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Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations.

Code ↔ Paper

2 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 2 matches
  1. [1] § Materials and methods › Structural-functional coupling: the graph harmonic model ↔ 01_SF_Coupling/lowhigh.m, lines 46–95 · score 0.68 · graph harmonic model, structural Laplacian, structural connectome, eigenmodes, SC, FC
  2. [2] § Materials and methods › Structural-functional coupling: the graph harmonic model ↔ 01_SF_Coupling/lowhigh.m, lines 97–146 · score 0.55 · functional connectivity profile, predictors, fit, linear, Laplacian, SC

Paper

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The authors' code

MATLAB · 208 lines · 8.1 KB · no license · 2 matches

  1. % =========================================================================
  2. % Structural-Functional (S-F) Coupling Calculation using Graph Harmonic Model
  3. % =========================================================================
  4. clear all; clc;
  6. % -------------------------------------------------------------------------
  7. % 1. Set Directory Paths (Please update these relative paths to your local data)
  8. % -------------------------------------------------------------------------
  9. FCdirpath = './Data_Sample/Fmri_fc'; % Path to functional connectivity matrices
  10. SCdirpath = './Data_Sample/DTI_data_sym'; % Path to structural connectivity matrices
  12. FCfiles = dir(FCdirpath); % List all files in the FC directory
  13. SCfiles = dir(SCdirpath); % List all files in the SC directory
  15. % Initialize variables
  16. allrsq = zeros(400, length(FCfiles)-2);
  17. allxx = zeros(400, length(FCfiles)-2);
  18. numflag = 0;
  20. % -------------------------------------------------------------------------
  21. % 2. Main Loop: Iterate through subjects and match multimodal data
  22. % -------------------------------------------------------------------------
  23. for ii = 1:length(FCfiles)
  24. flagsc = 0;
  25. flagfc = 0;
  26. flagmn = 1; % Status flag
  28. % Skip directories (. and ..)
  29. if FCfiles(ii).isdir
  30. continue;
  31. end
  33. fc_file_name = FCfiles(ii).name;
  35. % Extract FC subject ID
  36. tokens = regexp(fc_file_name, 'sub-(\d+)-FC', 'tokens');
  37. if isempty(tokens)
  38. continue;
  39. end
  40. fc_id = tokens{1}{1};
  41. fc_id_padded = sprintf('%05d', str2double(fc_id)); % e.g., '00009'
  42. subID = fc_id_padded;
  43. FCfiledir = fullfile(FCdirpath, fc_file_name);
  44. flagfc = 1;
  46. % Match corresponding SC file
  47. for ij = 1:length(SCfiles)
  48. if SCfiles(ij).isdir
  49. continue;
  50. end
  51. sc_file_name = SCfiles(ij).name;
  52. sc_tokens = regexp(sc_file_name, '^(\d+)_StructuralConnectome', 'tokens');
  53. if isempty(sc_tokens)
  54. continue;
  55. end
  56. sc_id = sc_tokens{1}{1};
  57. if strcmp(fc_id_padded, sc_id)
  58. SCfiledir = fullfile(SCdirpath, sc_file_name);
  59. flagsc = 1;
  60. break
  61. end
  62. end
  64. % Check if both modalities are successfully matched
  65. if flagfc * flagsc * flagmn == 1
  66. fc = importdata(FCfiledir);
  67. W = importdata(SCfiledir);
  68. Sc = W; Sc(Sc>0) = 1; % Structural binary matrix
  69. N = size(W,1); % Node number (e.g., 400 for Schaefer)
  71. disp(['Processing Subject ID: ', subID]);
  72. disp('Multimodal data matched successfully.');
  73. numflag = numflag + 1;
  74. fc(fc<0) = 0; % Exclude negative functional connections
  75. else
  76. continue
  77. end
  79. % Load nodal coordinates
  80. load coor.mat
  81. % coor represents x,y,z node coordinates | N x 3 matrix
  83. % ---------------------------------------------------------------------
  84. % 3. Graph Harmonic Model & Laplacian Eigenmodes
  85. % ---------------------------------------------------------------------
  86. A = W; A = -A;
  87. for i = 1:N
  88. A(i,i) = -sum(A(i,:));
  89. end
  90. B = A / max(eig(A)); % Structural Laplacian matrix
  91. [BEC, BE] = eig(B); % BEC-eigenvectors, BE-eigenvalues
  93. [a, b] = sort(diag(BE));
  94. BE_sort = diag(a);
  95. BEC_sort = BEC(:, b);
  97. % Diffusion map algorithm on functional network
  98. mappedX = diffusion_maps(fc, 10, 0.5, 1);
  99. x = mappedX(:, 1); % Node weights for first eigenvector
  100. xx = x * -1; % Reverse weights: positive = top of hierarchy
  101. allxx(:, numflag) = xx;
  103. % ---------------------------------------------------------------------
  104. % 4. Prediction Model (S-F Coupling Calculation)
  105. % ---------------------------------------------------------------------
