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Arousal-driven critical roaming reproduces human functional connectivity dynamics.

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 › FC, FCD, and FCD Speed ↔ dfc-walk/TS2dFCstream.m, the whole file · a weak match · score 0.54 · TS2FC, TS2dFCstream, frame, matrix
  2. [2] § Methods › FC, FCD, and FCD Speed ↔ dfc-walk/TS2dFCstream.m, the whole file · a weak match · score 0.53 · sliding window, FC matrices, overlapping

Paper

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

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The authors' code

MATLAB · 85 lines · 2.4 KB · CC-BY-4.0 · 2 matches

  1. function dFCstream = TS2dFCstream(TS, W, lag, format)
  3. % FUNCTION dFCstream = TS2dFCstream(TS, W, lag, format)
  4. % takes time-series (TS), size of window (W), and value of shift of window
  5. % (lag) as input and calculates dynamic functional connectivity stream
  6. % (dFCstream) as output.
  7. %
  8. % inputs: TS(t,n) --> rows are t different time-points;
  9. % columns are n different regions;
  10. % W --> size of window to slide;
  11. % lag --> shifting value, default is lag=W;
  12. % format --> '2D' (default) provides a [l]x[F] dFCstream
  13. % (FC vector over time) where l=n(n-1)/2 is the number
  14. % of links for n regions and F is the number of frames
  15. % depending on values of (t,W,lag);
  16. % '3D' provides a [n]x[n]x[F] dFCstream (FC matrix
  17. % over time).
  18. %
  19. % Example: dfcstream = TS2dFCstream(ts,10,10,'2D')
  20. % Example: dfcstream = TS2dFCstream(ts,10,[],'2D')
  21. % computes the dFCstream with window size 10 and without overlap between
  22. % windows. Since lag assumes a value equal to the default one and the
  23. % default is '2D', the syntax above is equivalent to the simpler syntax below:
  24. % Example: dfcstream = TS2dFCstream(ts,10)
  26. if ~exist('W','var') || isempty(W)
  27. disp('Provide at least a window size!!!')
  28. return
  29. end
  30. if ~exist('lag','var') || isempty(lag)
  31. lag = W; % default value for the size of sliding window if no argument for it is given
  32. end
  33. if ~exist('format','var') || isempty(format)
  34. format = '2D';
  35. end
  37. t = size(TS,1);
  38. n = size(TS,2);
  39. l = n*(n-1)/2;
  41. % calculate F
  42. wstart = 1;
  43. wstop = W;
  44. k = 0;
  45. while (wstop <= t)
  46. k = k+1;
  47. wstart = wstart + lag;
  48. wstop = wstop + lag;
  49. end
  50. kmax = k;
  52. % preallocate the dFCstream
  53. if (strcmp(format, '3D'))
  54. dFCstream = zeros(n,n,kmax);
  55. else
  56. dFCstream = zeros(l,kmax);
  57. end
  59. if (strcmp(format, '3D'))
  61. wstart = 1;
  62. wstop = W;
  63. k = 0;
  64. while (wstop <= t)
  65. k = k+1;
  66. % compute FC matrix for each lag column between wstart and wstop
  67. dFCstream(1:n, 1:n, k) = TS2FC(TS(wstart:wstop, :), '2D');
  68. wstart = wstart + lag;
  69. wstop = wstop + lag;
  70. end
  72. else
  74. wstart = 1;
  75. wstop = W;
  76. k = 0;
  77. while (wstop <= t)
  78. k = k+1;
  79. % compute FC vector for each lag column between wstart and wstop
  80. dFCstream(:, k) = TS2FC(TS(wstart:wstop, :), '1D');
  81. wstart = wstart + lag;
  82. wstop = wstop + lag;
  83. end
  85. end

TS2dFCstream.m, under CC-BY-4.0 · at the source

Overview

Authors: Anagh Pathak1, Demian Battaglia1
ORCID iDs: Anagh Pathak
  1. Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), Université de Strasbourg, CNRS, UMR 7364, Strasbourg, France
Institutions: Centre National de la Recherche Scientifique (France); Laboratoire de Neurosciences Cognitives et Adaptatives (France); Université de Strasbourg (France)
Journal: PLoS biology, volume 24, issue 9, article e3003916
Dates: received 24 January 2026; accepted 8 July 2026; published online 1 September 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pbio.3003916 · PMID 42678916 · PMCID PMC13533355 · OpenAlex W7204943783
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), systems (subfield)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Statistics, fMRI & imaging, Single-unit activity, calcium imaging
MeSH: Arousal*, Brain*, Nerve Net*, Connectome, Humans, Magnetic Resonance Imaging, Models, Neurological (* major topic)
Journal subjects: Computer and Information Sciences, Systems Science, Dynamical Systems, Physical Sciences, Mathematics, Nonlinear Dynamics, Biology and Life Sciences, Neuroscience, Brain Mapping, Functional Magnetic Resonance Imaging, Medicine and Health Sciences, Diagnostic Medicine, Diagnostic Radiology, Magnetic Resonance Imaging, Research and Analysis Methods, Imaging Techniques, Radiology and Imaging, Neuroimaging, Probability Theory, Statistical Distributions, Neural Networks, Mathematical and Statistical Techniques, Cluster Analysis, K Means Clustering, Population Biology, Population Dynamics, Cognitive Science, Cognitive Neuroscience, Cognitive Neurology, Cognitive Impairment, Neurology
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Fondation Vaincre Alzheimer; Agence Nationale de la Recherche (PEPR ANR-22-PESN-0012-BHT)
Citations: cited by 1 paper (Europe PMC); 87 references in the paper
Notices: A comment on this paper has been published (42685012, from Europe PMC)

