Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/18680
Title: A novel joint sparse partial correlation method for estimating group functional networks.
Authors: Liang, Xiaoyun;Connelly, Alan;Calamante, Fernando
Affiliation: Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Issue Date: Mar-2016
EDate: 2015-12-21
Citation: Human brain mapping 2016; 37(3): 1162-1177
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 optimal regularization parameters, a stability selection method is employed to extract networks. The proposed method is, therefore, denoted as JGMSS. By applying JGMSS across simulated datasets, the resulting networks show consistently higher accuracy and sensitivity than those estimated using an alternative approach (the elastic-net regularization with stability selection, ENSS). The robustness of the JGMSS is evidenced by the independence of the estimated networks to choices of the initial set of regularization parameters. The performance of JGMSS in estimating group networks is further demonstrated with in vivo fMRI data (ASL and BOLD), which show that JGMSS can more robustly estimate brain hub regions at group-level and can better control intersubject variability than it is achieved using ENSS.
URI: http://ahro.austin.org.au/austinjspui/handle/1/18680
DOI: 10.1002/hbm.23092
ORCID: 0000-0002-7550-3142
PubMed URL: 26859311
Type: Evaluation Studies
Journal Article
Research Support, Non-U.S. Gov't
Subjects: arterial spin labeling
connectome
functional connectivity
graphical models
sparse partial correlation
Appears in Collections:Journal articles

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