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Oxytocin selectively biases sensory-prefrontal communication through network-level suppression and theta coupling.

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 · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › LFP Acquisition and Analysis. › Preprocessing and spectral analysis. ↔ Supple_Figure3.m, the whole file · a weak match · score 0.87 · 35–50 Hz, 18–24 Hz, 24–32 Hz, 70–90 Hz, 8–12 Hz, 4–8 Hz · Report a mismatch
  2. [2] § Materials and Methods › LFP Acquisition and Analysis. › Preprocessing and spectral analysis. ↔ Figure2.m, lines 10 to 32 · score 0.86 · 35–50 Hz, 18–24 Hz, 24–32 Hz, 70–90 Hz, 8–12 Hz, 4–8 Hz · Report a mismatch
  3. [3] § Results › Oxytocin Enhances Auditory-Evoked Responses on a Background of Reduced Baseline Activity. ↔ Figure1.m, lines 378 to 486 · score 0.58 · Wilcoxon rank sum, Pre stim, baseline, windows, Horizontal, square · Report a mismatch
  4. [4] § Materials and Methods › LFP Acquisition and Analysis. › Network connectivity and directionality analysis. ↔ Supple_Figure4.m, lines 334 to 406 · score 0.53 · 5–20 min, 5 min · Report a mismatch
  5. [5] § Materials and Methods › LFP Acquisition and Analysis. › Network connectivity and directionality analysis. ↔ Supple_Figure3.m, the whole file · a weak match · score 0.51 · post injection, frequency band, channel, mPFC, GC, 20 min · Report a mismatch

