Statistics of brain networks

DANIEL FRAIMAN; NICOLAS FRAIMAN; RICARDO FRAIMAN

Bruselas

Conferencia; Brain Modes 2016; 2016

The study of random graphs and networks had an explosive development in the last coupleof decades. Meanwhile, techniques for the statistical analysis of these networks were lessdeveloped. In this work we focus on brain networks and study some statistical problems in anonparametric framework. We address the following questions: Given one or more samplesof brain networks, How to calculate a representative brain network? How to define a notionof variability for networks? How to identify a network outlier? How to test if the groups ofnetworks have the same probability law? How to perform classification? Answers to thesequestions provide an important step in the development of potential neuroimaging-basedtools for diagnosis. Finally, we show an application to electroencephalographic data to testwhether variability in functional brain networks implicated in Tango observation candiscriminate between groups differing in their level of expertise. We found that experts andbeginners significantly differed in the functional organization of task-relevant networks.Specifically, networks in expert Tango dancers exhibited less variability and a more robustfunctional architecture.