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Time-resolved EEG decoding reveals altered neural dynamics of affective semantic evaluation in depression and suicidality.

Code ↔ Paper

7 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 7 matches
  1. [1] § Methods › EEG recording and preprocessing ↔ Preprocessing/preprocPostICA.m, lines 3–70 · score 0.90 · artifact removal, preprocessing pipeline, linear trend, EEGLAB, Brainstorm, ICA
  2. [2] § Methods › EEG recording and preprocessing ↔ Preprocessing/preprocASR.m, lines 3–27 · score 0.86 · Clean_rawdata, Bad channels, EEGLAB, ASR, routine, detection
  3. [3] § Methods › Assessing group differences in spatiotemporal dynamics ↔ decodingBootsFeatures.m, lines 14–83 · score 0.67 · offset latency, peak amplitude, peak latency, onset latency, decoding
  4. [4] § Results › Group differences in affective semantic decoding ↔ decodingBootsFeatures.m, lines 14–83 · score 0.64 · offset latencies, peak amplitude, peak latency, onset latency, suicidal, depressed
  5. [5] § Methods › Assessing group differences in spatiotemporal dynamics ↔ draftFigurePlot.ipynb, lines 1978–2065 · score 0.53 · SVM weights, feature weight, absolute, sentence decoding, PC1, PC2
  6. [6] § Methods › Assessing group differences in spatiotemporal dynamics ↔ Statistics/bootsLatencyStat.ipynb, lines 91–189 · score 0.51 · paired bootstrap distributions, probability, Cliff, delta
  7. [7] § Results › Response time analysis during the Sentence Evaluation task ↔ draftFigurePlot.ipynb, lines 374–496 · score 0.51 · Mann Whitney, disagreeing, disagreement, EC, behavioral, sentences

