Exploring the Role of the Rich Club in Network Control of Neurocognitive States.

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 · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Materials and Methods › Connectome Reconstruction ↔ matlab/example_data_loaders/load_group_fc.m, the whole file · a weak match · score 0.54 · Desikan Killiany atlas, subparcellation, parcellations, matrix, connectivity
[2] § Materials and Methods › Regional Characteristics ↔ brainspace/datasets/base.py, lines 296–322 · score 0.53 · microstructural profile, functional connectivity, gradients, BrainSpace
[3] § Materials and Methods › Connectome Reconstruction ↔ matlab/example_data_loaders/load_parcellation.m, the whole file · a weak match · score 0.53 · Desikan Killiany atlas, subparcellation, parcellations
[4] § Materials and Methods › Null Models and Statistics ↔ Matlab/rotate_parcellation.m, lines 1–39 · score 0.50 · hemispheric symmetry, contiguity, rotates, maps

Paper

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

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The authors' code

MATLAB · 35 lines · 1.4 KB · BSD-3-Clause · 1 match

function conn_matrices = load_group_fc(name,parcel_number,group)
% LOAD_GROUP_FC loads group level connectivity matrices.
%
% conn_matrices = LOAD_GROUP_FC(name,parcel_number,group) loads sample
% group level connectivity matrices of the HCP dataset. Name can be set
% to 'vosdewael' for a subparcellation of the Desikan-Killiany atlas, or
% 'schaefer' for a functional parcellation; both may also be provided as
% a cell/string array. Parcel_number denotes the resolution of the
% parcellation. It is a vector containing any of the following values
% [100,200,300,400]. Group is either 'main' (default) or 'holdout'. Data
% from different subjects is loaded depending on the choice.
% conn_matrices is a structure array containing all the requested
% connectivity matrices.
%
% For more information, please consult our <a
% href="https://brainspace.readthedocs.io/en/latest/pages/matlab_doc/data_loaders/load_group_fc.html">ReadTheDocs</a>.
if nargin < 3
group = 'main';
end
if ~iscell(name) && ~isstring(name)
name = {name};
end
P = mfilename('fullpath');
brainspace_path = fileparts(fileparts(P));
data_path = [brainspace_path filesep 'datasets' filesep 'data' filesep group '_group'];
for ii = 1:numel(name)
for jj = 1:numel(parcel_number)
label = char(name{ii} + "_" + parcel_number(jj));
conn_matrices.(label) = load([data_path filesep label '_mean_connectivity_matrix.csv']);
end
end

load_group_fc.m at commit 8730de8, under BSD-3-Clause · at the source

Overview

Authors: Alina N. Podschun^1,2, Richard F. Betzel^3,4, Urs Braun^5,6, Sebastian Markett¹

ORCID iDs: Alina N. Podschun

Department of Psychology Humboldt‐Universität zu Berlin Berlin Germany
International Psychoanalytic University Berlin Berlin Germany
Department of Neuroscience University of Minnesota, Twin Cities Minneapolis Minnesota USA
Masonic Institute for the Developing Brain University of Minnesota, Twin Cities Minneapolis Minnesota USA
Department of Psychiatry and Psychotherapy Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg Mannheim Germany
Hector Institute of Artificial Intelligence in Psychiatry Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg Mannheim Germany

Institutions: Humboldt-Universität zu Berlin (Germany); International Psychoanalytic University Berlin (Germany); University of Minnesota (United States); Heidelberg University (Germany); Central Institute of Mental Health (Germany); Medizinische Fakultät Mannheim

Journal: Human brain mapping, volume 47, issue 4, article e70485

Dates: received 13 September 2025; accepted 14 February 2026; published online 26 February 2026; in print March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/hbm.70485 · PMID 41749476 · PMCID PMC12945927 · OpenAlex W7131859958

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), systems (subfield)

Methods: Graphs, fMRI & imaging, Statistics

MeSH: Brain*, Cerebral Cortex*, Connectome*, Nerve Net*, Adult, Female, Humans, Magnetic Resonance Imaging, Male (* major topic)

