OSCR

Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy.

Code ↔ Paper

17 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 17 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] § Methods › Entropy of dynamic connectivity › LEiDA-state Markov-rate ↔ copbet_py/functions/CopBET_LEiDA_transition_entropy.py, lines 1–33 · score 0.88 · phase coherence matrices, instantaneous phases, Hilbert transform, Leading Eigenvector, LEiDA, transition
  2. [2] § Methods › Entropy of dynamic connectivity › Integration/Segregation-state distribution ↔ copbet_py/functions/CopBET_temporal_entropy.py, lines 1–34 · score 0.84 · cartographic profile, sliding window, module degree, participation coefficient, Louvain, score
  3. [3] § Methods › Entropy of dynamic connectivity › Motif-connectivity distribution ↔ copbet_py/functions/CopBET_motif_connectivity_entropy.py, lines 1–44 · score 0.83 · overlapping sliding window, possible graph, partial correlation, window lengths, Shannon entropy, Motif
  4. [4] § Methods › Entropy of dynamic connectivity › Dynamic conditional correlation distribution ↔ CopBET_main_CH2016data.m, lines 128–160 · score 0.82 · medial frontal, subcortical cerebellar, ROI DCC, Shen, motor, edges
  5. [5] § Methods › Entropy of dynamic connectivity › Integration/Segregation-state distribution ↔ functions/CopBET_temporal_entropy.m, lines 87–181 · score 0.76 · cartographic profile, module degree, sliding, Louvain, modularity, dimensional
  6. [6] § Results › Motif-connectivity distribution entropy ↔ copbet_py/functions/CopBET_motif_connectivity_entropy.py, lines 52–107 · score 0.76 · 15–150 s, motif connectivity, window length, 15 s, ROI
  7. [7] § Methods › Entropy of dynamic connectivity › Meta-state complexity ↔ copbet_py/functions/CopBET_metastate_series_complexity.py, lines 1–37 · score 0.71 · LZ76 exhaustive, Lempel Ziv complexity, correlation distance, binary, sequence, clustered
  8. [8] § Methods › Entropy of dynamic connectivity › Meta-state complexity ↔ copbet_py/functions/helper_functions/lempel_ziv.py, lines 13–134 · score 0.70 · LZ76 exhaustive, Lempel Ziv complexity, binary sequence
  9. [9] § Methods › Entropy of regional dynamics › BOLD complexity ↔ copbet_py/functions/CopBET_time_series_complexity.py, lines 1–37 · score 0.67 · Hilbert transformed, Lempel Ziv complexity, amplitude, LZ78, space, temporal
  10. [10] § Methods › Entropy of static connectivity › Normalised Global Spatial Complexity ↔ copbet_py/functions/CopBET_von_Neumann_entropy.py, lines 31–69 · score 0.66 · Von Neumann entropy, zero eigenvalues, log, mask, sum, matrix
  11. [11] § Methods › Entropy of static connectivity › Von Neumann entropy ↔ copbet_py/functions/CopBET_von_Neumann_entropy.py, lines 31–69 · score 0.60 · von Neumann entropy, correlation matrix, log, eigenvalues
  12. [12] § Methods › Entropy of static connectivity › Out-network connectivity distribution entropy ↔ copbet_py/functions/CopBET_diversity_coefficient.py, lines 1–31 · score 0.60 · diversity coefficient, network Connectivity, modularity, ROI, matrix, Brain
  13. [13] § Methods › Entropy of static connectivity › Out-network connectivity distribution entropy ↔ functions/CopBET_diversity_coefficient.m, lines 49–87 · score 0.60 · diversity coefficient, connectivity matrix, Louvain, modularity, algorithm, scan
  14. [14] § Methods › Statistical model ↔ statistics/permlme.R, the whole file · a weak match · score 0.57 · nlme, Wald, model, covariates, residuals, regressed
  15. [15] § Methods › Correlation between metrics ↔ copbet_py/functions/CopBET_motif_connectivity_entropy.py, lines 1–44 · score 0.52 · motif connectivity, Pearson correlation, brain entropy, graph, windows
  16. [16] § Methods › Entropy of static connectivity › Degree distribution entropy ↔ copbet_py/functions/CopBET_degree_distribution_entropy.py, lines 34–73 · score 0.52 · Pearson correlation, distribution entropy, Shannon entropy, absolute, zero, thresholded
  17. [17] § Results › Von Neumann entropy ↔ copbet_py/functions/CopBET_von_Neumann_entropy.py, lines 1–28 · score 0.51 · Von Neumann entropy, correlation matrices, rho, Pearson

