TY - JOUR
T1 - Decentralized multi-site VBM analysis during adolescence shows structural changes linked to age, body mass index, and smoking
T2 - A COINSTAC analysis
AU - Gazula, Harshvardhan
AU - Holla, Bharath
AU - Zhang, Zuo
AU - Xu, Jiayuan
AU - Verner, Eric
AU - Kelly, Ross
AU - Schumann, Gunter
AU - Calhoun, Vince D.
N1 - Publisher Copyright:
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/11/18
Y1 - 2019/11/18
N2 - 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.
AB - 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.
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U2 - 10.1101/846386
DO - 10.1101/846386
M3 - Article
AN - SCOPUS:85095656785
JO - Advances in Water Resources
JF - Advances in Water Resources
SN - 0309-1708
ER -