Assessing graph models for description of brain networks

Both structural and functional brain networks have been investigated in the literature with enthusiasm via graph-theoretical methods. However, an important issue that has not been adequately addressed before is: what is the optimal graph model for describing brain networks, both in structural and functional aspects? We address this question in the following three aspects. First, multi-resolution structural brain networks are reconstructed via cortical surface parcellation based on white matter fiber density information. Second, the global and local graph properties of the constructed networks are measured using state-of-the-art graph analysis algorithms and tools, and are further compared with five popular random graph models. Third, a functional simulation study is conducted to evaluate the synchronizability of the five models. Our results suggest that the STICKY graph model fits brain networks the best in terms of global and local graph properties, and the fastest speed of functional synchronization.