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Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging.

Code ↔ Paper

2 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 2 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 › Preprocessing ↔ nipype/interfaces/t1prep/__init__.py, the whole file · a weak match · score 0.56 · preprocessing pipeline, fMRIPrep, Nipype
  2. [2] § Methods › Participants and data acquisition › OWN data ↔ nipype/interfaces/cat12/preprocess.py, lines 533–650 · score 0.55 · healthy controls, MNI space, MRI

Paper

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The authors' code

Python · 69 lines · 2.1 KB · Apache-2.0 · 1 match

  1. """Interfaces for T1Prep – T1-weighted MRI preprocessing pipeline (PyCAT).
  3. T1Prep performs skull-stripping, tissue segmentation, and cortical surface
  4. reconstruction using DeepMriPrep and the CAT-Surface library (via cat-surf).
  6. Sub-module interfaces
  7. ---------------------
  8. :class:`T1Prep`
  9. Full pipeline: skull-strip → segment → surface estimate. Wraps
  10. ``python -m t1prep.t1prep``.
  12. :class:`T1PrepSegment`
  13. Segmentation stage only. Wraps ``python -m t1prep.segment``.
  15. :class:`T1PrepSurfaceEstimation`
  16. Surface-estimation stage for one hemisphere. Wraps
  17. ``python -m t1prep.surface_estimation``.
  19. :class:`T1PrepRealignLongitudinal`
  20. Rigid realignment of longitudinal T1w time-points. Wraps
  21. ``python -m t1prep.realign_longitudinal``.
  23. Base classes
  24. ------------
  25. :class:`Info`
  26. T1Prep package version detection.
  28. :class:`T1PrepCommand`
  29. Base ``CommandLine`` for ``python -m t1prep.<module>`` invocations.
  31. A focused subset of the ``cat_surf`` API — the volume-denoising and volume/
  32. boundary registration interfaces needed to replace FreeSurfer / FSL (bbreg) /
  33. ANTs in an fMRIPrep-style pipeline — is exported directly from this package
  34. (:class:`CatSurfVolSanlm`, :class:`CatSurfBbreg`, :class:`CatSurfBbregDetectContrast`,
  35. :class:`CatSurfVolumeRegisterNmi`, :class:`CatSurfVolumeRegisterRobust`). The
  36. full per-function interface set is archived for reference in the CAT-Surface
  37. repository under ``cat_surface_cython/examples/nipype/``.
  39. """
  41. from .base import Info, T1PrepCommand
  42. from .preprocess import T1Prep, T1PrepSegment
  43. from .surface import T1PrepSurfaceEstimation
  44. from .longitudinal import T1PrepRealignLongitudinal
  45. from .cat_surf import (
  46. # Volume operations
  47. CatSurfVolSanlm,
  48. # Registration
  49. CatSurfBbreg,
  50. CatSurfBbregDetectContrast,
  51. CatSurfVolumeRegisterNmi,
  52. CatSurfVolumeRegisterRobust,
  53. )
  55. __all__ = [
  56. "Info",
  57. "T1PrepCommand",
  58. "T1Prep",
  59. "T1PrepSegment",
  60. "T1PrepSurfaceEstimation",
  61. "T1PrepRealignLongitudinal",
  62. # Volume operations
  63. "CatSurfVolSanlm",
  64. # Registration
  65. "CatSurfBbreg",
  66. "CatSurfBbregDetectContrast",
  67. "CatSurfVolumeRegisterNmi",
  68. "CatSurfVolumeRegisterRobust",
  69. ]

__init__.py at commit b02801e, under Apache-2.0 · at the source

Overview

Authors: Xiaojing Fang1, Olivia Schwemmer2, Abigail Hogan1, Michael Marxen1
ORCID iDs: Abigail Hogan
  1. Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Würzburger Straße 35, 01187 Dresden, Germany
  2. Department of Psychology, Technische Universität Dresden, Würzburger Straße 35, 01187 Dresden, Germany
Institutions: Technische Universität Dresden (Germany)
Journal: Biology of sex differences, volume 17, issue 1, article 76
Dates: received 20 October 2025; accepted 19 March 2026; published online 4 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13293-026-00891-z · PMID 41935261 · PMCID PMC13067390 · OpenAlex W4415830394
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.21203/rs.3.rs-7906846/v1
Status: code verified
Categories: fMRI (modality), human (organism)
Methods: Statistics, Machine learning, Connectivity, Graphs, fMRI & imaging
Keywords: Resting-state fMRI, Dynamic functional connectivity, Sliding window analysis, Sex difference, Functional integration, Functional segregation, Human brain connectome
MeSH: Brain*, Magnetic Resonance Imaging*, Sex Characteristics*, Adult, Connectome, Female, Humans, Male, Rest, Young Adult (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Deutsche Forschungsgemeinschaft (178833530, TRR 265, IRTG 2773, 402170461, 454245598, SFB940); Technische Universität Dresden
Citations: cited by 2 papers (Europe PMC); 52 references in the paper

Abstract

Background: Understanding the impact of biological sex on the functional organization and dynamics of the brain is crucial for elucidating sex-specific differences in cognitive functions and neuropsychiatric disorders. Systems neuroscience often models the brain as a network of interconnected brain regions with functional connectivity (FC), i.e., the correlation between signal time courses, serving as a measure of connection strength. FC matrices, here derived from resting-state functional magnetic resonance imaging (rs-fMRI), define a network graph that can be characterized by its level of module segregation or, inversely, integration. Such parameters can be generated for the full length of the acquired data (static) or for short periods implying dynamically changing brain states. We recently made the interesting observation in a separate study (N = 63) that measures of brain integration and segregation based on dynamic functional connectivity (dFC) data differed between sexes, while graph-based measures based on static FC (sFC) did not, which we investigated in more detail in this study.