Paper

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

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The authors' code

MATLAB · 121 lines · 3.9 KB · MIT · 2 matches

  1. %% Load dataset
  3. % Decoding data
  4. datPath = '/Users/woojaejeong/Desktop/Data/USC/DARPA-NEAT/Code/Decoding/svm/Result/svmDecoding_sentiment_sen_3pc_lepoch_congruency_linear_commonPCA.mat';
  5. wPath = '/Users/woojaejeong/Desktop/Data/USC/DARPA-NEAT/Code/Decoding/svm/Result/senPCAweight.mat';
  7. % Subject index
  8. indexPath = '/Users/woojaejeong/Desktop/Data/USC/DARPA-NEAT/Data/Behavior/subject_index.mat';
  10. load(fullfile(datPath))
  11. load(fullfile(indexPath))
  12. load(fullfile(wPath))
  14. %% Onset latency, offset latency, Peak latency, and Peak amplitude calculation
  16. rng('default')
  18. t = -199:4:1500;
  19. tPeak = [101:251];
  21. conId = find(subject_index == 1);
  22. depId = find(subject_index == 2);
  23. suiId = find(subject_index == 3);
  25. % Decoding
  26. conLat = []; conPeak = []; conPeakLat = []; conOff = [];
  27. depLat = []; depPeak = []; depPeakLat = []; depOff = [];
  28. suiLat = []; suiPeak = []; suiPeakLat = []; suiOff = [];
  30. for i = 1:1000
  31. fprintf(['Computing...(%d/1000)\n'], i);
  33. % Control
  34. rId = randi(length(conId),length(conId),1);
  35. conDecode = Decode(conId(rId),:);
  37. peak = mean(conDecode(:,tPeak),1);
  38. conPeakLat = [conPeakLat; t(tPeak(find(peak == max(peak),1)))];
  39. conPeak = [conPeak; max(peak)];
  41. [Sig, ~] = CBP_AB_single(conDecode, 'SVM lepoch');
  42. Sig = sigTimeGenerate(sort(Sig));
  43. sId = find(Sig(:,1) < conPeakLat(i) & Sig(:,2) > conPeakLat(i));
  44. conLat = [conLat; Sig(sId,1)];
  45. conOff = [conOff; Sig(sId,2)];
  47. % Depressed
  48. rId = randi(length(depId),length(depId),1);
  49. depDecode = Decode(depId(rId),:);
  51. peak = mean(depDecode(:,tPeak),1);
  52. depPeakLat = [depPeakLat; t(tPeak(find(peak == max(peak),1)))];
  53. depPeak = [depPeak; max(peak)];
  55. [Sig, ~] = CBP_AB_single(depDecode, 'SVM lepoch');
  56. Sig = sigTimeGenerate(sort(Sig));
  57. sId = find(Sig(:,1) < depPeakLat(i) & Sig(:,2) > depPeakLat(i));
  58. depLat = [depLat; Sig(sId,1)];
  59. depOff = [depOff; Sig(sId,2)];
  61. % Suicidal
  62. rId = randi(length(suiId),length(suiId),1);
  63. suiDecode = Decode(suiId(rId),:);
  65. peak = mean(suiDecode(:,tPeak),1);
  66. suiPeakLat = [suiPeakLat; t(tPeak(find(peak == max(peak),1)))];
  67. suiPeak = [suiPeak; max(peak)];
  69. [Sig, ~] = CBP_AB_single(suiDecode, 'SVM lepoch');
  70. Sig = sigTimeGenerate(sort(Sig));
  71. sId = find(Sig(:,1) < suiPeakLat(i) & Sig(:,2) > suiPeakLat(i));
  72. suiLat = [suiLat; Sig(sId,1)];
  73. suiOff = [suiOff; Sig(sId,2)];
  75. clc;
  76. end
  78. path = '/Users/woojaejeong/Desktop/Data/USC/DARPA-NEAT/Code/Decoding/svm/Result/svmLatencyBoots_commonPCA.mat';
  80. save(path, 'conLat','conPeak','conPeakLat',...
  81. 'depLat','depPeak','depPeakLat',...
  82. 'suiLat','suiPeak','suiPeakLat',...
  83. 'conOff', 'depOff', 'suiOff', '-v7.3');
  85. %% Correlation
  87. rho_pc1_con = []; rho_pc2_con = [];
  88. rho_pc1_dep = []; rho_pc2_dep = [];
  89. rho_pc1_sui = []; rho_pc2_sui = [];
  91. for i = 1:1000
  92. i
  93. rId = conId(randi(length(conId),length(conId),1));
  95. [r,~] = corr(mean(Decode(rId,:),1)', abs(mean(Weight(rId,:,1),1))', type = "Pearson");
  96. rho_pc1_con = [rho_pc1_con; r];
  98. [r,~] = corr(mean(Decode(rId,:),1)', abs(mean(Weight(rId,:,2),1))', type = "Pearson");
  99. rho_pc2_con = [rho_pc2_con; r];
  101. rId = depId(randi(length(depId),length(depId),1));
  103. [r,~] = corr(mean(Decode(rId,:),1)', abs(mean(Weight(rId,:,1),1))', type = "Pearson");
  104. rho_pc1_dep = [rho_pc1_dep; r];
  106. [r,~] = corr(mean(Decode(rId,:),1)', abs(mean(Weight(rId,:,2),1))', type = "Pearson");
  107. rho_pc2_dep = [rho_pc2_dep; r];
  109. rId = suiId(randi(length(suiId),length(suiId),1));
  111. [r,~] = corr(mean(Decode(rId,:),1)', abs(mean(Weight(rId,:,1),1))', type = "Pearson");
  112. rho_pc1_sui = [rho_pc1_sui; r];
  114. [r,~] = corr(mean(Decode(rId,:),1)', abs(mean(Weight(rId,:,2),1))', type = "Pearson");
  115. rho_pc2_sui = [rho_pc2_sui; r];
  116. end
  119. save("/Users/woojaejeong/Desktop/Data/USC/DARPA-NEAT/Code/Decoding/svm/Result/group_corr.mat",...
  120. 'rho_pc1_con','rho_pc1_dep','rho_pc1_sui',...
  121. 'rho_pc2_con','rho_pc2_dep','rho_pc2_sui', '-v7.3');