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

The brain's rich club is a network of particularly densely interconnected regions, metabolically costly to maintain but central to the balance between functional segregation and integration. We assessed whether the rich club can accordingly be described as a control center of the brain, and present a systematic analysis of its involvement in maintenance of and traversal between various cognitively relevant functional states. Brain states were defined based on fMRI task‐evoked and resting‐state patterns of activity as provided by the Human Connectome Project (HCP). Using tools from network control theory (NCT), we computed the necessary effort needed for control of dynamics when the rich club, versus a size‐matched set of low‐degree peripheral regions, was prohibited from exerting control over dynamics. Control energy needed to traverse functional states was significantly higher, and stability of states significantly lower, when the set of peripheral regions was prohibited from control. Findings were stable across various rich‐club and null model definitions and across different parameter settings. A region's contribution to optimal control processes was instead associated with its affiliation with certain intrinsic connectivity networks and its position on the visual‐sensorimotor, but not sensory‐transmodal cortical gradient. We accordingly report that the rich club was systematically less involved in control of dynamics than the size‐matched set of peripheral regions. These results do not negate an integratory role of the rich club, but question its proposed role as a driver of control. Indeed, if it would inhabit such a role, we would have expected opposite results. Our findings fit with a position describing the rich club as a passive “data‐highway” which, by means of its high connectivity, can be easily controlled by peripheral regions and thus facilitate relevant communication channels between them. We call for methodological expansions of the control theoretical toolbox allowing for elaborations on the temporal dynamics of control processes.

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 4 matches between paragraphs and lines of code.

ursbraun/network_control_and_dopamine

License: none: the authors keep all their rights

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 4514c232df1f5129e1e51b8916f1c24c01a4add5, 19 March 2021

Languages: MATLAB (4)

Size: 6 files, 4 scripts

Software Heritage: archived

Found in: the text, “Control Energy and Stability”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

5 files

compute_suboptimal_trajectories.m, MATLAB, 57 lines
continuous_to_discrete.m, MATLAB, 38 lines
optim_fun.m, MATLAB, 71 lines
variability_among_first_components.m, MATLAB, 59 lines
README.md, Text, 66 lines

At the source: github.com/ursbraun/network_control_and_dopamine

markett-lab/NetworkControlRichClub

License: GPL-3.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Commit: 5fa84d6554e2e8130b2677e758a35051aa05df4f, 23 August 2024

Languages: MATLAB (5)

Size: 21 files, 5 scripts

Software Heritage: not archived

Found in: the text, “Control Energy and Stability”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 30 September 2026: the link answers

30 September 2026: the link answers

7 files

scripts/example_nct_main.m, MATLAB, 210 lines
scripts/nct_analysis_global_regional.m, MATLAB, 76 lines
scripts/nct_analysis_subset.m, MATLAB, 55 lines
scripts/nct_analysis_subset_rotated.m, MATLAB, 70 lines
scripts/nct_main.m, MATLAB, 234 lines
LICENSE, License, 674 lines
README.md, Text, 44 lines

At the source: github.com/markett-lab/NetworkControlRichClub

MICA-MNI/BrainSpace

License: BSD-3-Clause

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 8730de88ae32c4f88eeaf16ef2a6e53c5c32dc34, 5 May 2026

Languages: Python (77), MATLAB (40), Jupyter (4)

Size: 373 files, 121 scripts

Software Heritage: not archived

Found in: the text, “Regional Characteristics”

Holds: README, license file, CITATION.cff, environment (Dockerfile, requirements.txt, setup.cfg, setup.py, docs/requirements.txt), tests, continuous integration, documentation, 4 notebooks

Tools: NumPy (52 files), BrainSpace (32 files), SciPy (18 files), scikit-learn (13 files), Matplotlib (9 files), NiBabel (3 files), Nilearn (3 files), GIfTI library for MATLAB (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

