Please use this identifier to cite or link to this item: http://ahro.austin.org.au/austinjspui/handle/1/17134
Title: A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.
Authors: Liang, Xiaoyun;Vaughan, David N;Connelly, Alan;Calamante, Fernando
Affiliation: The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
The Florey Department of Neuroscience and Mental Health Medicine, University of Melbourne, Melbourne, VIC, Australia
Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
Issue Date: 29-Dec-2017
EDate: 2017
Citation: Brain topography 2017; online first: 27 December
Abstract: The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.
URI: http://ahro.austin.org.au/austinjspui/handle/1/17134
DOI: 10.1007/s10548-017-0615-6
ORCID: http://orcid.org/0000-0002-1851-3408
PubMed URL: 29288387
PubMed URL: https://www.ncbi.nlm.nih.gov/pubmed/29288387
Type: Journal Article
Subjects: Brain connectome
Functional connectivity
Graphical model
Inter-subject variability
Network metric
Sparse group penalty
Temporal lobe epilepsy
Appears in Collections:Journal articles

Files in This Item:
There are no files associated with this item.


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