ReviewNeuroinformatics2022
Federated Analysis of Neuroimaging Data: A Review of the Field.
Review in Neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Advancing data protections for implantable brain-computer interfaces.Communications medicine · 2026Review
- Open neuroinformatics infrastructure ecosystem for federated multisite studies.bioRxiv : the preprint server for biology · 2026Article
- Federated learning frameworks: quality and interoperability for biomedical research.NAR genomics and bioinformatics · 2026Review
- dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.Bioinformatics (Oxford, England) · 2026Article
- Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study.Journal of medical Internet research · 2025Article
- Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics.Developmental cognitive neuroscience · 2024Review
- The BRAIN Initiative data-sharing ecosystem: Characteristics, challenges, benefits, and opportunities.eLife · 2024Article
- Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Privacy-Preserving Visualization of Brain Functional Connectivity.bioRxiv : the preprint server for biology · 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
- Efficient federated learning for distributed neuroimaging data.Frontiers in neuroinformatics · 2024Article
- PRIVACY-PRESERVING VISUALIZATION OF BRAIN FUNCTIONAL NETWORK CONNECTIVITY.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Elucidating salient site-specific functional connectivity features and site-invariant biomarkers in schizophrenia via deep neural networks.Scientific reports · 2023Article
- Medical Imaging Applications of Federated Learning.Diagnostics (Basel, Switzerland) · 2023Review
- Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.BMC medical informatics and decision making · 2023Article
- Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.Frontiers in neuroinformatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non-exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis software system, the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). In the end, we share insights on future research directions for federated analysis of neuroimaging 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.