Chen Yurong, Tang Haoteng, Guo Lei, Peven Jamie C, Huang Heng, Leow Alex D, Lamar Melissa, Zhan Liang
Department of Electrical and Computer Engineering, University of Pittsburgh, PA, USA.
Department of Psychology, University of Pittsburgh, PA, USA.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:288-291. doi: 10.1109/isbi45749.2020.9098552. Epub 2020 May 22.
Diffusion MRI-derived brain structural network has been widely used in brain research and community or modular structure is one of popular network features, which can be extracted from network edge-derived pathlengths. Conceptually, brain structural network edges represent the connecting strength between pair of nodes, thus non-negative. The pathlength. Many studies have demonstrated that each brain network edge can be affected by many confounding factors (e.g. age, sex, etc.) and this influence varies on each edge. However, after applying generalized linear regression to remove those confounding's effects, some network edges may become negative, which leads to barriers in extracting the community structure. In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity.
基于扩散磁共振成像的脑结构网络已广泛应用于脑研究，社区或模块结构是一种流行的网络特征，可从网络边衍生的路径长度中提取。从概念上讲，脑结构网络边代表节点对之间的连接强度，因此是非负的。路径长度。许多研究表明，每个脑网络边可能受到许多混杂因素（如年龄、性别等）的影响，且这种影响在每条边上各不相同。然而，在应用广义线性回归消除这些混杂效应后，一些网络边可能会变为负数，这给提取社区结构带来了障碍。在本研究中，我们提出了一种新颖的广义框架来解决从脑结构网络中提取模块结构时的负边问题。我们将我们的框架与传统的Q方法进行了比较。结果清楚地表明，我们的框架在稳定性和敏感性方面都具有显著优势。
