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Abstract #3059

A novel method for robust estimation of group functional connectivity based on a Joint Graphical Models approach

Xiaoyun Liang 1 , Alan Connelly 1,2 , and Fernando Calamante 1,2

1 Brain Research Institute, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia, 2 Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, VIC, Australia

In this study, we proposed a joint sparsity constraint method, JGMSS, to directly estimate networks at group-level. Simulated results demonstrate that JGMSS can achieve consistently higher accuracy and sensitivity than the previosuly proposed elastic net (EN) method. Estimated functional connectivity from in vivo data shows much less network variability across the selected range of threshold than EN does, suggesting that JGMSS is largely independent of threshold. Overall, JGMSS can robustly and reliably estimate functional connectivity at group-level.

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