Cheng Chen, Chen Junjie, Cao Xiaohua, Guo Hao
Department of Computer Science and Technology, Taiyuan University of TechnologyTaiyuan, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.
Department of Computer Science and Technology, Taiyuan University of Technology Taiyuan, China.
Front Neurosci. 2016 Dec 27;10:585. doi: 10.3389/fnins.2016.00585. eCollection 2016.
Anatomical distance has been widely used to predict functional connectivity because of the potential relationship between structural connectivity and functional connectivity. The basic implicit assumption of this method is "distance penalization." But studies have shown that one-parameter model (anatomical distance) cannot account for the small-worldness, modularity, and degree distribution of normal human brain functional networks. Two local information indices-common neighbor (CN) and preferential attachment index (PA), are introduced into the prediction model as another parameter to emulate many key topological of brain functional networks in the previous study. In addition to these two indices, many other local information indices can be chosen for investigation. Different indices evaluate local similarity from different perspectives. Currently, we still have no idea about how to select local information indices to achieve higher predicted accuracy of functional connectivity. Here, seven local information indices are chosen, including CN, hub depressed index (HDI), hub promoted index (HPI), Leicht-Holme-Newman index (LHN-I), Sørensen index (SI), PA, and resource allocation index (RA). Statistical analyses were performed on eight network topological properties to evaluate the predictions. Analysis shows that different prediction models have different performances in terms of simulating topological properties and most of the predicted network properties are close to the real data. There are four topological properties whose average relative error is less than 5%, including characteristic path length, clustering coefficient, global efficiency, and local efficiency. CN model shows the most accurate predictions. Statistical analysis reveals that five properties within the CN-predicted network do not differ significantly from the real data ( > 0.05, false-discovery rate method corrected for seven comparisons). PA model shows the worst prediction performance which was first applied in models of growth networks. Our results suggest that PA is not suitable for predicting connectivity in a small-world network. Furthermore, in order to evaluate the predictions rapidly, prediction power was proposed as an evaluation metric. The current study compares the predictions of functional connectivity with seven local information indices and provides a reference of method selection for construction of prediction models.
由于结构连通性与功能连通性之间存在潜在关系，解剖距离已被广泛用于预测功能连通性。该方法的基本隐含假设是“距离惩罚”。但研究表明，单参数模型（解剖距离）无法解释正常人类脑功能网络的小世界特性、模块化和度分布。在先前的研究中，将两个局部信息指标——共同邻居（CN）和优先连接指数（PA）作为另一个参数引入预测模型，以模拟脑功能网络的许多关键拓扑结构。除了这两个指标外，还可以选择许多其他局部信息指标进行研究。不同的指标从不同的角度评估局部相似性。目前，我们仍然不知道如何选择局部信息指标以实现更高的功能连通性预测准确性。在此，选择了七个局部信息指标，包括CN、中心抑制指数（HDI）、中心促进指数（HPI）、莱希特-霍尔姆-纽曼指数（LHN-I）、索伦森指数（SI）、PA和资源分配指数（RA）。对八个网络拓扑属性进行了统计分析以评估预测。分析表明，不同的预测模型在模拟拓扑属性方面具有不同的性能，并且大多数预测的网络属性与真实数据接近。有四个拓扑属性的平均相对误差小于5%，包括特征路径长度、聚类系数、全局效率和局部效率。CN模型显示出最准确的预测。统计分析表明，CN预测网络中的五个属性与真实数据没有显著差异（>0.05，经错误发现率方法校正用于七次比较）。PA模型显示出最差的预测性能，该模型最初应用于生长网络模型。我们的结果表明，PA不适合预测小世界网络中的连通性。此外，为了快速评估预测，提出了预测能力作为评估指标。当前研究比较了使用七个局部信息指标对功能连通性的预测，并为构建预测模型提供了方法选择的参考。
