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Telecommunication-inspired network models of healthy and diseased brains.

Code ↔ Paper

6 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 6 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Experimental evaluation: cases of individuals with healthy and diseased brains › General assessment ↔ BallFinal.m, lines 91–130 · score 0.90 · Lobo temporal lobe, Parietal lobe, Limbic lobe, Occipital lobe, Frontal lobe, NIfTI
  2. [2] § Methods: the human brain as a telecommunication network ↔ Emutual.m, lines 1–25 · score 0.60 · mutual information, edge weights, intra, RoIs, graph, activities
  3. [3] § Experimental evaluation: cases of individuals with healthy and diseased brains › General assessment ↔ AcsvImport.m, the whole file · a weak match · score 0.58 · Harvard Oxford, NiFti, numerical, Occipital, atlas, healthy
  4. [4] § Methods: the human brain as a telecommunication network › Time series discretization, RoI states and interactions: a deep theoretical analysis ↔ BallFinal.m, lines 132–214 · score 0.51 · fMRI, NIfTI, rows, discrete, matrix, patient
  5. [5] § Methods: the human brain as a telecommunication network › The theoretical FSAP for brain RoIs modeling ↔ BallFinal.m, lines 132–214 · score 0.51 · fMRI, NIfTI, sequence, window, discrete, RoIs
  6. [6] § Methods: the human brain as a telecommunication network › Time series discretization, RoI states and interactions: a deep theoretical analysis ↔ Emutual.m, lines 27–86 · score 0.51 · normalized mutual information, global, NMI

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 222 lines · 5.8 KB · no license · 3 matches

