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Abstract #2846

A novel threshold-free network-based statistical method: Demonstration and parameter optimisation using in vivo simulated pathology

Lea Vinokur 1,2 , Andrew Zalesky 3,4 , David Raffelt 1 , Robert Smith 1 , and Alan Connelly 1,2

1 The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia, 2 Department of Florey Neurosciences, University of Melbourne, Melbourne, Victoria, Australia, 3 Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia, 4 Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia

The connectome is becoming an increasingly popular tool to study brain connectivity. Case-control study at the level of individual connections is difficult due to a multiple comparisons problem. We propose a new method to combine Network Based Statistics, a statistical framework developed to adapt cluster-based inference to a network, with TFCE, a method to boost belief in signal clusters and remove the dependence on arbitrary thresholds. We apply the combined framework, denoted "NBS-TFCE", to in vivo structural connectivity data with synthetically introduced pathologies, to try to determine optimal parameters for performing NBS-TFCE on realistic connectivity matrices."

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