123 files

brainspace/__init__.py, Python, 5 lines
brainspace/_version.py, Python, 3 lines
brainspace/datasets/__init__.py, Python, 15 lines
brainspace/datasets/base.py, Python, 322 lines, 1 match
brainspace/examples/__init__.py, Python, 1 line
brainspace/examples/plot_tutorial0.py, Python, 182 lines
brainspace/examples/plot_tutorial1.py, Python, 84 lines
brainspace/examples/plot_tutorial2.py, Python, 253 lines
brainspace/examples/plot_tutorial3.py, Python, 304 lines
brainspace/gradient/__init__.py, Python, 18 lines
brainspace/gradient/alignment.py, Python, 266 lines
brainspace/gradient/embedding.py, Python, 477 lines
brainspace/gradient/gradient.py, Python, 334 lines
brainspace/gradient/kernels.py, Python, 98 lines
brainspace/gradient/utils.py, Python, 276 lines
brainspace/mesh/__init__.py, Python, 10 lines
brainspace/mesh/array_operations.py, Python, 976 lines
brainspace/mesh/mesh_cluster.py, Python, 252 lines
brainspace/mesh/mesh_correspondence.py, Python, 94 lines
brainspace/mesh/mesh_creation.py, Python, 110 lines
brainspace/mesh/mesh_elements.py, Python, 766 lines
brainspace/mesh/mesh_io.py, Python, 211 lines
brainspace/mesh/mesh_operations.py, Python, 587 lines
brainspace/null_models/__init__.py, Python, 12 lines
brainspace/null_models/moran.py, Python, 308 lines
brainspace/null_models/spin.py, Python, 347 lines
brainspace/null_models/variogram.py, Python, 2,005 lines
brainspace/plotting/__init__.py, Python, 6 lines
brainspace/plotting/base.py, Python, 501 lines
brainspace/plotting/colormaps.py, Python, 87 lines
brainspace/plotting/defaults_plotting.py, Python, 55 lines
brainspace/plotting/sphinx_gallery_scrapper.py, Python, 45 lines
brainspace/plotting/surface_plotting.py, Python, 633 lines
brainspace/plotting/utils.py, Python, 400 lines
brainspace/plotting/utils_qt.py, Python, 19 lines
brainspace/tests/__init__.py, Python, 1 line
brainspace/tests/test_aligned_lambdas.py, Python, 93 lines
brainspace/tests/test_alignment_methods.py, Python, 64 lines
brainspace/tests/test_alignment_options.py, Python, 51 lines
brainspace/tests/test_colormaps.py, Python, 43 lines
brainspace/tests/test_copy_methods.py, Python, 187 lines
brainspace/tests/test_datasets.py, Python, 134 lines
brainspace/tests/test_embedding_validation.py, Python, 66 lines
brainspace/tests/test_gradient.py, Python, 150 lines
brainspace/tests/test_gradient_path_inputs.py, Python, 133 lines
brainspace/tests/test_issue_133.py, Python, 37 lines
brainspace/tests/test_mesh.py, Python, 441 lines
brainspace/tests/test_null_models.py, Python, 260 lines
brainspace/tests/test_parcellation.py, Python, 313 lines
brainspace/tests/test_plotting.py, Python, 274 lines
brainspace/tests/test_vtk94_compatibility.py, Python, 208 lines
brainspace/tests/test_wrapping.py, Python, 192 lines
brainspace/utils/__init__.py, Python, 3 lines
brainspace/utils/parcellation.py, Python, 369 lines
brainspace/vtk_interface/__init__.py, Python, 14 lines
brainspace/vtk_interface/checks.py, Python, 142 lines
brainspace/vtk_interface/decorators.py, Python, 300 lines
brainspace/vtk_interface/io_support/__init__.py, Python, 8 lines
brainspace/vtk_interface/io_support/freesurfer_support.py, Python, 254 lines
brainspace/vtk_interface/io_support/gifti_support.py, Python, 159 lines
brainspace/vtk_interface/pipeline.py, Python, 352 lines
brainspace/vtk_interface/wrappers/__init__.py, Python, 78 lines
brainspace/vtk_interface/wrappers/actor.py, Python, 328 lines
brainspace/vtk_interface/wrappers/algorithm.py, Python, 327 lines
brainspace/vtk_interface/wrappers/base.py, Python, 732 lines