  1. %This script loads the workspace dataALL2.mat,
  2. %removes outliers, augments data and:
  3. %1) Represents the augmented data for patient i
  4. %2) Represents the data for Limbic RoIs of patient i
  5. %3) Discrtises data through the function buildMatrix, in function of the
  6. %chosen parameters states (states 4, W=20);
  7. %4) Plots the MTPM matrix elements
  8. %5) Saves the data workspaces in function of the selected patient
  10. clear
  11. close all;
  12. load ('dataALL2.mat');
  14. %Decide which class of patients to load
  15. over=0;%1 for over 70, 0 for under 70
  16. good=0;%1 for good, 0 for bad
  17. male=0;%1 male, 0 for female
  19. dataset=0;
  20. name='';
  21. if (over==0)&&(good==0)&&(male==0)
  22. dataset=u70bf;
  23. name='u70bf';
  24. elseif (over==0)&&(good==0)&&(male==1)
  25. dataset=u70bm;
  26. name='u70bm';
  27. elseif (over==0)&&(good==1)&&(male==0)
  28. dataset=u70gf;
  29. name='u70gf';
  30. elseif (over==0)&&(good==1)&&(male==1)
  31. dataset=u70gm;
  32. name='u70gm';
  33. elseif (over==1)&&(good==0)&&(male==0)
  34. dataset=o70bf;
  35. name='o70bf';
  36. elseif (over==1)&&(good==0)&&(male==1)
  37. dataset=o70bm;
  38. name='o70bm';
  39. elseif (over==1)&&(good==1)&&(male==0)
  40. dataset=o70gf;
  41. name='o70gf';
  42. elseif (over==1)&&(good==1)&&(male==1)
  43. dataset=o70gm;
  44. name='o70gm';
  45. end
  47. %In case of another implementation
  48. %assignin('base',var_name, value)
  49. %Not needed for now
  51. %Which patient to analyse (look at the dataset length)
  52. pat=2;
  53. patient=dataset{pat};
  54. name=[name '_' num2str(pat)];
  55. %It is assumed that for all patients we have the same RoIs and samples
  56. nRoIs=size(patient,2);
  57. nSamples=size(patient,1);
  58. %For the model
  59. states=4;
  60. %After interpolation
  61. sampling_time=0.5;%s with interpolation
  62. %the original is 1s
  63. %Observation window
  64. W=20;
  65. %Healthy
  66. %Table to array
  67. patient=patient{:,:};
  68. %Remove the heading index row
  69. patient(1,:)=[];
  71. %Interpolate values (for doubling samples) - Data augmentation
  72. patient_i1=[];
  73. for i=1:nRoIs
  74. patient_i1(:,i)=interpolate(patient(:,i));
  75. end
  77. %Remove the first 10 rows in order to clean starting outliers
  78. for j=1:40
  79. patient_i1(j,:)=[];
  80. end
  81. %end
  83. %doubling again the number of samples
  84. patient_i2=[];
  85. for i=1:nRoIs
  86. patient_i2(:,i)=interpolate(patient_i1(:,i));
  87. end
  88. %name_file=['data_' name '.mat'];
  89. %save (name_file);
  91. %Preliminary data plot
  92. plot(patient_i2(:,:))
  93. axis([20 470 min(min(patient_i2)) max(max(patient_i2))])
  94. set(gca,'FontSize',16,'FontWeight','bold')
  95. grid on
  96. title('Raw NIfTI data for patient, T=0.25s, w=470','FontSize',18,'FontWeight','bold');
  97. xlabel('Number of samples','FontSize',18,'FontWeight','bold')
  98. ylabel('Signal amplitude [a.u.]','FontSize',18,'FontWeight','bold')
  99. ax = gca;
  100. pause();
  102. for lobe=1:5
  103. lobe_curr={};
  104. sz=size(patient_i2);
  105. len=sz(2);
  106. for roi=1:len
  107. if (ismember(roi,harvard{lobe}))
  108. lobe_curr{end+1}=patient_i2(:,roi);
  109. end
  110. end
  111. %1Frontal lobe
  112. %2Lobo-temporale lobe
  113. %3Parietal lobe
  114. %4Occipital lobe
  115. %5Limbic lobe
  117. curr=cell2mat(lobe_curr);
  118. plot(curr,'LineWidth',1);
  119. axis([20 470 min(min(curr)) max(max(curr))])
  120. set(gca,'FontSize',16,'FontWeight','bold')
  121. grid on
  122. %Text should be adjusted in function of
  123. title('Raw NIfTI data for k=1, T=0.25s, w=470, Limbic lobe RoIs','FontSize',18,'FontWeight','bold');
  124. xlabel('Number of samples','FontSize',18,'FontWeight','bold')
  125. ylabel('Signal amplitude [a.u.]','FontSize',18,'FontWeight','bold')
  126. ax = gca;
  127. legend('27 - Planum Temporale', '28 - Planum Polare', "29 - Heschl' Gyrus", '30 - Temporal Pole', '34 - Cuneal Cortex', '35 - Lingual Gyrus');
  128. pause();
  129. hold off
  130. end
  132. %{
  133. figure('WindowState','maximized','Color',[1 1 1]);
  134. sizeA=120;
  135. x=double(1:sizeA);
  136. axes1 = axes;
  137. hold(axes1,'on');
  138. grid on
  139. roi=10;
  140. %plot(brain1sano(40:160),'Color','black','MarkerSize',8,'Marker','+','LineWidth',1);
  141. scatter(x,h1i(41:sizeA+40,roi),50,'filled','MarkerEdgeColor',[0 0 .6],...
  142. 'MarkerFaceColor',[.5 .5 .8],...
  143. 'LineWidth',1);
  144. box(axes1,'on');
  145. grid(axes1,'on');
  146. hold(axes1,'off');
  147. % Set the remaining axes properties
  148. set(axes1,'FontSize',34,'FontWeight','bold');
  149. axis([1 120 -2 1])
  150. title('fMRI->NIfTI RoI discrete sequence Example')
  151. xlabel('t [s]');
  152. ylabel('Signal amplitude [a.u.]')
  153. %}
  156. M={};
  157. sst={};
  158. thrs={};
  160. for i=1:nRoIs
  161. M{i}=[];
  162. sst{i}=[];
  163. thrs{i}=[];
  164. end
  166. %Creates structure to store matrix elements for each RoI/lobe
  167. for roi=1:nRoIs
  168. roi
  169. for j=1:W:length(patient_i2)-W
  170. [a,b,c]=buildMatrix(patient_i2((j:j+W),roi),states,sampling_time);
  171. M{roi}=[M{roi} a];
  172. sst{roi}=[sst{roi} b];
  173. thrs{end+1}=c;
  174. end
  175. end
  176. save('selected_patient.mat')
  178. col=1;
  179. row=1;
  180. figure
  181. element_all_lobe=[];%per lobe
  182. newcolors = {'#F00','#F80','#FF0','#0B0','#00F','#50F','#A0F','#A00','#A80','#AF0','#BB0', '#0BF','#A0F','#5FF','#AFF','#A0F','#A8F','#AFF','#BBF'};
  183. colororder(newcolors);
  184. for lobe=3:3
  185. aa=0;
  186. lobe
  187. color=1;
  188. for roi=1:nRoIs
  189. element_all_rois=[];%per roi
  190. hold on;
  191. if (ismember(roi,harvard{lobe}))
  192. roi
  193. array=M{roi};
  194. element=[];
  195. %Which element we want to plot
  196. %ii=2;
  197. %jj=1;
  198. for i=1:states:length(array)
  199. element=[element array(row,i+col-1)];
  200. end
  201. %plot(element);
  202. element_all_rois=[element_all_rois element];
  203. %make_pdf(element_all_rois);
  204. make_figure(element_all_rois,color);
  205. axis([0 22 0.5 1])
  206. pause()
  207. aa=aa+1;
  208. disp(['aa:' num2str(aa)]);
  209. color = color + 1;
  210. end
  211. %axis([0 1 0 0.5])
  212. hold off
  213. end
  214. %hist(element_all,10);
  216. %axis([1 length(element) 0 1])
  217. grid on
  218. pause()
  219. close all
  220. end
  221. name_file=['data_' name '.mat'];
  222. save (name_file);

