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

X Liang; DN Vaughan; A Connelly; F Calamante

Journal article

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

X Liang, DN Vaughan, A Connelly, F Calamante

Brain Topography | Published : 2018

DOI: 10.1007/s10548-017-0615-6

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..

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University of Melbourne Researchers

David Vaughan Author

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Grants

Awarded by Australian Research Council

Funding 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.