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Time-Varying Brain Functional Reconfiguration Patterns Associated With Fatigue in Multiple Sclerosis.

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 · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Functional MRI Analyses—Community Detection and Time‐Varying Network Connectivity ↔ CommunityDetection.m, the whole file · a weak match · score 0.77 · Community detection, assignment quality, network assignment, iterative, window, literature
  2. [2] § Methods › Validation Analyses ↔ Generate_surrogate.m, the whole file · a weak match · score 0.64 · generated surrogate, Fourier transformation, randomizing, models

Paper

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

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The authors' code

MATLAB · 118 lines · 4.7 KB · no license · 1 match

  1. function [netwassign_final,varargout] = CommunityDetection(timeseries,initial_assignment,window_size,step_size,varargin)
  2. %INPUT:
  3. % timeseries= t*n matrix containing the subjects' timeseries. t is the
  4. % number of timepoints and n the number of regions.
  5. % initial_assignment= n vector containing the initial assignment (e.g.
  6. % literature-based)
  7. % window_size= integer indicating the window-size
  8. % step_size= integer indicating the step-size
  9. %optional:
  10. % gausswin= use gaussian windows (true/false). Need weightedcorrs.m in
  11. % path (www.mathworks.com/matlabcentral/fileexchange/20846-weighted-correlation-matrix)
  12. % maxit= maximum number of iterations
  13. %
  14. %OUTPUT:
  15. % netwassign_final= n*w matrix containing the windowed network assignments.
  16. % n is the number of regions and w is the number of windows.
  17. %optional:
  18. % itw= the number of iterations until convergence for each window
  20. %Variable input arguments
  21. if nargin == 4
  22. gausswin=false;
  23. maxit=1000;
  24. elseif nargin == 5
  25. gausswin=varargin{1};
  26. maxit=1000;
  27. elseif nargin == 6
  28. gausswin=varargin{1};
  29. maxit=varargin{2};
  30. else
  31. error('incorrect number of input variables')
  32. end
  34. %Check variable sizes
  35. if length(initial_assignment)~=size(timeseries,2)
  36. error('Initial assignment variable does not have the same number of regions as the timeseries variable')
  37. elseif length(window_size)~=1; error('Window size: need single integer value')
  38. elseif length(step_size)~=1; error('Step size: need single integer value')
  39. elseif length(gausswin)~=1; error('Gaussian window: need single logical value')
  40. elseif length(maxit)~=1; error('Maximum number of iterations: need single integer value')
  41. elseif size(timeseries,1)<size(timeseries,2)
  42. warning('Warning: more regions in timeseries than timepoints, could indicate incorrect orientation (t*n)')
  43. end
  45. %Check variable datatypes
  46. if ~isnumeric(timeseries); error('Timeseries: wrong datatype entered')
  47. elseif ~isnumeric(initial_assignment) || ~all(mod(initial_assignment,1)==0); error('Initial assignment: wrong datatype entered')
  48. elseif ~isnumeric(window_size) || ~mod(window_size,1)==0; error('Window size: need single integer value')
  49. elseif ~isnumeric(step_size) || ~mod(step_size,1)==0; error('Step size: need single integer value')
  50. elseif ~islogical(gausswin); error('Gaussian window: need single logical value');
  51. elseif ~isnumeric(maxit) || ~mod(maxit,1)==0; error('Maximum number of iterations: need single integer value')
  52. end
  54. %Set gaussian window parameters if needed
  55. if gausswin
  56. window_shape=gausswin(window_size,(window_size-1)/6);window_shape=window_shape/mean(window_shape); % stdev = (N-1)/(2*alpha), so stdev=3
  57. end
  59. %Count number of networks/regions/windows
  60. num_netw=length(unique(initial_assignment));
  61. numreg=length(initial_assignment);
  62. numwin=length(1:step_size:(size(timeseries,1)-(window_size-1)));
  64. qq=0;itw=zeros(numwin,1);netwassign_final=zeros(numreg,numwin);
  65. for jj = 1:step_size:(size(timeseries,1)-(window_size-1)) %Create windows
  66. qq=qq+1;
  67. fmri_epoch=timeseries(jj:jj+(window_size-1),:);
  69. %Calculate connectivity
  70. if gausswin
  71. connect = abs(atanh(weightedcorrs(fmri_epoch,window_shape)));
  72. else
  73. connect = abs(atanh(corr(fmri_epoch)));
  74. end
  75. connect(logical(eye(size(connect)))) = NaN;
  77. costmat = zeros(numreg,1);
  78. netwassign=initial_assignment; % first set assignment to initial assignment
  79. tmporderprev=0; %keep track of worst-assigned region for each previous iteration
  81. % iteratively update the assignment until convergence
  82. for it=1:maxit
  84. % calculate the quality of each current assignment for all regions
  85. for ii = 1:numreg
  86. winode = mean(connect(ii,netwassign == netwassign(ii)),'omitnan');
  87. btnode = mean(connect(ii,netwassign ~= netwassign(ii)),'omitnan');
  88. costmat(ii) = (winode-btnode)/(winode+btnode);
  89. end
  91. % select the region that shows the worst assignment quality
  92. [~,tmporder]=sort(costmat);
  94. % stop the algorithm if the assignment is the same as the previous iteration (i.e. convergence)
  95. if tmporderprev~=tmporder(1)
  96. q=tmporder(1);
  97. else
  98. break
  99. end
  100. tmporderprev=q; % save worst assigned region for the next iteration
  102. % calculate the network with the best match for the worst region and reassign
  103. newcost = zeros(num_netw,1);
  104. for ii=1:num_netw
  105. newcost(ii) = mean(connect(q,netwassign==ii),'omitnan');
  106. end
  107. [~,z] = max(newcost); % determine best match
  108. netwassign(q)=z; % update assignment
  110. end
  111. itw(qq)=it;
  112. netwassign_final(:,qq)=netwassign; %save final assignment for each window
  113. end
  115. varargout{1}=itw;
  117. end

