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Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing.

Code ↔ Paper

10 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 10 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › Whole brain functional connectivity ↔ run3_aec_analysis.m, the whole file · a weak match · score 1.00 · Default_PFC_3, Default_PFCdPFCm_1, Default_Par_1, DorsAttn_FEF_1, Vis_9, Cont_PFCl_2
  2. [2] § Results › Whole brain functional connectivity ↔ run3_aec_analysis.m, the whole file · a weak match · score 1.00 · Default_PFC_7, Default_pCunPCC_2, DorsAttn_Post_4, SalVentAttn_FrOperIns_2, Vis_2, Vis_4
  3. [3] § Materials and methods › Food categorization task ↔ run1_preprocessing.m, lines 78–99 · score 0.96 · chocolate chip cookies, fried calamari, ice cream, potato chips, green olives, crackers
  4. [4] § Materials and methods › Food categorization task ↔ run1_preprocessing.m, lines 78–99 · score 0.95 · chocolate chip cookies, fried calamari, ice cream, potato chips, green olives, food categorization
  5. [5] § Materials and methods › EEG preprocessing ↔ run1_preprocessing.m, lines 39–50 · score 0.63 · EEGLAB, resampled, spherical, preprocessing, filtered, 48 Hz
  6. [6] § Results › Brain network connectivity ↔ run5_network_enrichment.m, lines 92–135 · score 0.63 · food taste categories, Fold enrichment, Network enrichment, functional network, marginal
  7. [7] § Results › Brain network connectivity ↔ run5_network_enrichment.m, lines 92–135 · score 0.61 · fold enrichment, Network enrichment, taste category, functional networks, foods
  8. [8] § Results › Brain network connectivity ↔ run5_network_enrichment.m, lines 10–55 · score 0.58 · DorsAttn, SalVentAttn, SomMot, Rows, Vis, Cont
  9. [9] § Materials and methods › Statistical analysis ↔ run5_network_enrichment.m, lines 10–55 · score 0.56 · Fold enrichment, network enrichment, binomial, probability, nodes
  10. [10] § Materials and methods › Cortical source localization ↔ run2_postprocessing.m, lines 103–127 · score 0.54 · head model, scalp, skull, Brainstorm