  106. % A multilinear model is used to predict the functional connection profile
  107. % of every node based on the 2nd-14th eigenmodes of structural Laplacian network.
  108. rsq = zeros(N, 1); % Node-wise vector
  110. % Define FC response (y) and SC predictors (x)
  111. for jj = 1:N
  112. y = fc(:, jj);
  113. x1 = zscore(BEC_sort(:, 2:14)); % Standardize predictors of 2nd-14th eigenmodes
  115. % Fit multiple regression (OLS, main effects only), excluding self-connections
  116. lm = fitlm(x1, y, 'Exclude', jj);
  117. rsq(jj) = lm.Rsquared.Ordinary; % Record R-squared for node jj
  118. end
  119. rsq1 = sqrt(real(rsq)); % Calculate S-F coupling value
  120. allrsq(:, numflag) = rsq1;
  122. end % End of subject loop
  124. % -------------------------------------------------------------------------
  125. % 5. Visualization & Statistics
  126. % -------------------------------------------------------------------------
  127. figure(3)
  128. rsq1 = BEC_sort(:,1);
  129. rsq1 = rsq1';
  130. x = coor(:,1); y = coor(:,2); z = coor(:,3);
  131. N1 = ceil(max(rsq1)*10000) - floor(min(rsq1)*10000);
  132. S = 200 * ones(size(x));
  134. % Color mapping (Red-Blue)
  135. mycolorpoint = [[255 135 158];[255 191 204];[250 235 214];[239 255 255];[64 104 224];[20 48 135]]/255;
  136. mycolorpoint = flipud(mycolorpoint);
  137. mycolorposition = [1 1000 round(N1/2)-100 round(N1/2)+100 N1-1000 N1];
  138. mycolormap_r = interp1(mycolorposition, mycolorpoint(:,1), 1:N1, 'linear', 'extrap');
  139. mycolormap_g = interp1(mycolorposition, mycolorpoint(:,2), 1:N1, 'linear', 'extrap');
  140. mycolormap_b = interp1(mycolorposition, mycolorpoint(:,3), 1:N1, 'linear', 'extrap');
  141. C = [mycolormap_r', mycolormap_g', mycolormap_b'];
  143. colormap(C);
  144. scatter3(x, y, z, S, C(round((rsq1-min(rsq1))*10000)+1,:), 'filled')
  145. axis off; colorbar('Ticks',[])
  146. figure(6); imagesc(rsq1);
  148. % -------------------------------------------------------------------------
  149. % Figure: R values anticorrelated with functional gradient
  150. % -------------------------------------------------------------------------
  151. figure;
  152. allrsq(:, numflag+1:end) = [];
  153. allxx(:, numflag+1:end) = [];
  154. y1 = mean(allrsq, 2);
  155. rsq1 = y1;
  156. xx = mean(allxx, 2);
  158. [rho, pval] = corr(xx, y1);
  159. lm = fitlm(xx, y1);
  160. xhat = linspace(min(xx), max(xx), 100);
  161. yhat = lm.Coefficients.Estimate(1) + (lm.Coefficients.Estimate(2) * xhat);
  163. plot(xx, y1, '.', 'Markersize', 10); hold on
  164. plot(xhat, yhat, 'LineWidth', 2)
  165. title(['rho = ' num2str(rho) ', p = ' num2str(pval)]);
  166. axis square
  167. xlabel('Gradient'); ylabel('S-F Coupling (R)');
  169. % -------------------------------------------------------------------------
  170. % Boxplot of R across functional networks
  171. % -------------------------------------------------------------------------
  172. load lab.mat
  173. load('rsn_mapping.mat');
  175. R_name = rsn_names; % Total region names
  176. R_n = length(R_name); % Total region number
  177. Rn_Ind = lab; % Network index for each node
  178. Rn_name = cell(length(rsq1), 1); % Network name for each node
  179. R_R = zeros(R_n, 1); % Median R
  180. R_A = zeros(R_n, 1); % Average R
  182. for i = 1:R_n
  183. R_R(i) = median(rsq1(Rn_Ind==i));
  184. R_A(i) = mean(rsq1(Rn_Ind==i));
  185. Rn_name(Rn_Ind==i) = R_name(i);
  186. end
  188. [temp, I] = sort(R_R, 'descend'); % Sort networks by R median
  189. a = rsq1; b = Rn_name;
  190. c = R_name(I); % Sorted network names
  191. Color = [219 2 10; 231 95 27; 238 146 43; 246 191 65; 246 236 84; 202 222 169; 147 205 137; 76 177 99]/255;
  192. Color = flipud(Color);
  194. figure;
  195. valid_idx = ~cellfun(@isempty, b);
  196. boxplot(a(valid_idx), b(valid_idx), 'Orientation', 'horizontal', 'GroupOrder', c, ...
  197. 'Colors', Color, 'BoxStyle', 'filled', 'Whisker', 0, 'OutlierSize', 2.5, ...
  198. 'Symbol', '.', 'MedianStyle', 'target');
  199. title('Structural-Functional Coupling across Networks');
  201. % -------------------------------------------------------------------------
  202. % 6. Save output variables to CSV
  203. % -------------------------------------------------------------------------
  204. csv_file_path = 'Output_SFC_Values.csv'; % Specify output CSV file path
  205. dlmwrite(csv_file_path, allrsq);
  206. disp('---------------------------------------------------');
  207. disp('Analysis completed. S-F coupling data successfully saved to CSV.');
  208. disp('---------------------------------------------------');