CommunityDetection.m at commit 1ec1cc1, no license · at the source

Overview

Authors: Stefanie Hechenberger1,2,3, Tommy A A Broeders4, Marloes D A Bet4, Birgit Helmlinger1,2, Christian Tinauer2, Stefan Ropele2, Bettina Heschl2, Sebastian Wurth2,5, Anna Damulina2, Michael Khalil2, Menno M Schoonheim4, Christian Enzinger2, Daniela Pinter1,2
ORCID iDs: Stefanie Hechenberger, Tommy A A Broeders, Marloes D A Bet, Birgit Helmlinger, Christian Tinauer, Stefan Ropele, Sebastian Wurth, Anna Damulina, Michael Khalil, Menno M Schoonheim, Christian Enzinger, Daniela Pinter
  1. Department of Neurology, Research Unit for Neuroplasticity and Repair, Medical University of Graz, Graz, Austria
  2. Department of Neurology, Medical University of Graz, Graz, Austria
  3. Clinical Department of Oncology, University Medical Center of Internal Medicine, Medical University of Graz, Graz, Austria
  4. Amsterdam UMC, Vrije Universiteit Amsterdam, Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam, the Netherlands
  5. Division of Neuroradiology and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
Institutions: University of Graz (Austria); Medical University of Graz (Austria); Amsterdam Neuroscience (Netherlands); Amsterdam University Medical Centers (Netherlands); Vrije Universiteit Amsterdam (Netherlands)
Journal: Human brain mapping, volume 47, issue 4, article e70480
Dates: received 14 November 2025; accepted 8 February 2026; published online 8 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/hbm.70480 · PMID 41797387 · PMCID PMC12968462 · OpenAlex W7134287170
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population)
Methods: Statistics, Smoothing, state filtering, decompositions, Connectivity, fMRI & imaging
Keywords: brain networks, fatigue, multiple sclerosis, time‐varying reconfigurations
MeSH: Brain*, Connectome*, Fatigue*, Multiple Sclerosis*, Nerve Net*, Adult, Cross-Sectional Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Time Factors (* major topic)
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 63 references in the paper

