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A conserved node degree-based backbone and flexible hub organization of brain connectome during naturalistic movie watching.

Code ↔ Paper

4 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 4 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Brain network construction ↔ stable_projects/brain_parcellation/Kong2019_MSHBM/step3_generate_ind_parcellations/CBIG_MSHBM_parameters_validation.m, lines 1–142 · score 0.62 · intra subject, inter subject functional, fMRI, functional connectivity, hemisphere, weighted
  2. [2] § Methods › Multivariate stimulus-brain canonical correlation analysis ↔ stable_projects/disorder_subtypes/Tang2020_ASDFactors/step3_analyses/behavioralAssociation/CBIG_ASDf_CCA_factorBehavior.m, the whole file · a weak match · score 0.58 · canonical correlation, structure coefficients, CCA, score, permutation, matrices
  3. [3] § Methods › Multivariate stimulus-brain canonical correlation analysis ↔ external_packages/matlab/non_default_packages/palm/palm-alpha109/palm_defaults.m, the whole file · a weak match · score 0.55 · canonical correlation, shuffled, CCA, fitting, FDR, Multivariate
  4. [4] § Methods › Brain network construction ↔ external_packages/matlab/non_default_packages/palm/palm-alpha109/palm_defaults.m, the whole file · a weak match · score 0.54 · Correlation coefficients, Fisher, Pearson, Oxford, FDR, transformed

