OSCR

Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial.

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] § Results › Secondary outcome (neural) ↔ Codes/MATLABcodes/PrePost_gPPI.m, lines 69–86 · score 0.77 · dorsal caudate, dorsal putamen, lateral amygdala, medial amygdala, ventral striatum, frontal
  2. [2] § Results › Secondary outcome (neural) ↔ Codes/MATLABcodes/Change_gPPI.m, lines 43–58 · score 0.76 · dorsal caudate, dorsal putamen, lateral amygdala, medial amygdala, ventral striatum, frontal
  3. [3] § Results › Secondary outcome (neural) ↔ Codes/MATLABcodes/calculate_and_plot_correlations_with_slope_test_Revisions.m, lines 1–41 · score 0.68 · left frontal, gPPI, left parietal, right frontal, right parietal, connectivity
  4. [4] § Results › Combined brain-behavior-stimulation analyses ↔ Codes/MATLABcodes/VAS_change_Revisions.m, lines 55–67 · score 0.53 · Pre Stimulation, Post Stimulation, fMRI, Day, Baseline, cue
  5. [5] § Method › Statistical analysis › Secondary hypothesis (neural) ↔ Codes/MATLABcodes/calculate_and_plot_correlations_with_slope_test_Revisions.m, lines 1–41 · score 0.51 · gPPI, matrix, subregions, CONN, correlation, striatum
  6. [6] § Method › Data collection › MRI data ↔ StimTool-DCR-python3/CueReactivity/CueReactivity.py, lines 26–34 · score 0.50 · drug cue reactivity, Craving ratings, fixation, block, stimulus, neutral
  7. [7] § Results › Combined brain-behavior-stimulation analyses ↔ Codes/MATLABcodes/Correlation_EF_FA_Revision.m, lines 127–176 · score 0.50 · electric field, post pre, EF, fit, regression, strength

