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Abstract #2833

A Comparison of Brain Subnetwork Extraction Methods

Elizabeth Ceiridwen Anne Powell1,2, Ferran Prados2,3, Baris Kanber2,3, Wallace Brownlee2, Sara Collorone2, Sebastien Ourselin3, Olga Ciccarelli2, Jonathan D Clayden4, Ahmed Toosy2, and Claudia Angela Gandini Wheeler-Kingshott2

1Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom, 3Translational Imaging Group, Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 4Developmental Imaging and Biophysics Section, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

In the complex network model of the brain it is often noted that a subset of nodes, or subnetwork, plays a central role in network architecture, whose damage could have a disproportionate effect on network resilience to injury. The identification of "important" nodes in a network is non-trivial though, and several fundamentally different methods exist; it is currently unclear to what extent these methods agree. In this work we demonstrate that subnetworks extracted using rich club and principal network analysis share 60% of nodes, suggesting a core subset of nodes are important to network architecture independently of analysis model.

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