Evidence map›Paper›PMID 40305522›Full record

ArticlePLoS biology2025

Removing facial features from structural MRI images biases visual quality assessment.

Céline Provins, Élodie Savary, Thomas Sanchez, Emeline Mullier, Jaime Barranco, Elda Fischi-Gómez, Yasser Alemán-Gómez, Jonas Richiardi, Russell A Poldrack, Patric Hagmann and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PLoS biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Céline ProvinsDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0000-0002-1668-9629
Élodie SavaryDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Thomas SanchezDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Emeline MullierDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Jaime BarrancoDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Elda Fischi-GómezDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Yasser Alemán-GómezDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Jonas RichiardiDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Russell A PoldrackDepartment of Psychology, Stanford University, Stanford, California, United States of America.
Patric HagmannDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Oscar EstebanDepartment of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID https://orcid.org/0000-0001-8435-6191

Funding

NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and speciesRF1MH121867 · NIMH · STANFORD UNIVERSITY · PI POLDRACK, RUSSELL A, ROKEM, ARIEL SHALOM · 2021 to 2022
$1.6M
NIMH NIH HHS RF1 MH121867
6 · The paper itself

Abstract

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals' privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N = 185). By comparing two linear mixed-effects models, we determined that four trained human raters' perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N = 581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQC's quality metrics were mostly insensitive to defacing.

Indexed as

BrainFaceMagnetic Resonance ImagingNeuroimagingAdultFemaleHumansImage Processing, Computer-AssistedMale

Identifiers

PMID40305522
PMCPMC12143891

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.