Fitting networks models for functional brain connectivity

Abstract

Functional connectivity of the human brain and the hierarchical modular architecture of functional networks can be investigated using functional magnetic resonance imaging (fMRI). Various network models, such as power-law networks and modular networks have been explored before to study brain networks. In order to investigate the plausibility of modeling functional brain networks with network models based on distribution of node degree and connection weights, we will compute the goodness-of-fit of several network models on resting-state fMRI scans gathered in the Human Connectome Project. Our experiments suggest that the power-law networks and stochastic block models aptly fit functional connectivity of the subjects and the stochastic block models have the potential to detect functional modules of the brain.