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

Python · 231 lines · 7.1 KB · no license · 3 matches

  1. """
  2. CopBET_motif_connectivity_entropy
  3. ===================================
  4. Copenhagen Brain Entropy Toolbox: Motif-connectivity entropy.
  6. Evaluates motif-connectivity entropy as in Tagliazucchi et al., 2014.
  7. Non-overlapping sliding windows of varying lengths are slid across the
  8. data. In each window, the partial correlation between the 4 ROIs
  9. (conditioned on the remaining ROIs and a motion confound) is binarized
  10. by p-value. The resulting 6-bit connectivity pattern is matched to one
  11. of 64 possible graphs. Shannon entropy of the graph distribution is
  12. returned per window length. The mean across window lengths is also returned.
  14. NOTE: This function requires exactly 4 ROIs. If more are provided, the
  15. first 4 are used unless roi_indices is specified.
  17. Input
  18. -----
  19. sessions : list of np.ndarray, each shape (T, N) with N >= 4
  20. ROI mean time series per session (demeaned, standardized recommended).
  21. motion : list of np.ndarray or None
  22. Motion confound time series, each shape (T, n_confounds).
  23. If None, partial correlations are replaced by Pearson correlations.
  24. TR : float
  25. Repetition time in seconds (default 2.0).
  26. roi_indices : list of int or None
  27. Indices of the 4 ROIs to use. Default: [0, 1, 2, 3].
  29. Returns
  30. -------
  31. entropy : list of dict with keys:
  32. 'per_window_length' : np.ndarray, shape (n_wl,) — entropy per window length
  33. 'mean' : float — mean entropy across window lengths
  35. Reference
  36. ---------
  37. Tagliazucchi et al., 2014.
  39. Please cite McCulloch, Olsen et al., 2023 if you use CopBET.
  40. """
  42. import numpy as np
  43. from scipy import stats
  44. from itertools import product
  47. # All 64 possible binary graphs on 6 edges
  48. _POSSIBLE_GRAPHS = np.array(list(product([0, 1], repeat=6)), dtype=float).T # (6, 64)
  49. _ROI_PAIRS = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]
  52. def CopBET_motif_connectivity_entropy(sessions, motion=None, TR=2.0,
  53. roi_indices=None,
  54. window_lengths_sec=None):
  55. """
  56. Compute motif-connectivity Shannon entropy.
  58. Parameters
  59. ----------
  60. sessions : list of np.ndarray, each shape (T, N), N >= 4
  61. motion : list of np.ndarray or None
  62. Motion confound series per session, shape (T, n_confounds).
  63. TR : float
  64. roi_indices : list of int or None
  65. Indices for the 4 ROIs. Default [0, 1, 2, 3].
  66. window_lengths_sec : array-like or None
  67. Window lengths in seconds. Default: 15 to 150 seconds step 1.
  69. Returns
  70. -------
  71. entropy : list of dict
  72. """
  73. if roi_indices is None:
  74. roi_indices = [0, 1, 2, 3]
  75. if window_lengths_sec is None:
  76. window_lengths_sec = np.arange(15, 151)
  78. if motion is None:
  79. motion = [None] * len(sessions)
  81. entropy = []
  83. for ses, ts in enumerate(sessions):
  84. ts = np.asarray(ts, dtype=float)
  86. # Extract 4 ROIs, demean, standardize
  87. data4 = ts[:, roi_indices] # (T, 4)
  88. data4 = (data4 - data4.mean(axis=0)) / (data4.std(axis=0) + 1e-10)
  89. data4 = data4.T # (4, T)
  91. rp = motion[ses]
  92. if rp is not None:
  93. rp = np.asarray(rp, dtype=float)
  94. rp = _fwd_calc(rp)[:, np.newaxis] # (T, 1)
  95. else:
  96. rp = np.zeros((ts.shape[0], 1))
  98. word_counts = _state_distribution(data4, rp, TR, window_lengths_sec)
  99. ent = _shannon_entropy(word_counts)
  101. entropy.append({
  102. 'per_window_length': ent,
  103. 'mean': float(np.nanmean(ent)),
  104. 'window_lengths_sec': window_lengths_sec,