Paper

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The authors' code

MATLAB · 162 lines · 5.4 KB · MIT · 4 matches

  1. %% Network Enrichment Visualization
  3. network_names = {'Cont', 'Default', 'DorsAttn', 'Limbic', 'SalVentAttn', 'SomMot', 'Vis'};
  4. network_sizes = [13, 24, 15, 5, 12, 14, 17];
  5. num_networks = numel(network_names);
  6. total_nodes = sum(network_sizes);
  8. conditions = {'Sweet', 'Sour', 'Salty'};
  10. %% ========== INSERT YOUR OBSERVED DATA BELOW ==========
  11. % Rows: conditions (Sweet, Sour, Salty)
  12. % Columns: networks (Cont, Default, DorsAttn, Limbic, SalVentAttn, SomMot, Vis)
  14. observed_data = [
  15. NaN, NaN, NaN, NaN, NaN, NaN, NaN; % Sweet
  16. NaN, NaN, NaN, NaN, NaN, NaN, NaN; % Sour
  17. NaN, NaN, NaN, NaN, NaN, NaN, NaN % Salty
  18. ];
  20. fold_enrichment = zeros(3, num_networks);
  21. p_values = zeros(3, num_networks);
  22. expected_values = zeros(3, num_networks);
  24. for c = 1:3
  25. observed = observed_data(c, :);
  26. k = sum(observed);
  28. for j = 1:num_networks
  29. N = network_sizes(j);
  30. x = observed(j);
  32. % Expected
  33. expected = (N / total_nodes) * k;
  34. expected_values(c, j) = expected;
  35. expected_prob = N / total_nodes;
  37. % Fold enrichment
  38. if expected > 0
  39. fold_enrichment(c, j) = x / expected;
  40. else
  41. fold_enrichment(c, j) = 0;
  42. end
  44. % Binomial test (right-tailed)
  45. p_value = binocdf(x-1, k, expected_prob, 'upper');
  46. p_values(c, j) = p_value;
  47. end
  48. end
  50. figure('Position', [100, 100, 1600, 700]);
  52. % Color scheme
  53. colors = [0.2 0.6 0.8; % Sweet - blue
  54. 0.9 0.5 0.2; % Sour - orange
  55. 0.3 0.7 0.4]; % Salty - green
  57. %% Panel A: Fold Enrichment by Network (Grouped Bar)
  58. subplot(1, 2, 1);
  59. bar_data = fold_enrichment';
  60. b = bar(bar_data, 'grouped');
  62. for c = 1:3
  63. b(c).FaceColor = colors(c, :);
  64. b(c).EdgeColor = 'none';
  65. end
  67. % Add reference line at 1.0 (no enrichment)
  68. hold on;
  69. yline(1.0, 'k--', 'LineWidth', 2, 'Alpha', 0.7);
  70. text(0.6, 1.10, 'No enrichment', 'FontSize', 11, 'Color', [0.3 0.3 0.3]);
  72. for j = 1:num_networks
  73. for c = 1:3
  74. if p_values(c, j) < 0.05
  75. x_offset = j + (c-2)*0.27;
  76. y_offset = fold_enrichment(c, j) + 0.15;
  77. text(x_offset, y_offset, '*', 'FontSize', 18, ...
  78. 'HorizontalAlignment', 'center', 'Color', 'k', 'FontWeight', 'bold');
  79. end
  80. end
  81. end
  83. legend(conditions, 'Location', 'northwest', 'FontSize', 14);
  84. set(gca, 'XTickLabel', network_names, 'XTickLabelRotation', 45, 'FontSize', 15);
  85. xlabel('Functional network', 'FontSize', 18, 'FontWeight', 'bold');
  86. ylabel('Fold enrichment', 'FontSize', 18, 'FontWeight', 'bold');
  87. ylim([0, max(fold_enrichment(:))*1.25]);
  88. grid on;
  89. box on;
  90. set(gca, 'LineWidth', 1.2);
  92. %% Panel B: Significant Enrichments Only
  93. subplot(1, 2, 2);
  94. sig_mask = p_values < 0.05;
  95. marginal_mask = (p_values >= 0.05) & (p_values < 0.10); % marginal significance
  96. sig_enrichment = fold_enrichment .* sig_mask;
  98. % Create custom colormap: white for non-significant, hot colors for significant
  99. custom_cmap = [1 1 1; hot(256)];
  100. imagesc(sig_enrichment);
  101. colormap(gca, custom_cmap);
  102. cb = colorbar;
  103. cb.Label.String = 'Fold enrichment (p < 0.05 only)';
  104. cb.Label.FontSize = 14;
  105. cb.Label.FontWeight = 'bold';
  106. caxis([0, 3.5]);
  108. hold on;
  109. for i = 1:3
  110. for j = 1:num_networks
  111. if sig_mask(i, j)
  112. text(j, i, sprintf('%.2f\n(p=%.3f)', fold_enrichment(i,j), p_values(i,j)), ...
  113. 'HorizontalAlignment', 'center', 'Color', 'k', ...
  114. 'FontWeight', 'bold', 'FontSize', 11);
  115. elseif marginal_mask(i, j)
  116. % Show marginal significance with dashed border
  117. rectangle('Position', [j-0.45, i-0.45, 0.9, 0.9], ...
  118. 'EdgeColor', [0.5 0.5 0.5], 'LineWidth', 2.5, 'LineStyle', '--');
  119. text(j, i, sprintf('%.2f†\n(p=%.3f)', fold_enrichment(i,j), p_values(i,j)), ...
  120. 'HorizontalAlignment', 'center', 'Color', [0.2 0.2 0.2], ...
  121. 'FontSize', 11, 'FontWeight', 'bold');
  122. end
  123. end
  124. end
  126. set(gca, 'XTick', 1:num_networks, 'XTickLabel', network_names, ...
  127. 'YTick', 1:3, 'YTickLabel', conditions, 'XTickLabelRotation', 45, ...
  128. 'FontSize', 15);
  129. xlabel('Functional network', 'FontSize', 18, 'FontWeight', 'bold');
  130. ylabel('Food taste category', 'FontSize', 18, 'FontWeight', 'bold');
  131. set(gca, 'LineWidth', 1.2);
  133. annotation('textbox', [0.71, 0.85, 0.15, 0.05], 'String', '† marginal (p < 0.10)', ...
  134. 'EdgeColor', 'none', 'FontSize', 11, 'Color', [0.4 0.4 0.4], ...
  135. 'HorizontalAlignment', 'right');
  137. %% Print summary
  138. fprintf('\n========== SIGNIFICANT ENRICHMENTS (p < 0.05) ==========\n');
  139. for c = 1:3
  140. fprintf('\n%s Condition:\n', conditions{c});
  141. sig_idx = find(p_values(c, :) < 0.05);
  142. if isempty(sig_idx)
  143. fprintf(' No significant enrichments\n');
  144. else
  145. for j = sig_idx
  146. fprintf(' %s: %.2fx enriched (observed=%d, expected=%.1f, p = %.4f)\n', ...
  147. network_names{j}, fold_enrichment(c, j), ...
  148. observed_data(c, j), expected_values(c, j), p_values(c, j));
  149. end
  150. end
  151. end
  152. fprintf('========================================================\n\n');
  154. % Summary statistics
  155. fprintf('Summary:\n');
  156. fprintf('Total significant enrichments: %d out of %d tests\n', sum(sig_mask(:)), numel(sig_mask));
  157. fprintf('By condition: Sweet=%d, Sour=%d, Salty=%d\n', ...
  158. sum(sig_mask(1,:)), sum(sig_mask(2,:)), sum(sig_mask(3,:)));
  160. %% Save figure as PNG
  161. print('network_enrichment_analysis3', '-dpng', '-r600');
  162. fprintf('\nFigure saved as: network_enrichment_analysis.png (600 dpi)\n');

