Record Detail

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