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

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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.

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Notes

  1. Note that comparisons to partial correlation based on inverse covariance matrix using L1 regularization is less relevant due to the limitation of L1-norm Liang et al. (2016). A novel joint sparse partial correlation method for estimating group functional networks. Hum Brain Mapp, 37, 1162–77; they were therefore not included in the comparison study.

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Acknowledgements

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.

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Correspondence to Xiaoyun Liang.

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Liang, X., Vaughan, D.N., Connelly, A. et al. A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies. Brain Topogr 31, 364–379 (2018). https://doi.org/10.1007/s10548-017-0615-6

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  • DOI: https://doi.org/10.1007/s10548-017-0615-6

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