中国神经再生研究(英文版)

中国神经再生研究(英文版) ›› 2019, Vol. 14 ›› Issue (10): 1805-1813.doi: 10.4103/1673-5374.257538

• 原著:退行性病与再生 • 上一篇    下一篇

脑网络建模分析阿尔茨海默病的发展机制

  

  • 出版日期:2019-10-15 发布日期:2019-10-15
  • 基金资助:

    中央高校基础研究基金项目（N161608001，N171903002）

Brain networks modeling for studying the mechanism underlying the development of Alzheimer’s disease

Shuai-Zong Si, Xiao Liu, Jin-Fa Wang, Bin Wang, Hai Zhao   

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China
  • Online:2019-10-15 Published:2019-10-15
  • Contact: Xiao Liu, Doctoral candidate, liu.xiao.xiao.1881@gmail.com; Bin Wang, Doctoral candidate, bin.wang.brilliant@gmail.com.
  • Supported by:

    This study was supported in part by Fundamental Research Funds for the Central Universities in China, No. N161608001 and No. N171903002.

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摘要：

阿尔茨海默病是一种与年龄相关的神经退行性疾病，可导致认知和记忆功能减退。尽管已发现阿尔茨海默病患者的脑网络连接中发生了变化，但至今鲜有人能够对导致阿尔茨海默病脑网络产生的本质原因进行合理的解释。此病例对照研究于2018年在中国东北大学完成，纳入数据来源于涵盖遗传学、影像学和临床数据的开放共享数据集，ADNI数据集的数据。此次试验中从ADNI数据集收集97名受试者数据，按照参与者情况分为对照组(n=52，男20例，女32例，年龄73.90 ± 4.72岁)和阿尔茨海默病组(n=45，男23例，女22例，年龄74.85 ± 5.66岁)，以图理论为理论基础，从脑网建模的角度出发，提出了一种基于朴素贝叶斯理论的动态脑网络模型——基于朴素贝叶斯的脑网络模型。试验结果表明，与传统方法相比，所提出的模型聚集系数、特征路径长度、网络效益、模块度等多种属性方面很好拟合阿尔茨海默病患者的脑网络，能够有效模拟由正常到阿尔茨海默病状态人脑的变化规律，这对了解大脑组织结构与认知、行为等功能间的关系提供了帮助。

orcid: 0000-0002-4143-7757 (Bin Wang)

 

关键词: 阿尔茨海默病, 图理论, 功能磁共振成像, 网络模型, 连接预测, 朴素贝叶斯分析, 拓扑结构, 解剖距离, 整体效率, 局部效率, 神经再生

Abstract:

Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions. Al¬though connections between changes in brain networks of Alzheimer’s disease patients have been established, the mechanisms that drive these alterations remain incompletely understood. This study, which was conducted in 2018 at Northeastern University in China, included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative dataset covering genetics, imaging, and clinical data. All participants were divided into two groups: normal control (n = 52; 20 males and 32 females; mean age 73.90 ± 4.72 years) and Alzheimer’s disease (n = 45, 23 males and 22 females; mean age 74.85 ± 5.66). To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients, we proposed a local naïve Bayes brain network model based on graph theory. Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined, including cluster¬ing coefficient, modularity, characteristic path length, network efficiency, betweenness, and degree distribution compared with empirical methods. This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients. Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.

Key words: nerve regeneration, Alzheimer’s disease, graph theory, functional magnetic resonance imaging, network model, link prediction, naïve Bayes, topological structures, anatomical distance, global efficiency, local efficiency, neural regeneration