OSCR

Evolutionary signatures in deep white matter architecture: A comparative study of humans and chimpanzees.

Code ↔ Paper

7 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 7 matches
  1. [1] § Material and Methods › Chimpanzee cohort and analysis ↔ dmri/src/library/gkg-dmri-tractography/RegularizedDeterministicTractographyAlgorithm.h, lines 1–64 · score 0.78 · regularized deterministic tractography, aperture angle, GFA threshold, streamline, algorithm, voxel
  2. [2] § Material and Methods › Chimpanzee cohort and analysis ↔ dmri/src/library/gkg-dmri-tractography/RegularizedDeterministicTractographyAlgorithm_i.h, lines 428–509 · score 0.75 · regularized deterministic tractography, aperture angle, orientation distribution functions, GFA, algorithm, ODF
  3. [3] § Material and Methods › Human cohort and analysis › Fiber clustering ↔ dmri/src/plugin/gkg-dmri-plugin-functors/DwiIntraSubjectFiberClustering/DwiIntraSubjectFiberClusteringCommand.h, lines 1–72 · score 0.62 · connectivity matrix, fiber length, parcels, watershed, clustering, density
  4. [4] § Material and Methods › Comparison of human and chimpanzee DWMBs › Morphological analysis using isomaps ↔ pcpm/distance/libpointmatcher.py, lines 260–318 · score 0.58 · affine transformation matrices, point cloud
  5. [5] § Material and Methods › Human cohort and analysis › Fiber clustering ↔ dmri/src/plugin/gkg-dmri-plugin-functors/DwiIntraSubjectFiberClustering/DwiIntraSubjectFiberClusteringCommand.h, lines 1–72 · score 0.54 · fiber length ranges, right hemispheres, cerebellum, density, clustering, mask
  6. [6] § Material and Methods › Human cohort and analysis ↔ mri-reconstruction/src/library/gkg-mri-reconstruction-io/GehcSignaPFileHeader.h, lines 934–993 · score 0.52 · spin echo, TE, pulsed, MRI
  7. [7] § Material and Methods › Human cohort and analysis › Fiber clustering ↔ dmri/src/library/gkg-dmri-bundle-measure/BundleAffinityMatrix.h, lines 43–102 · score 0.52 · affinity matrix, centroids, distances, fiber, bundle

