OSCR

Distinct modes of dopamine modulation on striatopallidal synaptic transmission.

Code ↔ Paper

11 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 11 matches · 4 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Clustering analysis of strontium miniature responses ↔ Quantal Clustering Analysis/analysis_main.m, lines 139–178 · score 0.88 · Heat maps, horizontal bars, permutation cloud, red stars, Z2, Holm
  2. [2] § Methods › Parameter optimization ↔ modeling/scripts/fit.py, lines 14–54 · score 0.67 · calcium channel open, calcium influx, open probability, optimized, model, Quinpirole
  3. [3] § Methods › Parameter optimization ↔ modeling/scripts/fit.py, lines 14–54 · score 0.66 · model parameters, open probability, optimizations, fitting, ratio, error
  4. [4] § Methods › Mathematical framework ↔ modeling/scripts/config.py, lines 5–11 · score 0.58 · Hill coefficient, Release probability, calcium
  5. [5] § Methods › Parameter optimization ↔ modeling/scripts/config.py, lines 36–46 · score 0.57 · baseline release probability, bounds, optimized, depressing
  6. [6] § Methods › Spot detection and synapse extraction analysis ↔ Striatopallidal synapse analysis_IHC/extpregfp.m, the whole file · a weak match · score 0.56 · top hat, hard thresholding, noise, holes, filling, suppressing
  7. [7] § Methods › Spot detection and synapse extraction analysis ↔ Striatopallidal synapse analysis_IHC/extprerfp.m, the whole file · a weak match · score 0.56 · top hat, hard thresholding, noise, holes, filling, suppressing
  8. [8] § Methods › Clustering analysis of strontium miniature responses ↔ Quantal Clustering Analysis/recursiveKMeansAuto.m, lines 6–33 · score 0.52 · Calinski Harabasz, optimal, Recursive, clustering
  9. [9] § Methods › Spot detection and synapse extraction analysis ↔ Striatopallidal synapse analysis_IHC/extpregfp.m, the whole file · a weak match · score 0.52 · Top hat, Hard thresholding, enhancement, denoising, synapse
  10. [10] § Methods › Spot detection and synapse extraction analysis ↔ Striatopallidal synapse analysis_IHC/extprerfp.m, the whole file · a weak match · score 0.52 · Top hat, Hard thresholding, enhancement, denoising, synapse
  11. [11] § Methods › Clustering analysis of strontium miniature responses ↔ Quantal Clustering Analysis/analysis_main.m, lines 1–14 · score 0.51 · Calinski Harabasz, split, Recursive, clustering

