ArticleFrontiers in neuroscience2016
COINSTAC: A Privacy Enabled Model and Prototype for Leveraging and Processing Decentralized Brain Imaging Data.
Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 4 of them syntheses that pooled it.
What it found
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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
60 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- ENIGMA + COINSTAC: Improving Findability, Accessibility, Interoperability, and Re-usability.Neuroinformatics · 2022Pooled it
- The ENIGMA Brain Injury working group: approach, challenges, and potential benefits.Brain imaging and behavior · 2021Pooled it
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.Translational psychiatry · 2020Pooled it
- MindGrab: A spectrally-motivated architecture for accessible deep learning in neuroimaging.NeuroImage · 2026Article
- Open neuroinformatics infrastructure ecosystem for federated multisite studies.bioRxiv : the preprint server for biology · 2026Article
- NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis.bioRxiv : the preprint server for biology · 2026Article
- Real-world federated learning for brain imaging scientists.Frontiers in digital health · 2026Article
- The Marriage of Neurotechnologies and Artificial Intelligence: Ethical, regulatory, and technological aspects.IEEE signal processing magazine · 2025Article
- International initiatives to enhance awareness and uptake of open research in psychology: a systematic mapping review.Royal Society open science · 2025Review
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Privacy-Preserving Visualization of Brain Functional Connectivity.bioRxiv : the preprint server for biology · 2024Article
- How does the macroenvironment influence brain and behaviour-a review of current status and future perspectives.Molecular psychiatry · 2024Review
- The Aphasia Recovery Cohort, an open-source chronic stroke repository.Scientific data · 2024Article
- A federated learning architecture for secure and private neuroimaging analysis.Patterns (New York, N.Y.) · 2024Article
- Cortical similarities in psychiatric and mood disorders identified in federated VBM analysis via COINSTAC.Patterns (New York, N.Y.) · 2024Article
- Decentralized Mixed Effects Modeling in COINSTAC.Neuroinformatics · 2024Article
- iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices.Epilepsia · 2024Article
- PRIVACY-PRESERVING VISUALIZATION OF BRAIN FUNCTIONAL NETWORK CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Federated Privacy-Preserving Visualization: A Vision Paper.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and "closed" repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to "pooled-data" solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.
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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.