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

C/C++ header · 128 lines · 5.1 KB · other · 2 matches

  1. #ifndef _gkg_dmri_plugin_functors_DwiIntraSubjectFiberClustering_DwiIntraSubjectFiberClusteringCommand_h_
  2. #define _gkg_dmri_plugin_functors_DwiIntraSubjectFiberClustering_DwiIntraSubjectFiberClusteringCommand_h_
  3. #include <gkg-communication-command/Command.h>
  4. #include <gkg-core-pattern/Creator.h>
  5. #include <gkg-processing-coordinates/Vector3d.h>
  6. #include <string>
  7. #include <vector>
  8. namespace gkg
  9. {
  10. class DwiIntraSubjectFiberClusteringCommand :
  11. public Command,
  12. public Creator2Arg< DwiIntraSubjectFiberClusteringCommand,
  13. Command,
  14. int32_t,
  15. char** >,
  16. public Creator1Arg< DwiIntraSubjectFiberClusteringCommand,
  17. Command,
  18. const Dictionary& >
  19. {
  20. public:
  21. DwiIntraSubjectFiberClusteringCommand( int32_t argc,
  22. char* argv[],
  23. bool loadPlugin = false,
  24. bool removeFirst = true );
  25. DwiIntraSubjectFiberClusteringCommand(
  26. const std::vector< std::string >& fileNameInputBundleMaps,
  27. const std::string& fileNameBrainSubDivisionMask,
  28. const std::string& fileNameTransform3dBrainSubDivisionMaskToBundle,
  29. const std::string& outputDirectoryName,
  30. float fiberResamplingStep,
  31. int16_t rightHemisphereLabel,
  32. int16_t leftHemisphereLabel,
  33. int16_t cerebellumLabel,
  34. int16_t corpusCallosumLabel,
  35. int16_t discardedLabel,
  36. float hemisphereThreshold,
  37. float corpusCallosumThreshold,
  38. float cerebellumThreshold,
  39. float lowerLength,
  40. float upperLength,
  41. int32_t fiberLengthRangeCount,
  42. const Vector3d< double >& parcellationResolution,
  43. const int32_t& parcellationFiberCountThreshold,
  44. const int32_t& parcelVoxelCount,
  45. const int32_t& parcellationKMeansIterationCount,
  46. const float& parcellationRatioOfDistanceVariance,
  47. const float& connectivityMatrixThresholdRatio,
  48. const int32_t& averageClusterSize,
  49. const int32_t& isolatedClusterMinimumSize,
  50. const int32_t& minimumClusterSize,
  51. const float& minimumParcelSizeRatio,
  52. const float& minimumPercentageOfFiberLengthBelongingToCluster,
  53. bool performWatershed,
  54. const int32_t& minimumFiberCountInCluster,
  55. const float& extremityLength,
  56. const float& extremityDensityMapThreshold,
  57. bool intermediate,
  58. const std::string& outputVolumeFormat,
  59. const std::string& outputBundleFormat,
  60. bool ascii,
  61. bool verbose );
  62. DwiIntraSubjectFiberClusteringCommand( const Dictionary& parameters );
  63. virtual ~DwiIntraSubjectFiberClusteringCommand();
  64. static std::string getStaticName();
  65. protected:
  66. friend class Creator2Arg< DwiIntraSubjectFiberClusteringCommand, Command,
  67. int32_t, char** >;
  68. friend class Creator1Arg< DwiIntraSubjectFiberClusteringCommand, Command,
  69. const Dictionary& >;
  70. void parse();
  71. void execute(
  72. const std::vector< std::string >& fileNameInputBundleMaps,
  73. const std::string& fileNameBrainSubDivisionMask,
  74. const std::string& fileNameTransform3dBrainSubDivisionMaskToBundle,
  75. const std::string& outputDirectoryName,
  76. float fiberResamplingStep,
  77. int16_t rightHemisphereLabel,
  78. int16_t leftHemisphereLabel,
  79. int16_t cerebellumLabel,
  80. int16_t corpusCallosumLabel,
  81. int16_t discardedLabel,
  82. float hemisphereThreshold,
  83. float corpusCallosumThreshold,
  84. float cerebellumThreshold,
  85. float lowerLength,
  86. float upperLength,
  87. int32_t fiberLengthRangeCount,
  88. const Vector3d< double >& parcellationResolution,
  89. const int32_t& parcellationFiberCountThreshold,
  90. const int32_t& parcelVoxelCount,
  91. const int32_t& parcellationKMeansIterationCount,
  92. const float& parcellationRatioOfDistanceVariance,
  93. const float& connectivityMatrixThresholdRatio,
  94. const int32_t& averageClusterSize,
  95. const int32_t& isolatedClusterMinimumSize,
  96. const int32_t& minimumClusterSize,
  97. const float& minimumParcelSizeRatio,
  98. const float& minimumPercentageOfFiberLengthBelongingToCluster,
  99. bool performWatershed,
  100. const int32_t& minimumFiberCountInCluster,
  101. const float& extremityLength,
  102. const float& extremityDensityMapThreshold,
  103. bool intermediate,
  104. const std::string& outputVolumeFormat,
  105. const std::string& outputBundleFormat,
  106. bool ascii,
  107. bool verbose );
  108. };
  109. }
  110. #endif

DwiIntraSubjectFiberClusteringCommand.h at commit d1669f8, under other · at the source

Overview

Authors: Maëlig Chauvel1,2, Ivy Uszynski1, Marco Pascucci1, Yann Leprince3, Bastien Herlin1, Denis Rivière1, Jean-François Mangin1, William D Hopkins4, Cyril Poupon1
  1. BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, Gif-sur-Yvette, France
  2. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  3. UNIACT, NeuroSpin, Université Paris-Saclay, INSERM, CEA, Gif-sur-Yvette, France
  4. Department of Comparative Medicine, Michale E Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, TX, United States
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1154
Dates: received 5 August 2025; accepted 19 January 2026; published online 11 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1154 · PMID 41836920 · PMCID PMC12980544 · OpenAlex W7128622658
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), non-human primate (organism), cognitive (subfield)
Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: white matter, chimpanzee, fiber clustering, isomap, neuro-evolution
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: cited by 1 paper (Europe PMC); 83 references in the paper