brainspace/vtk_interface/wrappers/data_object.py, Python, 561 lines
brainspace/vtk_interface/wrappers/lookup_table.py, Python, 80 lines
brainspace/vtk_interface/wrappers/misc.py, Python, 111 lines
brainspace/vtk_interface/wrappers/property.py, Python, 30 lines
brainspace/vtk_interface/wrappers/renderer.py, Python, 250 lines
brainspace/vtk_interface/wrappers/utils.py, Python, 255 lines
docs/conf.py, Python, 249 lines
docs/python_doc/auto_examples/plot_tutorial0.ipynb, Jupyter, 187 lines
docs/python_doc/auto_examples/plot_tutorial0.py, Python, 182 lines
docs/python_doc/auto_examples/plot_tutorial1.ipynb, Jupyter, 92 lines
docs/python_doc/auto_examples/plot_tutorial1.py, Python, 84 lines
docs/python_doc/auto_examples/plot_tutorial2.ipynb, Jupyter, 272 lines
docs/python_doc/auto_examples/plot_tutorial2.py, Python, 253 lines
docs/python_doc/auto_examples/plot_tutorial3.ipynb, Jupyter, 303 lines
docs/python_doc/auto_examples/plot_tutorial3.py, Python, 304 lines
matlab/@GradientMaps/GradientMaps.m, MATLAB, 317 lines
matlab/@GradientMaps/fit.m, MATLAB, 142 lines
matlab/analysis_code/@variogram/variogram.m, MATLAB, 339 lines
matlab/analysis_code/compute_mem.m, MATLAB, 141 lines
matlab/analysis_code/diffusion_mapping.m, MATLAB, 86 lines
matlab/analysis_code/graph_is_connected.m, MATLAB, 29 lines
matlab/analysis_code/labelmean.m, MATLAB, 94 lines
matlab/analysis_code/laplacian_eigenmaps.m, MATLAB, 72 lines
matlab/analysis_code/moran_randomization.m, MATLAB, 100 lines
matlab/analysis_code/procrustes_alignment.m, MATLAB, 106 lines
matlab/analysis_code/spin_permutations.m, MATLAB, 105 lines
matlab/example_data_loaders/load_conte69.m, MATLAB, 30 lines
matlab/example_data_loaders/load_gradient.m, MATLAB, 18 lines
matlab/example_data_loaders/load_group_fc.m, MATLAB, 35 lines, 1 match
matlab/example_data_loaders/load_group_mpc.m, MATLAB, 28 lines
matlab/example_data_loaders/load_marker.m, MATLAB, 19 lines
matlab/example_data_loaders/load_mask.m, MATLAB, 20 lines
matlab/example_data_loaders/load_parcellation.m, MATLAB, 29 lines, 1 match
matlab/plot_data/@plot_hemispheres/colorlimits.m, MATLAB, 28 lines
matlab/plot_data/@plot_hemispheres/colormaps.m, MATLAB, 30 lines
matlab/plot_data/@plot_hemispheres/labels.m, MATLAB, 51 lines
matlab/plot_data/@plot_hemispheres/plot_hemispheres.m, MATLAB, 177 lines
matlab/plot_data/@plot_hemispheres/private/make_surface_plot.m, MATLAB, 27 lines
matlab/plot_data/@plot_hemispheres/private/plotter.m, MATLAB, 68 lines
matlab/plot_data/@plot_hemispheres/private/process_views.m, MATLAB, 50 lines
matlab/plot_data/gradient_in_euclidean.m, MATLAB, 108 lines
matlab/plot_data/scree_plot.m, MATLAB, 18 lines
matlab/surface_manipulation/SurfStatReadSurf1.m, MATLAB, 164 lines
matlab/surface_manipulation/SurfStatWriteSurf1.m, MATLAB, 96 lines
matlab/surface_manipulation/combine_surfaces.m, MATLAB, 29 lines
matlab/surface_manipulation/convert_surface.m, MATLAB, 122 lines
matlab/surface_manipulation/full2parcel.m, MATLAB, 13 lines
matlab/surface_manipulation/parcel2full.m, MATLAB, 41 lines
matlab/surface_manipulation/read_surface.m, MATLAB, 13 lines
matlab/surface_manipulation/split_surfaces.m, MATLAB, 41 lines
matlab/surface_manipulation/surface_to_graph.m, MATLAB, 56 lines
matlab/surface_manipulation/write_surface.m, MATLAB, 11 lines
matlab/tests/@datasets_tests/datasets_tests.m, MATLAB, 83 lines
matlab/tests/@diffusion_mapping_tests/diffusion_mapping_tests.m, MATLAB, 57 lines
matlab/tests/@utils_tests/utils_tests.m, MATLAB, 34 lines
setup.py, Python, 86 lines
LICENSE, License, 29 lines
README.rst, Text, 43 lines

