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Dopamine-related alterations in functional brain network dynamic reconfiguration in Parkinson's disease.

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  1. [1] § Methods › Dynamic network reconfiguration measures ↔ HelperFunctions/multiord.m, the whole file · a weak match · score 0.58 · multilayer community detection, resolution parameters, Intralayer, correlation, temporal, GenLouvain
  2. [2] § Methods › Dynamic network reconfiguration measures ↔ HelperFunctions/multiord_f.m, the whole file · a weak match · score 0.58 · multilayer community detection, resolution parameters, Intralayer, correlation, temporal, GenLouvain

Paper

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The authors' code

MATLAB · 133 lines · 5.5 KB · BSD-2-Clause · 1 match

  1. function [B,twom] = multiord(A,gamma,omega)
  2. %MULTIORD returns multilayer Newman-Girvan modularity matrix for ordered layers, matrix version
  3. % Works for directed or undirected networks
  4. %
  5. % Version: 2.2.0
  6. % Date: Thu 11 Jul 2019 12:25:42 CEST
  7. %
  8. % Input: A: Cell array of NxN adjacency matrices for each layer of an
  9. % ordered multilayer (directed or undirected) network
  10. % gamma: intralayer resolution parameter
  11. % omega: interlayer coupling strength
  12. %
  13. % Output: B: [NxT]x[NxT] flattened modularity tensor for the
  14. % multilayer network with uniform ordinal coupling (T is
  15. % the number of layers of the network)
  16. % mm: normalisation constant
  17. %
  18. % Example of usage: [B,mm]=multiord(A,gamma,omega);
  19. % [S,Q]= genlouvain(B); % see iterated_genlouvain.m and
  20. % postprocess_temporal_multilayer.m for how to improve output
  21. % multilayer partition
  22. % Q=Q/mm;
  23. % S=reshape(S,N,T);
  24. %
  25. % [B,mm] = MULTIORD(A,GAMMA, OMEGA) with A a cell array of square
  26. % (symmetric or assymetric) matrices of equal size each representing a
  27. % directed or undirected network "layer" computes the Newman Girvan multilayer
  28. % modularity matrix using the quality function described in Mucha et al.
  29. % 2010, with intralayer resolution parameter GAMMA, and with interlayer
  30. % coupling OMEGA connecting nearest-neighbor ordered layers. The null
  31. % model used for the quality function is the Newman-Girvan null model
  32. % (see e.g. Bazzi et al. for other possible null models). Once the
  33. % mulilayer modularity matrix is computed, optimization can be performed
  34. % by the generalized Louvain code GENLOUVAIN or ITERATED_GENLOUVAIN. The
  35. % sparse output matrix B can be used with other heuristics, provided the
  36. % same mapping is used to go from the multilayer tensor to the multilayer
  37. % flattened matrix. That is, the node-layer tuple (i,s) is mapped to
  38. % i + (s-1)*N. [Note that we can define a mapping between a multilayer
  39. % partition S_m stored as an N by T matrix and the corresponding flattened
  40. % partition S stored as an NT by 1 vector. In particular S_m = reshape(S,N,T)
  41. % and S = S_m(:).]
  42. %
  43. % See also
  44. % genlouvain heuristics: GENLOUVAIN, ITERATED_GENLOUVAIN
  45. % multilayer wrappers: MULTICAT, MULTICATF, MULTIORDF
  46. % other heuristics: SPECTRAL23
  47. % Kernighan-Lin improvement: KLNB
  48. %
  49. % Notes:
  50. % The matrices in the cell array A are assumed to be square,
  51. % and of equal size. These assumptions are not checked here.
  52. %
  53. % This code assumes that the sparse quality/modularity matrix B will
  54. % fit in memory and proceeds to build that matrix. For larger systems,
  55. % try MULTIORD_F for undirected layer networks and MULTIORDDIR_F
  56. % for directed layer networks.
  57. %
  58. % This code serves as a template and can be modified for situations
  59. % with other wrinkles (e.g., different intralayer null models [see eg
  60. % Bazzi et al. 2016 for examples], different numbers of nodes from
  61. % layer-to-layer, or systems which are both multiplex and longitudinal).
  62. % That is, this code is only a starting point; it is by no means