Abstract

Despite near-identical genetics, humans and chimpanzees display striking cognitive differences, thought to emerge from overall brain size variation and subtle divergences in brain connectivity. We present a comparative analysis of deep white matter bundle (DWMB) morphology in 39 in vivo chimpanzees and 39 humans, using diffusion MRI and a novel isomap-based shape analysis pipeline. After mapping DWMBs into a shared anatomical space via sulcus-informed diffeomorphic registration, we identified robust species-specific differences across key frontal tracts. We focused on four frontal tracts due to their roles in fronto-parietal and fronto-temporal connectivity supporting language, executive function, and socio-emotional processing, with the arcuate fasciculus serving as an internal control given its well-established species differences. Notably, the arcuate fasciculus in humans exhibited greater curvature, volume, and temporal extension—traits absent in chimpanzees and consistent with its role in language. The uncinate and inferior fronto-occipital fasciculus revealed distinct cross-species expansions and lateralization was observed for the frontal aslants and inferior fronto-occipital fasciculus in chimpanzees. These results provide the first high-dimensional morphological mapping of DWMBs across species, uncovering evolutionary adaptations in frontal connectivity and lateralization that likely underlie human-specific cognitive abilities.

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 7 matches between paragraphs and lines of code.

framagit.org/cpoupon/gkg

License: other
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: d1669f8d9da159e1372e18dcd9d8cb6da9d3660b, 3 September 2026
Languages: C/C++ (2575), Python (72), C++ (46), Shell (15), CUDA (1)
Size: 5,430 files, 2,709 scripts
Software Heritage: not archived
Found in: the text, “Chimpanzee cohort and analysis”
Holds: license file, tests
Not found: README, CITATION.cff, environment file, continuous integration, documentation
Tools: Matplotlib (1 file)
Availability: 2 checks, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
  • 27 September 2026: unreachable at the last attempt
2,000 files

neurospin/point-cloud-pattern-mining

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 60c0f9aa56b59e83ed4b72d9dbd9f2c36363bd53, 10 July 2023
Languages: Python (27)
Size: 37 files, 27 scripts
Software Heritage: archived
Found in: “Data and Code Availability”
Holds: README, environment (setup.py), tests, documentation
Not found: license file, CITATION.cff, continuous integration
Tools: NumPy (17 files), pandas (11 files), SciPy (5 files), Matplotlib (2 files), scikit-learn (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
28 files

Zenodo 7253494

License: other-open
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: NumPy (17 files), pandas (11 files), SciPy (4 files), Matplotlib (2 files), scikit-learn (2 files)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
28 files
At the source:

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:

  • 3 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,053 scripts, each with its path and the digest of its content;
  • 7 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

Datasets cited

Data and Code Availability

The chimpanzee DWMB atlas used in this study is available on the Zenodo platform at https://doi.org/10.5281/zenodo.7147789. The human DWMB is available on the Zenodo platform at https://doi.org/10.5281/zenodo.7308510. The results of the registration between human and chimpanzee as well as transformation files are available on zenodo at https://doi.org/10.5281/zenodo.13935442. The construction of atlases is based on the analysis of the anatomical and diffusion MRI dataset using the tractography and fiber clustering tools available from the Ginkgo toolbox (Ginkgo Team, BAOBAB, NeuroSpin, Paris-Saclay University, CNRS, CEA, https://framagit.org/cpoupon/gkg). The isomap algorithm used for this analysis is given on the neurospin/github repository: https://github.com/neurospin/point-cloud-pattern-mining.

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, pages, dates, 9 authors, 5 keywords, 78 references.

