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Data quality biases normative models derived from fetal brain MRI.

Code ↔ Paper

8 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 8 matches
  1. [1] § Methods › Data processing › Segmentation ↔ scripts/plots_and_analyses/supp_plot_iterative_removal.r, lines 1–80 · score 0.76 · cortical gray matter, eCSF, basal ganglia, lateral ventricles, brainstem, thalamus
  2. [2] § Methods › Data processing › Segmentation ↔ scripts/plots_and_analyses/1_plot_main.r, lines 1–39 · score 0.75 · cortical gray matter, eCSF, basal ganglia, lateral ventricles, brainstem, thalamus
  3. [3] § Results › Poor-quality images can bias normative trajectories ↔ notebooks/1_harmonize_data.ipynb, lines 539–558 · score 0.74 · cortical gray matter, eCSF, basal ganglia, lateral ventricles, cerebellar volume, brainstem
  4. [4] § Results › Poor-quality images can bias normative trajectories ↔ scripts/plots_and_analyses/1_plot_main.r, lines 1–39 · score 0.74 · cortical gray matter, eCSF, basal ganglia, lateral ventricles, cerebellar volume, brainstem
  5. [5] § Methods › Data processing › Super-resolution reconstruction ↔ fetpype/pipelines/full_pipeline.py, lines 26–104 · score 0.65 · bias field correction, brain extraction, denoising, resolution, preprocessing, stacks
  6. [6] § Results › Poor-quality images can bias normative trajectories ↔ scripts/plots_and_analyses/supp_plot_iterative_removal.r, lines 1–80 · score 0.60 · lateral ventricles volume, white matter volume, cerebellar volume, confidence, bootstrap, centile
  7. [7] § Methods › Analysis of the impact of image quality on normative models › Harmonization on volumetric measurements ↔ notebooks/1_harmonize_data.ipynb, lines 197–283 · score 0.58 · ComBat, GroundTruth, GAM, smoothing, spline, harmonized
  8. [8] § Methods › Analysis of the impact of image quality on normative models › QC subgroups definition ↔ notebooks/1_harmonize_data.ipynb, lines 36–86 · score 0.50 · global quality score, excellent quality, cohorts, scans

Paper

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

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The authors' code

Jupyter notebook · 814 lines · 38 KB · no license · 3 matches

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It can be read at the source: notebooks/1_harmonize_data.ipynb.

Overview

Authors: Thomas Sanchez1,2, Angeline Mihailov3, Gerard Martí-Juan4, Nadine Girard3,5, Aurélie Manchon3,5, Mathieu Milh6,7, Elisenda Eixarch8,9,10, Vincent Dunet2, Mériam Koob2, Léo Pomar11,12, Joanna Sichitiu11, Miguel A. González Ballester4,13, Oscar Camara4, Gemma Piella4, Meritxell Bach Cuadra1,2, Guillaume Auzias3
ORCID iDs: Thomas Sanchez, Angeline Mihailov, Gerard Martí-Juan, Nadine Girard, Aurélie Manchon, Mathieu Milh, Elisenda Eixarch, Vincent Dunet, Mériam Koob, Léo Pomar, Joanna Sichitiu, Miguel A. González Ballester, Oscar Camara, Gemma Piella, Meritxell Bach Cuadra, Guillaume Auzias
13 affiliations
  1. CIBM Center for Biomedical Imaging, Lausanne, Switzerland
  2. Department of Medical Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  3. Institut de Neurosciences de la Timone, CNRS, Aix-Marseille Université, Marseille, France
  4. BCN MedTech, Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
  5. APHM, Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital de la Timone, Aix-Marseille University, Marseille, France
  6. APHM, Service de Neurologie Pédiatrique, Hôpital de la Timone, Aix-Marseille University, Marseille, France
  7. Aix Marseille University, Inserm, Marseille, France
  8. BCNatal | Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu, Universitat de Barcelona), Barcelona, Spain
  9. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
  10. Centre for Biomedical Research on Rare Diseases (CIBERER), Barcelona, Spain
  11. Department Woman-Mother-Child, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  12. School of Health Sciences (HESAV), HES-SO, University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
  13. ICREA, Barcelona, Spain
Institutions: Centre Hospitalier Universitaire Vaudois (Switzerland); Centre d'Imagerie BioMedicale (Switzerland); University of Lausanne (Switzerland); Centre National de la Recherche Scientifique (France); Aix-Marseille Université (France); Institut de Neurosciences de la Timone (France); Universitat Pompeu Fabra (Spain); Hôpital de la Timone (France); Inserm (France); Hospital Sant Joan de Déu Barcelona (Spain); Centre for Biomedical Network Research on Rare Diseases (Spain); Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (Spain); Universitat de Barcelona (Spain); HES-SO University of Applied Sciences and Arts Western Switzerland (Switzerland); Haute École de Santé Vaud; Institució Catalana de Recerca i Estudis Avançats (Spain)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1337
Dates: received 22 January 2026; accepted 17 July 2026; published online 14 August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1337 · PMID 42609916 · PMCID PMC13479347 · OpenAlex W7171370507
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality)
Methods: Connectivity, Statistics, Physiology & signal measures
Keywords: fetal, brain MRI, normative modeling, image artifacts, quality control
Topic: Fetal and Pediatric Neurological Disorders (Pediatrics, Perinatology and Child Health, Medicine), according to OpenAlex
Funding: Swiss National Science Foundation (31NE30_203977, 205320-215641); Agence Nationale de la Recherche (ANR-21-NEU2-0005, ANR-19-CE45-0014); Instituto de Salud Carlos III (AC21_2/00016); Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033)
Citations: not cited yet (Europe PMC); 42 references in the paper