At the source: github.com/MICA-MNI/BrainSpace

frantisekvasa/rotate_parcellation

License: MIT

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 65673ea7f47fca36b2982df669fc649b9a4bc5da, 29 June 2023

Languages: MATLAB (3), R (2)

Size: 9 files, 5 scripts

Software Heritage: archived

Found in: the text, “Null Models and Statistics”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: FreeSurfer (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

7 files

Matlab/centroid_extraction_sphere.m, MATLAB, 33 lines
Matlab/perm_sphere_p.m, MATLAB, 93 lines
Matlab/rotate_parcellation.m, MATLAB, 167 lines, 1 match
R/perm.sphere.p.R, R, 52 lines
R/rotate.parcellation.R, R, 216 lines
LICENSE, License, 21 lines
README.md, Text, 55 lines

At the source: github.com/frantisekvasa/rotate_parcellation

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:

4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
135 scripts, each with its path and the digest of its content;
4 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

No dataset and no data link were found in the paper.

Data Availability Statement

The data that support the findings of this study are openly available in the Human Connectome Project at https://db.humanconnectome.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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 9 MeSH terms, 97 references.

Cite

This paper

Podschun, A. N., Betzel, R. F., Braun, U., & Markett, S. (2026). Exploring the Role of the Rich Club in Network Control of Neurocognitive States. Human brain mapping, 47(4), e70485. https://doi.org/10.1002/hbm.70485

BibTeX

@article{podschun2026exploring, author = {Podschun, Alina N. and Betzel, Richard F. and Braun, Urs and Markett, Sebastian}, title = {{Exploring the Role of the Rich Club in Network Control of Neurocognitive States}}, journal = {Human brain mapping}, year = {2026}, month = mar, volume = {47}, number = {4}, pages = {e70485}, publisher = {Wiley}, issn = {1065-9471}, doi = {10.1002/hbm.70485}, url = {https://doi.org/10.1002/hbm.70485}, pmid = {41749476}, pmcid = {PMC12945927} }

RIS

TY - JOUR AU - Podschun, Alina N. AU - Betzel, Richard F. AU - Braun, Urs AU - Markett, Sebastian TI - Exploring the Role of the Rich Club in Network Control of Neurocognitive States T2 - Human brain mapping J2 - Hum Brain Mapp PY - 2026 DA - 2026/03/01 VL - 47 IS - 4 SP - e70485 SN - 1065-9471 PB - Wiley DO - 10.1002/hbm.70485 UR - https://doi.org/10.1002/hbm.70485 LA - en ER -

CSL-JSON

{ "id": "10.1002/hbm.70485", "type": "article-journal", "title": "Exploring the Role of the Rich Club in Network Control of Neurocognitive States", "container-title": "Human brain mapping", "author": [ { "family": "Podschun", "given": "Alina N." }, { "family": "Betzel", "given": "Richard F." }, { "family": "Braun", "given": "Urs" }, { "family": "Markett", "given": "Sebastian" } ], "container-title-short": "Hum Brain Mapp", "volume": "47", "issue": "4", "page": "e70485", "DOI": "10.1002/hbm.70485", "PMID": "41749476", "PMCID": "PMC12945927", "ISSN": "1065-9471", "publisher": "Wiley", "URL": "https://doi.org/10.1002/hbm.70485", "language": "en", "issued": { "date-parts": [ [ 2026, 3, 1 ] ] } }

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Exploring the Role of the Rich Club in Network Control of Neurocognitive States.

Code ↔ Paper

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Overview

Abstract

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ursbraun/network_control_and_dopamine

markett-lab/NetworkControlRichClub

MICA-MNI/BrainSpace

frantisekvasa/rotate_parcellation

Tracing map

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