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Salience Network Dynamics Across Spontaneous Attacks, Interictal Rest and Interictal Pain Imagery in Menstrually Related Migraine.

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 · 9 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Materials and Methods › MRI Data Preprocessing ↔ main2.sh, lines 1–57 · score 0.83 · spatial smoothing, middle volume, Brain Extraction, Linear, distortion, BET
  2. [2] § Materials and Methods › MRI Data Preprocessing ↔ perform_bet_fast.sh, the whole file · a weak match · score 0.81 · bias field, Brain Extraction, FAST, tissue, BET, FNIRT
  3. [3] § Materials and Methods › Dynamic Functional Connectivity (dFC) › Extraction of Dynamic Functional Connectivity (dFC) Eigenvectors ↔ utilities/analyses_scripts/EigenVectors_VoxelSpace.m, the whole file · a weak match · score 0.63 · phase coherence, leading eigenvector, LEiDA, analysing, scans, matrices
  4. [4] § Materials and Methods › Dynamic Functional Connectivity (dFC) ↔ LEiDA_Start.m, the whole file · a weak match · score 0.63 · fractional occupancy, leading eigenvector, cluster centroid, repetition, fMRI, TRs
  5. [5] § Materials and Methods › Dynamic Functional Connectivity (dFC) › dFC State Assignment to SN ↔ utilities/analyses_scripts/Overlap_LEiDA_Yeo.m, the whole file · a weak match · score 0.58 · cluster centroid, LEiDA, zero, clustering solutions, overlap, correlation
  6. [6] § Materials and Methods › MRI Data Acquisition ↔ preprocess_mig.sh, lines 47–88 · score 0.56 · Fieldmap magnitude, MPRAGE, EPI, head, volumes, phase
  7. [7] § Materials and Methods › Dynamic Functional Connectivity (dFC) › Extraction of Dynamic Functional Connectivity (dFC) Eigenvectors ↔ utilities/analyses_scripts/EigenVectors_VoxelSpace.m, the whole file · a weak match · score 0.54 · Hilbert Transform, phase coherence, Eigenvectors
  8. [8] § Materials and Methods › Dynamic Functional Connectivity (dFC) ↔ LEiDA_Start.m, the whole file · a weak match · score 0.54 · leading eigenvector dynamic, LEiDA toolbox, MATLAB, temporal
  9. [9] § Materials and Methods › Dynamic Functional Connectivity (dFC) › Extraction of Dynamic Functional Connectivity (dFC) Eigenvectors ↔ utilities/analyses_scripts/LEiDA_data.m, the whole file · a weak match · score 0.53 · Hilbert Transform, phase coherence, Eigenvectors
  10. [10] § Materials and Methods › Dynamic Functional Connectivity (dFC) › Extraction of Dynamic Functional Connectivity (dFC) Eigenvectors ↔ utilities/analyses_scripts/LEiDA_data.m, the whole file · a weak match · score 0.53 · Hilbert transform, brain areas, scan, Eigenvectors, phase
  11. [11] § Materials and Methods › Dynamic Functional Connectivity (dFC) ↔ LEiDA_AnalysisCentroid.m, the whole file · a weak match · score 0.53 · leading eigenvector dynamic, LEiDA toolbox, MATLAB

