OSCR

Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder.

Code ↔ Paper

5 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 5 matches
  1. [1] § Results › Right amygdala ablation reduces negative interpretation bias in the index patient: neural evidence ↔ IndexPatient_example_code.m, lines 61–149 · score 0.65 · fearful faces, happy faces, post ablation, left amygdala, ablated, ERPs
  2. [2] § Results › Right amygdala ablation reduces negative interpretation bias in the index patient: behavioral evidence ↔ IndexPatient_example_code.m, lines 61–149 · score 0.63 · pre ablation, fearful faces, happy faces, post ablation, 40 %, 50 %
  3. [3] § Methods › Electrophysiology in emotional processing ↔ Dependencies/dicom2nifti/dicm_dict.m, lines 206–265 · score 0.55 · baseline corrected, segments, zero, filter, signals, duration
  4. [4] § Methods › Electrophysiology in emotional processing ↔ Dependencies/dicom2nifti/dicm_dict.m, lines 206–265 · score 0.55 · baseline corrected, segments, zero, filter, signals, duration
  5. [5] § Methods › The index patient ↔ Components/Output/MatOutput.m, lines 35–115 · score 0.52 · FreeSurfer, amplified, hemispheres, implanted, localize, EEG

Paper

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The authors' code

MATLAB · 150 lines · 5.1 KB · no license · 2 matches

  2. % This script reproduces the behavioral and ERP analyses for the index patient
  3. % in the facial emotion discrimination task.
  5. % It computes block-level behavioral metrics (fear-choice rate, reaction time, confidence),
  6. % and extracts happy vs. fearful L-ERPs for each SEEG channel.
  8. % All analyses use the data structure indexPatient.mat containing SEEG signals,
  9. % event information, and channel locations.
  11. % Tao Xie, 11/17/2025
  13. clc,clear;
  14. load('indexPatient.mat');
  16. %% behaveral analysis
  17. typ = {'pre','post'};
  18. run = 1:4;
  19. fearVal = [0 30 40 50 60 70 100];
  20. rlt = struct; kk=0;
  21. for indxTyp = 1:length(typ)
  22. for indxRun = 1:length(run)
  23. locTyp = cellfun(@(x)strcmp(x,typ{indxTyp}),{eeg.event.session});
  24. locRun = [eeg.event.runNum]==run(indxRun);
  25. trial = eeg.event(locTyp&locRun);
  26. trial = trial([trial.validity]==1);
  27. if ~isempty(trial)
  28. kk = kk+1;
  29. rlt(kk).typ = typ{indxTyp};
  30. rlt(kk).runNum = run(indxRun);
  31. for i = 1:length(fearVal)
  32. loc = [trial.fear]==fearVal(i);
  33. % fear chose rate (%)
  34. rlt(kk).fchoRate(i,1) = sum([trial(loc).em]==37)/sum(loc)*100;
  35. % reaction time
  36. val = [trial(loc).emRT];
  37. rlt(kk).rt_mean(i,1) = mean(val);
  38. % confidence
  39. val = [trial(loc).cf]-37;
  40. rlt(kk).cf_mean(i,1) = mean(val);
  41. end
  42. end
  43. end
  44. end
  46. figure; hold on;
  47. col = [0 0 0; 0 0 0; 0 0 0; 1 0 0; 1 0 0; 1 0 0; 1 0 0; ];
  48. markTyp = {'o','square','diamond','o','square','diamond','hexagram'};
  49. ha = [];
  50. for indx = 1:length(rlt)
  51. if indx<=3; s = -1.5; else; s = 1.5; end
  52. ha(indx) = plot(fearVal+s,rlt(indx).fchoRate,markTyp{indx},'markerfacecolor','none','markeredgecolor',col(indx,:),'markersize',15,'LineWidth',2);
  53. nam{indx} = [rlt(indx).typ 'Abl : block ' num2str(rlt(indx).runNum)];
  54. end
  55. grid on;
  56. xlabel('Fear intensity level (%)'); ylabel('Percentage of faces judged as fearful (%)');
  57. legend(ha,nam,'Location','northeastoutside');
  58. set(gca,'fontsize',18,'xlim',[-5 105],'ylim',[-5 110],'xtick',fearVal,'ytick',0:20:100,'TickLength',[0.03 0.03]);