Paper

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The authors' code

MATLAB · 120 lines · 8.8 KB · MIT · 2 matches

  1. function opts = palm_defaults
  2. % Set up PALM defaults.
  3. %
  4. % _____________________________________
  5. % Anderson M. Winkler
  6. % FMRIB / University of Oxford
  7. % Oct/2014
  8. % http://brainder.org
  10. % - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  11. % PALM -- Permutation Analysis of Linear Models
  12. % Copyright (C) 2015 Anderson M. Winkler
  13. %
  14. % This program is free software: you can redistribute it and/or modify
  15. % it under the terms of the GNU General Public License as published by
  16. % the Free Software Foundation, either version 3 of the License, or
  17. % any later version.
  18. %
  19. % This program is distributed in the hope that it will be useful,
  20. % but WITHOUT ANY WARRANTY; without even the implied warranty of
  21. % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
  22. % GNU General Public License for more details.
  23. %
  24. % You should have received a copy of the GNU General Public License
  25. % along with this program. If not, see <http://www.gnu.org/licenses/>.
  26. % - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  28. % Define some defaults and organise all as a struct
  29. opts.o = 'palm'; % Default output string.
  30. opts.nP0 = 10000; % Number of permutations
  31. opts.lx = true; % Lexicographic permutations?
  32. opts.cmcp = false; % Use Conditional Monte Carlo (ignore repeated perms)?
  33. opts.cmcx = false; % Use Conditional Monte Carlo (ignore repeated elements in X)?
  34. opts.twotail = false; % Do a two-tailed t-test for the t-contrasts?
  35. opts.concordant = false; % Favour hypotheses with the same sign?
  36. opts.tonly = false; % Run only the t-contrasts?
  37. opts.fonly = false; % Run only the F-contrasts?
  38. opts.pmethodp = 'Guttman'; % Method to partition the model to define the permutation set
  39. opts.pmethodr = 'Beckmann'; % Method to partition the model for the actual regression
  40. opts.rmethod = 'Freedman-Lane'; % Regression/permutation method.
  41. opts.rfallback = 'terBraak'; % Regression/permutation method if correcting over contrasts
  42. opts.NPC = false; % Do non-parametric combination?
  43. opts.MV = false; % Do classical multivariate inference? (MANOVA/MANCOVA)
  44. opts.CCA = false; % Do canonical correlation analysis? (CCA)
  45. opts.PLS = false; % Do partial least squares regression? (PLS)
  46. opts.mvstat = 'auto'; % Default method for MANOVA/MANCOVA. The "auto" will use either Hotelling's T^2 or Wilks lambda depending on rank(C). For approx.noperm, this will use Pillai.
  47. opts.npcmethod = 'Fisher'; % Combination method.
  48. opts.cfallback = 'Tippett'; % (not currently used)
  49. opts.npcmod = false; % FWER correction over input modalities?
  50. opts.npccon = false; % FWER correction over contrasts?
  51. opts.savepara = false; % Save parametric p-values too?
  52. opts.saveglm = false; % Save COPEs and VARCOPEs in the 1st permutation?
  53. opts.savecdf = false; % Save 1-p instead of true p.
  54. opts.savelogp = false; % Convert p-values to -log10(p).
  55. opts.savepartial = false; % If running NPC or MV, save partial tests?
  56. opts.saveunivariate = true; % Save univariate stats when using multivariate stats?
  57. opts.savedof = false; % save a file with the degrees of freedom?
  58. opts.savemask = false; % Save the masks?
  59. opts.forcemaskinter = false; % Force an intersection mask across modalities and evperdat?
  60. opts.saveperms = false; % Save permutation images?
  61. opts.savemetrics = false; % Save permutation metrics?
  62. opts.saveuncorrected = true; % Save uncorrected p-values?
  63. opts.corrmod = false; % FWER correction over modalities?
  64. opts.corrcon = false; % FWER correction over contrasts?
  65. opts.FDR = false; % FDR adjustment?
  66. opts.cluster.stat = 'extent'; % Method to do the cluster-level statistic
  67. opts.cluster.uni.do = false; % Do cluster statistic for the t-stat?
  68. opts.cluster.npc.do = false; % Do cluster statistic for the NPC z-stat?
  69. opts.cluster.mv.do = false; % Do cluster statistic for the MV z-stat?
  70. opts.tfce.stat = 'tfce'; % Method to do TFCE
  71. opts.tfce.uni.do = false; % Do TFCE?
  72. opts.tfce.npc.do = false; % Do TFCE for NPC?
  73. opts.tfce.mv.do = false; % Do TFCE for MV?
  74. opts.tfce.H = 2; % TFCE H parameter
  75. opts.tfce.E = 0.5; % TFCE E parameter
  76. opts.tfce.conn = 6; % TFCE connectivity neighbourhood
  77. opts.tfce.deltah = 'auto'; % Delta-h of the TFCE equation
  78. opts.tableasvolume = false; % Has the option -tfce1d been given?
  79. opts.useniiclass = true; % Use the NIFTI class (saves memory)
  80. opts.inormal = false; % Do an inverse-normal transformation?
  81. opts.inormal_meth = 'Waerden'; % Method for the inverse-normal transformation.
  82. opts.inormal_quanti = true; % Treat inputs as quantitative for the inormal?
  83. opts.probit = false; % Do a probit transformation?
  84. opts.seed = 0; % Seed for the random number generator
  85. opts.demean = false; % Mean-center?
  86. opts.vgdemean = false; % Mean-center within VG?
  87. opts.ev4vg = false; % Add one EV for each VG?
  88. opts.removevgbysize = 0; % Remove VGs smaller than a given size?
  89. opts.zstat = false; % Convert G-stat to z-stat?
  90. opts.SwE = false; % Whole block shuffling (for legacy format)?
  91. opts.pearson = false; % Compute the Pearson's correlation coefficient (R^2 if rank(C)>1)?
  92. opts.noranktest = false; % Don't test the rank(Y) before doing MANOVA/MANCOVA.
  93. opts.evperdat = false; % Use one (single!) EV per datum?
  94. opts.transposedata = false; % transpose data if 2D?
  95. opts.verbosefilenames = false; % use filenames with _i%d, _c%d, etc when there are more than one?
  96. opts.syncperms = false; % synchronize permutations (this only affects designs that can actually be synced
  97. opts.designperinput = false; % use one design for each input?
  98. opts.showprogress = true; % print progress as the permutations are performed? (use -quiet to disable)
  99. opts.inputmv = false; % treat each column of the sole input as a separate input for MV/NPC/CCA?
  100. opts.reversemasks = false; % Reverse the masks?
  101. opts.precision = []; % Precision? Can be 'single', 'double', or [] for what the file defines.
  102. % Approximation options:
  103. opts.accel.negbin = 0; % Run a negbin scheme
  104. opts.accel.negbin_nexced = 2; % Number of exceedances for the negbin mode
  105. opts.accel.tail = false; % Use a Pareto tail approximation to the FWER-adjusted p-values?
  106. opts.accel.tail_thr = 0.10; % P-values below this will be approximated using tail approximation
  107. opts.accel.noperm = false; % Compute approximate p-vals without doing any permutation
  108. opts.accel.gamma = false; % Do a gamma-fit after just a few permutations
  109. opts.accel.lowrank = false; % Try a low rank approximation
  110. opts.accel.lowrank_val = NaN; % Use NaN for N*(N+1)/2. Values <=1 are fraction of voxels to use; vals >1 are number of voxels to use.
  111. opts.accel.lowrank_recon = false; % Reconstruct past permutations in the new basis? This is very slow.
  112. opts.accel.G1out = false; % Exclude (true) or not (false) the unpermuted statistic in the null distribution for tail and gamma?
  114. % Missing data options:
  115. opts.missingdata = false; % Are there missing data?
  116. opts.mcar = false; % Data missing completely at random?
  117. opts.npcmethodmiss = 'Fisher'; % Combination method for missing data.
  119. % Note that there are no adjustable defaults for EE, ISE, whole or within.
  120. % These are hard coded and not meant to be ever changed (EE is default, within-block is also default).