Paper

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The authors' code

MATLAB · 208 lines · 8.1 KB · MIT · 2 matches

  1. %% -------------------------------------------------------------------------
  2. % Cluster‑Region Distinctiveness Analysis
  3. % -------------------------------------------------------------------------
  4. % • Load four brain regions (DL, VL, DM, VM)
  5. % • Clean 3‑D features (AMP, RT, DT) and z‑standardize
  6. % • Recursive k‑means sub‑clustering (Calinski‑Harabasz split rule)
  7. % • χ² (df = 3) test per cluster → Region‑by‑cluster Z‑matrix
  8. % • Region‑wise ΣZ² + pairwise ΔS permutation tests (Holm/FDR)
  9. % -------------------------------------------------------------------------
  10. % Author : Minseok Jeong
  11. % Date : 2025-06-13
  12. % -------------------------------------------------------------------------
  14. clear; clc; close all;
  16. %% 0 | Load ----------------------------------------------------------------
  17. load('GPe_SR2+_mini.mat'); % RESULT.DL / VL / DM / VM
  19. %% 1 | Concatenate regions -------------------------------------------------
  20. regions = {'DL','VL','DM','VM'};
  21. X = []; % raw feature matrix
  22. RL = []; % region label (1–4)
  23. for r = 1:numel(regions)
  24. condNames = fields(RESULT.(regions{r}));
  25. for k = 1:numel(condNames)
  26. T = RESULT.(regions{r}).(condNames{k});
  27. X = [X; [T.AMP, T.RT, T.DT]];
  28. RL = [RL; r*ones(height(T),1)];
  29. end
  30. end
  32. %% 2 | Cleaning ------------------------------------------------------------
  33. X = X(~any(isnan(X),2),:); % drop rows with NaN
  34. RL = RL(~any(isnan(X),2));
  35. flag = isoutlier(X,'percentiles',[0.01 99.99]);
  36. X = X(~any(flag,2),:);
  37. RL = RL(~any(flag,2));
  38. fprintf('Rows kept after cleaning: %d\n',size(X,1));
  40. %% 3 | Z‑score -------------------------------------------------------------
  41. [Xz,mu,sigma] = zscore(X);
  43. %% 3' | PCA ------------------------------------------------------------
  44. [coeff, score, latent, tsquared, explained] = pca(X);
  46. [coeff_z, score_z, latent_z, tsquared_z, explained_z] = pca(Xz);
  48. % --- Explained Variance Bar Graph (Scree Plot) ---
  49. figure;
  50. bar(explained);
  51. hold on;
  52. cumulativeExplained = cumsum(explained);
  53. plot(1:numel(explained), cumulativeExplained, ':o', 'LineWidth', 1, 'Color', 'r');
  54. xlabel('Principal Component');
  55. ylabel('Variance Explained (%)');
  56. title('Scree Plot: Explained Variance by Principal Component (Raw data)');
  57. grid on;
  58. legend('Individual Variance', 'Cumulative Variance');
  60. figure;
  61. bar(explained_z);
  62. hold on;
  63. cumulativeExplained_z = cumsum(explained_z);
  64. plot(1:numel(explained_z), cumulativeExplained_z, ':o', 'LineWidth', 1, 'Color', 'r');
  65. xlabel('Principal Component');
  66. ylabel('Variance Explained (%)');
  67. title('Scree Plot: Explained Variance by Principal Component (Z-scored data)');
  68. grid on;
  69. legend('Individual Variance', 'Cumulative Variance');
  71. % --- Extract first two principal components ---
  72. X_2d = score(:,1:2);
  73. % --- Visualization ---
  76. figure;
  77. scatter(X_2d(:,1), X_2d(:,2), 5, 'filled');
  78. xlabel('Principal Component 1');
  79. ylabel('Principal Component 2');
  80. title('PCA 2D Projection');
  81. grid on;
  83. %% 4 | Recursive k‑means ---------------------------------------------------
  84. finalLab = recursiveKMeansAuto(Xz,10,500,0.05);
  85. fprintf('Final #clusters = %d\n',numel(unique(finalLab)));
  86. tabulate(finalLab)
  88. % Extended Data Fig.8a
  89. figure; scatter3(Xz(:,1),Xz(:,2),Xz(:,3),6,finalLab,'filled');
  90. title('Final recursive k‑means clusters');
  92. %% 5 | χ² test per cluster -------------------------------------------------
  93. C = numel(unique(finalLab)); R = 4;
  94. N = numel(RL);
  95. p_r = accumarray(RL,1,[R,1])/N;
  96. p_min = min(p_r);
  97. minCell = 20;
  98. minClustSize = ceil(minCell / p_min);
  99. chiObs = NaN(C,1); Zmat = NaN(C,R);
  100. for c = 1:C
  101. idx = finalLab==c; if sum(idx)<minClustSize, continue, end
  102. tbl = [accumarray(RL(idx),1,[R,1],@sum,0), accumarray(RL(~idx),1,[R,1],@sum,0)];
  103. if any(tbl(:)<minCell), continue, end
  104. expT = sum(tbl,2).*sum(tbl,1)/sum(tbl,'all');
  105. chiObs(c)=sum((tbl-expT).^2./(expT+eps),'all');
  106. Zmat(c,:)=(tbl(:,1)-expT(:,1))./sqrt(expT(:,1)+eps);
  107. end
