ReviewHuman brain mapping2022
Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed.
Review in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 84 papers, 5 of them syntheses that pooled 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.
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
84 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Advancing image-based meta-analysis through systematic use of crowdsourced NeuroVault data.Scientific reports · 2025Pooled it
- Neuroimage analysis using artificial intelligence approaches: a systematic review.Medical & biological engineering & computing · 2024Pooled it
- Barriers impeding research data sharing on chronic disease prevention among the older adults in low-and middle-income countries: a systematic review.Frontiers in public health · 2024Pooled it
- Parsing variability in borderline personality disorder: a meta-analysis of neuroimaging studies.Translational psychiatry · 2021Pooled it
- Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC.Nature protocols · 2026Review
- Reconciling global collaboration and data sovereignty in neuroscience.Nature neuroscience · 2026Review
- Advancing data protections for implantable brain-computer interfaces.Communications medicine · 2026Review
- Radiologists' memory as a data protection risk: a worst-case stress test for chest radiograph re-identification.Insights into imaging · 2026Article
- Article
- Autism data sharing: Benefits, challenges, and recommendations.PLOS digital health · 2026Review
- A maturity model framework for federated networks of trusted research environments.Frontiers in digital health · 2026Article
- IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.Frontiers in neuroinformatics · 2026Article
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond.Scientific data · 2025Article
- Article
- An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function.bioRxiv : the preprint server for biology · 2025Article
- Correction to "Data Sharing and Privacy Issues in Neuroimaging Research: Opportunities, Obstacles, Challenges, and Monsters Under the Bed".Human brain mapping · 2025Article
- Effectiveness of fMRI-based Neurofeedback Therapy on Depression: A Systematic Review.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Review
- Ethical and regulatory requirements for conducting researcher-driven large-scale multinational genetic haematological studies: the INHERENT experience.Health research policy and systems · 2025Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Collaborative networks and data sharing initiatives are broadening the opportunities for the advancement of science. These initiatives offer greater transparency in science, with the opportunity for external research groups to reproduce, replicate, and extend research findings. Further, larger datasets offer the opportunity to identify homogeneous patterns within subgroups of individuals, where these patterns may be obscured by the heterogeneity of the neurobiological measure in smaller samples. However, data sharing and data pooling initiatives are not without their challenges, especially with new laws that may at first glance appear quite restrictive for open science initiatives. Interestingly, what is key to some of these new laws (i.e, the European Union's general data protection regulation) is that they provide greater control of data to those who "give" their data for research purposes. Thus, the most important element in data sharing is allowing the participants to make informed decisions about how they want their data to be used, and, within the law of the specific country, to follow the participants' wishes. This framework encompasses obtaining thorough informed consent and allowing the participant to determine the extent that they want their data shared, many of the ethical and legal obstacles are reduced to just monsters under the bed. In this manuscript we discuss the many options and obstacles for data sharing, from fully open, to federated learning, to fully closed. Importantly, we highlight the intersection of data sharing, privacy, and data ownership and highlight specific examples that we believe are informative to the neuroimaging community.
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.