This study applies graph theory to the analysis of resting state functional connectivity MRI (rs-fcMRI) brain networks. Graph theory is used to analyze rs-fcMRI data because it can adequately describe the brain as a complex network, and model properties at the level of the entire graph, subgraphs, or individual nodes.The paper tries to solve one problem in using graph theory to study functional brain organization - the difficulty of defining individual nodes that make up a brain network.
Many language studies in Cognitive Neuroscience focus on a specific feature of language (syntactic role, part of speech, etc), and try to design artificial stimuli that only varies in that specific feature, and trying to decipher which part of the brain is responsible for the varied response. However, these approaches risks not being very ecologically valid, and it is unclear how the results fit into how natural language works in the brain. This study tries to find a comprehensive model of language in the brain with regards to natural language stimuli.