Abstract

Fatigue affects between 36.5% and 78% of people with multiple sclerosis (pwMS) and significantly impairs daily life. The neurobiological mechanisms underlying fatigue remain poorly understood and information about the time‐varying communication between brain regions and the networks they form may offer new insights into the complex pathology of MS‐related fatigue. Brain regions continuously reconfigure how they communicate within distinct networks (i.e., time‐varying reconfigurations) and aberrant time‐varying reconfigurations may contribute to the perception of fatigue in pwMS. This study aimed to explore if and how time‐varying reconfigurations are associated with fatigue in pwMS. In this cross‐sectional study, 155 pwMS (62% female; age = 39 ± 10 years; disease duration = 10 ± 8 years; median EDSS = 1.0 ± 2.0) and 48 healthy controls (HC) (71% female; age = 33 ± 10 years) underwent clinical, neuropsychological, and (resting‐state functional) MRI assessments. Fatigue was evaluated with the “Fatigue Scale for Motor and Cognitive Function”, comprising total, motor, and cognitive fatigue scores. Time‐varying connectivity was derived using a sliding‐window approach, with data‐driven assignment of brain regions to one of eight resting‐state networks for each window. Promiscuity (dispersion of reconfigurations), flexibility (frequency of reconfigurations), cohesion (joint reconfigurations), and disjointedness (independent reconfigurations) described the time‐varying reconfigurations of the whole brain and its networks. Among pwMS, 57% reported experiencing at least mild total fatigue (motor: 60%, cognitive: 57%). Higher total fatigue was correlated with greater global promiscuity (r = 0.21, p = 0.032) and disjointedness (r = 0.24, p = 0.008). Similarly, higher motor fatigue was associated with greater global promiscuity (r = 0.25, p = 0.008), flexibility (r = 0.21, p = 0.032), and disjointedness (r = 0.28, p < 0.001). The associations with disjointedness remained significant even after controlling for demographics, clinical measures, and structural brain damage, such as lesion load and atrophy (total fatigue: adj.R 2 = 0.23, β = 0.17, p = 0.033; motor fatigue: adj.R 2 = 0.38, β = 0.16, p = 0.026). Network‐level analyses in pwMS revealed that higher total (r = 0.25, p = 0.016) and motor (r = 0.25, p = 0.016) fatigue were associated with greater limbic network promiscuity. No significant correlations were found for cognitive fatigue in pwMS, or for total, motor, and cognitive fatigue in HC (all p > 0.05). Elevated levels of fatigue, particularly motor fatigue, in pwMS were linked to more unstable network reconfigurations, particularly of regions in the limbic network, possibly reflecting dysfunctional reward processing. More frequent dispersion requires more energy and may therefore contribute to increased fatigue.

Trial Registration: This project was pre‐registered at ClinicalTrials.gov (registration number: NCT04892134)

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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

taabroeders/recon_dyn_ms

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1ec1cc113823e7f27ea8ba87a1c72171031c6ea2, 7 September 2021
Languages: MATLAB (2)
Size: 2 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Functional MRI Analyses—Community Detection and ”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/taabroeders/Recon_Dyn_MS/blob/main/CommunityDetection.m

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 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);
  • 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 data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Reproduced under the paper's license (CC BY), from the paper cited above.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 4 keywords, 13 MeSH terms, 60 references.

Cite

This paper

Hechenberger, S., Broeders, T. A. A., Bet, M. D. A., Helmlinger, B., Tinauer, C., Ropele, S., Heschl, B., Wurth, S., Damulina, A., Khalil, M., Schoonheim, M. M., Enzinger, C., & Pinter, D. (2026). Time-Varying Brain Functional Reconfiguration Patterns Associated With Fatigue in Multiple Sclerosis. Human brain mapping, 47(4), e70480. https://doi.org/10.1002/hbm.70480

BibTeX

@article{hechenberger2026time,
author = {Hechenberger, Stefanie and Broeders, Tommy A A and Bet, Marloes D A and Helmlinger, Birgit and Tinauer, Christian and Ropele, Stefan and Heschl, Bettina and Wurth, Sebastian and Damulina, Anna and Khalil, Michael and Schoonheim, Menno M and Enzinger, Christian and Pinter, Daniela},
title = {{Time-Varying Brain Functional Reconfiguration Patterns Associated With Fatigue in Multiple Sclerosis}},
journal = {Human brain mapping},
year = {2026},
month = mar,
volume = {47},
number = {4},
pages = {e70480},
publisher = {Wiley},
issn = {1065-9471},
doi = {10.1002/hbm.70480},
url = {https://doi.org/10.1002/hbm.70480},
pmid = {41797387},
pmcid = {PMC12968462}
}

RIS

TY - JOUR
AU - Hechenberger, Stefanie
AU - Broeders, Tommy A A
AU - Bet, Marloes D A
AU - Helmlinger, Birgit
AU - Tinauer, Christian
AU - Ropele, Stefan
AU - Heschl, Bettina
AU - Wurth, Sebastian
AU - Damulina, Anna
AU - Khalil, Michael
AU - Schoonheim, Menno M
AU - Enzinger, Christian
AU - Pinter, Daniela
TI - Time-Varying Brain Functional Reconfiguration Patterns Associated With Fatigue in Multiple Sclerosis
T2 - Human brain mapping
J2 - Hum Brain Mapp
PY - 2026
DA - 2026/03/01
VL - 47
IS - 4
SP - e70480
SN - 1065-9471
PB - Wiley
DO - 10.1002/hbm.70480
UR - https://doi.org/10.1002/hbm.70480
LA - en
ER -

CSL-JSON

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