Brain Topography

, Volume 31, Issue 3, pp 364–379 | Cite as

A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies

  • Xiaoyun Liang
  • David N. Vaughan
  • Alan Connelly
  • Fernando Calamante
Original Paper


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.


Functional connectivity Brain connectome Sparse group penalty Graphical model Network metric Inter-subject variability Temporal lobe epilepsy 



We are grateful to the National Health and Medical Research Council (NHMRC) of Australia, the Australian Research Council (ARC), and the Victorian Government’s Operational Infrastructure Support Program for their support. Patient data used in this study was acquired as part of NHMRC program Grant 628952 and Project Grant 1081151 led by Professor Graeme Jackson.

Supplementary material

10548_2017_615_MOESM1_ESM.doc (2.8 mb)
Supplementary material 1 (DOC 2883 KB)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.The Florey Institute of Neuroscience and Mental HealthHeidelbergAustralia
  2. 2.Department of NeurologyAustin HealthMelbourneAustralia
  3. 3.The Florey Department of Neuroscience and Mental Health Medicine, University of MelbourneMelbourneAustralia
  4. 4.Department of Medicine, Austin Health and Northern HealthUniversity of MelbourneMelbourneAustralia

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