  105. })
  107. return entropy
  110. def _fwd_calc(rp):
  111. """
  112. Compute framewise displacement from 6 motion parameters (3 trans, 3 rot).
  113. Rotation parameters are converted from radians to mm (r=50 mm).
  114. """
  115. radius = 50.0
  116. ts = rp.copy()
  117. ts[:, 3:6] = (2 * radius * np.pi / 360) * ts[:, 3:6] * (180 / np.pi)
  118. dts = np.diff(ts, axis=0)
  119. fwd = np.concatenate([[0], np.sum(np.abs(dts), axis=1)])
  120. fwd = (fwd - fwd.mean()) / (fwd.std() + 1e-10)
  121. return fwd
  124. def _state_distribution(data, rp, TR, window_lengths_sec):
  125. """
  126. For each window length, count occurrences of each 64-bit graph pattern.
  128. Parameters
  129. ----------
  130. data : np.ndarray, shape (4, T)
  131. rp : np.ndarray, shape (T, n_confounds)
  132. TR : float
  133. window_lengths_sec : array-like
  135. Returns
  136. -------
  137. word_counts : np.ndarray, shape (n_wl, 64)
  138. """
  139. T = data.shape[1]
  140. eff_wl = np.round(np.asarray(window_lengths_sec) / TR).astype(int)
  141. eff_wl = np.maximum(eff_wl, 5) # minimum window of 5 TRs
  143. word_counts = np.zeros((len(window_lengths_sec), 64), dtype=int)
  145. for wl_idx, wl in enumerate(eff_wl):
  146. if wl_idx > 0 and eff_wl[wl_idx] == eff_wl[wl_idx - 1]:
  147. word_counts[wl_idx] = word_counts[wl_idx - 1]
  148. continue
  150. # Non-overlapping windows
  151. window_starts = np.arange(0, T - wl + 1, wl)
  153. for ws in window_starts:
  154. data_win = data[:, ws:ws + wl] # (4, wl)
  155. rp_win = np.abs(rp[ws:ws + wl]) # (wl, n_confounds)
  157. graph_bits = np.zeros(6, dtype=int)
  158. for pair_idx, (i, j) in enumerate(_ROI_PAIRS):
  159. # Confound: all other ROIs + motion
  160. other_rois = [k for k in range(4) if k != i and k != j]
  161. confounds = np.hstack([
  162. data_win[other_rois, :].T, # (wl, 2)
  163. rp_win # (wl, n_confounds)
  164. ]) # (wl, n_confounds+2)
  166. xi = data_win[i, :]
  167. xj = data_win[j, :]
  169. try:
  170. r, p = _partial_corr(xi, xj, confounds)
  171. graph_bits[pair_idx] = int(p < 0.05 / 6) # Bonferroni
  172. except Exception:
  173. graph_bits[pair_idx] = 0
  175. # Find matching graph pattern
  176. diff = np.sum(np.abs(_POSSIBLE_GRAPHS - graph_bits[:, np.newaxis]), axis=0)
  177. word_idx = np.argmin(diff)
  178. word_counts[wl_idx, word_idx] += 1
  180. return word_counts
  183. def _partial_corr(x, y, z):
  184. """
  185. Compute partial correlation of x and y controlling for z.
  187. Parameters
  188. ----------
  189. x, y : np.ndarray, shape (T,)
  190. z : np.ndarray, shape (T, k)
  192. Returns
  193. -------
  194. r : float, p : float
  195. """
  196. T = len(x)
  197. if z.shape[1] == 0:
  198. return stats.pearsonr(x, y)
  200. # Residualize x and y on z
  201. z_with_intercept = np.column_stack([np.ones(T), z])
  202. px = np.linalg.lstsq(z_with_intercept, x, rcond=None)[0]
  203. py = np.linalg.lstsq(z_with_intercept, y, rcond=None)[0]
  204. res_x = x - z_with_intercept @ px
  205. res_y = y - z_with_intercept @ py
  207. return stats.pearsonr(res_x, res_y)
  210. def _shannon_entropy(word_counts):
  211. """
  212. Compute Shannon entropy of graph distribution for each window length.
  214. Parameters
  215. ----------
  216. word_counts : np.ndarray, shape (n_wl, 64)
  218. Returns
  219. -------
  220. ent : np.ndarray, shape (n_wl,)
  221. """
  222. ent = np.zeros(word_counts.shape[0])
  223. for wl in range(word_counts.shape[0]):
  224. total = word_counts[wl].sum()
  225. if total == 0:
  226. ent[wl] = np.nan
  227. continue
  228. prob = word_counts[wl] / total
  229. mask = prob > 0
  230. ent[wl] = np.sum(prob[mask] * np.log2(1.0 / prob[mask]))
  231. return ent