Paper

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The authors' code

MATLAB · 149 lines · 5.9 KB · no license · 2 matches

  1. %% GC plot
  2. % addpath hb_sptools/
  3. load('data/processed/gc_cleaned.mat')
  5. inj_time = meta_info.inj_time;
  6. drug = meta_info.drug;
  7. skip_ratio = meta_info.skip_ratio;
  10. isEmptyGC = cellfun(@isempty, gc(:,1));
  11. valid_fileIdx = find((skip_ratio <= 10) & ~isEmptyGC);
  12. drug = drug(valid_fileIdx);
  13. inj_time = inj_time(valid_fileIdx,:);
  16. gc_all = nan([512, 3, 2, length(valid_fileIdx), 3]);
  17. for newIdx = 1:length(valid_fileIdx)
  18. fileIdx = valid_fileIdx(newIdx);
  19. gc_data = real(gc{fileIdx,1});
  20. t = gc{fileIdx,2};
  22. if inj_time(newIdx,1) > 0
  23. inj_start_end = hb_findIdx(inj_time(newIdx,:), t);
  24. for timeIdx = 1:3
  25. switch(timeIdx)
  26. case 1
  27. time_win = inj_start_end(1) - 20*60 + 1 : inj_start_end(1);
  28. time_win = time_win(time_win > 0);
  29. case 2
  30. time_win = inj_start_end(end) : inj_start_end(end) + 20*60;
  31. case 3
  32. time_win = inj_start_end(end) + 20*60 : length(t);
  33. end
  34. indiv_gc = nanmedian(gc_data(time_win,:,:,:), 1);
  35. gc_all(:,:,:,newIdx,timeIdx) = indiv_gc;
  36. end
  37. end
  38. end
  40. oxy = find(drug == 1);
  41. sal = find(drug == 0);
  47. fig = figure(2); clf;
  48. set(fig, 'Color', 'w', 'Position', [269 517 918 506]);
  50. % 공통 스타일 파라미터
  51. c_oxy = [0.85 0.2 0.2]; c_sal = [0.3 0.3 0.3];
  52. std_lw = 1; std_fs = 10;
  53. freq_line = 1:55;
  54. freq_bar = 1:121;
  55. freq_bands = {[4 8], [8 12], [18 24], [24 32], [35 50], [70 90]};
  56. band_names = {'\theta_{low}', '\theta_{high}', '\beta_{low}', '\beta_{high}', '\gamma_{low}', '\gamma_{high}'};
  57. chan_labels = {'mPFC', 'BLA', 'AC'};
  58. chan_comb = [1 2; 1 3; 2 3]; % 1:mPFC-BLA, 2:mPFC-AC, 3:BLA-AC
  61. sal_idx = find(drug == 0); sal_idx([4, 13, 14, 16, 18, 19]) = [];
  62. oxy_idx = find(drug == 1); oxy_idx([2, 16]) = [];
  65. % [2] Main Plotting Loop (3 Rows x 4 Columns)
  66. for combIdx = 1:3
  67. for dirIdx = 1:2
  69. % 데이터 추출 (timeIdx=2: Post-injection)
  70. raw_sal = squeeze(gc_all(:, combIdx, dirIdx, sal_idx, 2))';
  71. raw_oxy = squeeze(gc_all(:, combIdx, dirIdx, oxy_idx, 2))';
  73. % ---------------------------------------------------
  74. % ---------------------------------------------------
  75. sp_line = (combIdx-1)*4 + dirIdx;
  76. subplot(3, 4, sp_line); hold on; axis square;
  79. ms_sal = smoothdata(nanmean(raw_sal(:, freq_line), 1), 'movmean', 9);
  80. ss_sal = smoothdata(nanstd(raw_sal(:, freq_line), 0, 1)/sqrt(size(raw_sal,1)), 'movmean', 5);
  81. ms_oxy = smoothdata(nanmean(raw_oxy(:, freq_line), 1), 'movmean', 9);
  82. ss_oxy = smoothdata(nanstd(raw_oxy(:, freq_line), 0, 1)/sqrt(size(raw_oxy,1)), 'movmean', 5);
  85. fill([freq_line, fliplr(freq_line)], [ms_sal+ss_sal, fliplr(ms_sal-ss_sal)], c_sal, 'FaceAlpha', 0.2, 'EdgeColor', 'none');
  86. plot(freq_line, ms_sal, 'Color', c_sal, 'LineWidth', std_lw);
  87. fill([freq_line, fliplr(freq_line)], [ms_oxy+ss_oxy, fliplr(ms_oxy-ss_oxy)], c_oxy, 'FaceAlpha', 0.2, 'EdgeColor', 'none');
  88. plot(freq_line, ms_oxy, 'Color', c_oxy, 'LineWidth', std_lw);
  91. for f = 1:length(freq_line)
  92. [~, p_val] = ttest2(raw_sal(:, f), raw_oxy(:, f));
  93. if p_val < 0.05
  94. star_col = 'k'; % 기본 검정색
  95. if ms_oxy(f) > ms_sal(f), star_col = c_oxy; end % Oxy > Sal일 때 빨간색
  96. text(freq_line(f), 0.23, '*', 'HorizontalAlignment', 'center', ...
  97. 'FontSize', 10, 'Color', star_col, 'FontWeight', 'normal');
  98. end
  99. end
  102. set(gca, 'Box', 'off', 'TickDir', 'in', 'LineWidth', std_lw, 'FontSize', std_fs, 'XLim', [1 55], 'YLim', [0.05 0.25]);
  103. from_node = chan_labels{chan_comb(combIdx, dirIdx)};
  104. to_node = chan_labels{chan_comb(combIdx, 3-dirIdx)};
  105. title(sprintf('%s \\rightarrow %s', from_node, to_node), 'FontSize', std_fs, 'FontWeight', 'normal');
  106. if dirIdx == 1, ylabel('GC (a.u.)'); end
  107. if combIdx == 3, xlabel('Freq (Hz)'); end
  109. sp_bar = (combIdx-1)*4 + dirIdx + 2;
  110. subplot(3, 4, sp_bar); hold on;
  112. m_vals = []; s_vals = []; p_vals = [];
  113. for b = 1:6
  114. f_idx = hb_findIdx(freq_bands{b}, freq_bar);
  115. val_sal = nanmean(raw_sal(:, f_idx), 2);
  116. val_oxy = nanmean(raw_oxy(:, f_idx), 2);
  117. m_vals(b, :) = [nanmean(val_sal), nanmean(val_oxy)];
  118. s_vals(b, :) = [nanstd(val_sal)/sqrt(sum(~isnan(val_sal))), nanstd(val_oxy)/sqrt(sum(~isnan(val_oxy)))];
  119. p_vals(b) = ranksum(val_sal, val_oxy);
  120. end
  122. % Bar Plotting
  123. x = 1:6; bw = 0.35;
  124. b1 = bar(x - bw/2, m_vals(:,1), bw, 'FaceColor', c_sal, 'FaceAlpha', 0.4, 'EdgeColor', 'none');
  125. b2 = bar(x + bw/2, m_vals(:,2), bw, 'FaceColor', c_oxy, 'FaceAlpha', 0.4, 'EdgeColor', 'none');
  126. errorbar(x - bw/2, m_vals(:,1), s_vals(:,1), 'k.', 'LineWidth', 0.8, 'CapSize', 0);
  127. errorbar(x + bw/2, m_vals(:,2), s_vals(:,2), 'k.', 'LineWidth', 0.8, 'CapSize', 0);
  129. for b = 1:6
  130. if p_vals(b) < 0.05
  131. star_col = 'k';
  132. if m_vals(b, 2) > m_vals(b, 1), star_col = c_oxy; end % Oxy > Sal일 때 빨간색
  133. text(b, max(m_vals(b,:)) + s_vals(b, 1) + 0.02, '*', ...
  134. 'HorizontalAlignment', 'center', 'FontSize', 12, 'Color', star_col);
  135. end
  136. end
  138. set(gca, 'Box', 'off', 'TickDir', 'in', 'LineWidth', std_lw, 'FontSize', std_fs, 'XTick', x, 'XTickLabel', band_names, 'YLim', [0 0.25]);
  139. xtickangle(45);
  140. title(sprintf('%s \\rightarrow %s', from_node, to_node), 'FontSize', std_fs, 'FontWeight', 'normal');
  141. if dirIdx == 1, ylabel('GC (a.u.)'); end
  142. end
  143. end
  145. %%
  146. exportgraphics(fig, 'Supple_Figure3_260130a.pdf', 'ContentType', 'vector');
  147. exportgraphics(fig, 'Supple_Figure3_260130a.tif', 'Resolution', 600);

