Assessing theoretical graph models for characterizing structural networks of human brain

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 structural brain networks? In this paper, we perform a comparative study to address this problem. First of all, we localized large-scale cortical regions of interest (ROIs) by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of the brain network ROIs in previous studies. Then, the structural brain network of each subject was constructed based on diffusion tensor imaging (DTI) data. Afterwards, by using the state-of-the-art graph analysis algorithms and tools, we measured the global and local graph properties of the constructed structural brain networks, and further compared with seven popular theoretical graph models. Our experimental results suggest that SF-GD and STICKY models have better performances in characterizing the structural brain network of human brain among the seven theoretical graph models compared in this study.