Paper

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

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The authors' code

MATLAB · 200 lines · 8.1 KB · no license · 2 matches

  1. function calculate_and_plot_correlations_with_slope_test_Revision()
  2. % ---------------------------
  3. % Load VAS
  4. % ---------------------------
  5. filename = '/Volumes/ExtremeSSDD/LIBR_tACS/VAS/Craving_Final.xlsx';
  6. data = readtable(filename);
  8. % ---------------------------
  9. % Load gPPI (CONN) matrices
  10. % Z is [nROI x nROI x nSubjects] in CONN ROI results
  11. % ---------------------------
  12. load('/Volumes/ExtremeSSDD/LIBR_tACS_CONN/conn_project01_LIBR_tACS/results/firstlevel/gPPI_BNA/resultsROI_Condition001.mat'); Opioid_Pre = Z;
  13. load('/Volumes/ExtremeSSDD/LIBR_tACS_CONN/conn_project01_LIBR_tACS/results/firstlevel/gPPI_BNA/resultsROI_Condition002.mat'); Neutral_Pre = Z;
  14. load('/Volumes/ExtremeSSDD/LIBR_tACS_CONN/conn_project01_LIBR_tACS/results/firstlevel/gPPI_BNA/resultsROI_Condition003.mat'); Opioid_Post = Z;
  15. load('/Volumes/ExtremeSSDD/LIBR_tACS_CONN/conn_project01_LIBR_tACS/results/firstlevel/gPPI_BNA/resultsROI_Condition004.mat'); Neutral_Post= Z;
  17. OvN_Pre = Opioid_Pre - Neutral_Pre;
  18. OvN_Post = Opioid_Post - Neutral_Post;
  19. OvN_Change = OvN_Post - OvN_Pre; % [ROI x ROI x subj]
  20. Conn_var = OvN_Change;
  22. % ---------------------------
  23. % Group info (match your coding)
  24. % ---------------------------
  25. % data.Group contains 'A' (Sham) and 'B' (Active)
  26. groups = {'A','B'};
  27. groupLabels = {'Sham','Active'};
  28. groupColorsHex = {'#F8766D', '#00BFC4'};
  29. groupColors = cellfun(@hex2rgb, groupColorsHex, 'UniformOutput', false);
  31. % ---------------------------
  32. % ROI labels (same as yours)
  33. % ---------------------------
  34. regionsOfInterest = [13, 14, 211, 212, 213, 214, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258];
  35. regionNames = {
  36. 'Left VMPFC', 'Right VMPFC', 'Left medial Amyg', 'Right medial Amyg', 'Left lateral Amyg',...
  37. 'Right lateral Amyg', 'Bilateral VMPFC', 'Right Parietal', 'Right Frontal', 'Left Parietal',...
  38. 'Left Frontal', 'rVS', 'Striatum_subregions.STR1_r', 'Striatum_subregions.STR1_l', ...
  39. 'Striatum_subregions.STR2_r', 'Striatum_subregions.STR2_l', 'Striatum_subregions.STR3_r',...
  40. 'Striatum_subregions.STR3_l'
  41. };
  43. % ROI pairs (same as yours)
  44. roiPairs = [
  45. 249, 256;
  46. 247, 250;
  47. 248, 13;
  48. 248, 212;
  49. 248, 251;
  50. 248, 258;
  51. 251, 211;
  52. 251, 258;
  53. 252, 257;
  54. 252, 258;
  55. 253, 257;
  56. 254, 214
  57. ];
  59. % Which VAS column to use (you used 'Change')
  60. timePoint = 'Change'; % must be a column name in the Craving_Final.xlsx
  62. % ---------------------------
  63. % IMPORTANT: subject alignment
  64. % ---------------------------
  65. % Assumption: CONN subject dimension matches the subject order in your
  66. % Craving_Final.xlsx. If not, you MUST add an explicit mapping here.
  67. nConnSubj = size(Conn_var, 3);
  68. if height(data) ~= nConnSubj
  69. warning('Row count in VAS table (%d) does not match CONN subjects (%d). You may need an ID->index mapping.', height(data), nConnSubj);
  70. end
  72. % Build group categorical for regression
  73. % Baseline group will be Sham ('A') unless you reorder categories
  74. GroupCat = categorical(data.Group, groups, groupLabels); % A->Sham, B->Active
  76. % Output table to save interaction tests
  77. results = table();
  79. for k = 1:size(roiPairs,1)
  80. roi1 = roiPairs(k,1);
  81. roi2 = roiPairs(k,2);
  83. % Extract connectivity for ALL subjects in the same row order as "data"
  84. connAll = squeeze(Conn_var(roi1, roi2, :)); % [nSubj x 1]
  85. vasAll = data.(timePoint);
  87. % Remove NaNs (both conn and vas)
  88. valid = ~isnan(connAll) & ~isnan(vasAll) & ~isundefined(GroupCat);
  89. connAll = connAll(valid);
  90. vasAll = vasAll(valid);
  91. grpAll = GroupCat(valid);
  93. % Need at least some subjects per group
  94. if numel(connAll) < 6 || numel(unique(grpAll)) < 2
  95. fprintf('Skipping ROI %d-%d: not enough valid data or only one group present.\n', roi1, roi2);
  96. continue;
  97. end
  99. % ---------------------------
  100. % Reviewer-requested test:
  101. % One model with interaction (difference in slopes)
  102. % ---------------------------
  103. T = table(connAll, grpAll, vasAll, 'VariableNames', {'Conn','Group','VAS'});
  104. mdl = fitlm(T, 'VAS ~ Conn*Group'); % includes Conn, Group, Conn:Group
  106. % Get interaction p-value
  107. coef = mdl.Coefficients;
  108. % Term name will look like 'Conn:Group_Active' depending on MATLAB version
  109. termNames = coef.Properties.RowNames;
  110. intIdx = find(contains(termNames,'Conn:Group'), 1);
  111. if isempty(intIdx)
  112. error('Could not find interaction term in model coefficients. Terms: %s', strjoin(termNames', ', '));
  113. end
  114. pInteraction = coef.pValue(intIdx);
  115. betaInteraction = coef.Estimate(intIdx);
  117. % Optional: within-group correlations/slopes (to match your plot labels)
  118. rVals = nan(2,1); pVals = nan(2,1);
  119. slopes = nan(2,1);
  121. % Plot
  122. figure; hold on;
  124. for g = 1:2
  125. thisLabel = groupLabels{g};
  126. thisColor = groupColors{g};
  128. idxG = (grpAll == thisLabel);
  129. x = connAll(idxG);
  130. y = vasAll(idxG);
  132. if numel(x) > 1
  133. [rVals(g), pVals(g)] = corr(x(:), y(:), 'Type','Pearson');
  135. % Within-group slope (simple lm)
  136. mdlG = fitlm(x, y);
  137. slopes(g) = mdlG.Coefficients.Estimate(2);
  139. scatter(x, y, 36, 'MarkerFaceColor', thisColor, 'MarkerEdgeColor', thisColor);
  141. x_fit = linspace(min(x), max(x), 100)';
  142. [y_fit, y_ci] = predict(mdlG, x_fit);
  144. plot(x_fit, y_fit, 'Color', thisColor, 'LineWidth', 1.5);
  145. fill([x_fit; flipud(x_fit)], [y_ci(:,1); flipud(y_ci(:,2))], thisColor, ...
  146. 'FaceAlpha', 0.10, 'EdgeColor','none');
  147. end
  148. end
  150. grid on; grid minor;
  152. roi1Name = regionNames{regionsOfInterest == roi1};
  153. roi2Name = regionNames{regionsOfInterest == roi2};
  155. xlabel(sprintf('Δ gPPI (Opioid–Neutral) Post–Pre: %s ↔ %s', roi1Name, roi2Name), 'Interpreter','none');
  156. ylabel(sprintf('VAS %s', timePoint), 'Interpreter','none');
  158. title(sprintf('ROI %d–%d | Slope difference test: p_{int}=%.3g', roi1, roi2, pInteraction), 'Interpreter','none');
  160. % Text box: within-group r/p + interaction p
  161. xL = xlim; yL = ylim;
  162. xText = xL(1) + 0.03*(xL(2)-xL(1));
  163. yText = yL(2) - 0.05*(yL(2)-yL(1));
  164. text(xText, yText, sprintf('Interaction (Conn×Group): \\beta=%.3f, p=%.3g', betaInteraction, pInteraction), ...
  165. 'FontSize', 11, 'BackgroundColor','white');
  167. if ~isnan(rVals(1))
  168. text(xText, yText - 0.08*(yL(2)-yL(1)), sprintf('Sham: r=%.2f, p=%.3g', rVals(1), pVals(1)), ...
  169. 'Color', groupColors{1}, 'FontSize', 11, 'BackgroundColor','white');
  170. end
  171. if ~isnan(rVals(2))
  172. text(xText, yText - 0.16*(yL(2)-yL(1)), sprintf('Active: r=%.2f, p=%.3g', rVals(2), pVals(2)), ...
  173. 'Color', groupColors{2}, 'FontSize', 11, 'BackgroundColor','white');
  174. end
  176. hold off;
  178. % Save results row
  179. newRow = table(roi1, roi2, string(roi1Name), string(roi2Name), ...
  180. betaInteraction, pInteraction, ...
  181. slopes(1), slopes(2), rVals(1), pVals(1), rVals(2), pVals(2), ...
  182. 'VariableNames', {'ROI1','ROI2','ROI1_Name','ROI2_Name', ...
  183. 'Beta_Interaction','P_Interaction', ...
  184. 'Slope_Sham','Slope_Active', ...
  185. 'r_Sham','p_Sham','r_Active','p_Active'});
  186. results = [results; newRow]; %#ok<AGROW>
  187. end
  189. % Write out a summary table (recommended for your revision)
  190. outCSV = fullfile(pwd, sprintf('SlopeDifference_ConnxGroup_%s.csv', timePoint));
  191. writetable(results, outCSV);
  192. fprintf('\nSaved interaction-test summary to: %s\n', outCSV);
  193. end
  196. function rgb = hex2rgb(hex)
  197. hex = char(hex);
  198. if hex(1) == '#', hex = hex(2:end); end
  199. rgb = reshape(sscanf(hex, '%2x')/255, 1, 3);
  200. end