  109. %% 6 | Region‑wise ΣZ² + permutation --------------------------------------
  110. valid = ~isnan(Zmat(:,1));
  111. Sobs = nansum(Zmat(valid,:).^2,1);
  112. B = 1e4; rng(4); n = numel(RL);
  113. Sperm = zeros(B,R);
  114. for b = 1:B
  115. perm = RL(randperm(n));
  116. tbl = accumarray([finalLab,perm],1,[C,R]);
  117. expT = sum(tbl,2).*sum(tbl,1)/sum(tbl,'all');
  118. Z = (tbl-expT)./sqrt(expT+eps);
  119. Sperm(b,:) = sum(Z(valid,:).^2,1);
  120. end
  121. pPerm = (sum(Sperm>=Sobs,1)+1)/(B+1);
  123. fprintf('\nRegion S_obs p_perm\n');
  124. for r = 1:R, fprintf('%6d %7.1f %.4f\n',r,Sobs(r),pPerm(r)); end
  128. %% 7 | Pairwise ΔS permutation + Holm / BH --------------------------------
  129. Pairs = nchoosek(1:R,2); m = size(Pairs,1); rawP = zeros(m,1);
  130. for i = 1:m
  131. a=Pairs(i,1); b=Pairs(i,2);
  132. rawP(i) = (sum(abs(Sperm(:,a)-Sperm(:,b))>=abs(Sobs(a)-Sobs(b)))+1)/(B+1);
  133. end
  134. [pSrt,ix] = sort(rawP); holm=min(1,cummax(pSrt.*(m:-1:1)')); pHolm=zeros(m,1); pHolm(ix)=holm;
  135. qBH = mafdr(rawP,'BHFDR',true);
  136. Tpairs = table(Pairs(:,1),Pairs(:,2),rawP,pHolm,qBH,'VariableNames',{'R_A','R_B','pRaw','pHolm','qBH'});
  137. disp(Tpairs);
  139. %% 8 | Visualisations ------------------------------------------------------
  140. % 8‑a ΔS heat‑map (row>col = red)
  141. Delta = NaN(R); pMat=NaN(R);
  142. for i=1:m
  143. a=Pairs(i,1); b=Pairs(i,2); d=Sobs(a)-Sobs(b);
  144. Delta(a,b)= d; Delta(b,a)=-d; pMat(a,b)=pHolm(i); pMat(b,a)=pHolm(i);
  145. end
  146. Delta(1:R+1:end)=0;
  148. % Extended Data Fig.8c
  149. figure('Name','DeltaS heat‑map'); imagesc(Delta); axis square; colormap(redbluecmap);
  150. cb=colorbar; cb.Label.String='ΔS (S_r - S_s)';
  151. set(gca,'XTick',1:R,'XTickLabel',{'DL','VL','DM','VM'},'YTick',1:R,'YTickLabel',{'DL','VL','DM','VM'});
  152. title('ΔS between Regions (red: row greater)'); hold on;
  153. [row,col]=find(pMat<0.05); plot(col,row,'k*','MarkerSize',8,'LineWidth',1.2);
  154. rectangle('Position',[0.5 3.5 R 1],'EdgeColor','k','LineWidth',1.4);
  155. rectangle('Position',[3.5 0.5 1 R],'EdgeColor','k','LineWidth',1.4);
  157. % 8‑b Normalised ΔS bar (Region‑4 focus)
  158. % Extended Data Fig.8b
  159. figure('Name','VM ΔS bar');
  160. bar(abs(Delta(4,[1 2 3]))/Sobs(4),'FaceColor',[0.4 0.6 1]);
  161. set(gca,'XTick',1:3,'XTickLabel',{'VM‑DL','VM‑VL','VM‑DM'});
  162. ylim([0, 1.0])
  163. ylabel('|ΔS| / S_V_M'); title('Distinctiveness of VM vs others');
  165. % 8‑c Region ΣZ² horizontal bar
  166. % Extended Data Fig.8d
  167. figure('Name','ΣZ² bar'); [Ssort,ord]=sort(Sobs,'descend');
  168. barh(Ssort,'FaceColor',[.7 .7 .7]); hold on; barh(find(ord==4),Ssort(ord==4),'r');
  169. set(gca,'YTick',1:R,'YTickLabel',compose('R%d',ord)); xlabel('ΣZ²'); title('Region‑wise distinctiveness');
  170. text(Ssort+5,1:R,compose('p=%.4f',pPerm(ord)),'FontSize',8);
  172. % 8‑d Permutation cloud vs observed ΣZ²
  173. % Extended Data Fig.8e
  174. figure('Name','Permutation cloud'); hold on;
  175. for r=1:R, scatter(r*ones(B,1),Sperm(:,r),4,[.8 .8 .8],'filled'); end
  176. plot(1:R,Sobs,'r*','MarkerSize',10,'LineWidth',1.1);
  177. xlim([0.5 R+0.5]); ylabel('ΣZ²'); set(gca,'XTick',1:R,'XTickLabel',{'DL','VL','DM','VM'});
  178. title('Permutation distribution of ΣZ² (red star = observed)');
  180. %% 9 | Save figures and data ------------------------------------------------
  181. figHandles = findall(groot, 'Type', 'Figure');
  182. if isempty(figHandles)
  183. fprintf('No figures to save.\n');
  184. else
  185. for i = 1:length(figHandles)
  186. figTitle = figHandles(i).CurrentAxes.Title.String;
  187. if ~isempty(figTitle)
  188. % Replace spaces with underscores for the filename
  189. filename = strrep(figTitle, ' ', '_');
  190. filename = strrep(filename, '(', '');
  191. filename = strrep(filename, ')', '');
  192. filename = strrep(filename, ':', '');
  193. % Save the figure as a PDF with 600 DPI
  194. print(figHandles(i), filename, '-dpdf', '-r600');
  195. end
  196. end
  197. end
  199. % Save resulting data
  200. save('analysis_results.mat', 'Xz', 'Zmat', 'Sobs', 'Sperm', 'Delta', 'pPerm','pMat');
  202. %% -------------------------------------------------------------------------
  203. function [p,chi2,df] = chi2gof2D(tbl)
  204. expT=sum(tbl,2).*sum(tbl,1)/sum(tbl,'all');
  205. chi2=sum((tbl-expT).^2 ./ (expT+eps),'all');
  206. df=(size(tbl,1)-1)*(size(tbl,2)-1); p=1-chi2cdf(chi2,df);
  207. end

