Si Shuaizong, Wang Bin, Liu Xiao, Yu Chong, Ding Chao, Zhao Hai
School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China.
Entropy (Basel). 2019 Mar 20;21(3):300. doi: 10.3390/e21030300.
Alzheimer's disease (AD) is a progressive disease that causes problems of cognitive and memory functions decline. Patients with AD usually lose their ability to manage their daily life. Exploring the progression of the brain from normal controls (NC) to AD is an essential part of human research. Although connection changes have been found in the progression, the connection mechanism that drives these changes remains incompletely understood. The purpose of this study is to explore the connection changes in brain networks in the process from NC to AD, and uncovers the underlying connection mechanism that shapes the topologies of AD brain networks. In particular, we propose a mutual information brain network model (MINM) from the perspective of graph theory to achieve our aim. MINM concerns the question of estimating the connection probability between two cortical regions with the consideration of both the mutual information of their observed network topologies and their Euclidean distance in anatomical space. In addition, MINM considers establishing and deleting connections, simultaneously, during the networks modeling from the stage of NC to AD. Experiments show that MINM is sufficient to capture an impressive range of topological properties of real brain networks such as characteristic path length, network efficiency, and transitivity, and it also provides an excellent fit to the real brain networks in degree distribution compared to experiential models. Thus, we anticipate that MINM may explain the connection mechanism for the formation of the brain network organization in AD patients.
阿尔茨海默病（AD）是一种进行性疾病，会导致认知和记忆功能衰退问题。AD患者通常会失去自理日常生活的能力。探索大脑从正常对照（NC）发展到AD的过程是人类研究的重要组成部分。尽管在这个过程中已经发现了连接变化，但其驱动这些变化的连接机制仍未完全理解。本研究的目的是探索从NC到AD过程中脑网络的连接变化，并揭示塑造AD脑网络拓扑结构的潜在连接机制。具体而言，我们从图论的角度提出了一种互信息脑网络模型（MINM）来实现我们的目标。MINM关注在考虑两个皮质区域观察到的网络拓扑的互信息及其在解剖空间中的欧几里得距离的情况下，估计它们之间连接概率的问题。此外，MINM考虑在从NC阶段到AD阶段的网络建模过程中同时建立和删除连接。实验表明，MINM足以捕捉真实脑网络的一系列令人印象深刻的拓扑特性，如特征路径长度、网络效率和传递性，并且与经验模型相比，它在度分布方面也能很好地拟合真实脑网络。因此，我们预计MINM可能解释AD患者脑网络组织形成的连接机制。
