Abstract #3745

A novel sparse partial correlation method for simultaneous estimation of functional networks in group comparisons

Xiaoyun Liang¹, David Vaughan^2,3, Alan Connelly^1,4, and Fernando Calamante^1,4

¹Imaging Division, Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, ²Epilepsy Division, Florey Institute of Neuroscience and Mental Health, Melbourne, Australia, ³Department of Neurology, Austin Health, Melbourne, Australia, ⁴Department of Medicine, University of Melbourne, Melbourne, Australia

We propose a novel approach, Graphical-LAsso with Stability-Selection (GM-GLASS), by employing sparse group penalties for simultaneously estimating networks from healthy control and patient groups. Simulations demonstrate that both GM-GLASS and JGMSS outperform Fisher Z-transform. Our in vivo results further show that GM-GLASS yields highest contrast of network metrics between groups, demonstrating the superiority of GM-GLASS in detecting significance group differences over JGMSS and Fisher Z-transform. Overall, by controlling confounding variations between subjects, and therefore enhancing the statistical power, our simulated and in vivo results demonstrate that GM-GLASS provides a robust approach for conducting group comparison studies.

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