lowhigh.m at commit 7924672, no license · at the source

Overview

Authors: Jian Zhang1, Xujing Nie1, Shuyue Fu1, Yinping Lu2, Tiantian Liu1, Zhilin Zhang2, Jinglong Wu1, Xuezhi Tong3, Tianyi Yan1
  1. School of Medical Science and Engineering, Beijing Institute of Technology, Beijing, 100081 China
  2. Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055 China
  3. Department of Neurosurgery, China-Japan Friendship Hospital, Beijing, 100029 China
Institutions: Beijing Institute of Technology (China); Chinese Academy of Sciences (China); Shenzhen Institutes of Advanced Technology (China); China-Japan Friendship Hospital (China)
Journal: The journal of headache and pain, volume 27, issue 1, article 154
Dates: received 6 January 2026; accepted 2 June 2026; published online 6 June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1186/s10194-026-02419-7 · PMID 42251257 · PMCID PMC13251101 · OpenAlex W7163739652
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), structural MRI / diffusion (modality), human (organism), pain (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Connectivity, fMRI & imaging, Smoothing, state filtering, decompositions, Machine learning
Keywords: Structural-functional coupling, Graph harmonic model, Brain network, Multimodal MRI, Trigeminal neuralgia, Adaptive reorganization
MeSH: Brain*, Nerve Net*, Neuronal Plasticity*, Transcriptome*, Trigeminal Neuralgia*, Adult, Aged, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged (* major topic)
Topic: Trigeminal Neuralgia and Treatments (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Key-Area Research and Development Program of Guangdong Province (2023B0303030002); the National Natural Science Foundation of China (62406025, 62373056, 62306035, 62336002); STI2030-Major Projects (2022ZD0208500); Shenzhen Basic Research Program (JCYJ20241202124804007)
Citations: not cited yet (Europe PMC); 58 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

