Connectivity-Based Parcellation of the Brain using the In nite Relational Model and Bayesian Community Detection

Abstract

A main interest in cognitive neuroscience is to understand how the brain is segregated into regions that subserve particular functions. There are different ways of approaching this, one of them is to cluster together brain regions that show similar structural connectivity patterns. The resulting clusters are taken to represent functionally distinct areas of the brain based on the assumption that structure and function are intimately related. In this thesis, structural connectivity data of the brain was used to compare two Bayesian clustering algorithms: the Infinite Relational model and Bayesian Community Detection. The maximum a posteriori estimates of twenty subjects, required by simulated annealing, were used to compare both models. It was found that Bayesian Community Detection produces significantly more clusters than the Infinite Relational Model while at the same time performing just as well as the Infinite Relational Model in terms of reproducibility.