CopBET_motif_connectivity_entropy.py at commit eb4e455, no license · at the source

Overview

Authors: Drummond E-Wen McCulloch1,2, Anders Stevnhoved Olsen1,3, Brice Ozenne1,4, Kristian Larsen1,2, Dea Siggaard Stenbæk1,5, Sophia Armand1,5, Martin Korsbak Madsen1,6, Gitte Moos Knudsen1,7, Patrick MacDonald Fisher1,8
ORCID iDs: Drummond E-Wen McCulloch, Anders Stevnhoved Olsen, Gitte Moos Knudsen, Patrick MacDonald Fisher
  1. Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet,Copenhagen, Denmark
  2. Faculty of Health and Medical Sciences, University of Copenhagen,Copenhagen, Denmark
  3. Department of Applied Mathematics and Computer Science, Technical University of Denmark,Kgs. Lyngby, Denmark
  4. Section of Biostatistics, Department of Public Health, University of Copenhagen,Copenhagen, Denmark
  5. Department of Psychology, University of Copenhagen,Copenhagen, Denmark
  6. Department of Psychiatry Odense-Svendborg, University Hospital of Southern Denmark,Svendborg, Denmark
  7. Department of Clinical Medicine, University of Copenhagen,Copenhagen, Denmark
  8. Department of Drug Design and Pharmacology, University of Copenhagen,Copenhagen, Denmark
Institutions: Copenhagen University Hospital (Denmark); University of Copenhagen (Denmark); Technical University of Denmark (Denmark); Odense University Hospital (Denmark)
Journal: Nature communications, volume 17, issue 1, article 7940
Dates: received 16 September 2025; accepted 29 May 2026; published online 24 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-74215-5 · PMID 42343098 · PMCID PMC13448839 · OpenAlex W7165778510
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), computational (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Complexity, Preprocessing, Graphs, fMRI & imaging
Keywords: Computational neuroscience, Neurology, Neuroscience
MeSH: Brain*, Hallucinogens*, Magnetic Resonance Imaging*, Psilocybin*, Adult, Brain Mapping, Entropy, Female, Humans, Male, Young Adult (* major topic)
Topic: Psychedelics and Drug Studies (Clinical Psychology, Psychology), according to OpenAlex
Funding: Innovation Fund Denmark (4108–00004B); Danmarks Frie Forskningsfond (6110–00518B, 8020-00414B); Rigshospitalet (R259-A11532, R130A5324); H2020 Marie Skłodowska‐Curie Actions (746,850); Compass Pathways Ltd
Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

A prominent theory of psychedelics is that they increase brain entropy. Thirteen studies have evaluated psychedelic effects on fMRI brain entropy, each applying a distinct measure. Here we evaluated these metrics in an independent 28-participant healthy cohort with 121 pre- and post-psilocybin fMRI scans. We assessed relations between brain entropy and objective and subjective psychedelic drug effects using linear mixed-effects models. All metrics were evaluated using two parcellation strategies and 7 denoising pipelines. We observed consistent significant positive associations for Shannon entropy of the spatial eigendistribution of the time by voxel matrix, path-length, instantaneous correlations, brain-state switching, and sample entropy at short time-scales. We consistently did not observe significant effects for 8 of 14 entropy metrics and observe inconsistent positive effects for Lempel-Ziv complexity of the BOLD signal. Brain entropy quantifications showed limited inter-measure correlations. Our observations support a nuanced acute psychedelic effect on brain entropy, empirically demonstrating that these metrics do not reflect a singular construct.

