In the recent past, there has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such framework, Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC), provides a platform to analyze neuroimaging data stored locally across multiple organizations without the need for pooling the data at any point during the analysis. In this paper, we perform a decentralized voxel-based morphometry analysis of structural magnetic resonance imaging data across two different sites to understand the structural changes in the brain as linked to age, body mass index and smoking. Results produced by the decentralized analysis are contrasted with similar findings in literature. This work showcases the potential benefits of performing multi-voxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
- Immunology and Microbiology(all)
- Pharmacology, Toxicology and Pharmaceutics(all)