  59. saveas(gcf,'Behavioral_preVSpost','png')
  61. %% ERP analysis
  62. typ = {'pre','post'};
  63. cat = {[0 30 40];[60 70 100]}; % happy/fearful
  64. rlt = struct;
  65. for ch = 1:eeg.nbchan
  66. for indxTyp = 1:length(typ)
  67. for indxCat = 1:length(cat)
  68. locTyp = cellfun(@(x)strcmp(x,typ{indxTyp}),{eeg.event.session});
  69. locCat = false(1,length(locTyp));
  70. for i = 1:length(cat{indxCat})
  71. locCat = locCat | [eeg.event.fear]==cat{indxCat}(i);
  72. end
  73. dat = squeeze(eeg.data(ch,:,locTyp&locCat));
  74. rlt(ch).(typ{indxTyp})(indxCat,:) = mean(dat,2);
  75. end
  76. end
  77. end
  79. % A sample channel in the left amygdala
  80. figure;
  81. ch = 2;
  82. subplot(1,2,1); hold on;
  83. datH = rlt(ch).pre(1,:);
  84. datF = rlt(ch).pre(2,:);
  85. plot(eeg.times,double(datH),'Color',[0 0 0],'LineWidth',3);
  86. plot(eeg.times,double(datF),'Color',[0 1 0],'LineWidth',3);
  87. % define the lab
  88. if eeg.chanlocs(ch).tala(1)<0; lr='L'; else; lr='R'; end
  89. lab = [lr ': ' eeg.chanlocs(ch).annotation{:}];
  90. title(['PreAbl: ' lab]); ylim([-1.2 0.5]); xlim([-200 800])
  91. xlabel('Time (ms)'); ylabel('Evoked potentials (normalized)')
  92. set(gca,'fontsize',14);
  94. subplot(1,2,2); hold on;
  95. datH = rlt(ch).post(1,:);
  96. datF = rlt(ch).post(2,:);
  97. ha = [];
  98. ha(1) = plot(eeg.times,double(datH),'Color',[0 0 0],'LineWidth',3);
  99. ha(2) = plot(eeg.times,double(datF),'Color',[0 1 0],'LineWidth',3);
  100. % define the lab
  101. if eeg.chanlocs(ch).tala(1)<0; lr='L'; else; lr='R'; end
  102. lab = [lr ': ' eeg.chanlocs(ch).annotation{:}];
  103. title(['PostAbl: ' lab]); ylim([-1.2 0.5]); xlim([-200 800])
  104. xlabel('Time (ms)'); ylabel('Evoked potentials (normalized)')
  105. legend(ha,{'Happy faces', 'Fearful faces'},'Location','northeast')
  106. set(gca,'fontsize',14);
  107. saveas(gcf,'exampleERP_preVSpost','png')
  110. % all channel: pre Ablation
  111. figure('Position',[10 10 2400 1200]);
  112. tiledlayout(10,16, 'TileSpacing','compact', 'Padding','compact');
  113. for ch = 1:eeg.nbchan
  114. ax = nexttile; hold on;
  115. datH = rlt(ch).pre(1,:);
  116. datF = rlt(ch).pre(2,:);
  117. %figure;hold on;
  118. plot(eeg.times,double(datH),'Color',[0 0 0],'LineWidth',3);
  119. plot(eeg.times,double(datF),'Color',[0 1 0],'LineWidth',3);
  120. % define the lab
  121. if eeg.chanlocs(ch).tala(1)<0; lr='L'; else; lr='R'; end
  122. lab = [lr ': ' eeg.chanlocs(ch).annotation{:}];
  123. set(gca,'xtick',[],'ytick',[]);
  124. title(lab);
  125. ylim([-1 1])
  126. end
  127. saveas(gcf,'allERP_preAbl','png')
  129. % all channel: post Ablation
  130. figure('Position',[10 10 2400 1200]);
  131. t = tiledlayout(10,16, 'TileSpacing','compact', 'Padding','compact');
  132. ablatedChan = {'AR2','AR3','AR4','AR5','AR6'};
  133. for ch = 1:eeg.nbchan
  134. ax = nexttile; hold on;
  135. datH = rlt(ch).post(1,:);
  136. datF = rlt(ch).post(2,:);
  137. if sum(ismember(ablatedChan,eeg.chanlocs(ch).labels))==0
  138. %figure;hold on;
  139. plot(eeg.times,double(datH),'Color',[0 0 0],'LineWidth',3);
  140. plot(eeg.times,double(datF),'Color',[0 1 0],'LineWidth',3);
  141. % define the lab
  142. if eeg.chanlocs(ch).tala(1)<0; lr='L'; else; lr='R'; end
  143. lab = [lr ': ' eeg.chanlocs(ch).annotation{:}];
  144. set(gca,'xtick',[],'ytick',[]);
  145. title(lab);
  146. ylim([-1 1])
  147. end
  148. end
  149. saveas(gcf,'allERP_postAbl','png')