Supple_Figure3.m at commit 29001f3, no license · at the source

Overview

Authors: DaYoung Jung1,2,3, Hio-Been Han4, Jungyoung Kim1,5, Ji Hyung Kim2, Robert C Froemke3,6, Jee Hyun Choi1,7
ORCID iDs: DaYoung Jung, Hio-Been Han, Jungyoung Kim, Ji Hyung Kim, Robert C Froemke, Jee Hyun Choi
  1. Computational Cognitive & Systems Neuroscience Laboratory, Brain Science Institute, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea
  2. Department of Biotechnology, Korea University, Seoul 02841, Republic of Korea
  3. Department of Neuroscience, New York University, Grossman School of Medicine, New York University, New York, NY 10016
  4. School of Convergence, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
  5. Department of Bio and Brain Engineering Program, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
  6. Department of Otolaryngology–Head and Neck Surgery, New York University Grossman School of Medicine, Neuroscience Institute, New York University, New York, NY 10016
  7. Division of Artificial Intelligence and Robotics, University of Science and Technology, Daejeon 34113, Republic of Korea
Institutions: Korea University (South Korea); Korea University (Japan); New York University (United States); Korea Institute of Science and Technology (South Korea); Seoul National University of Science and Technology (South Korea); Korea Advanced Institute of Science and Technology (South Korea); Korea University of Science and Technology (South Korea)
Journal: Proceedings of the National Academy of Sciences of the United States of America, volume 123, issue 24, article e2603966123
Dates: received 3 February 2026; accepted 7 May 2026; published online 9 June 2026; in print 16 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1073/pnas.2603966123 · PMID 42263121 · PMCID PMC13273337 · OpenAlex W7164029112
Open access: hybrid, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: extracellular electrophysiology (units, LFP) (modality), human (organism), mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Machine learning, Connectivity, Statistics
Keywords: oxytocin, neural oscillations, network dynamics, local field potentials
MeSH: Auditory Cortex*, Oxytocin*, Prefrontal Cortex*, Theta Rhythm*, Animals, Female, Local Field Potential Measurement, Male, Mice, Mice, Inbred C57BL, Nerve Net (* major topic)
Topic: Neuroendocrine regulation and behavior (Endocrine and Autonomic Systems, Neuroscience), according to OpenAlex
Funding: Human Frontier Science Program (RGP006/2025); National Research Foundation of Korea (RS-2022-NR070539, RS-2024-00460958); Korea Institute of Science and Technology (2E32901); Seoul National University of Science and Technology (RS2025)
Citations: not cited yet (Europe PMC); 86 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