palm_defaults.m at commit 35b5664, under MIT · at the source

Overview

Authors: Xuehu Wei1,2,3, Laura Rigolo1, Colin P Galvin1, Einat Liebenthal2,3, Yanmei Tie1,3
ORCID iDs: Xuehu Wei, Yanmei Tie
  1. Department of Neurosurgery, Brigham and Women’s Hospital, Boston, Massachusetts, USA
  2. McLean Imaging Center, McLean Hospital, Belmont, Massachusetts, USA
  3. Harvard Medical School, Boston, Massachusetts, USA
Institutions: Brigham and Women's Hospital (United States); Harvard University (United States); McLean Hospital (United States)
Journal: NeuroImage, volume 339, article 122171
Dates: published online 20 August 2026; in print 1 October 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.neuroimage.2026.122171 · PMID 42624004 · PMCID PMC13591665 · OpenAlex W7203837377
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fMRI (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Graphs, fMRI & imaging
Keywords: Naturalistic stimuli, Movie-watching fMRI, Functional connectome, Inter-subject functional correlation, Degree-based backbone, Rich-club organization, Stimulus-brain association
MeSH: Brain*, Connectome*, Motion Pictures*, Nerve Net*, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Institutes of Health (R01DC020965, R21NS075728); NIDCD NIH HHS (R01 DC020965); NINDS NIH HHS (R21 NS075728)
Citations: not cited yet (Europe PMC); 78 references in the paper

Abstract

How does the brain organize and transmit information during naturalistic conditions? Using large-scale movie-watching fMRI data spanning 14 diverse clips, we revealed a degree-based dual architecture of the functional connectome under naturalistic stimuli. During movie watching, the brain consistently expresses a conserved network backbone characterized by persistently high node degree in temporal and occipital sensory cortices and parietal association regions, whereas anterior higher-order regions show relatively lower node degree and substantially greater variability across different clips. We further found that this backbone links brain network organizational patterns to stimulus features of the clips, particularly audiovisual features capturing human presence and social communication. Building on this conserved backbone, different movie clips recruit distinct sets of rich-club hubs, reflecting a flexible hub organization distributed across superior temporal gyrus, temporo-parieto-occipital junction, precuneus/posterior cingulate cortex, intraparietal sulcus, and visual motion-sensitive regions. These hubs serve as key integrative nodes whose connectivity statistically mediates the relationship between stimulus features and cross-network integration. The conserved backbone and rich-club organization were reproduced in an independent movie-watching dataset analyzed with an identical pipeline, providing partial validation of these topological findings. Together, these findings reveal a network-level organizing principle in which a conserved backbone supports stable large-scale coordination, while flexible hub organization enables feature-specific coupling between stimulus features and distributed brain networks.