Abstract

Normative modeling is increasingly used to characterize typical growth trajectories and identify atypical neurodevelopment, including early brain development using magnetic resonance imaging (MRI) acquired before birth. Recent work has emphasized the importance of large sample sizes for accurate and robust centile estimation. In this study, we investigate how image quality influences fetal brain normative models, a critical factor in this context where MRI is acquired on a moving fetus in utero. Using a multi-centric cohort of 635 fetal MRI scans, we applied a standardized visual quality control (QC) protocol with continuous quality ratings. We fit normative models for multiple brain structures under progressively relaxed QC stringency, and quantified the deviations in centile estimates relative to a high-quality reference subgroup. Our results showed that including lower-quality data systematically biased normative centiles, with the strongest effects observed in the outer centiles, particularly the lower tail (1st–10th). Bias increased progressively as QC stringency was relaxed and could not be attributed solely to the number of scans used to fit the models. Quality-induced bias was structure dependent, and often not visually apparent at the segmentation level. These findings highlight that image quality is an important source of bias in normative fetal brain modeling, and that increasing sample size at the expense of quality may systematically affect centile estimates, potentially jeopardizing the utility of the model.

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 8 matches between paragraphs and lines of code.

fetpype/fetpype

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 5c99de9a3b35734b2fa71b5fcde2920cb9b1d93b, 3 September 2026
Languages: Python (27)
Size: 99 files, 27 scripts
Software Heritage: archived
Found in: the end of the paper
Holds: README, license file, CITATION.cff, environment (pyproject.toml), tests, continuous integration, documentation
Tools: Nipype (10 files), NiBabel (2 files), NumPy (2 files), FSL (1 file), pandas (1 file), PyBIDS (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
29 files
At the source: github.com/fetpype/fetpype

fetpype/normative_modeling_data_quality

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 24d93cbe628568ac5ff3df680bd6046fdd8e393e, 22 July 2026
Languages: R (10), Jupyter (1), Python (1)
Size: 13 files, 12 scripts
Software Heritage: not archived
Found in: “Data and Code Availability”
Holds: README, 1 notebook
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (10 files), ggplot2 (9 files), patchwork (6 files), cowplot (4 files), mgcv (4 files), neuroHarmonize (2 files), NumPy (2 files), pandas (2 files), scikit-learn (2 files), Matplotlib (1 file), nlme (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
13 files, not copied: shown from their source

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At the source: github.com/fetpype/normative_modeling_data_quality

Zenodo 15696638

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file, 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 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
At the source:

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

Tracing map

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

Datasets cited

Data and Code Availability

Tabular data (estimated volumes data and corresponding data quality for each scan) are available on a Zenodo repository at https://doi.org/10.5281/zenodo.21485414. The code is publicly available on GitHub at https://github.com/fetpype/normative_modeling_data_quality. Sharing of the reconstructed images and segmentations requires data transfer agreements with each of the centers.

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

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 16 authors, 5 keywords, 4 funders, 42 references.

Cite

This paper

Sanchez, T., Mihailov, A., Martí-Juan, G., Girard, N., Manchon, A., Milh, M., Eixarch, E., Dunet, V., Koob, M., Pomar, L., Sichitiu, J., González Ballester, M. A., Camara, O., Piella, G., Bach Cuadra, M., & Auzias, G. (2026). Data quality biases normative models derived from fetal brain MRI. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1337. https://doi.org/10.1162/imag.a.1337

BibTeX

@article{sanchez2026data,
author = {Sanchez, Thomas and Mihailov, Angeline and Martí-Juan, Gerard and Girard, Nadine and Manchon, Aurélie and Milh, Mathieu and Eixarch, Elisenda and Dunet, Vincent and Koob, Mériam and Pomar, Léo and Sichitiu, Joanna and González Ballester, Miguel A. and Camara, Oscar and Piella, Gemma and Bach Cuadra, Meritxell and Auzias, Guillaume},
title = {{Data quality biases normative models derived from fetal brain MRI}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = aug,
volume = {4},
pages = {IMAG.a.1337},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1337},
url = {https://doi.org/10.1162/imag.a.1337},
pmid = {42609916},
pmcid = {PMC13479347}
}

RIS

TY - JOUR
AU - Sanchez, Thomas
AU - Mihailov, Angeline
AU - Martí-Juan, Gerard
AU - Girard, Nadine
AU - Manchon, Aurélie
AU - Milh, Mathieu
AU - Eixarch, Elisenda
AU - Dunet, Vincent
AU - Koob, Mériam
AU - Pomar, Léo
AU - Sichitiu, Joanna
AU - González Ballester, Miguel A.
AU - Camara, Oscar
AU - Piella, Gemma
AU - Bach Cuadra, Meritxell
AU - Auzias, Guillaume
TI - Data quality biases normative models derived from fetal brain MRI
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/08/14
VL - 4
SP - IMAG.a.1337
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1337
UR - https://doi.org/10.1162/imag.a.1337
LA - en
ER -

CSL-JSON

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