BallFinal.m at commit 7bc9946, no license · at the source

Overview

Authors: Peppino Fazio1,2, Maria Mannone1,3,4,5, Norbert Marwan4,5, Patrizia Ribino3, Miralem Mehic2,6, Abdalla Swikir7, Danilo Amendola8, Pietro Riello1, Miroslav Voznak2
  1. DSMN, Ca’ Foscari University of Venice, Via Torino 155, 30170 Mestre (VE), Italy
  2. Department of Telecommunications, VSB - Technical University of Ostrava, 17. listopadu 15, 70800 Ostrava, Czechia
  3. ICAR, National Research Council (CNR), Palermo, Italy
  4. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Berlin, Germany
  5. Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany
  6. Department of Telecommunications, Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
  7. Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE
  8. Joint Research Centre, European Commission, Rue du Champ de Mars 21, 1050 Brussels, Belgium
Institutions: VSB - Technical University of Ostrava (Czechia); Ca' Foscari University of Venice (Italy); University of Potsdam (Germany); Leibniz Association (Germany); National Research Council (Italy); Potsdam Institute for Climate Impact Research (Germany); University of Sarajevo (Bosnia & Herzegovina); Mohamed bin Zayed University of Artificial Intelligence (United Arab Emirates); European Commission (Belgium); Joint Research Center (Belgium); Joint Research Centre (Italy); Joint Research Centre (Germany); Directorate-General Joint Research Centre (Belgium); Joint Research Centre (Netherlands)
Journal: Scientific reports, volume 16, issue 1, article 19686
Dates: received 25 September 2025; accepted 23 April 2026; published online 28 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-50758-x · PMID 42049884 · PMCID PMC13314952 · OpenAlex W7156726621
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational modeling (no new data) (modality), human (organism), computational (subfield)
Methods: Connectivity, fMRI & imaging
Keywords: Biological neural network, Biological routing, Channel modeling, Communication theory, Finite state process, Computational biology and bioinformatics, Engineering, Mathematics and computing, Neuroscience
MeSH: Brain*, Brain Diseases*, Models, Neurological*, Nerve Net*, Humans, Neural Networks, Computer, Neurons (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Union (CZ.10.03.01/00/22 003/0000048); NextGenerationEU (B83C22004880006)
Citations: cited by 1 paper (Europe PMC); 78 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 6 matches between paragraphs and lines of code.

