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Comparative Systematic Analysis of Gray Matter Biophysical Models on a Public Dataset.

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Paper

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The authors' code

Python · 1 line · 0 B · Apache-2.0

__init__.py at commit 1ed059d, under Apache-2.0 · at the source

Overview

Authors: Santiago Mezzano1,2,3, Quentin Uhl1,2, Tommaso Pavan1,2, Jasmine Nguyen‐Duc1,2, Hansol Lee4,5, Susie Huang4,5, Ileana Jelescu1,2
ORCID iDs: Santiago Mezzano, Hansol Lee, Ileana Jelescu
  1. Department of Radiology Lausanne University Hospital (CHUV) Lausanne Switzerland
  2. Faculty of Biology and Medicine University of Lausanne Lausanne Switzerland
  3. School of Psychology University of Auckland Auckland New Zealand
  4. Athinoula A. Martinos Center for Biomedical Imaging Charlestown Massachusetts USA
  5. Department of Radiology, Massachusetts General Hospital Harvard Medical School Boston Massachusetts USA
Institutions: University of Auckland (New Zealand); Centre Hospitalier Universitaire Vaudois (Switzerland); University of Lausanne (Switzerland); Harvard University (United States); Massachusetts General Hospital (United States); Athinoula A. Martinos Center for Biomedical Imaging (United States)
Journal: Magnetic resonance in medicine, volume 96, issue 5, pages 2278-2291
Dates: received 13 January 2026; accepted 29 June 2026; published online 16 July 2026; in print November 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/mrm.70507 · PMID 42464030 · PMCID PMC13527255 · OpenAlex W4411683434
Open access: hybrid, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.06.19.660512
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: biophysical modeling, diffusion MRI, gray matter microstructure, NEXI
MeSH: Diffusion Magnetic Resonance Imaging*, Gray Matter*, Image Processing, Computer-Assisted*, Adult, Algorithms, Brain, Connectome, Female, Humans, Male, Neuroimaging (* major topic)
Journal subjects: Imaging Methodology
Topic: Bioinformatics and Genomic Networks (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Swiss Secretariat for Education, Research and Innovation (MB22.00032); Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (194260)
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Purpose: Biophysical models of diffusion tailored to characterize gray matter (GM) microstructure are gaining traction in the neuroimaging community. NEXI, SMEX, SANDI, and SANDIX represent recent efforts to account for different microstructural features, such as soma contributions and inter‐compartment exchange, in the diffusion MRI (dMRI) signal. The purpose of this work is to provide a comparative evaluation of these four GM diffusion models.

Methods: A comparative analysis of NEXI, SMEX, SANDI, and SANDIX was performed using a single, publicly available in vivo human dataset, the Connectome Diffusion Microstructure Dataset (CDMD), acquired with two diffusion times. Cortical microstructure metrics were estimated in 26 healthy subjects using the open‐source Gray Matter Swiss Knife toolbox, and goodness of fit, anatomical patterns, and consistency with previous studies were evaluated.

Results: CDMD data yielded GM parameter estimates consistent with values reported in previous studies across all four models. NEXI and SMEX produced similar cortical anatomical patterns, with consistent regional distributions across diffusion times. Goodness of fit varied across models, with NEXI showing the best fit, followed by SMEX, and finally SANDIX. SANDI parameter estimates showed strong dependence on diffusion‐time selection and fitting algorithm.

Conclusion: This retrospective cross‐model analysis establishes the feasibility of estimating exchange models from only two diffusion times and highlights trade‐offs in biological specificity, model complexity, and fitting robustness, which are critical considerations when selecting a model for future clinical and research applications.

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

Repository

Its files are read in the Code ↔ Paper reader above.

QuentinUhl/graymatter_swissknife

License: Apache-2.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 1ed059d44a4f4951d6106c1b2df6105d38e5697c, 6 August 2025
Languages: Python (69)
Size: 93 files, 69 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, license file, environment (pyproject.toml), tests
Not found: CITATION.cff, continuous integration, documentation
Tools: NumPy (48 files), SciPy (11 files), NiBabel (7 files), Matplotlib (2 files), scikit-learn (2 files), XGBoost (2 files)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
71 files
At the source: github.com/QuentinUhl/graymatter_swissknife

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 69 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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 Statement

The CDMD dataset used in this study is publicly available at https://springernature.figshare.com/collections/_/5315474. The Gray Matter Swiss Knife toolbox used for model fitting is openly available at https://github.com/QuentinUhl/graymatter_swissknife. Code for model comparisons, statistical analyses, plotting, and cortical ribbon metric extraction can be made available upon request to the corresponding author.

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

Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 11 MeSH terms, 2 funders, 47 references.

Cite

This paper

Mezzano, S., Uhl, Q., Pavan, T., Nguyen‐Duc, J., Lee, H., Huang, S., & Jelescu, I. (2026). Comparative Systematic Analysis of Gray Matter Biophysical Models on a Public Dataset. Magnetic resonance in medicine, 96(5), 2278-2291. https://doi.org/10.1002/mrm.70507

BibTeX

@article{mezzano2026comparative,
author = {Mezzano, Santiago and Uhl, Quentin and Pavan, Tommaso and Nguyen‐Duc, Jasmine and Lee, Hansol and Huang, Susie and Jelescu, Ileana},
title = {{Comparative Systematic Analysis of Gray Matter Biophysical Models on a Public Dataset}},
journal = {Magnetic resonance in medicine},
year = {2026},
month = jul,
volume = {96},
number = {5},
pages = {2278--2291},
publisher = {Wiley},
issn = {0740-3194},
doi = {10.1002/mrm.70507},
url = {https://doi.org/10.1002/mrm.70507},
pmid = {42464030},
pmcid = {PMC13527255}
}

RIS

TY - JOUR
AU - Mezzano, Santiago
AU - Uhl, Quentin
AU - Pavan, Tommaso
AU - Nguyen‐Duc, Jasmine
AU - Lee, Hansol
AU - Huang, Susie
AU - Jelescu, Ileana
TI - Comparative Systematic Analysis of Gray Matter Biophysical Models on a Public Dataset
T2 - Magnetic resonance in medicine
J2 - Magn Reson Med
PY - 2026
DA - 2026/07/16
VL - 96
IS - 5
SP - 2278
EP - 2291
SN - 0740-3194
PB - Wiley
DO - 10.1002/mrm.70507
UR - https://doi.org/10.1002/mrm.70507
LA - en
ER -

CSL-JSON

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