calculate_and_plot_correlations_with_slope_test_Revisions.m at commit 7aba61f, no license · at the source

Overview

Authors: Ghazaleh Soleimani1, Rayus Kuplicki2, Martin P. Paulus2, Hamed Ekhtiari2,3
ORCID iDs: Ghazaleh Soleimani, Rayus Kuplicki, Martin P. Paulus, Hamed Ekhtiari
  1. Department of Biomedical Engineering, University of Minnesota,Minneapolis, MN USA
  2. Laureate Institute for Brain Research (LIBR),Tulsa, OK USA
  3. Department of Psychiatry, University of Texas Southwestern (UTSW),Dallas, TX USA
Institutions: University of Minnesota (United States); Laureate Institute for Brain Research (United States); The University of Texas Southwestern Medical Center (United States)
Journal: Molecular psychiatry, volume 31, issue 10, pages 6056-6068
Dates: received 5 September 2025; accepted 16 June 2026; published online 26 June 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41380-026-03694-1 · PMID 42362768 · PMCID PMC13569433 · OpenAlex W7166075431
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), other (modality), human (organism), other condition (population), pain (population), clinical / translational (subfield)
Methods: Statistics, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: Addiction, Psychology
MeSH: Opioid-Related Disorders*, Adult, Amygdala, Brain, Corpus Striatum, Craving, Cues, Frontal Lobe, Humans, Magnetic Resonance Imaging, Male, Nerve Net, Neural Pathways, Parietal Lobe, Theta Rhythm, Transcranial Direct Current Stimulation (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Brain and Behavior Research Foundation; MnDrive Brain Conditions
Citations: not cited yet (Europe PMC); 91 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 7 matches between paragraphs and lines of code.

