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Psilocybin shapes the slow, global propagation of brain activity over the cortical layout of 5HT2a receptors.

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  1. [1] § Methods › Cortical gradients ↔ run_gradients.m, the whole file · a weak match · score 0.74 · BrainSpace, diffusion embedding, gradient score, alignment, Schaefer, components · Report a mismatch

Paper

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The authors' code

MATLAB · 150 lines · 4.7 KB · other · 1 match

  1. % addpath BrainSpace toolbox to path
  2. % Schaefer parcellation file should be on path
  4. load conn_Schaef
  5. [surf_lh, surf_rh] = load_conte69();
  8. parcelSch=ft_read_cifti('Schaefer2018_200Parcels_7Networks_order.dlabel.nii');
  9. labeling = parcelSch.parcels(1:64984);
  11. % All_subjects
  12. cont1 = 1;
  13. cont2 = 1;
  14. cont3 = 1;
  15. cont4 = 1;
  16. cont5 = 1;
  18. for i =1:size(connAll,1)
  19. Mx_z_sparse = squeeze(connAll(i,1,:,:));
  20. symmetric_matrix = Mx_z_sparse + Mx_z_sparse';
  21. symmetric_matrix(eye(size(symmetric_matrix)) == 1) = 0;
  22. non_zero_indices = any(symmetric_matrix, 2);
  23. filtered_matrix = symmetric_matrix(non_zero_indices, non_zero_indices);
  24. % Normalize each row of the filtered matrix
  25. if ~isempty(filtered_matrix)
  26. norms = sqrt(sum(filtered_matrix.^2, 2));
  27. normalized_matrix = filtered_matrix ./ norms;
  29. connmatrixAll_B1{cont1} = normalized_matrix;
  30. cont1 = cont1 + 1;
  31. end
  32. clear normalized_matrix
  33. Mx_z_sparse = squeeze(connAll(i,2,:,:));
  34. symmetric_matrix = Mx_z_sparse + Mx_z_sparse';
  35. symmetric_matrix(eye(size(symmetric_matrix)) == 1) = 0;
  36. non_zero_indices = any(symmetric_matrix, 2);
  37. filtered_matrix = symmetric_matrix(non_zero_indices, non_zero_indices);
  38. % Normalize each row of the filtered matrix
  39. if ~isempty(filtered_matrix)
  40. norms = sqrt(sum(filtered_matrix.^2, 2));
  41. normalized_matrix = filtered_matrix ./ norms;
  43. connmatrixAll_B2{cont2} = normalized_matrix;
  44. cont2 = cont2 + 1;
  45. end
  46. clear normalized_matrix
  48. Mx_z_sparse = squeeze(connAll(i,3,:,:));
  49. symmetric_matrix = Mx_z_sparse + Mx_z_sparse';
  50. symmetric_matrix(eye(size(symmetric_matrix)) == 1) = 0;
  51. non_zero_indices = any(symmetric_matrix, 2);
  52. filtered_matrix = symmetric_matrix(non_zero_indices, non_zero_indices);
  53. if ~isempty(filtered_matrix) % Check if there are non-zero rows/columns
  54. norms = sqrt(sum(filtered_matrix.^2, 2));
  55. normalized_matrix = filtered_matrix ./ norms;
  56. connmatrixAll_B3{cont3} = normalized_matrix;
  57. cont3 = cont3 + 1;
  58. end
  59. clear normalized_matrix
  61. Mx_z_sparse = squeeze(connAll(i,4,:,:));
  62. symmetric_matrix = Mx_z_sparse + Mx_z_sparse';
  63. symmetric_matrix(eye(size(symmetric_matrix)) == 1) = 0;
  64. non_zero_indices = any(symmetric_matrix, 2);
  65. filtered_matrix = symmetric_matrix(non_zero_indices, non_zero_indices);
  66. if ~isempty(filtered_matrix) % Check if there are non-zero rows/columns
  67. norms = sqrt(sum(filtered_matrix.^2, 2));
  68. normalized_matrix = filtered_matrix ./ norms;
  70. connmatrixAll_D{cont4} = normalized_matrix;
  71. cont4 = cont4 + 1;
  72. end
  73. clear normalized_matrix
  76. Mx_z_sparse = squeeze(connAll(i,5,:,:));
  77. symmetric_matrix = Mx_z_sparse + Mx_z_sparse';
  78. symmetric_matrix(eye(size(symmetric_matrix)) == 1) = 0;
  79. non_zero_indices = any(symmetric_matrix, 2);
  80. filtered_matrix = symmetric_matrix(non_zero_indices, non_zero_indices);
  81. if ~isempty(filtered_matrix) % Check if there are non-zero rows/columns
  82. norms = sqrt(sum(filtered_matrix.^2, 2));
  83. normalized_matrix = filtered_matrix ./ norms;
  85. connmatrixAll_M{cont5} = normalized_matrix;
  86. cont5 = cont5 + 1;;
  87. end
  88. clear normalized_matrix
  90. end
  91. Gp = GradientMaps('kernel','cs','approach','diffusionEmbedding','alignment','pa','n_components',5);
  93. % Calculate gradient scores
  95. Gp = Gp.fit(connmatrixAll_B1);
  96. for i= 1:11
  97. pg0(i,:)=Gp.aligned{i}(:,1);
  98. end
  100. Gp = Gp.fit(connmatrixAll_B2);
  101. for i= 1:7
  102. pg(i,:)=Gp.aligned{i}(:,1);
  103. end
  105. Gp = Gp.fit(connmatrixAll_B3);
  106. for i= 1:7
  107. pg2(i,:)=Gp.aligned{i}(:,1);
  108. end
  110. Gp = Gp.fit(connmatrixAll_D);
  111. for i= 1:11
  112. pgPSI(i,:)=Gp.aligned{i}(:,1);
  113. end
  115. Gp = Gp.fit(connmatrixAll_M);
  116. for i= 1:11
  117. pgMET(i,:)=Gp.aligned{i}(:,1);
  118. end
  121. plot_hemispheres([nanmean([-pg; -pg0;pg2]); nanmean(-pgMET); nanmean(pgPSI)]', ...
  122. {surf_lh,surf_rh},'parcellation',labeling, ...
  123. 'labeltext',{'PLCB','MET','PSI'});
  125. % Calculate gradient score difference
  127. Gp = Gp.fit(connmatrixAll_B1);
  128. for i = 1:length(connmatrixAll_B1)
  129. scoredif0(i) = max(Gp.aligned{i}(:,1))-min(Gp.aligned{i}(:,1));
  130. end
  132. Gp = Gp.fit(connmatrixAll_B2);
  133. for i = 1:length(connmatrixAll_B2)
  134. scoredif(i) = max(Gp.aligned{i}(:,1))-min(Gp.aligned{i}(:,1));
  135. end
  137. Gp = Gp.fit(connmatrixAll_B3);
  138. for i = 1:length(connmatrixAll_B3)
  139. scoredif2(i) = max(Gp.aligned{i}(:,1))-min(Gp.aligned{i}(:,1));
  140. end
  142. Gp = Gp.fit(connmatrixAll_D);
  143. for i = 1:length(connmatrixAll_D)
  144. scoredifPSI(i) = max(Gp.aligned{i}(:,1))-min(Gp.aligned{i}(:,1));
  145. end
  147. Gp = Gp.fit(connmatrixAll_M);
  148. for i = 1:length(connmatrixAll_M)
  149. scoredifMET(i) = max(Gp.aligned{i}(:,1))-min(Gp.aligned{i}(:,1));
  150. end