Methods: We preregistered a replication of our analysis from the small sample in N = 501 subjects of the Human Connectome Project dataset. We performed cross-sectional comparisons between sexes of the static rs-fMRI graph parameters modularity and global efficiency, as well as the dFC parameters state prevalence, mean dwell time, mean inter-state transition time, and variability derived from a two-state model. Additionally, we explored whether sex differences in 66 cognitive and behavioral parameters are mediated by the FC integration measure with the strongest sex effect.

Results: All static and dynamic measures of integration/segregation showed higher levels of functional integration in males, with effect sizes up to 0.60 for the dFC parameter prevalence. For three of the 66 explored cognitive and behavioral parameters, we observed that the prevalence of the integrated state mediated the sex difference: dexterity, agreeableness, and self-reported aggression.

Conclusion: We found consistent evidence across two datasets that rs-fMRI-based measures of brain integration are increased in males. An exploratory analysis, which requires replication, suggests that such differences mediate personality differences. This study highlights that biological sex differences in brain functional organization may contribute to sex-typical behaviors.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13293-026-00891-z.

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 2 matches between paragraphs and lines of code.

Zenodo 4035081

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Preprocessing”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Nipype (102 files), NumPy (87 files), NiBabel (51 files), SciPy (17 files), FSL (15 files), NetworkX (14 files), DIPY (11 files), ANTs (7 files), FreeSurfer (6 files), Matplotlib (6 files), SPM (6 files), Nilearn (2 files), pandas (2 files), PyBIDS (2 files), AFNI (1 file), DataLad (1 file), pydicom (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
1,326 files
At the source: zenodo.org/record/4035081#.X7Ty32hKhPY

OSF 286fb

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: osf.io/286fb

OSF c3xvt

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 2 files, 0 scripts
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: osf.io/c3xvt

OSF 6gswx

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 3 files
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: osf.io/6gswx

OSF mu3st

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 4 files
Software Heritage: not checked
Found in: the references
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: osf.io/mu3st

nipy/nipype

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b02801e61e94301514e58212ee69c29f110318a4, 23 September 2026
Languages: Python (1370), Shell (20), MATLAB (2), JavaScript (1)
Size: 1,892 files, 1,393 scripts
Software Heritage: archived
Found in: the Zenodo archive record
Holds: README, license file, environment (pyproject.toml, uv.lock, doc/requirements.txt, nipype/interfaces/dipy/setup.py), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: Nipype (114 files), NumPy (86 files), NiBabel (50 files), FSL (16 files), SciPy (16 files), NetworkX (12 files), DIPY (11 files), ANTs (8 files), SPM (8 files), FreeSurfer (6 files), Matplotlib (6 files), Nilearn (2 files), pandas (2 files), PyBIDS (2 files), AFNI (1 file), DataLad (1 file), h5py (1 file), pydicom (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
1,395 files
At the source: github.com/nipy/nipype/tree/1.5.1

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:

  • 6 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2,717 scripts, each with its path and the digest of its content;
  • 2 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 availability

This study was preregistered after an analysis of the OWN data [22]. All extracted parameters, including time series for connectivity matrices and the required software are openly available at OSF [52]. The raw MRI data from our acquisition (i.e., OWN data) are not available since complex brain images may contain fingerprint-like information, which could lead to reidentification of the subjects. Discussions on how to treat such data at the university level are ongoing. Individual requests for data access can be sent to the corresponding author. OWN data time-courses and matrices are available at OSF [23] including static and dynamic parameters. The code to produce dFC parameters is available on OSF [52]. The HCP data are publicly available [21].

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

  • Funding: added Deutsche Forschungsgemeinschaft: 178833530, TRR 265, IRTG 2773, 402170461, 454245598, SFB940; Technische Universität Dresden

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 7 keywords, 10 MeSH terms, 45 references.

Cite

This paper

Fang, X., Schwemmer, O., Hogan, A., & Marxen, M. (2026). Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging. Biology of sex differences, 17(1), 76. https://doi.org/10.1186/s13293-026-00891-z

BibTeX

@article{fang2026sex,
author = {Fang, Xiaojing and Schwemmer, Olivia and Hogan, Abigail and Marxen, Michael},
title = {{Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging}},
journal = {Biology of sex differences},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {76},
publisher = {BMC},
issn = {2042-6410},
doi = {10.1186/s13293-026-00891-z},
url = {https://doi.org/10.1186/s13293-026-00891-z},
pmid = {41935261},
pmcid = {PMC13067390}
}

RIS

TY - JOUR
AU - Fang, Xiaojing
AU - Schwemmer, Olivia
AU - Hogan, Abigail
AU - Marxen, Michael
TI - Sex differences in dynamic and static measures of brain integration derived from resting-state functional magnetic resonance imaging
T2 - Biology of sex differences
J2 - Biol Sex Differ
PY - 2026
DA - 2026/04/04
VL - 17
IS - 1
SP - 76
SN - 2042-6410
PB - BMC
DO - 10.1186/s13293-026-00891-z
UR - https://doi.org/10.1186/s13293-026-00891-z
LA - en
ER -

CSL-JSON

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