Gao Hongchang, Cai Chengtao, Yan Jingwen, Yan Lin, Cortes Joaquin Goni, Wang Yang, Nie Feiping, West John, Saykin Andrew, Shen Li, Huang Heng
Computer Science and Engineering, University of Texas at Arlington, TX, USA.
Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA.
Med Image Comput Comput Assist Interv. 2015;9350:169-76. doi: 10.1007/978-3-319-24571-3_21.
Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.
在人类连接组研究中，缺乏用于分析复杂生物网络的计算工具。特别是，如何发现一组受试者共有的脑网络模式是一个具有挑战性的计算神经科学问题。尽管一些单图聚类方法可以扩展以解决多图情况，但发现的网络模式往往不均衡，例如孤立点。为了解决这些问题，我们提出了一种新颖的指标约束平衡多图归一化割方法，以从目标受试者组的连通性脑网络中识别连接组模块模式。我们通过分析加权纤维连通性网络对我们的方法进行了评估。
