Provins Céline

Highlights

Enthusiastic about developing pipelines drawing from significantly contributing to magnetic resonance imaging data processing software using Python and Git toolings during Ph.D. Appreciated for excellent organizational and managing skills that ensure a reliable and timely delivery of results. Recognized to show good communication skills whether it is to collaborate and coordinate with different stakeholders or to present clear results to a broad audience.

My motto

Continuous learning, whilst delivering meaningful projects with existing and new expertise

News

May 30, 2025	New paper out in PLOS Biology! 🧠 Defacing MRI images is essential to protect participant privacy, but are there unintended consequences? In this registered report, we showed that defacing significantly alters manual quality assessments of structural MRI images: human raters evaluated the same images differently depending on whether facial features were present. This suggests that defacing removes not only identifiable information but also visual cues critical for assessing image quality. The risk? Poor-quality images may persist despite quality control, potentially undermining the reliability of the study. As a consequence, we recommend to perform quality control before defacing. This work is also the first paper we brought to completion as a registered report, which was not short of challenges, but we learned a ton in the process. Huge thanks to my coauthors for their precious guidance and time investment in this project.
May 04, 2025	Just back from three inspiring days in Boston, where I had the chance to collaboratively contribute to the NiPreps software ecosystem! I spent the time coding alongside like-minded peers, learning from domain experts, and expanding the scope of our tools. My contribution was a deep-dive discussion on the needs to assess quality of Positron Emission Tomography (PET) imaging. This led me to develop a new workflow—drawing inspiration from MRIQC’s functional processing—to generate quality reports tailored to PET. Curious about the progress? You can follow the pull request here! Also had the pleasure of reconnecting with old friends. Fun times ! :)
Apr 29, 2025	Australia here I come! I am thrilled to have been awarded a travel grant to attend the Annual Meeting of the Organization for Human Brain Mapping (OHBM) 2025 in Brisbane, Australia. This opportunity allows me to pursue a long-standing ambition: to premiere the open release of our large multi-modal MRI dataset, the Human Connectome Phantom (HCPh) dataset. My goal is to engage with the neuroimaging community and encourage the adoption of this dataset to explore important and fascinating research questions. At OHBM, I’ll participate in BrainHack—a three-day collaborative coding event—present a poster showcasing our first analyses of the HCPh dataset related to the intra-subject reliability of functional MRI, and, give a talk to share our latest insights drawn from this unique resource.
Mar 06, 2025	Officially Dr. Provins!
Jan 14, 2025	I successfully passed my private defense! The committee praised my rigorous methodology, the clarity of my thesis manuscript and presentation, and my substantial contributions to the field. Wrapping up this Ph.D. has been both a challenge and a privilege. These four years were filled with hard work, learning, and moments of doubt, but also excitement, discovery, and growth. I’m proud of what I’ve accomplished and grateful for everyone who supported me along the way—now onto the final step, the public defense!

Selected Publications

Front. Neuroimaging

Quality control in functional MRI studies with MRIQC and fMRIPrep

Céline Provins, Eilidh MacNicol, Saren H. Seeley, and 2 more authors

Frontiers in Neuroimaging , 2023

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The implementation of adequate quality assessment (QA) and quality control (QC) protocols within the magnetic resonance imaging (MRI) research workflow is resource- and time-consuming and even more so is their execution. As a result, QA/QC practices highly vary across laboratories and “MRI schools”, ranging from highly specialized knowledge spots to environments where QA/QC is considered overly onerous and costly despite evidence showing that below-standard data increase the false positive and false negative rates of the final results. Here, we demonstrate a protocol based on the visual assessment of images one-by-one with reports generated by MRIQC and fMRIPrep, for the QC of data in functional (blood-oxygen dependent-level; BOLD) MRI analyses. We particularize the proposed, open-ended scope of application to whole-brain voxel-wise analyses of BOLD to correspondingly enumerate and define the exclusion criteria applied at the QC checkpoints. We apply our protocol on a composite dataset (n = 181 subjects) drawn from open fMRI studies, resulting in the exclusion of 97% of the data (176 subjects). This high exclusion rate was expected because subjects were selected to showcase artifacts. We describe the artifacts and defects more commonly found in the dataset that justified exclusion. We moreover release all the materials we generated in this assessment and document all the QC decisions with the expectation of contributing to the standardization of these procedures and engaging in the discussion of QA/QC by the community.
Nature Methods

Reliability characterization of MRI measurements for analyses of brain networks on a human phantom

Céline Provins, Hélène Lajous, Elodie Savary, and 7 more authors

Stage 1 Registered Report accepted in principle at Nature Methods , 2023

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Network-based approaches are widely adopted to model functional and structural ‘connectivity’ of the living brain, extracted noninvasively with magnetic resonance imaging (MRI). However, these analyses —on functional and structural networks— render unreliable at the finer temporal, spatial, and brain-parcellation scales. Consequently, the clinical translation of these analyses has yet to materialize meaningfully, and interpretation of the skyrocketing production of scientific literature requires caution. We will characterize relevant sources of variability and assess the reliability of structural and functional networks extracted from MRI with the repeated acquisition of a single, healthy individual, whom we regard as the ‘Human Connectome Phantom’. Two comprehensive MRI protocols will be executed across three different devices ( 48, 12, and 12 sessions, respectively) while recording a wealth of physiological signals to help model corresponding spurious effects on brain networks. To maximize reuse, e.g., as a benchmark reference, a baseline for machine learning models, or a source of prior knowledge, we will openly share all data and their derivatives. By systematically assessing spurious sources of variability throughout the neuroimaging workflow, we will deliver reliability margins of brain networks that inform future research and contribute to the standardization of ‘connectivity measurement’.
PLOS Biology

Defacing biases in manual and automatic quality assessments of structural MRI with MRIQC

Céline Provins, Elodie Savary, Thomas Sanchez, and 8 more authors

Stage 1 Registered Report accepted in principle at PLOS Biology , 2023

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A critical requirement 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 comparing their ratings on the same set of images in two conditions: “nondefaced” (that is, 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.