Work

Defacing biases manual and automated quality assessments of structural MRI with MRIQC

Year: 2023

Type: preprint

Abstract: Defacing (i.e. removing facial features) from structural imaging has become a necessary step before data sharing to ensure participants’ anonymity. This process has proven to have some deleterious effects on the downstream research workflow. Here, we present an exploratory analysis prior to testing the hypothesis that both quality ratings by human experts and the image quality metrics (IQMs) that MRIQC extracts are affected by defacing. We found sufficient evidence in a small sample that there might be an effect. Therefore, we will pre-register and carry out a confirmatory analysis on a larger, unseen, sample.

DOI: 10.31219/osf.io/t9ehk

Authors: Céline Provins, Yasser Alemán-Gómez, Martine Cleusix, Raoul Jenni, Jonas Richiardi, Patric Hagmann, Oscar Esteban

Institutions: Centre Hospitalier Universitaire Vaudois, University of Lausanne

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, National Institute of Mental Health

Awards: Uncovering the interplay of structure, function, and dynamics of brain connectivity using MRI, NIPreps: integrating neuroimaging preprocessing workflows across modalities, populations, and species

OpenAlex ID: W4388911526

Cites: 7

Cited by: 4

Related to: 10