  63. % exhaustive.
  64. %
  65. % By using this code, the user implicitly acknowledges that the authors
  66. % accept no liability associated with that use. (What are you doing
  67. % with it anyway that might cause there to be a potential liability?!?)
  68. %
  69. % References:
  70. % Blondel, Vincent D., Jean-Loup Guillaume, Renaud Lambiotte, and
  71. % Etienne Lefebvre, "Fast unfolding of communities in large networks,"
  72. % Journal of Statistical Mechanics: Theory and Experiment, P10008
  73. % (2008).
  74. %
  75. % Fortunato, Santo, "Community detection in graphs," Physics Reports
  76. % 486, 75-174 (2010).
  77. %
  78. % Good, Benjamin H., Yves-Alexandre de Montjoye, and Aaron Clauset,
  79. % "Performance of modularity maximization in practical contexts,"
  80. % Physical Review E 81, 046106 (2010).
  81. %
  82. % Newman, Mark E. J. and Michelle Girvan. "Finding and Evaluating
  83. % Community Structure in Networks", Physical Review E 69, 026113 (2004).
  84. %
  85. % Elizabeth A. Leicht and Mark E. J. Newman. "Community structure in
  86. % Directed Networks", Physical Review Letters 100, 118703 (2008).
  87. %
  88. % Mucha, Peter J., Thomas Richardson, Kevin Macon, Mason A. Porter, and
  89. % Jukka-Pekka Onnela. "Community Structure in Time-Dependent,
  90. % Multiscale, and Multiplex Networks," Science 328, 876-878 (2010).
  91. %
  92. % Bazzi, Marya, Mason A. Porter, Stacy Williams, Mark McDonald, Daniel
  93. % J. Fenn, and Sam D. Howison. "Community Detection in Temporal
  94. % Multilayer Networks, with an Application to Correlation Networks",
  95. % MMS: A SIAM Interdisciplinary Journal 14, 1-41 (2016).
  96. %
  97. % Porter, M. A., J. P. Onnela, and P. J. Mucha, "Communities in
  98. % networks," Notices of the American Mathematical Society 56, 1082-1097
  99. % & 1164-1166 (2009).
  100. %
  101. % Acknowledgments:
  102. % Thank you to Dani Bassett, Jesse Blocher, Bruce Rogers, and Simi Wang
  103. % for their collaborative help which led to significant cleaning up
  104. % of earlier versions of our multilayer community detection codes.
  107. if nargin<2
  108. gamma=1;
  109. end
  111. if nargin<3
  112. omega=1;
  113. end
  115. N=length(A{1});
  116. T=length(A);
  118. if length(gamma)==1
  119. gamma=repmat(gamma,T,1);
  120. end
  122. B=spalloc(N*T,N*T,N*N*T+2*N*T);
  123. twom=0;
  124. for s=1:T
  125. kout=sum(A{s},1);
  126. kin=sum(A{s},2);
  127. mm=sum(kout);
  128. twom=twom+mm;
  129. indx=[1:N]+(s-1)*N;
  130. B(indx,indx)=(A{s}+A{s}')/2-gamma(s)/2.*((kin*kout+kout'*kin')/mm);
  131. end
  132. B = B + omega*spdiags(ones(N*T,2),[-N,N],N*T,N*T);
  133. twom=twom+2*N*(T-1)*omega;

multiord.m at commit 0fb0aa8, under BSD-2-Clause · at the source

Overview

Authors: AmirHussein Abdolalizadeh1, Micha Burkhardt2, Paria Jahansa3, Carsten Gießing1,4, Karsten Witt4,5, Christiane M Thiel1,4
ORCID iDs: Micha Burkhardt
  1. Biological Psychology Lab, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
  2. Psychological Methods and Statistics, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
  3. Mathematical Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
  4. Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
  5. Department of Neurology, School of Medicine and Health Sciences, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
Institutions: Carl von Ossietzky Universität Oldenburg (Germany)
Journal: NPJ Parkinson's disease, volume 12, issue 1, article 175
Dates: received 27 November 2025; accepted 27 June 2026; published online 21 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41531-026-01466-w · PMID 42481479 · PMCID PMC13388714 · OpenAlex W7169913901
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), Parkinson's (population)
Methods: Statistics, Connectivity, fMRI & imaging
Keywords: Diseases, Neurology, Neuroscience
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (456732630)
Citations: not cited yet (Europe PMC); 94 references in the paper
Research resources: RRID:SCR_002502, RRID:SCR_006431, RRID:SCR_016216, RRID:SCR_017427