analysis_main.m at commit 0bf0930, under MIT · at the source

Overview

Authors: Youngeun Lina Lee1, Maria Reva2, Ki Jung Kim3, Hyun-Jin Kim1, Yemin Kim1, Eunjeong Cho1, Minseok Jeong1, Youngjong Kwak4, Kyungjae Myung5,6, Yulong Li7, Seung Eun Lee8, Dong Pyo Jang4, C. Justin Lee3, Christian Lüscher2,9, Jae-Ick Kim1
ORCID iDs: Youngeun Lina Lee, Ki Jung Kim, Kyungjae Myung, Yulong Li, C. Justin Lee, Christian Lüscher, Jae-Ick Kim
  1. Department of Biological Sciences, Ulsan National Institute of Science and Technology,Ulsan, Republic of Korea
  2. Department of Basic Neurosciences, Faculty of Medicine, University of Geneva,Geneva, Switzerland
  3. Center for Cognition and Sociality, Institute for Basic Science,Daejeon, Republic of Korea
  4. Department of Biomedical Engineering, Hanyang University,Seoul, Republic of Korea
  5. Center for Genomic Integrity, Institute for Basic Science,Ulsan, Republic of Korea
  6. Department of Biomedical Engineering, Ulsan National Institute for Science and Technology,Ulsan, Republic of Korea
  7. State Key Laboratory of Membrane Biology, Peking University School of Life Sciences,Beijing, China
  8. Research Animal Resource Center, Korea Institute of Science and Technology,Seoul, Republic of Korea
  9. Clinic of Neurology, Department of Clinical Neurosciences, Geneva University Hospital,Geneva, Switzerland
Institutions: Ulsan National Institute of Science and Technology (South Korea); University of Geneva (Switzerland); Institute for Basic Science (South Korea); Hanyang University (South Korea); Peking University (China); Korea Institute of Science and Technology (South Korea); University Hospital of Geneva (Switzerland)
Journal: Nature communications, volume 17, issue 1, article 4826
Dates: received 4 April 2025; accepted 20 March 2026; published online 3 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71426-8 · PMID 41932937 · PMCID PMC13223316 · OpenAlex W7148963028
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: intracellular / patch clamp (modality), mouse (organism), cellular / molecular (subfield)
Methods: Statistics, Machine learning, Preprocessing, Evoked potentials, Connectivity, fMRI & imaging, Single-unit activity, calcium imaging
Keywords: Neuroscience, Synaptic transmission
MeSH: Corpus Striatum*, Dopamine*, Globus Pallidus*, Synaptic Transmission*, Animals, Male, Mice, Mice, Inbred C57BL, Optogenetics, Oxidopamine, Patch-Clamp Techniques, Receptors, Dopamine D2, Receptors, Dopamine D4, Synapses (* major topic)
Topic: Neurotransmitter Receptor Influence on Behavior (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation of Korea (NRF-2021M3A9G8022960 / RS-2021-NR056530); Samsung Future Technology Promotion Project (Project No.: SRFC-IT2302-02); Institute for Basic Science (IBS-R022-D1)
Citations: not cited yet (Europe PMC); 101 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 11 matches between paragraphs and lines of code.