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jeelabKIST/Oxytocin-LFP

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 29001f394a4e32196283dedbaed55c4730e28a43, 21 April 2026
Languages: MATLAB (9)
Size: 10 files, 9 scripts
Software Heritage: not archived
Found in: the references
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (9 files), Image Processing Toolbox (3 files), shadedErrorBar (2 files), Signal Processing Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/jeelabKIST/Oxytocin-LFP

The paper's code and data availability statement is in the Data section.

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Read it in the paper: doi.org/10.1073/pnas.2603966123.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 4 keywords, 11 MeSH terms, 4 funders, 84 references.

Cite

This paper

Jung, D., Han, H.-B., Kim, J., Kim, J. H., Froemke, R. C., & Choi, J. H. (2026). Oxytocin selectively biases sensory-prefrontal communication through network-level suppression and theta coupling. Proceedings of the National Academy of Sciences of the United States of America, 123(24), e2603966123. https://doi.org/10.1073/pnas.2603966123

BibTeX

@article{jung2026oxytocin,
author = {Jung, DaYoung and Han, Hio-Been and Kim, Jungyoung and Kim, Ji Hyung and Froemke, Robert C and Choi, Jee Hyun},
title = {{Oxytocin selectively biases sensory-prefrontal communication through network-level suppression and theta coupling}},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
year = {2026},
month = jun,
volume = {123},
number = {24},
pages = {e2603966123},
publisher = {National Academy of Sciences},
issn = {0027-8424},
doi = {10.1073/pnas.2603966123},
url = {https://doi.org/10.1073/pnas.2603966123},
pmid = {42263121},
pmcid = {PMC13273337}
}

RIS

TY - JOUR
AU - Jung, DaYoung
AU - Han, Hio-Been
AU - Kim, Jungyoung
AU - Kim, Ji Hyung
AU - Froemke, Robert C
AU - Choi, Jee Hyun
TI - Oxytocin selectively biases sensory-prefrontal communication through network-level suppression and theta coupling
T2 - Proceedings of the National Academy of Sciences of the United States of America
J2 - Proc Natl Acad Sci U S A
PY - 2026
DA - 2026/06/09
VL - 123
IS - 24
SP - e2603966123
SN - 0027-8424
PB - National Academy of Sciences
DO - 10.1073/pnas.2603966123
UR - https://doi.org/10.1073/pnas.2603966123
LA - en
ER -

CSL-JSON

{
"id": "10.1073/pnas.2603966123",
"type": "article-journal",
"title": "Oxytocin selectively biases sensory-prefrontal communication through network-level suppression and theta coupling",
"container-title": "Proceedings of the National Academy of Sciences of the United States of America",
"author": [
{
"family": "Jung",
"given": "DaYoung"
},
{
"family": "Han",
"given": "Hio-Been"
},
{
"family": "Kim",
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{
"family": "Kim",
"given": "Ji Hyung"
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{
"family": "Froemke",
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},
{
"family": "Choi",
"given": "Jee Hyun"
}
],
"container-title-short": "Proc Natl Acad Sci U S A",
"volume": "123",
"issue": "24",
"page": "e2603966123",
"DOI": "10.1073/pnas.2603966123",
"PMID": "42263121",
"PMCID": "PMC13273337",
"ISSN": "0027-8424",
"publisher": "National Academy of Sciences",
"URL": "https://doi.org/10.1073/pnas.2603966123",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
9
]
]
}
}

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