run_gradients.m at commit 78a1477, under other · at the source

Overview

Authors: Verónica Mäki-Marttunen1
ORCID iDs: Verónica Mäki-Marttunen
  1. Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
Institutions: Oslo University Hospital (Norway)
Journal: Communications biology, volume 9, issue 1, article 672
Dates: received 2 January 2026; accepted 12 March 2026; published online 26 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s42003-026-09912-4 · PMID 41882058 · PMCID PMC13183898 · OpenAlex W7140297251
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: fMRI (modality), human (organism), cellular / molecular (subfield)
Methods: Statistics, Preprocessing, Machine learning
Keywords: Consciousness, Neurophysiology
MeSH: Brain*, Hallucinogens*, Psilocybin*, Receptor, Serotonin, 5-HT2A*, Brain Mapping, Humans, Magnetic Resonance Imaging, Serotonin 5-HT2 Receptor Agonists (* major topic)
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Citations: cited by 2 papers (Europe PMC); 75 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 1 match between paragraphs and lines of code.

Zenodo 18933134

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (3 files), Statistics and Machine Learning Toolbox (3 files), BrainSpace (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
6 files
At the source:

veronicamaki-marttunen/psilocybin-shapes-propagation-of-brain-activity

License: other
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 78a14774c72610b058a2616d9e1fe4c05dc5306d, 22 September 2026
Languages: MATLAB (4)
Size: 6 files, 4 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (3 files), Statistics and Machine Learning Toolbox (3 files), BrainSpace (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
6 files
At the source: github.com/VeronicaMaki-Marttunen/Psilocybin-shapes-propagation-of-brain-activity/tree/v1.0.0

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

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Read it in the paper: doi.org/10.1038/s42003-026-09912-4.

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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:

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

Read it in the paper: doi.org/10.1038/s42003-026-09912-4.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 2 keywords, 8 MeSH terms, 74 references.

Cite

This paper

Mäki-Marttunen, V. (2026). Psilocybin shapes the slow, global propagation of brain activity over the cortical layout of 5HT2a receptors. Communications biology, 9(1), 672. https://doi.org/10.1038/s42003-026-09912-4

BibTeX

@article{makimarttunen2026psilocybin,
author = {Mäki-Marttunen, Verónica},
title = {{Psilocybin shapes the slow, global propagation of brain activity over the cortical layout of 5HT2a receptors}},
journal = {Communications biology},
year = {2026},
month = mar,
volume = {9},
number = {1},
pages = {672},
publisher = {Nature Publishing Group},
issn = {2399-3642},
doi = {10.1038/s42003-026-09912-4},
url = {https://doi.org/10.1038/s42003-026-09912-4},
pmid = {41882058},
pmcid = {PMC13183898}
}

RIS

TY - JOUR
AU - Mäki-Marttunen, Verónica
TI - Psilocybin shapes the slow, global propagation of brain activity over the cortical layout of 5HT2a receptors
T2 - Communications biology
J2 - Commun Biol
PY - 2026
DA - 2026/03/26
VL - 9
IS - 1
SP - 672
SN - 2399-3642
PB - Nature Publishing Group
DO - 10.1038/s42003-026-09912-4
UR - https://doi.org/10.1038/s42003-026-09912-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s42003-026-09912-4",
"type": "article-journal",
"title": "Psilocybin shapes the slow, global propagation of brain activity over the cortical layout of 5HT2a receptors",
"container-title": "Communications biology",
"author": [
{
"family": "Mäki-Marttunen",
"given": "Verónica"
}
],
"container-title-short": "Commun Biol",
"volume": "9",
"issue": "1",
"page": "672",
"DOI": "10.1038/s42003-026-09912-4",
"PMID": "41882058",
"PMCID": "PMC13183898",
"ISSN": "2399-3642",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s42003-026-09912-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
26
]
]
}
}

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