基于局域社团的人类脑功能网络生成模型

摘要 研究了人类脑功能区域间拓扑结构与解剖结构两种因素对脑功能网络建模的影响，提出了基于局域社团的人类脑功能网络生成模型.模型中的局域社团拓扑结构采用功能区域间的共同邻居及邻居间的局域连接表示，解剖结构用人脑区域间的解剖距离代表.为了衡量模型生成网络与基于fMRI数据构建的真实数据网络之间的相似性，提出了用于校验网络间接近程度的相似性能量指标.实验结果表明，相比传统生成模型，基于局域社团的脑功能网络生成模型在网络效益、聚集系数、模块性、度分布等属性方面都能够更精确地模拟真实数据网络.
Abstract：The effects of both regional topological structures and anatomical structures on human brain functional networks modeling were investigated， and generative models of human brain functional networks based on local communities were proposed. The local community topologies of the models are measured by not only the common neighbors between the two functional regions but also the connections among the neighbors. And the anatomical structures are represented by the anatomical distance between the two brain regions. In addition， the similarity energy index was proposed to evaluate the similarity between the generated network and the real data network based on functional magnetic resonance images(fMRI). The results show that the generative models based on local communities provide a good fit to the real data network in terms of network efficiency， clustering coefficient， modularity and degree distribution compared with traditional generated models.