Abstract #2833

A Comparison of Brain Subnetwork Extraction Methods

Elizabeth Ceiridwen Anne Powell^1,2, Ferran Prados^2,3, Baris Kanber^2,3, 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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