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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