Cite

This paper

Chauvel, M., Uszynski, I., Pascucci, M., Leprince, Y., Herlin, B., Rivière, D., Mangin, J.-F., Hopkins, W. D., & Poupon, C. (2026). Evolutionary signatures in deep white matter architecture: A comparative study of humans and chimpanzees. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1154. https://doi.org/10.1162/imag.a.1154

BibTeX

@article{chauvel2026evolutionary,
author = {Chauvel, Maëlig and Uszynski, Ivy and Pascucci, Marco and Leprince, Yann and Herlin, Bastien and Rivière, Denis and Mangin, Jean-François and Hopkins, William D and Poupon, Cyril},
title = {{Evolutionary signatures in deep white matter architecture: A comparative study of humans and chimpanzees}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = mar,
volume = {4},
pages = {IMAG.a.1154},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1154},
url = {https://doi.org/10.1162/imag.a.1154},
pmid = {41836920},
pmcid = {PMC12980544}
}

RIS

TY - JOUR
AU - Chauvel, Maëlig
AU - Uszynski, Ivy
AU - Pascucci, Marco
AU - Leprince, Yann
AU - Herlin, Bastien
AU - Rivière, Denis
AU - Mangin, Jean-François
AU - Hopkins, William D
AU - Poupon, Cyril
TI - Evolutionary signatures in deep white matter architecture: A comparative study of humans and chimpanzees
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/03/11
VL - 4
SP - IMAG.a.1154
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1154
UR - https://doi.org/10.1162/imag.a.1154
LA - en
ER -

CSL-JSON

{
"id": "10.1162/imag.a.1154",
"type": "article-journal",
"title": "Evolutionary signatures in deep white matter architecture: A comparative study of humans and chimpanzees",
"container-title": "Imaging neuroscience (Cambridge, Mass.)",
"author": [
{
"family": "Chauvel",
"given": "Maëlig"
},
{
"family": "Uszynski",
"given": "Ivy"
},
{
"family": "Pascucci",
"given": "Marco"
},
{
"family": "Leprince",
"given": "Yann"
},
{
"family": "Herlin",
"given": "Bastien"
},
{
"family": "Rivière",
"given": "Denis"
},
{
"family": "Mangin",
"given": "Jean-François"
},
{
"family": "Hopkins",
"given": "William D"
},
{
"family": "Poupon",
"given": "Cyril"
}
],
"container-title-short": "Imaging Neurosci (Camb)",
"volume": "4",
"page": "IMAG.a.1154",
"DOI": "10.1162/imag.a.1154",
"PMID": "41836920",
"PMCID": "PMC12980544",
"ISSN": "2837-6056",
"publisher": "MIT Press",
"URL": "https://doi.org/10.1162/imag.a.1154",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
11
]
]
}
}

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/s41592-026-03159-x [code]
Siibra: a software tool suite for realizing a Multilevel Human Brain Atlas from complex data resources.
Journal: Nature methods
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references, author Yann Leprince
[2] doi:10.1016/j.ynirp.2026.100360 [code]
CIVET-Chimp: An automated pipeline for MRI-based cortical surface extraction in chimpanzees.
Journal: Neuroimage. Reports
In common: NumPy, non-human primate, 6 references
[3] doi:10.1073/pnas.2530123123
Individual differences in speech monitoring: Functional and structural correlates of delayed auditory feedback.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: cognitive, 5 references
[4] doi:10.1016/j.isci.2026.116671 [code]
A high-resolution functional network-organized atlas of human superficial white matter from ultra-high-field diffusion MRI.
Journal: iScience
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references
[5] doi:10.1162/imag.a.1316 [code]
Longitudinal MRI template of the baboon brain from birth to adolescence.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: pandas, SciPy, Matplotlib, 1 other tool, non-human primate, 2 references
[6] doi:10.1038/s41467-026-71404-0 [code]
Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control.
Journal: Nature communications
In common: scikit-learn, pandas, SciPy, 2 other tools, cognitive, 1 reference
[7] doi:10.1038/s41467-026-75745-8 [code]
A language network in the individualized functional connectomes of 1199 human brains doing arbitrary tasks.
Journal: Nature communications
In common: scikit-learn, pandas, SciPy, 2 other tools, cognitive, 1 reference
[8] doi:10.1162/imag.a.1288
Neighborhood disadvantage and brain myelination: Insights from infancy to childhood.
Journal: Imaging neuroscience (Cambridge, Mass.)
In common: 4 references
[9] doi:10.1038/s41467-026-75585-6 [code]
Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease.
Journal: Nature communications
In common: scikit-learn, pandas, SciPy, 2 other tools, 2 references
[10] doi:10.1038/s41467-026-73947-8 [code]
Frontal cortex organization supporting audiovisual processing during naturalistic viewing.
Journal: Nature communications
In common: SciPy, Matplotlib, NumPy, cognitive, 2 references

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.