Please use this identifier to cite or link to this item:
Title: A novel joint sparse partial correlation method for estimating group functional networks.
Austin Authors: Liang, Xiaoyun;Connelly, Alan;Calamante, Fernando
Affiliation: Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, Victoria, Australia
The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Department of Medicine, Northern Health, University of Melbourne, Melbourne, Victoria, Australia
Issue Date: Mar-2016 2015-12-21
Publication information: 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.
DOI: 10.1002/hbm.23092
ORCID: 0000-0002-7550-3142
Journal: Human brain mapping
PubMed URL: 26859311
Type: Journal Article
Subjects: arterial spin labeling
functional connectivity
graphical models
sparse partial correlation
Appears in Collections:Journal articles

Show full item record

Page view(s)

checked on Feb 6, 2023

Google ScholarTM


Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.