Abstract

Dopaminergic degeneration in Parkinson’s disease disrupts large-scale brain networks, yet how dopamine loss and its treatment shape the brain’s dynamic reconfiguration over time remains unknown. We combined resting-state fMRI with dopamine transporter scan in 136 drug-naive patients and 20 healthy controls from the PPMI cohort to determine how dopamine transporter availability relates to dynamic network reconfiguration, indexed by how brain regions switch communities over time. Patients showed reduced modular reconfiguration in the default-mode network. Dopamine transporter availability was differentially associated with reconfiguration, showing negative associations in visual and positive associations in limbic networks. Cognitive performance correlated with attention network reconfiguration, whereas motor impairment tracked dopamine loss. Longitudinal analyses in a subset with one year follow-up (n = 29) showed that network reconfiguration increased with dopaminergic decline, and medication modulated these dynamics toward the pattern seen in healthy controls. Our findings demonstrate that network reconfiguration captures dopamine-sensitive and cognition-relevant alterations in early Parkinson’s disease.

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.

GenLouvain/GenLouvain

License: BSD-2-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0fb0aa8bccae4ffb5a840c8f68f8dd48f1293de2, 14 January 2024
Languages: MATLAB (32), C++ (5), C/C++ (4), C (3)
Size: 61 files, 44 scripts
Software Heritage: not archived
Found in: the text, “Dynamic network reconfiguration measures”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
46 files
At the source: github.com/GenLouvain/GenLouvain

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;
  • 44 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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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

Data used in this project were downloaded from the Parkinson’s Progression Markers Initiative (PPMI) database (https://ppmi-info.org/).

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 3 keywords, 1 funder, 82 references, 4 RRIDs.

Cite

This paper

Abdolalizadeh, A., Burkhardt, M., Jahansa, P., Gießing, C., Witt, K., & Thiel, C. M. (2026). Dopamine-related alterations in functional brain network dynamic reconfiguration in Parkinson's disease. NPJ Parkinson's disease, 12(1), 175. https://doi.org/10.1038/s41531-026-01466-w

BibTeX

@article{abdolalizadeh2026dopamine,
author = {Abdolalizadeh, AmirHussein and Burkhardt, Micha and Jahansa, Paria and Gießing, Carsten and Witt, Karsten and Thiel, Christiane M},
title = {{Dopamine-related alterations in functional brain network dynamic reconfiguration in Parkinson's disease}},
journal = {NPJ Parkinson's disease},
year = {2026},
month = jul,
volume = {12},
number = {1},
pages = {175},
publisher = {Nature Publishing Group},
issn = {2373-8057},
doi = {10.1038/s41531-026-01466-w},
url = {https://doi.org/10.1038/s41531-026-01466-w},
pmid = {42481479},
pmcid = {PMC13388714}
}

RIS

TY - JOUR
AU - Abdolalizadeh, AmirHussein
AU - Burkhardt, Micha
AU - Jahansa, Paria
AU - Gießing, Carsten
AU - Witt, Karsten
AU - Thiel, Christiane M
TI - Dopamine-related alterations in functional brain network dynamic reconfiguration in Parkinson's disease
T2 - NPJ Parkinson's disease
J2 - NPJ Parkinsons Dis
PY - 2026
DA - 2026/07/21
VL - 12
IS - 1
SP - 175
SN - 2373-8057
PB - Nature Publishing Group
DO - 10.1038/s41531-026-01466-w
UR - https://doi.org/10.1038/s41531-026-01466-w
LA - en
ER -

CSL-JSON

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