A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks in group comparison studies

Journal article


Liang, Xiaoyun, Vaughan, David N., Connelly, Alan and Calamante, Fernando. (2018) A novel group-fused sparse partial correlation method for simultaneous estimation of functional networks in group comparison studies. Brain Topography. 31(3), pp. 364 - 379. https://doi.org/10.1007/s10548-017-0615-6
AuthorsLiang, Xiaoyun, Vaughan, David N., Connelly, Alan and Calamante, Fernando
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.

Keywordsfunctional connectivity; brain connectome; sparse group penalty; graphical model; network metric; inter-subject variability; temporal lobe epilepsy
Year2018
JournalBrain Topography
Journal citation31 (3), pp. 364 - 379
PublisherSpringer New York LLC
ISSN0896-0267
Digital Object Identifier (DOI)https://doi.org/10.1007/s10548-017-0615-6
Scopus EID2-s2.0-85039722497
Page range364 - 379
Research GroupMary MacKillop Institute for Health Research
Grant IDnhmrc/628952
nhmrc/1081151
Place of publicationUnited States of America
EditorsC. Michel and M. Murray
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