ArticleNeuroimage. Reports2021
Pseudonymisation of neuroimages and data protection:
Article in Neuroimage. Reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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.
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.
Who cites it
22 citing papers in PubMed.
- Radiology research platform: automating multicenter radiological research workflows using a cloud-native platform.European radiology · 2026Article
- Accelerating Research on Brain Aging: Enabling Brain Imaging Data Sharing in the Open Science Landscape.European journal of neurology · 2026Review
- Radiologists' memory as a data protection risk: a worst-case stress test for chest radiograph re-identification.Insights into imaging · 2026Article
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Unlocking Health Data for Research: Legal, Technical, and Organisational Lessons from a Belgian Interdisciplinary Case Study.Journal of healthcare informatics research · 2026Article
- Pitfalls of defacing whole-head MRI: re-identification risk with diffusion models and compromised research potential.Computers in biology and medicine · 2025Article
- Recent advances in 3D printing applications for CNS tumours.European journal of medical research · 2025Review
- A labeled Clinical-MRI dataset of Nigerian brains.Scientific data · 2025Article
- Data Hazards as An Ethical Toolkit for Neuroscience.Neuroethics · 2025Article
- Can I have your data? Recommendations and practical tips for sharing neuroimaging data upon a direct personal request.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Ethical implications of neurotechnology in industry-academia partnerships: Insights from patient and research participant interviews.PloS one · 2025Article
- ezBIDS: Guided standardization of neuroimaging data interoperable with major data archives and platforms.Scientific data · 2024Article
- Bridging Huntington's disease research with big data science: Harmonized neuroimaging datasets from multiple studies.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Demystifying the likelihood of reidentification in neuroimaging data: A technical and regulatory analysis.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Ethics Along the Continuum of Research Involving Persons with Disorders of Consciousness.Neurocritical care · 2023Article
- Article
- The R package for DICOM to brain imaging data structure conversion.Scientific data · 2023Article
- Article
- Towards an understanding of global brain data governance: ethical positions that underpin global brain data governance discourse.Frontiers in big data · 2023Article
- FAIR human neuroscientific data sharing to advance AI driven research and applications: Legal frameworks and missing metadata standards.Frontiers in genetics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
For a number of years, facial features removal techniques such as 'defacing', 'skull stripping' and 'face masking/blurring', were considered adequate privacy preserving tools to openly share brain images. Scientifically, these measures were already a compromise between data protection requirements and research impact of such data. Now, recent advances in machine learning and deep learning that indicate an increased possibility of re-identifiability from defaced neuroimages, have increased the tension between open science and data protection requirements. Researchers are left pondering how best to comply with the different jurisdictional requirements of anonymization, pseudonymisation or de-identification without compromising the scientific utility of neuroimages even further. In this paper, we present perspectives intended to clarify the meaning and scope of these concepts and highlight the privacy limitations of available pseudonymisation and de-identification techniques. We also discuss possible technical and organizational measures and safeguards that can facilitate sharing of pseudonymised neuroimages without causing further reductions to the utility of the data.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.