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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