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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Click here for more information on becoming a member.
