Statistical Inference in Functional Networks

Specialeforsvar ved Fayi Zhang

Titel: Statistical Inference in Functional Networks

Abstract: The brain is a complex network of connected components, whose interactions evolve dynamically to cooperatively perform specific functions. Building the functional network can help us understand how our brain works and conduct certain tasks. There are many methods that can help build the network. In this project, I will introduce a  statisti-cally principled approach to build the functional network. This analysis is based on the data recorded from 64 sensors during a repeated behavior task. In this project, I will first simulate EEG data with different signal-to-noise ratios (SNRs), correlation ratios and show that this method successfully identifies functional networks and edge densities of confi-dence for these data. After that, I will employ a principled technique to establish functional networks based on predetermined regions of interest using canonical correlation and analyze the dynamic functional networks associated with it. Finally, I will apply the use of these methods on the real data and build the functional networks for the visual cortex and the whole brain, I will also analyze how the functional networks differ for different stimulus 

 

 

Vejleder: Susanne Ditlevsen
Censor:   Birger Stjernholm Madsen, Novozymes