基于网络效益的人类脑功能网络的小世界度量

‘Small-world-ness’ metric for human brain functional networks based on network efficiency

人类脑功能网络的小世界拓扑组织形式能够促进信息在脑功能区域间以低能量成本和较短连接代价实现高效的转发与集成。然而,使用传统的小世界网络度量指标对人类脑功能网络的小世界特性进行判定时,存在着判定精确度不高的问题。针对此问题,并考虑到人脑工作的高效率特性,提出基于网络效益的小世界度量指标(Efficiency based small world index, ESW)。通过与传统的基于聚集系数、特征路径长度的度量指标以及新型的小世界指数σ、ω进行对比,验证ESW对不同边密度和不同节点规模下仿真网络进行小世界判定的有效性。同时,对真实的人类脑功能性网络的小世界特性进行判定。首先,发现不同阈值下健康志愿者(Healthy volunteers, HV)真实脑功能网络小世界特性的变化规律;然后,通过对比分析HV与患有儿童精神分裂症(Childhood onset schizophrenia, COS)患者以及患有注意力不足过动症(Attention deficit hyperactivity disorder, ADHD)患者的小世界特性,发现COS患者以及ADHD患者的小世界特性明显减弱。关于人类脑功能网络小世界特性的研究,为相关研究人员从网络的拓扑组织形态探索人脑的工作与连接模式提供了参考。

Small-world structures of human brain functional networks can promote the information forwarding and integration among brain regions with low energy consumption and connection cost. However, traditional metrics have the problem of low accuracy during‘small-world-ness’ measurement of human brain functional networks. In order to solve this problem, we proposed a‘small-world-ness’ metric named efficiency based small world(ESW) index with the consideration of the high efficiency characteristic of human brain. The validity of the ESW on simulation networks under different edge densities and node scales is verified by comparing with the traditional measurement including clustering coefficient, characteristic path length, new small world index σ and ω. Simultaneously, the‘small-world-ness’ of real world human brain functional networks is verified. The variation on the‘small-world-ness’ of healthy volunteers(HV)with different thresholds is found firstly. Then, by comparing and 1 analyzing the‘small-world-ness’ of HV, patients with childhood onset schizophrenia(COS) and patients with attention deficit hyperactivity disorder(ADHD), it is showed that the‘small-world-ness’ of COS and ADHD patients weakens. The study on the small-world-ness of human brain functional networks provides a reference for the relevant researchers, which means we can explore the working and connection patterns of human brain from the aspect of network topologies.