ArticleFrontiers in neuroinformatics2014
Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation.
Article in Frontiers in neuroinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Privacy-Preserving Visualization of Brain Functional Connectivity.bioRxiv : the preprint server for biology · 2024Article
- Decentralized Mixed Effects Modeling in COINSTAC.Neuroinformatics · 2024Article
- Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing.Frontiers in neuroinformatics · 2024Article
- PRIVACY-PRESERVING VISUALIZATION OF BRAIN FUNCTIONAL NETWORK CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains.Neuroinformatics · 2023Article
- Privacy-preserving quality control of neuroimaging datasets in federated environments.Human brain mapping · 2022Article
- Federated Analysis of Neuroimaging Data: A Review of the Field.Neuroinformatics · 2022Review
- NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.Neuroinformatics · 2022Article
- Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed.Human brain mapping · 2022Review
- Project, toolkit, and database of neuroinformatics ecosystem: A summary of previous studies on "Frontiers in Neuroinformatics".Frontiers in neuroinformatics · 2022Review
- Pseudonymisation of neuroimages and data protection:Neuroimage. Reports · 2021Article
- Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis.Neuroinformatics · 2021Article
- NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data.Neuroinformatics · 2021Article
- Decentralized dynamic functional network connectivity: State analysis in collaborative settings.Human brain mapping · 2020Article
- From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research.Frontiers in neuroinformatics · 2019Review
- Distributed Differentially-Private Algorithms for Matrix and Tensor Factorization.IEEE journal of selected topics in signal processing · 2018Article
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.NeuroImage · 2017Review
- COINSTAC: Decentralizing the future of brain imaging analysis.F1000Research · 2017Article
- Article
- Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication.Translational psychiatry · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the "small N" problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in the function of the brain. When it is possible, open data sharing provides the most benefits. However, some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries-the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.
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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.