He Yongquan, Zhang Li, Fang Shan, Zeng Yaqin, Yang Wei, Chen Weidong, Shao Yuling, Cheng Ruidong, Ye Xiangming, Xu Dongrong
Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai 200062, P. R. China.
Department of Rehabilitation Medicine, Zhejiang Province People's Hospital, Hangzhou Medical College, Hangzhou 310014, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Apr 25;39(2):237-247. doi: 10.7507/1001-5515.202103079.
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.
随着大脑发育、疾病和衰老过程，脑功能网络会随时间发生变化。然而，用于评估个体脑功能网络之间差异（或相似性）的大多数现有测量方法都是针对静态网络的特征描述，而这些方法不适用于通常涉及长时间跨度和大规模随时间演变的脑网络动态特征。当前研究提出了一种用于测量动态脑网络相似性的指标，称为动态网络相似性（DNS）。它通过结合动态网络的“演化”和“结构”属性来测量相似性。生成了四组具有不同演化和结构属性（变化幅度、变化趋势、连接强度分布、连接强度范围）的模拟动态网络，以验证DNS的性能。此外，从13名接受经颅直流电刺激（tDCS）治疗的中风患者获取的真实世界成像数据集，用于进一步验证所提出的方法，并与为静态网络相似性开发的传统相似性测量方法进行比较。结果表明，DNS与动态网络的变化幅度、变化趋势、连接强度分布和连接强度范围显著相关。DNS能够适当地测量tDCS治疗前后患者网络变化动态的显著相似性。然而，传统方法却失败了，这表明tDCS治疗前后的数据存在显著差异。实验结果表明，DNS可以稳健地测量动态网络演化和结构属性的相似性。新方法似乎优于传统方法，因为新方法能够评估动态功能成像数据的时间相似性。