Abstract

Ongoing brain activity displays rich temporal variability associated with efficient cognition, with functional connectivity (FC) continually reconfiguring over time. The resulting functional connectivity dynamics (FCD) specifically show complex, fat-tailed statistics that alternate between persistent epochs and faster reconfiguration transients. While nonlinear whole-brain models tuned nearby a critical point have reproduced some aspects of FCD, they fall short of capturing its full temporal complexity. We propose that slow fluctuations in arousal offer a biologically plausible mechanism for exploring critical regimes in large-scale brain dynamics and thus enrich FCD. Using a connectome-based model of coupled cortical populations, we identified phase boundaries where system dynamics transition between regimes of faster or slower FCD. We then phenomenologically incorporated arousal changes, modeling them as stochastic fluctuations in key parameters such as cortical excitability, input gain, and noise amplitude. This explicitly time-dependent formulation enables the system to roam dynamically across regime boundaries, flexibly tuning its distance from critical transition lines and producing intermittent transitions that mirror the stochastic evolution observed in empirical FCD. Fitting these models to human resting-state fMRI and performing model comparison, we find that arousal-driven models more accurately reproduce the distinctive quantitative features of FCD, with the greatest improvements coming from the previously poorly accounted fat-tailed portions of the distributions. Together, these results suggest that arousal fluctuations—likely mediated by changes in neuromodulatory tone—shape the brain’s attractor landscape over time, expanding the repertoire of accessible functional network states and providing a mechanistic basis for the complexity of spontaneous functional dynamics.

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

Repositories

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

Zenodo 20798972

License: CC-BY-4.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (2 files), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
29 files
At the source: doi.org/10.5281/zenodo.20798972

juanitacabral/NetworkModel_Toolbox

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: f044a937ba64743935de790c79a74669ac8b4064, 21 January 2019
Languages: MATLAB (6), Python (2)
Size: 12 files, 8 scripts
Software Heritage: not archived
Found in: the text, “Neural mass modeling”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (2 files), Signal Processing Toolbox (1 file), Matplotlib (1 file), SciPy (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
8 files
At the source: github.com/juanitacabral/NetworkModel_Toolbox

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

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 36 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

The code used for all analyses and simulations is publicly available at https://doi.org/10.5281/zenodo.20798972. Data underlying the figures are provided in the Supporting information.

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, 2 authors, 7 MeSH terms, 2 funders, 78 references, 1 integrity notice.

Cite

This paper

Pathak, A., & Battaglia, D. (2026). Arousal-driven critical roaming reproduces human functional connectivity dynamics. PLoS biology, 24(9), e3003916. https://doi.org/10.1371/journal.pbio.3003916

BibTeX

@article{pathak2026arousal,
author = {Pathak, Anagh and Battaglia, Demian},
title = {{Arousal-driven critical roaming reproduces human functional connectivity dynamics}},
journal = {PLoS biology},
year = {2026},
month = sep,
volume = {24},
number = {9},
pages = {e3003916},
publisher = {PLOS},
issn = {1544-9173},
doi = {10.1371/journal.pbio.3003916},
url = {https://doi.org/10.1371/journal.pbio.3003916},
pmid = {42678916},
pmcid = {PMC13533355}
}

RIS

TY - JOUR
AU - Pathak, Anagh
AU - Battaglia, Demian
TI - Arousal-driven critical roaming reproduces human functional connectivity dynamics
T2 - PLoS biology
J2 - PLoS Biol
PY - 2026
DA - 2026/09/01
VL - 24
IS - 9
SP - e3003916
SN - 1544-9173
PB - PLOS
DO - 10.1371/journal.pbio.3003916
UR - https://doi.org/10.1371/journal.pbio.3003916
LA - en
ER -

CSL-JSON

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"DOI": "10.1371/journal.pbio.3003916",
"PMID": "42678916",
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