decodingBootsFeatures.m at commit 1f15690, under MIT · at the source

Overview

Authors: Woojae Jeong1,2, Aditya Kommineni2, Kleanthis Avramidis3, Colin McDaniel4,5, Donald Berry6, Myzelle Hughes4,5, Thomas McGee7, Elsi Kaiser8, Dani Byrd8, Assal Habibi4,5, B. Rael Cahn9, Idan A. Blank7, Kristina Lerman3,6, Dimitrios Pantazis10, Sudarsana R. Kadiri2, Takfarinas Medani2, Shrikanth Narayanan2,3,5,6,8, Richard M. Leahy1,2
ORCID iDs: Woojae Jeong, Colin McDaniel, Idan A. Blank, Dimitrios Pantazis, Shrikanth Narayanan
  1. Alfred E. Mann Department of Biomedical Engineering, University of Southern California,Los Angeles, CA USA
  2. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California,Los Angeles, CA USA
  3. Thomas Lord Department of Computer Science, University of Southern California,Los Angeles, CA USA
  4. Brain and Creativity Institute, University of Southern California,Los Angeles, CA USA
  5. Department of Psychology, University of Southern California,Los Angeles, CA USA
  6. Information Science Institute, University of Southern California,Marina Del Rey, CA USA
  7. Department of Psychology, University of California, Los Angeles,Los Angeles, CA USA
  8. Department of Linguistics, University of Southern California,Los Angeles, CA USA
  9. Department of Psychiatry and Behavioral Sciences, University of Southern California,Los Angeles, CA USA
  10. McGovern Institute for Brain Research, Massachusetts Institute of Technology,Cambridge, MA USA
Institutions: University of Southern California (United States); University of California, Los Angeles (United States); Massachusetts Institute of Technology (United States)
Journal: Communications biology, volume 9, issue 1, article 908
Dates: received 14 October 2025; accepted 10 April 2026; published online 30 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-10108-z · PMID 42062432 · PMCID PMC13338046 · OpenAlex W7159636669
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), other condition (population), depression (population), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, Single-unit activity, calcium imaging, Physiology & signal measures
Keywords: Neural decoding, Cognitive control, Consciousness, Diagnostic markers, Disorders of consciousness
MeSH: Depression*, Electroencephalography*, Semantics*, Suicidal Ideation*, Adult, Emotions, Female, Humans, Male, Young Adult (* major topic)
Topic: Emotion and Mood Recognition (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: United States Department of Defense | Defense Advanced Research Projects Agency (DARPA) (N660012324006); U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB) (R01EB026299)
Citations: cited by 2 papers (Europe PMC); 90 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.

Repository

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

lyricists/PRECOG

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 1f156909179396b11e240c983ba8191b00cc4e2b, 4 March 2026
Languages: MATLAB (13), Jupyter (2), Python (2)
Size: 19 files, 17 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, 2 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brainstorm (4 files), EEGLAB (4 files), NumPy (4 files), FieldTrip (3 files), scikit-learn (3 files), SciPy (3 files), pandas (2 files), statsmodels (2 files), Matplotlib (1 file), MNE-Python (1 file), seaborn (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
19 files
At the source: github.com/lyricists/PRECOG

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s42003-026-10108-z.

Tracing map

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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;
  • 17 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability statement

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  • it points to a dataset: Zenodo 19363840
  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s42003-026-10108-z.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 18 authors, 5 keywords, 10 MeSH terms, 2 funders, 78 references.

Cite

This paper

Jeong, W., Kommineni, A., Avramidis, K., McDaniel, C., Berry, D., Hughes, M., McGee, T., Kaiser, E., Byrd, D., Habibi, A., Cahn, B. R., Blank, I. A., Lerman, K., Pantazis, D., Kadiri, S. R., Medani, T., Narayanan, S., & Leahy, R. M. (2026). Time-resolved EEG decoding reveals altered neural dynamics of affective semantic evaluation in depression and suicidality. Communications biology, 9(1), 908. https://doi.org/10.1038/s42003-026-10108-z

BibTeX

@article{jeong2026time,
author = {Jeong, Woojae and Kommineni, Aditya and Avramidis, Kleanthis and McDaniel, Colin and Berry, Donald and Hughes, Myzelle and McGee, Thomas and Kaiser, Elsi and Byrd, Dani and Habibi, Assal and Cahn, B. Rael and Blank, Idan A. and Lerman, Kristina and Pantazis, Dimitrios and Kadiri, Sudarsana R. and Medani, Takfarinas and Narayanan, Shrikanth and Leahy, Richard M.},
title = {{Time-resolved EEG decoding reveals altered neural dynamics of affective semantic evaluation in depression and suicidality}},
journal = {Communications biology},
year = {2026},
month = apr,
volume = {9},
number = {1},
pages = {908},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-10108-z},
url = {https://doi.org/10.1038/s42003-026-10108-z},
pmid = {42062432},
pmcid = {PMC13338046}
}

RIS

TY - JOUR
AU - Jeong, Woojae
AU - Kommineni, Aditya
AU - Avramidis, Kleanthis
AU - McDaniel, Colin
AU - Berry, Donald
AU - Hughes, Myzelle
AU - McGee, Thomas
AU - Kaiser, Elsi
AU - Byrd, Dani
AU - Habibi, Assal
AU - Cahn, B. Rael
AU - Blank, Idan A.
AU - Lerman, Kristina
AU - Pantazis, Dimitrios
AU - Kadiri, Sudarsana R.
AU - Medani, Takfarinas
AU - Narayanan, Shrikanth
AU - Leahy, Richard M.
TI - Time-resolved EEG decoding reveals altered neural dynamics of affective semantic evaluation in depression and suicidality
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/04/30
VL - 9
IS - 1
SP - 908
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-10108-z
UR - https://doi.org/10.1038/s42003-026-10108-z
LA - en
ER -

CSL-JSON

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