rkuplicki/LIBR_MOCD

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 9ebf1903ca778933d569ffa25952df38839fdba3, 27 April 2022
Languages: Python (20), Shell (2), R (2), MATLAB (1)
Size: 1,253 files, 25 scripts
Software Heritage: not archived
Found in: the text, “Limitations and future directions”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: PsychoPy (10 files), NumPy (4 files), cowplot (2 files), ggplot2 (2 files), tidyverse (2 files), data.table (1 file), ggpubr (1 file), lavaan (1 file), lme4 (1 file), lmerTest (1 file), multcomp (1 file), nlme (1 file), reshape2 (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
26 files
At the source: github.com/rkuplicki/LIBR_MOCD

soleimanighazaleh/frontoparietal-tacs-oud

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 7aba61f406533b76a5c85f2a46cdaca738fb0440, 30 December 2025
Languages: MATLAB (21), Shell (5)
Size: 161 files, 26 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: AFNI (5 files), Statistics and Machine Learning Toolbox (5 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/SoleimaniGhazaleh/Frontoparietal-tACS-OUD/tree/main

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 51 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);
  • 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.

Code and data availability statement

The paper has a code and data 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/s41380-026-03694-1.

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Springer Nature

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 2 keywords, 16 MeSH terms, 2 funders, 88 references.

Cite

This paper

Soleimani, G., Kuplicki, R., Paulus, M. P., & Ekhtiari, H. (2026). Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial. Molecular psychiatry, 31(10), 6056-6068. https://doi.org/10.1038/s41380-026-03694-1

BibTeX

@article{soleimani2026targeting,
author = {Soleimani, Ghazaleh and Kuplicki, Rayus and Paulus, Martin P. and Ekhtiari, Hamed},
title = {{Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial}},
journal = {Molecular psychiatry},
year = {2026},
month = jun,
volume = {31},
number = {10},
pages = {6056--6068},
publisher = {Springer Nature},
issn = {1359-4184},
doi = {10.1038/s41380-026-03694-1},
url = {https://doi.org/10.1038/s41380-026-03694-1},
pmid = {42362768},
pmcid = {PMC13569433}
}

RIS

TY - JOUR
AU - Soleimani, Ghazaleh
AU - Kuplicki, Rayus
AU - Paulus, Martin P.
AU - Ekhtiari, Hamed
TI - Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial
T2 - Molecular psychiatry
J2 - Mol Psychiatry
PY - 2026
DA - 2026/06/26
VL - 31
IS - 10
SP - 6056
EP - 6068
SN - 1359-4184
PB - Springer Nature
DO - 10.1038/s41380-026-03694-1
UR - https://doi.org/10.1038/s41380-026-03694-1
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41380-026-03694-1",
"type": "article-journal",
"title": "Targeting cortico-striatal-amygdalar networks via theta-band frontoparietal synchronization in opioid use disorder: a randomized tACS-fMRI Trial",
"container-title": "Molecular psychiatry",
"author": [
{
"family": "Soleimani",
"given": "Ghazaleh"
},
{
"family": "Kuplicki",
"given": "Rayus"
},
{
"family": "Paulus",
"given": "Martin P."
},
{
"family": "Ekhtiari",
"given": "Hamed"
}
],
"container-title-short": "Mol Psychiatry",
"volume": "31",
"issue": "10",
"page": "6056-6068",
"DOI": "10.1038/s41380-026-03694-1",
"PMID": "42362768",
"PMCID": "PMC13569433",
"ISSN": "1359-4184",
"publisher": "Springer Nature",
"URL": "https://doi.org/10.1038/s41380-026-03694-1",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
26
]
]
}
}

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