In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer’s disease, Parkinson’s disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.

近年来，神经退行性疾病（即阿尔茨海默病、帕金森病、轻度认知障碍）和精神障碍（即抑郁症、焦虑症和精神分裂症）患者数量急剧增加。研究人员发现，复杂网络分析可以揭示大脑功能网络的拓扑结构，如小世界、无标度等。在脑部疾病的研究中，发现这些拓扑结构发生了不同程度的异常变化。因此，脑功能网络的研究不仅可以为理解神经精神疾病的病理机制提供新的视角，而且可以为早期诊断提供帮助。本文围绕人脑功能网络的研究，综述了近年来的研究成果。首先介绍了复杂网络理论下脑功能网络的研究背景以及拓扑特性在脑疾病研究中的重要作用。其次，论文描述了如何利用神经图像数据构建大脑功能网络。第三，介绍了功能网络分析的常用方法，包括网络结构分析和疾病分类。第四，研究脑功能网络在脑功能疾病病理研究、分析和诊断中的作用。最后，论文对脑功能网络的现有研究进行了总结，并指出了存在的问题和未来的研究方向。