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 17 matches between paragraphs and lines of code.

anders-s-olsen/CopBET

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: eb4e4559bd9cdcc7b70bfd6c8b6a428044e98380, 13 August 2026
Languages: MATLAB (329), C/C++ (38), C++ (32), JavaScript (30), Python (19), Shell (4), R (1)
Size: 793 files, 453 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, environment (copbet_py/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: Brain Connectivity Toolbox (42 files), Statistics and Machine Learning Toolbox (24 files), NumPy (17 files), Optimization Toolbox (9 files), Tools for NIfTI and ANALYZE image (MATLAB) (7 files), SciPy (7 files), Image Processing Toolbox (6 files), Signal Processing Toolbox (3 files), scikit-learn (3 files), Parallel Computing Toolbox (2 files), Matplotlib (1 file), NetworkX (1 file), nlme (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
454 files
At the source: github.com/anders-s-olsen/CopBET

Zenodo 19914432

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Code availability”
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 (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

Code availability

We shared relevant analysis scripts with original authors, hoping to ensure as much as possible that our computations aligned with original reports; we are thankful for the feedback we received. All functions used to derive entropy estimates from pre-processed data have been compiled into the Copenhagen Brain Entropy Toolbox (CopBET), a Matlab-based toolbox that can be found here: https://github.com/anders-s-olsen/CopBET and 10.5281/zenodo.19914432. The permutation testing code is also available here. Code for other statistical analyses and figures can be made available upon request.

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

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;
  • 453 scripts, each with its path and the digest of its content;
  • 17 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 data that support the findings of this study are available from the corresponding author upon request to the CIMBI database95. The raw neuroimaging data generated in this study have been deposited in the Cimbi database, https://www.nru.dk/cimbidb. The data are available under restricted access due to privacy and European General Data Protection Regulation (GDPR) restrictions, access can be obtained by a Cimbi database request: https://nru.dk/index.php/research-menu/research-groups/115-the-cimbi-database-and-biobank. Database requests may be approved based on the perceived scientific value of the proposed research project, and decisions are made within a few weeks. The subsequent legal approval process may take several months, but once granted, data will be available for as long as the proposed research project requires. The statistical output data generated in this study are provided in the Supplementary 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, 9 authors, 3 keywords, 11 MeSH terms, 5 funders, 93 references.

Cite

This paper

McCulloch, D. E.-W., Olsen, A. S., Ozenne, B., Larsen, K., Stenbæk, D. S., Armand, S., Madsen, M. K., Knudsen, G. M., & Fisher, P. M. (2026). Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy. Nature communications, 17(1), 7940. https://doi.org/10.1038/s41467-026-74215-5

BibTeX

@article{mcculloch2026multi,
author = {McCulloch, Drummond E-Wen and Olsen, Anders Stevnhoved and Ozenne, Brice and Larsen, Kristian and Stenbæk, Dea Siggaard and Armand, Sophia and Madsen, Martin Korsbak and Knudsen, Gitte Moos and Fisher, Patrick MacDonald},
title = {{Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy}},
journal = {Nature communications},
year = {2026},
month = jun,
volume = {17},
number = {1},
pages = {7940},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-74215-5},
url = {https://doi.org/10.1038/s41467-026-74215-5},
pmid = {42343098},
pmcid = {PMC13448839}
}