Paper

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

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The authors' code

MATLAB · 149 lines · 5.4 KB · MIT · 2 matches

  1. function EigenVectors_VoxelSpace(data_dir,save_dir,leida_dir,selectedK)
  2. %
  3. % This function loads the fMRI signal in each voxel of a brain template.
  4. % Resizes the template to the 10mm MNI space. Computes the fMRI phase
  5. % leading eigenvectors for each TR for all participants. Then computes the
  6. % mean leading eigenvector for each LEiDA state across conditions.
  7. %
  8. % INPUT:
  9. % data_dir directory where the fMRI data in NIFTI format is saved
  10. % save_dir directory to save the new data
  11. % leida_dir directory where the results from running LEiDA are saved
  12. % selectedK value of K to be further analysed
  13. %
  14. % OUTPUT:
  15. % V1_MNI10mm leading eigenvectors in MNI 10mm space
  16. % mean_V1 mean leading eigenvectors based on the time courses of PL
  17. % states obtained from running the K-means algorithm
  18. %
  19. % Author: Joana Cabral, University of Minho, [email hidden]
  20. % Miguel Farinha, University of Minho, [email hidden]
  22. % INPUT EXAMPLES:
  23. % data_dir = 'D:/LEiDA_Toolbox/Outputs/dparsf/nofilt_noglobal/func_preproc/';
  24. % save_dir = 'D:/LEiDA_Toolbox/ABIDE_dparsf_MNI10mm/';
  25. % leida_dir = 'D:/LEiDA_Toolbox/LEiDA_Results_ABIDE_dparsf_AAL120/';
  26. % selectedK = 15;
  28. % File with the Kmeans results (output from LEiDA_cluster.m)
  29. file_cluster = 'LEiDA_Clusters.mat';
  31. % Load required data:
  32. if isfile([leida_dir file_cluster])
  33. load([leida_dir file_cluster], 'Kmeans_results', 'rangeK');
  34. end
  36. % Gather the PL state time courses across all participants
  37. state_time_idxs = Kmeans_results{rangeK == selectedK}.IDX;
  39. % Get number of files in folder
  40. data_info = dir([data_dir '*.nii.gz']);
  41. num_subjs = numel(data_info);
  43. % Load 10mm MNI voxel space
  44. MNI10mm_Mask = niftiread('MNI152_T1_10mm_brain_mask.nii');
  45. sz = size(MNI10mm_Mask); % size of the 10mm MNI mask
  46. ind_voxels = find(MNI10mm_Mask(:) > 0); % find the non-zero elements in the mask
  47. n_voxels = length(ind_voxels);
  49. % Matrix to store the leading eigenvectors of all subjects at each TR
  50. V1_all = zeros(length(state_time_idxs)*2,n_voxels);
  52. t_all = 0;
  53. for s = 1:num_subjs
  55. file = data_info(s).name;
  56. [~, baseFileName, ~] = fileparts(file);
  57. ix = strfind(baseFileName,'_'); % get the underscore locations
  58. if length(ix) == 4 % File with names like CMU_a_0050
  59. saveFileName = baseFileName(1:ix(3)); % return the substring up to 3rd underscore
  60. else
  61. saveFileName = baseFileName(1:ix(2)); % return the substring up to 2nd underscore
  62. end
  64. if size(file,1)
  65. disp(['Participant ' saveFileName(1:end-1) ':']);
  67. % Read the nii file
  68. fMRI_MNI = niftiread([data_dir file]);
  69. T = size(fMRI_MNI,4); % number of volumes
  71. disp('- Resizing NIFTI file to MNI 10mm space');
  72. % Files will be resized in order to be accomodated to the MNI10mm template
  73. fMRI_MNI10mm = zeros(sz(1), sz(2), sz(3), T);
  74. for t = 1:T
  75. fMRI_MNI10mm(:,:,:,t) = imresize3(fMRI_MNI(:,:,:,t),sz);
  76. end
  77. clear fMRI_MNI
  79. disp('- Computing the fMRI signal phases using the Hilbert transform');
  80. % Store the fMRI signal phase using the Hilbert transform
  81. fMRI_ts = zeros(n_voxels,T);
  83. for v = 1:n_voxels
  84. [I1,I2,I3] = ind2sub(sz,ind_voxels(v));
  85. fMRI_ts(v,:) = squeeze(fMRI_MNI10mm(I1,I2,I3,:))';
  86. end
  87. clear fMRI_MNI10mm
  89. disp('- Saving fMRI time series in MNI 10mm space');
  90. save([save_dir saveFileName 'MNI10mm'], 'fMRI_ts')
  92. disp('- Computing the leading eigenvectors');
  93. % De-meaning the fMRI signal
  94. for v = 1:n_voxels
  95. fMRI_ts(v,:) = fMRI_ts(v,:) - mean(fMRI_ts(v,:));
  96. end
  98. % Get the fMRI signal phase using the Hilbert transform
  99. for v = 1:n_voxels
  100. fMRI_ts(v,:) = angle(hilbert(fMRI_ts(v,:)));
  101. end
  103. % Compute leading eigenvector of each phase coherence matrix
  104. for t = 2:T-1 % exclude 1st and last TR of each fMRI signal
  106. % Save the leading eigenvector for time t
  107. [v1,~] = eigs(cos(fMRI_ts(:,t) - fMRI_ts(:,t)'),1);
  109. if sum(v1) > 0 % for eigenvectors with sum of entries > 0
  110. v1 = -v1;
  111. end
  113. t_all = t_all + 1; % time point in V1_all
  115. % row t_all correponds to the computed eigenvector at time t for subject s
  116. V1_all(t_all,:) = v1;
  117. end
  118. end
  119. end
  120. % Reduce size in case some scans have less TRs than tmax
  121. % In this case, these lines of code will not result in changes
  122. V1_all(t_all+1:end,:) = [];
  124. disp(' ');
  125. disp('- Saving leading eigenvectors in MNI 10mm space');
  126. save([leida_dir 'V1_all_MNI10mm'], 'V1_all', '-v7.3')
  129. % Save the mean PL states in MNI 10mm space
  130. mean_V1 = zeros(selectedK,n_voxels);
  132. disp(' ');
  133. if size(V1_all,1) ~= size(state_time_idxs,2)
  134. error('- Number of leading eigenvectors and length of state time courses do not coincide');
  135. else
  136. for k = 1:selectedK
  137. disp(['- Computing mean leading eigenvector for PL state ' num2str(k)]);
  138. idx_k = state_time_idxs == k;
  139. mean_V1(k,:) = mean(V1_all(idx_k,:),1);
  140. end
  141. end
  142. disp(' ');
  143. disp(['- Saving the mean leading eigenvectors for K = ' num2str(selectedK)]);
  144. % Create a directory to store results for defined value of K
  145. if ~exist([leida_dir 'K' num2str(selectedK) '/'], 'dir')
  146. mkdir([leida_dir 'K' num2str(selectedK) '/']);
  147. end
  148. K_dir = [leida_dir 'K' num2str(selectedK) '/'];
  149. save([K_dir 'V1_VoxelSpace'], 'mean_V1', 'ind_voxels', 'MNI10mm_Mask');