medusamedusa/time_series_from_AD_fMRI_

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 69f289c2a294cf210b2c30ac2c298b5a51488a80, 29 January 2026
Languages: Jupyter (1)
Size: 2 files, 1 script
Software Heritage: not archived
Found in: the text, “Methods: the human brain as a telecommunication ”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NiBabel (1 file), Nilearn (1 file), pandas (1 file), pydicom (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
2 files
At the source: codeberg.org/medusamedusa/time_series_from_AD_fMRI_

medusamedusa/fMRI_telecommunications

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 7bc9946666280e8685f6b73d7fc368c692903529, 29 January 2026
Languages: MATLAB (26), Jupyter (1)
Size: 80 files, 27 scripts
Software Heritage: not archived
Found in: the text, “Specific analysis and STATNET characterization”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (1 file), NiBabel (1 file), Nilearn (1 file), pandas (1 file), pydicom (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
28 files
At the source: github.com/medusamedusa/fMRI_telecommunications

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

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;
  • 28 scripts, each with its path and the digest of its content;
  • 6 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

Datasets cited

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:

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

Read it in the paper: doi.org/10.1038/s41598-026-50758-x.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 9 keywords, 7 MeSH terms, 2 funders, 69 references.

Cite

This paper

Fazio, P., Mannone, M., Marwan, N., Ribino, P., Mehic, M., Swikir, A., Amendola, D., Riello, P., & Voznak, M. (2026). Telecommunication-inspired network models of healthy and diseased brains. Scientific reports, 16(1), 19686. https://doi.org/10.1038/s41598-026-50758-x

BibTeX

@article{fazio2026telecommunication,
author = {Fazio, Peppino and Mannone, Maria and Marwan, Norbert and Ribino, Patrizia and Mehic, Miralem and Swikir, Abdalla and Amendola, Danilo and Riello, Pietro and Voznak, Miroslav},
title = {{Telecommunication-inspired network models of healthy and diseased brains}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {19686},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-50758-x},
url = {https://doi.org/10.1038/s41598-026-50758-x},
pmid = {42049884},
pmcid = {PMC13314952}
}

RIS

TY - JOUR
AU - Fazio, Peppino
AU - Mannone, Maria
AU - Marwan, Norbert
AU - Ribino, Patrizia
AU - Mehic, Miralem
AU - Swikir, Abdalla
AU - Amendola, Danilo
AU - Riello, Pietro
AU - Voznak, Miroslav
TI - Telecommunication-inspired network models of healthy and diseased brains
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/28
VL - 16
IS - 1
SP - 19686
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-50758-x
UR - https://doi.org/10.1038/s41598-026-50758-x
LA - en
ER -

CSL-JSON

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"title": "Telecommunication-inspired network models of healthy and diseased brains",
"container-title": "Scientific reports",
"author": [
{
"family": "Fazio",
"given": "Peppino"
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{
"family": "Mannone",
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{
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{
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}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "19686",
"DOI": "10.1038/s41598-026-50758-x",
"PMID": "42049884",
"PMCID": "PMC13314952",
"ISSN": "2045-2322",
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"issued": {
"date-parts": [
[
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28
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]
}
}

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