RIS

TY - JOUR
AU - McCulloch, Drummond E-Wen
AU - Olsen, Anders Stevnhoved
AU - Ozenne, Brice
AU - Larsen, Kristian
AU - Stenbæk, Dea Siggaard
AU - Armand, Sophia
AU - Madsen, Martin Korsbak
AU - Knudsen, Gitte Moos
AU - Fisher, Patrick MacDonald
TI - Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/06/24
VL - 17
IS - 1
SP - 7940
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-74215-5
UR - https://doi.org/10.1038/s41467-026-74215-5
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41467-026-74215-5",
"type": "article-journal",
"title": "Multi-metric evaluations of acute psychedelic effects on fMRI brain entropy",
"container-title": "Nature communications",
"author": [
{
"family": "McCulloch",
"given": "Drummond E-Wen"
},
{
"family": "Olsen",
"given": "Anders Stevnhoved"
},
{
"family": "Ozenne",
"given": "Brice"
},
{
"family": "Larsen",
"given": "Kristian"
},
{
"family": "Stenbæk",
"given": "Dea Siggaard"
},
{
"family": "Armand",
"given": "Sophia"
},
{
"family": "Madsen",
"given": "Martin Korsbak"
},
{
"family": "Knudsen",
"given": "Gitte Moos"
},
{
"family": "Fisher",
"given": "Patrick MacDonald"
}
],
"container-title-short": "Nat Commun",
"volume": "17",
"issue": "1",
"page": "7940",
"DOI": "10.1038/s41467-026-74215-5",
"PMID": "42343098",
"PMCID": "PMC13448839",
"ISSN": "2041-1723",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41467-026-74215-5",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41591-026-04287-9 [code]
An international mega-analysis of psychedelic drug effects on brain circuit function.
Journal: Nature medicine
In common: scikit-learn, SciPy, Matplotlib, 1 other tool, fMRI, 15 references, author Drummond E-Wen McCulloch
[2] doi:10.1038/s41586-026-10910-z [code]
Psychedelics align brain activity with context.
Journal: Nature
In common: Image Processing Toolbox, Statistics and Machine Learning Toolbox, SciPy, 1 other tool, fMRI, 14 references
[3] doi:10.1038/s41467-026-75890-0
Serotonergic psychedelics induce distinct patterns of metabolic activity and covariance within biologically informed rat brain networks.
Journal: Nature communications
In common: 8 references, author Patrick M Fisher
[4] doi:10.1016/j.nicl.2026.104012 [code]
Structural-functional multilayer brain network properties and outcome of combined repetitive transcranial magnetic stimulation and psychotherapy for obsessive-compulsive disorder.
Journal: NeuroImage. Clinical
In common: Brain Connectivity Toolbox, nlme, NetworkX, 7 other tools, fMRI, 5 references
[5] doi:10.1162/imag.a.1278 [code]
Young and old adult brains experience opposite effects of acute sleep restriction on the functional connectivity network.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: Brain Connectivity Toolbox, Parallel Computing Toolbox, NetworkX, 6 other tools, fMRI, 5 references
[6] doi:10.1002/hbm.70596 [code]
Modeled Long-Term Effects of Psilocybin on Dynamic Activity and Effective Connectivity of Fronto-Striatal-Thalamic Circuits.
Journal: Human brain mapping
In common: 11 references
[7] doi:10.1002/hbm.70483 [code]
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation.
Journal: Human brain mapping
In common: Tools for NIfTI and ANALYZE image (MATLAB), Parallel Computing Toolbox, Image Processing Toolbox, 6 other tools, fMRI, 5 references
[8] doi:10.3389/fnhum.2026.1874178
Longitudinal neural and psychological changes following psilocybin under compassion imagery priming.
Journal: Frontiers in human neuroscience
In common: fMRI, 10 references
[9] doi:10.1038/s41398-026-04025-2 [code]
Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.
Journal: Translational psychiatry
In common: Brain Connectivity Toolbox, Tools for NIfTI and ANALYZE image (MATLAB), Image Processing Toolbox, 5 other tools, 4 references
[10] doi:10.1038/s41467-026-74565-0 [code]
The functional neurobiology of dispositions towards negative emotions.
Journal: Nature communications
In common: Brain Connectivity Toolbox, nlme, Optimization Toolbox, 6 other tools, 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.