run5_network_enrichment.m, under MIT · at the source

Overview

Authors: Manyoel Lim1,2, Min Jung Kim3,4,2
ORCID iDs: Manyoel Lim
  1. Food Processing Research Group, Food Convergence Research Division, Korea Food Research Institute, Wanju-gun, 55365 Republic of Korea
  2. Korea Food Research Institute, 245, Nongsaengmyeong-ro, Wanju-gun, 55365 Republic of Korea
  3. Aging Research Group, Food Functionality Research Division, Korea Food Research Institute, Wanju-gun, 55365 Republic of Korea
  4. Department of Food Biotechnology, University of Science & Technology, Daejeon, 34113 Republic of Korea
Institutions: Korea Food Research Institute (South Korea); Korea University of Science and Technology (South Korea)
Journal: Scientific reports, volume 16, issue 1, article 18626
Dates: received 19 September 2025; accepted 16 April 2026; published online 22 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-49773-9 · PMID 42020504 · PMCID PMC13270053 · OpenAlex W7155179898
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Preprocessing
Keywords: Visual food perception, Taste, Brain network, Functional connectivity, Electroencephalography, Neuroscience, Psychology
MeSH: Brain*, Cues*, Nerve Net*, Taste*, Taste Perception*, Visual Perception*, Adult, Brain Mapping, Electroencephalography, Female, Food, Humans, Male, Young Adult (* major topic)
Topic: Biochemical Analysis and Sensing Techniques (Nutrition and Dietetics, Nursing), according to OpenAlex
Funding: Korea Food Research Institute (Main Research Program (E0232201))
Citations: not cited yet (Europe PMC); 49 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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Zenodo 19383880

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (5)
Size: 5 files, 5 scripts
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Brainstorm (2 files), EEGLAB (1 file), ICLabel (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.19383880

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

Tracing map

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  • 5 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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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/s41598-026-49773-9.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 7 keywords, 14 MeSH terms, 1 funder, 47 references.

Cite

This paper

Lim, M., & Kim, M. J. (2026). Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing. Scientific reports, 16(1), 18626. https://doi.org/10.1038/s41598-026-49773-9

BibTeX

@article{lim2026large,
author = {Lim, Manyoel and Kim, Min Jung},
title = {{Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {18626},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-49773-9},
url = {https://doi.org/10.1038/s41598-026-49773-9},
pmid = {42020504},
pmcid = {PMC13270053}
}

RIS

TY - JOUR
AU - Lim, Manyoel
AU - Kim, Min Jung
TI - Large-scale brain network interactions differentiate sweet, sour, and salty food cues during visual processing
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/22
VL - 16
IS - 1
SP - 18626
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-49773-9
UR - https://doi.org/10.1038/s41598-026-49773-9
LA - en
ER -

CSL-JSON

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"container-title": "Scientific reports",
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"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "18626",
"DOI": "10.1038/s41598-026-49773-9",
"PMID": "42020504",
"PMCID": "PMC13270053",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-49773-9",
"language": "en",
"issued": {
"date-parts": [
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2026,
4,
22
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]
}
}

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