Zenodo 18608880

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (13 files), Statistics and Machine Learning Toolbox (12 files), NumPy (5 files), pandas (2 files), SciPy (2 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
61 files
At the source:

yelee03153/Striatopallidalsynapse

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 0bf0930c05476d30f9b17e3c84c18bbac8765e03, 11 February 2026
Languages: MATLAB (53), Python (7)
Size: 774 files, 60 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: license file, environment (modeling/requirements.txt)
Not found: README, CITATION.cff, tests, continuous integration, documentation
Tools: Image Processing Toolbox (13 files), Statistics and Machine Learning Toolbox (12 files), NumPy (5 files), pandas (2 files), SciPy (2 files), Matplotlib (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
61 files
At the source: github.com/yelee03153/Striatopallidalsynapse.git

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/s41467-026-71426-8.

Tracing map

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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;
  • 120 scripts, each with its path and the digest of its content;
  • 11 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

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

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

Read it in the paper: doi.org/10.1038/s41467-026-71426-8.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 15 authors, 2 keywords, 14 MeSH terms, 3 funders, 100 references.

Cite

This paper

Lee, Y. L., Reva, M., Kim, K. J., Kim, H.-J., Kim, Y., Cho, E., Jeong, M., Kwak, Y., Myung, K., Li, Y., Lee, S. E., Jang, D. P., Lee, C. J., Lüscher, C., & Kim, J.-I. (2026). Distinct modes of dopamine modulation on striatopallidal synaptic transmission. Nature communications, 17(1), 4826. https://doi.org/10.1038/s41467-026-71426-8

BibTeX

@article{lee2026distinct,
author = {Lee, Youngeun Lina and Reva, Maria and Kim, Ki Jung and Kim, Hyun-Jin and Kim, Yemin and Cho, Eunjeong and Jeong, Minseok and Kwak, Youngjong and Myung, Kyungjae and Li, Yulong and Lee, Seung Eun and Jang, Dong Pyo and Lee, C. Justin and Lüscher, Christian and Kim, Jae-Ick},
title = {{Distinct modes of dopamine modulation on striatopallidal synaptic transmission}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {4826},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71426-8},
url = {https://doi.org/10.1038/s41467-026-71426-8},
pmid = {41932937},
pmcid = {PMC13223316}
}

RIS

TY - JOUR
AU - Lee, Youngeun Lina
AU - Reva, Maria
AU - Kim, Ki Jung
AU - Kim, Hyun-Jin
AU - Kim, Yemin
AU - Cho, Eunjeong
AU - Jeong, Minseok
AU - Kwak, Youngjong
AU - Myung, Kyungjae
AU - Li, Yulong
AU - Lee, Seung Eun
AU - Jang, Dong Pyo
AU - Lee, C. Justin
AU - Lüscher, Christian
AU - Kim, Jae-Ick
TI - Distinct modes of dopamine modulation on striatopallidal synaptic transmission
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/03
VL - 17
IS - 1
SP - 4826
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71426-8
UR - https://doi.org/10.1038/s41467-026-71426-8
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-71426-8",
"type": "article-journal",
"title": "Distinct modes of dopamine modulation on striatopallidal synaptic transmission",
"container-title": "Nature communications",
"author": [
{
"family": "Lee",
"given": "Youngeun Lina"
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{
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"given": "Maria"
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{
"family": "Kim",
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{
"family": "Kim",
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},
{
"family": "Kim",
"given": "Yemin"
},
{
"family": "Cho",
"given": "Eunjeong"
},
{
"family": "Jeong",
"given": "Minseok"
},
{
"family": "Kwak",
"given": "Youngjong"
},
{
"family": "Myung",
"given": "Kyungjae"
},
{
"family": "Li",
"given": "Yulong"
},
{
"family": "Lee",
"given": "Seung Eun"
},
{
"family": "Jang",
"given": "Dong Pyo"
},
{
"family": "Lee",
"given": "C. Justin"
},
{
"family": "Lüscher",
"given": "Christian"
},
{
"family": "Kim",
"given": "Jae-Ick"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "4826",
"DOI": "10.1038/s41467-026-71426-8",
"PMID": "41932937",
"PMCID": "PMC13223316",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-71426-8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
3
]
]
}
}

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