Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there\r\nis an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing\r\nthe structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, largescale\r\ncortical regions of interest (ROIs) are localized by recently developed and validated brain reference system named Dense\r\nIndividualized Common Connectivity-based Cortical Landmarks (DICCCOL) to address the limitations in the identification of\r\nthe brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI)\r\ndata. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the stateof-\r\nthe-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition,\r\nwe compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY) and scale-free\r\ngene duplication model (SF-GD), that have higher similarity with the real structural brain networks in terms of global and local\r\ngraph properties. Our experimental results suggest that among the seven theoretical graphmodels compared in this study, STICKY\r\nand SF-GD models have better performances in characterizing the structural human brain network.
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