A novel joint sparse partial correlation method for estimating group functional networks.

ABSTRACT: Advances in graph theory have provided a powerful tool to characterize brain networks. In particular, functional networks at group-level have great appeal to gain further insight into complex brain function, and to assess changes across disease conditions. These group networks, however, often have two main limitations. First, they are popularly estimated by directly averaging individual networks that are compromised by confounding variations. Secondly, functional networks have been estimated mainly through Pearson cross-correlation, without taking into account the influence of other regions. In this study, we propose a sparse group partial correlation method for robust estimation of functional networks based on a joint graphical models approach. To circumvent the issue of choosing the opti

SUBMITTER: Liang X 

PROVIDER: S-EPMC8022278 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature