Decentralized Mixed Effects Modeling in COINSTAC-Reference-Cited by-同舟云学术

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Decentralized Mixed Effects Modeling in COINSTAC

Published:2024-03-01 Issue:2 Volume:22 Page:163-175
ISSN:1559-0089
Container-title:Neuroinformatics
language:en
Short-container-title:Neuroinform

Author:

Basodi Sunitha,Raja Rajikha,Gazula Harshvardhan,Romero Javier Tomas,Panta Sandeep,Maullin-Sapey Thomas,Nichols Thomas E.,Calhoun Vince D.

Funder

Tri-institutional Center for Translational Research in Neuroimaging and Data Science, United States

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s12021-024-09657-7.pdf

Reference42 articles.

1. Aledhari, M., Razzak, R., Parizi, R. M., & Saeed, F. (2020). Federated learning: A survey on enabling technologies, protocols, and applications. IEEE Access, 8, 140699–140725.

2. Basodi, S., Raja, R., Ray, B., Gazula, H., Sarwate, A. D., Plis, S., Liu, J., Verner, E., & Calhoun, V. D. (2022). Decentralized brain age estimation using mri data. Neuroinformatics, pages 1–10.

3. Bearden, C. E., & Thompson, P. M. (2017). Emerging global initiatives in neurogenetics: the enhancing neuroimaging genetics through meta-analysis (enigma) consortium. Neuron, 94(2), 232–236.

4. Beckmann, C., Jenkinson, M., & Smith, S. (2003a). General multilevel linear modeling for group analysis in fmri. NeuroImage, 20, 1052–63.

5. Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2003b). General multilevel linear modeling for group analysis in fmri. Neuroimage, 20(2), 1052–1063.

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