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

starz0275/TN_SF_Code

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 792467264236fd6c11ba671b2933efe3d9ae173c, 9 March 2026
Languages: MATLAB (3)
Size: 10 files, 3 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (3 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
4 files
At the source: github.com/starz0275/TN_SF_Code

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

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 3 scripts, each with its path and the digest of its content;
  • 2 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/10.1186/s10194-026-02419-7.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 6 keywords, 13 MeSH terms, 4 funders, 56 references.

Cite

This paper

Zhang, J., Nie, X., Fu, S., Lu, Y., Liu, T., Zhang, Z., Wu, J., Tong, X., & Yan, T. (2026). Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations. The journal of headache and pain, 27(1), 154. https://doi.org/10.1186/s10194-026-02419-7

BibTeX

@article{zhang2026transcriptional,
author = {Zhang, Jian and Nie, Xujing and Fu, Shuyue and Lu, Yinping and Liu, Tiantian and Zhang, Zhilin and Wu, Jinglong and Tong, Xuezhi and Yan, Tianyi},
title = {{Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations}},
journal = {The journal of headache and pain},
year = {2026},
month = jun,
volume = {27},
number = {1},
pages = {154},
publisher = {BMC},
issn = {1129-2369},
doi = {10.1186/s10194-026-02419-7},
url = {https://doi.org/10.1186/s10194-026-02419-7},
pmid = {42251257},
pmcid = {PMC13251101}
}

RIS

TY - JOUR
AU - Zhang, Jian
AU - Nie, Xujing
AU - Fu, Shuyue
AU - Lu, Yinping
AU - Liu, Tiantian
AU - Zhang, Zhilin
AU - Wu, Jinglong
AU - Tong, Xuezhi
AU - Yan, Tianyi
TI - Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations
T2 - The journal of headache and pain
J2 - J Headache Pain
PY - 2026
DA - 2026/06/06
VL - 27
IS - 1
SP - 154
SN - 1129-2369
PB - BMC
DO - 10.1186/s10194-026-02419-7
UR - https://doi.org/10.1186/s10194-026-02419-7
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s10194-026-02419-7",
"type": "article-journal",
"title": "Transcriptional correlates of structure-function coupling plasticity in trigeminal neuralgia: unveiling the synaptic and metabolic associations",
"container-title": "The journal of headache and pain",
"author": [
{
"family": "Zhang",
"given": "Jian"
},
{
"family": "Nie",
"given": "Xujing"
},
{
"family": "Fu",
"given": "Shuyue"
},
{
"family": "Lu",
"given": "Yinping"
},
{
"family": "Liu",
"given": "Tiantian"
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{
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"given": "Zhilin"
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{
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"given": "Jinglong"
},
{
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"given": "Xuezhi"
},
{
"family": "Yan",
"given": "Tianyi"
}
],
"container-title-short": "J Headache Pain",
"volume": "27",
"issue": "1",
"page": "154",
"DOI": "10.1186/s10194-026-02419-7",
"PMID": "42251257",
"PMCID": "PMC13251101",
"ISSN": "1129-2369",
"publisher": "BMC",
"URL": "https://doi.org/10.1186/s10194-026-02419-7",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
6
]
]
}
}

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