Abstract:

Complex network analysis, based on graph theory and neuroimaging data, allows investigators to describe the large-scale brain networks using several topological characteristics. This approach overcomes the shortcoming of traditional neuroimaging research that can only focus on a few brain regions either individually or in combination. This review concluded includes the following issues: 1) the concepts of complex brain network analysis; 2) the researches of depression depressive disorder based on graph theory ; 3) weaknesses, conclusions and future directions of brain network research. Convergent evidence shows that Major Depressive Disorder (MDD) patients show small-world architecture in brain networks, but some of their nodal characteristics evolved with the development of disease, and the whole brain network tended to be a random network. Regional abnormality mainly existed within the default mode network (DMN) and prefrontal-limbic circuits. In future studies, construction of whole brain networks in cognitive tasks and Minimum Spanning Tree (MST) may provide more information about the disrupted brain connectome of MDD.

Key words: major depression disorder, complex brain network, graph theory, human connectome