Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/17134
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dc.contributor.authorLiang, Xiaoyun-
dc.contributor.authorVaughan, David N-
dc.contributor.authorConnelly, Alan-
dc.contributor.authorCalamante, Fernando-
dc.date2017-
dc.date.accessioned2018-02-07T22:16:48Z-
dc.date.available2018-02-07T22:16:48Z-
dc.date.issued2017-12-29-
dc.identifier.citationBrain topography 2018; 31(3): 364-379-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/17134-
dc.description.abstractThe 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.-
dc.language.isoeng-
dc.subjectBrain connectome-
dc.subjectFunctional connectivity-
dc.subjectGraphical model-
dc.subjectInter-subject variability-
dc.subjectNetwork metric-
dc.subjectSparse group penalty-
dc.subjectTemporal lobe epilepsy-
dc.titleA Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.-
dc.typeJournal Article-
dc.identifier.journaltitleBrain topography-
dc.identifier.affiliationThe Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia-
dc.identifier.affiliationDepartment of Neurology, Austin Health, Heidelberg, Victoria, Australia-
dc.identifier.affiliationThe Florey Department of Neuroscience and Mental Health Medicine, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.affiliationDepartment of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia-
dc.identifier.pubmedurihttps://pubmed.ncbi.nlm.nih.gov/29288387-
dc.identifier.doi10.1007/s10548-017-0615-6-
dc.identifier.orcid0000-0002-1851-3408-
dc.identifier.pubmedid29288387-
dc.type.austinJournal Article-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.languageiso639-1en-
item.openairetypeJournal Article-
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