EigenVectors_VoxelSpace.m at commit ab03cbb, under MIT · at the source

Overview

Authors: Inês Esteves1, Alexandre Perdigão1, Ana R Fouto1,2, Amparo Ruiz-Tagle1,3, Gina Caetano1, Joana Cabral1, Isabel Pavão Martins4, Raquel Gil-Gouveia5,6, César Caballero-Gaudes7,8, Patrícia Figueiredo1
ORCID iDs: Raquel Gil-Gouveia
  1. Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico – Universidade de Lisboa, Lisbon, Portugal
  2. Algarve Biomedical Center, Faro, Portugal
  3. Faculdade de Ciências da Saúde, Universidade Europeia, Lisbon, Portugal
  4. Centro de Estudos Egas Moniz e Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa (FMUL), Lisbon, Portugal
  5. Neurology Department, Hospital da Luz, Lisbon, Portugal
  6. Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisbon, Portugal
  7. Basque Center on Cognition, Brain and Language, Donostia - San Sebastian, Spain
  8. Ikerbasque, Basque Foundation for Science, Bilbao, Spain
Institutions: University of Lisbon (Portugal); Instituto Superior Técnico (Portugal); Algarve Biomedical Center (Portugal); Universidade Europeia (Portugal); Instituto de Medicina Molecular João Lobo Antunes; Universidade Católica Portuguesa (Portugal); Hospital da Luz (Portugal); Ikerbasque (Spain); Basque Center on Cognition, Brain and Language (Spain)
Journal: Brain topography, volume 39, issue 6, article 94
Dates: received 26 March 2026; accepted 21 August 2026; published online 4 September 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1007/s10548-026-01247-x · PMID 42693231 · PMCID PMC13541788 · OpenAlex W7207652057
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), epilepsy (population), pain (population)
Methods: Spectral & time-frequency, Statistics, Machine learning, Connectivity, fMRI & imaging
Keywords: Pain imagery, fMRI, Migraine, Resting state, Dynamic functional connectivity, LEiDA
MeSH: Brain*, Imagination*, Migraine Disorders*, Nerve Net*, Pain*, Adult, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Rest, Young Adult (* major topic)
Topic: Migraine and Headache Studies (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: LARSyS FCT funding (DOI: 10.54499/LA/P/0083/2020, 10.54499/UIDP/50009/2020, and 10.54499/UIDB/50009/2020); PRR project Center for Responsible AI (grant C645008882-00000055); Fundação para a Ciência e a Tecnologia (grants PTDC/EMD-EMD/29675/2017, LISBOA-01-0145-FEDER-029675, grants PD/BD/150356/2019, PTDC/EMD-EMD/29675/2017)
Citations: not cited yet (Europe PMC); 83 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.

martaxavier/fMRI-Preprocessing

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 44551d735d8193b571348d5b0c59d2422acc47ca, 27 January 2026
Languages: Shell (28), MATLAB (2)
Size: 45 files, 30 scripts
Software Heritage: not archived
Found in: the text, “MRI Data Preprocessing”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FSL (19 files), FreeSurfer (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
31 files, not copied: shown from their source

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At the source: github.com/martaxavier/fMRI-Preprocessing

PSYMARKER/leida-matlab

License: MIT
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ab03cbb6f987a80fd65ed1aebe75419b660b907e, 29 January 2025
Languages: MATLAB (49)
Size: 55 files, 49 scripts
Software Heritage: not checked
Found in: the text, “Dynamic Functional Connectivity (dFC)”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (13 files), Image Processing Toolbox (11 files), Signal Processing Toolbox (3 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
51 files
At the source: github.com/PSYMARKER/leida-matlab

martaxavier/fmri-preprocessing](https:

License: none: the authors keep all their rights
State: the link is dead, verified on 26 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link is dead
  • 26 September 2026: the link is dead
At the source: github.com/martaxavier/fMRI-Preprocessing](https:/github.com/martaxavier/fMRI-Preprocessing)

psymarker/leida-matlab](https:

License: none: the authors keep all their rights
State: the link is dead, verified on 26 September 2026
Evidence: found in the paper
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link is dead
  • 26 September 2026: the link is dead
At the source: github.com/PSYMARKER/leida-matlab](https:/github.com/PSYMARKER/leida-matlab)

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

Tracing map

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What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 79 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

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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/10.1007/s10548-026-01247-x.

Versions

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Version 3, 28 September 2026

  • Publisher: — → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 6 keywords, 12 MeSH terms, 3 funders, 78 references.

Cite

This paper

Esteves, I., Perdigão, A., Fouto, A. R., Ruiz-Tagle, A., Caetano, G., Cabral, J., Martins, I. P., Gil-Gouveia, R., Caballero-Gaudes, C., & Figueiredo, P. (2026). Salience Network Dynamics Across Spontaneous Attacks, Interictal Rest and Interictal Pain Imagery in Menstrually Related Migraine. Brain topography, 39(6), 94. https://doi.org/10.1007/s10548-026-01247-x

BibTeX

@article{esteves2026salience,
author = {Esteves, Inês and Perdigão, Alexandre and Fouto, Ana R and Ruiz-Tagle, Amparo and Caetano, Gina and Cabral, Joana and Martins, Isabel Pavão and Gil-Gouveia, Raquel and Caballero-Gaudes, César and Figueiredo, Patrícia},
title = {{Salience Network Dynamics Across Spontaneous Attacks, Interictal Rest and Interictal Pain Imagery in Menstrually Related Migraine}},
journal = {Brain topography},
year = {2026},
month = sep,
volume = {39},
number = {6},
pages = {94},
publisher = {Springer Science+Business Media},
issn = {0896-0267},
doi = {10.1007/s10548-026-01247-x},
url = {https://doi.org/10.1007/s10548-026-01247-x},
pmid = {42693231},
pmcid = {PMC13541788}
}

RIS

TY - JOUR
AU - Esteves, Inês
AU - Perdigão, Alexandre
AU - Fouto, Ana R
AU - Ruiz-Tagle, Amparo
AU - Caetano, Gina
AU - Cabral, Joana
AU - Martins, Isabel Pavão
AU - Gil-Gouveia, Raquel
AU - Caballero-Gaudes, César
AU - Figueiredo, Patrícia
TI - Salience Network Dynamics Across Spontaneous Attacks, Interictal Rest and Interictal Pain Imagery in Menstrually Related Migraine
T2 - Brain topography
J2 - Brain Topogr
PY - 2026
DA - 2026/09/04
VL - 39
IS - 6
SP - 94
SN - 0896-0267
PB - Springer Science+Business Media
DO - 10.1007/s10548-026-01247-x
UR - https://doi.org/10.1007/s10548-026-01247-x
LA - en
ER -

CSL-JSON

{
"id": "10.1007/s10548-026-01247-x",
"type": "article-journal",
"title": "Salience Network Dynamics Across Spontaneous Attacks, Interictal Rest and Interictal Pain Imagery in Menstrually Related Migraine",
"container-title": "Brain topography",
"author": [
{
"family": "Esteves",
"given": "Inês"
},
{
"family": "Perdigão",
"given": "Alexandre"
},
{
"family": "Fouto",
"given": "Ana R"
},
{
"family": "Ruiz-Tagle",
"given": "Amparo"
},
{
"family": "Caetano",
"given": "Gina"
},
{
"family": "Cabral",
"given": "Joana"
},
{
"family": "Martins",
"given": "Isabel Pavão"
},
{
"family": "Gil-Gouveia",
"given": "Raquel"
},
{
"family": "Caballero-Gaudes",
"given": "César"
},
{
"family": "Figueiredo",
"given": "Patrícia"
}
],
"container-title-short": "Brain Topogr",
"volume": "39",
"issue": "6",
"page": "94",
"DOI": "10.1007/s10548-026-01247-x",
"PMID": "42693231",
"PMCID": "PMC13541788",
"ISSN": "0896-0267",
"publisher": "Springer Science+Business Media",
"URL": "https://doi.org/10.1007/s10548-026-01247-x",
"language": "en",
"issued": {
"date-parts": [
[
2026,
9,
4
]
]
}
}

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