IndexPatient_example_code.m, no license · at the source

Overview

Authors: Tao Xie1,2, Sanne J H van Rooij3, Sai Sun4,5, Noah K Bryson1,2,6, Phillip Demarest1,2,6, Haeorum Park1,2, Luigi Maccotta5,7, Shuo Wang5, Peter Brunner1,2,6, Jon T Willie1,2,6,7,8,9
ORCID iDs: Tao Xie, Sanne J H van Rooij, Sai Sun, Noah K Bryson, Phillip Demarest, Haeorum Park, Luigi Maccotta, Shuo Wang, Peter Brunner, Jon T Willie
  1. Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO USA
  2. National Center for Adaptive Neurotechnologies, St. Louis, MO USA
  3. Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA USA
  4. Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan
  5. Department of Radiology, Washington University School of Medicine, St. Louis, MO USA
  6. Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO USA
  7. Department of Neurology, Washington University School of Medicine, St. Louis, MO USA
  8. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
  9. Department of Neurosurgery, University of Texas at Austin Dell Medical School, Austin, TX USA
Institutions: Washington University in St. Louis (United States); Emory University (United States); Frontier Research Institute for Interdisciplinary Sciences, Tohoku University (Japan); The University of Texas at Austin (United States)
Journal: Nature communications, volume 17, issue 1, article 7868
Dates: received 4 June 2025; accepted 21 May 2026; published online 22 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-74099-5 · PMID 42331801 · PMCID PMC13443746 · OpenAlex W7165520890
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), other condition (population), epilepsy (population), clinical / translational (subfield)
Methods: Spectral & time-frequency, Connectivity, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Post-traumatic stress disorder, Neural circuits
MeSH: Amygdala*, Radiofrequency Ablation*, Stress Disorders, Post-Traumatic*, Drug Resistant Epilepsy, Electroencephalography, Epilepsy, Evoked Potentials, Facial Expression, Fear, Humans, Male, Prospective Studies (* major topic)
Topic: Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes (Experimental and Cognitive Psychology, Psychology), according to OpenAlex
Funding: U.S. Department of Health & Human Services | National Institutes of Health (NIH) (K01-MH121653, R01-MH120194, R01-EB026439, U24-NS109103, U01-NS108916, U01-NS128612, P41-EB018783, R01-EY037195, R01-MH129426); NINDS NIH HHS (U01 NS108916, U01 NS128612, U24 NS109103); NEI NIH HHS (R01 EY037195); NIMH NIH HHS (K01 MH121653, R01 MH120194, R01 MH129426); NIBIB NIH HHS (P41 EB018783, R01 EB026439)
Citations: not cited yet (Europe PMC); 107 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.

Repositories

Its files are read in the Code ↔ Paper reader above, with 5 matches between paragraphs and lines of code.

OSF 63tym

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 4 files, 1 script
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: osf.io/63tym/

Zenodo 7486842

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (15 files), Statistics and Machine Learning Toolbox (8 files), FreeSurfer (7 files), SPM (6 files), Image Processing Toolbox (4 files), ANTs (3 files), Parallel Computing Toolbox (2 files), GIfTI library for MATLAB (1 file), Psychtoolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
446 files
At the source:

neurotechcenter/vera

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 20b412e5d428577a9067d9bcd850747fa445c988, 25 August 2026
Languages: MATLAB (531), Shell (11), C (6), C/C++ (1), Java (1)
Size: 1,009 files, 550 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, CITATION.cff, documentation, 28 notebooks
Not found: environment file, tests, continuous integration
Tools: Tools for NIfTI and ANALYZE image (MATLAB) (18 files), FreeSurfer (13 files), Statistics and Machine Learning Toolbox (8 files), Image Processing Toolbox (6 files), SPM (6 files), ANTs (3 files), Parallel Computing Toolbox (2 files), dcm2niix (1 file), GIfTI library for MATLAB (1 file), Psychtoolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
552 files
At the source: github.com/neurotechcenter/VERA/tree/doi-release

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/s41467-026-74099-5.

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 995 scripts, each with its path and the digest of its content;
  • 5 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 statement

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  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1038/s41467-026-74099-5.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 12 MeSH terms, 5 funders, 100 references.

Cite

This paper

Xie, T., van Rooij, S. J. H., Sun, S., Bryson, N. K., Demarest, P., Park, H., Maccotta, L., Wang, S., Brunner, P., & Willie, J. T. (2026). Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder. Nature communications, 17(1), 7868. https://doi.org/10.1038/s41467-026-74099-5

BibTeX

@article{xie2026right,
author = {Xie, Tao and van Rooij, Sanne J H and Sun, Sai and Bryson, Noah K and Demarest, Phillip and Park, Haeorum and Maccotta, Luigi and Wang, Shuo and Brunner, Peter and Willie, Jon T},
title = {{Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7868},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74099-5},
url = {https://doi.org/10.1038/s41467-026-74099-5},
pmid = {42331801},
pmcid = {PMC13443746}
}

RIS

TY - JOUR
AU - Xie, Tao
AU - van Rooij, Sanne J H
AU - Sun, Sai
AU - Bryson, Noah K
AU - Demarest, Phillip
AU - Park, Haeorum
AU - Maccotta, Luigi
AU - Wang, Shuo
AU - Brunner, Peter
AU - Willie, Jon T
TI - Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/22
VL - 17
IS - 1
SP - 7868
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74099-5
UR - https://doi.org/10.1038/s41467-026-74099-5
LA - en
ER -

CSL-JSON

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"title": "Right amygdala ablation reduces maladaptive negative interpretation bias and symptoms in a patient with post-traumatic stress disorder",
"container-title": "Nature communications",
"author": [
{
"family": "Xie",
"given": "Tao"
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{
"family": "van Rooij",
"given": "Sanne J H"
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{
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"given": "Sai"
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{
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"volume": "17",
"issue": "1",
"page": "7868",
"DOI": "10.1038/s41467-026-74099-5",
"PMID": "42331801",
"PMCID": "PMC13443746",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
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"language": "en",
"issued": {
"date-parts": [
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2026,
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}
}

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