ArticleF1000Research2017
COINSTAC: Decentralizing the future of brain imaging analysis.
Article in F1000Research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- NeuroFLAME: A Scalable, Privacy-Preserving Federated Framework for Secure, Reproducible, and Multi-Site Neuroimaging Analysis.bioRxiv : the preprint server for biology · 2026Article
- Neuroanatomical Correlates of Negative Symptoms in Schizophrenia.bioRxiv : the preprint server for biology · 2025Article
- Challenging the status quo: A guide to open and reproducible neuroimaging for early career researchers.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Privacy-Preserving Visualization of Brain Functional Connectivity.bioRxiv : the preprint server for biology · 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
- PRIVACY-PRESERVING VISUALIZATION OF BRAIN FUNCTIONAL NETWORK CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Efficient federated learning for distributed neuroimaging data.Frontiers in neuroinformatics · 2024Article
- Federated Privacy-Preserving Visualization: A Vision Paper.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
- Five negative symptom domains are differentially associated with resting state amplitude of low frequency fluctuations in Schizophrenia.Psychiatry research. Neuroimaging · 2023Article
- Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.Frontiers in neuroinformatics · 2023Article
- The spectrum of data sharing policies in neuroimaging data repositories.Human brain mapping · 2022Review
- Federated Analysis of Neuroimaging Data: A Review of the Field.Neuroinformatics · 2022Review
- Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis.Neuroinformatics · 2021Article
- Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples.Current opinion in neurology · 2021Review
- Decentralized distribution-sampled classification models with application to brain imaging.Journal of neuroscience methods · 2020Article
- Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity.Frontiers in neuroinformatics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
In the era of Big Data, sharing neuroimaging data across multiple sites has become increasingly important. However, researchers who want to engage in centralized, large-scale data sharing and analysis must often contend with problems such as high database cost, long data transfer time, extensive manual effort, and privacy issues for sensitive data. To remove these barriers to enable easier data sharing and analysis, we introduced a new, decentralized, privacy-enabled infrastructure model for brain imaging data called COINSTAC in 2016. We have continued development of COINSTAC since this model was first introduced. One of the challenges with such a model is adapting the required algorithms to function within a decentralized framework. In this paper, we report on how we are solving this problem, along with our progress on several fronts, including additional decentralized algorithms implementation, user interface enhancement, decentralized regression statistic calculation, and complete pipeline specifications.
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.