Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs) are localized 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, we construct structural brain networks based on diffusion tensor imaging (DTI) data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free gene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network. 1. Introduction The human brain is intrinsically organized into distinct large-scale functional networks, and cognitive functions arise from the dynamic interactions of distributed brain areas operating in these networks [1]. New advances in neuroimaging techniques have shown the possibility of systematic exploring the human brain formal complex network perspective. Graph theory provides a theoretical framework in which the topological properties of the brain networks can be examined such as centrality, clustering, efficiency, hierarchy, modularity, robustness, small-worldness, and synchronizability [2], and it can reveal important information about both the global and local organizations of the human brain networks. The improved characterization of brain networks achieved via graph-theoretical methods provides not only parsimonious accounts of normal cognitive processes [3], but also novel insights into psychiatric and neurological disorders such as Alzheimer’s disease [4, 5], multiple sclerosis [6], and attention-deficit disorder [7]. Many complex systems show remarkably similar
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