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 4 matches between paragraphs and lines of code.

thomasyeolab/cbig

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 35b5664bec8822e2f77da5e090e96f91d0095be6, 31 August 2026
Languages: MATLAB (2100), Shell (651), Python (571), C (113), C++ (65), C/C++ (62), R (25), Jupyter (5)
Size: 9,187 files, 3,592 scripts
Software Heritage: not archived
Found in: the text, “Data preprocessing”
Holds: README, license file, environment (external_packages/python/mapalign-master/requirements.txt, external_packages/python/mapalign-master/setup.py, external_packages/python/yapf-master/setup.cfg, external_packages/python/yapf-master/setup.py), tests, documentation, 2 notebooks
Not found: CITATION.cff, continuous integration
Tools: NumPy (130 files), PyTorch (122 files), FreeSurfer (93 files), SciPy (61 files), FieldTrip (60 files), Statistics and Machine Learning Toolbox (50 files), FSL (44 files), SPM (40 files), GIfTI library for MATLAB (10 files), Image Processing Toolbox (8 files), Connectome Workbench (8 files), scikit-learn (5 files), AFNI (2 files), Matplotlib (2 files), Tools for NIfTI and ANALYZE image (MATLAB) (2 files), ANTs (1 file), NiBabel (1 file), Nilearn (1 file), tedana (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2,000 files
At the source: github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/CBIG_fMRI_Preproc2016

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

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Data

Datasets cited

Data and code availability

The data used in this study are publicly available from the Human Connectome Project (HCP) database. Access to the HCP dataset requires registration and adherence to the data use terms (https://www.humanconnectome.org/).

The in-house dataset and custom code used for data processing and analysis are available from the corresponding author upon reasonable request.

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 2, 28 September 2026

  • Publisher: n/a → Elsevier BV

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 7 keywords, 10 MeSH terms, 3 funders, 71 references.

Cite

This paper

Wei, X., Rigolo, L., Galvin, C. P., Liebenthal, E., & Tie, Y. (2026). A conserved node degree-based backbone and flexible hub organization of brain connectome during naturalistic movie watching. NeuroImage, 339, 122171. https://doi.org/10.1016/j.neuroimage.2026.122171

BibTeX

@article{wei2026conserved,
author = {Wei, Xuehu and Rigolo, Laura and Galvin, Colin P and Liebenthal, Einat and Tie, Yanmei},
title = {{A conserved node degree-based backbone and flexible hub organization of brain connectome during naturalistic movie watching}},
journal = {NeuroImage},
year = {2026},
month = aug,
volume = {339},
pages = {122171},
publisher = {Elsevier BV},
issn = {1053-8119},
doi = {10.1016/j.neuroimage.2026.122171},
url = {https://doi.org/10.1016/j.neuroimage.2026.122171},
pmid = {42624004},
pmcid = {PMC13591665}
}

RIS

TY - JOUR
AU - Wei, Xuehu
AU - Rigolo, Laura
AU - Galvin, Colin P
AU - Liebenthal, Einat
AU - Tie, Yanmei
TI - A conserved node degree-based backbone and flexible hub organization of brain connectome during naturalistic movie watching
T2 - NeuroImage
J2 - Neuroimage
PY - 2026
DA - 2026/08/20
VL - 339
SP - 122171
SN - 1053-8119
PB - Elsevier BV
DO - 10.1016/j.neuroimage.2026.122171
UR - https://doi.org/10.1016/j.neuroimage.2026.122171
LA - en
ER -

CSL-JSON

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