Brain functional network changes over time along with the process of brain development, disease, and aging. However, most of the available measurements for evaluation of the difference (or similarity) between the individual brain functional networks are for charactering static networks, which do not work with the dynamic characteristics of the brain networks that typically involve a long-span and large-scale evolution over the time. The current study proposes an index for measuring the similarity of dynamic brain networks, named as dynamic network similarity (DNS). It measures the similarity by combining the "evolutional" and "structural" properties of the dynamic network. Four sets of simulated dynamic networks with different evolutional and structural properties (varying amplitude of changes, trend of changes, distribution of connectivity strength, range of connectivity strength) were generated to validate the performance of DNS. In addition, real world imaging datasets, acquired from 13 stroke patients who were treated by transcranial direct current stimulation (tDCS), were used to further validate the proposed method and compared with the traditional similarity measurements that were developed for static network similarity. The results showed that DNS was significantly correlated with the varying amplitude of changes, trend of changes, distribution of connectivity strength and range of connectivity strength of the dynamic networks. DNS was able to appropriately measure the significant similarity of the dynamics of network changes over the time for the patients before and after the tDCS treatments. However, the traditional methods failed, which showed significantly differences between the data before and after the tDCS treatments. The experiment results demonstrate that DNS may robustly measure the similarity of evolutional and structural properties of dynamic networks. The new method appears to be superior to the traditional methods in that the new one is capable of assessing the temporal similarity of dynamic functional imaging data.
大脑的功能网络会随脑发育、病变、衰老等时间过程改变。现有针对个体间脑功能网络变化的差异（或相似）度量大都是用于评估网络的静态特性的，不适用于评估脑功能网络沿时间轴发生的大跨度、大规模的演变而形成的动态特性。本文提出了一种用于度量脑网络动态相似性的动态网络相似度（DNS）指标。该指标通过结合动态网络的演化和结构特征进行相似度度量。通过四组具有不同演化和结构特征（变化幅度、变化趋势、连接强度分布、连接强度跨度）的模拟动态网络验证了DNS指标的性能。此外，还使用了一组采用经颅直流电刺激（tDCS）治疗的13名中风患者之间脑功能网络的真实数据对DNS指标进行了检验，并与传统静态网络相似度方法作了比较。结果表明DNS指标与模拟动态网络的变化幅度、变化趋势、连接强度分布、连接强度跨度均显著相关。使用DNS指标，可以发现中风患者在tDCS治疗前后运动网络的动态演变具有较强相似性；而利用传统静态网络相似度则不能反映这一动态特性，所得到的患者子组间的相似度结果在治疗前与治疗后差异较大。实验结果表明，DNS指标能够较准确地反映动态网络的演化及结构特性，具有较强的鲁棒性。这一新指标克服了传统静态网络相似度度量方法缺乏总体评估时序脑功能数据能力的缺点。.
Keywords: Brain functional network; Dynamic evolution; Functional magnetic resonance imaging; Graph theory; Similarity.