang, Z., & Qiu, S. (2026). Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging. Psychoradiology, 6, kkag009. https://doi.org/10.1093/psyrad/kkag009

BibTeX

@article{lu2026brain,
author = {Lu, Ruoxi and Li, Jianyu and Li, Yiran and Zeng, Xinglin and Guo, Yan and Li, Danian and Cui, Ying and Liang, Xinyu and Zhang, Hanyue and Wang, Jing and Cheng, Baohua and Liu, Yujie and Wang, Ze and Qiu, Shijun},
title = {{Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging}},
journal = {Psychoradiology},
year = {2026},
month = mar,
volume = {6},
pages = {kkag009},
publisher = {Oxford University Press},
issn = {2634-4416},
doi = {10.1093/psyrad/kkag009},
url = {https://doi.org/10.1093/psyrad/kkag009},
pmid = {42017053},
pmcid = {PMC13092983}
}

RIS

TY - JOUR
AU - Lu, Ruoxi
AU - Li, Jianyu
AU - Li, Yiran
AU - Zeng, Xinglin
AU - Guo, Yan
AU - Li, Danian
AU - Cui, Ying
AU - Liang, Xinyu
AU - Zhang, Hanyue
AU - Wang, Jing
AU - Cheng, Baohua
AU - Liu, Yujie
AU - Wang, Ze
AU - Qiu, Shijun
TI - Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging
T2 - Psychoradiology
J2 - Psychoradiology
PY - 2026
DA - 2026/03/06
VL - 6
SP - kkag009
SN - 2634-4416
PB - Oxford University Press
DO - 10.1093/psyrad/kkag009
UR - https://doi.org/10.1093/psyrad/kkag009
LA - en
ER -

CSL-JSON

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"id": "10.1093/psyrad/kkag009",
"type": "article-journal",
"title": "Brain entropy as a biomarker of major depression in adolescents and young adults: insights from multimodal resting-state functional magentic resonance imaging",
"container-title": "Psychoradiology",
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"family": "Li",
"given": "Danian"
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