Brain Network Modeling Based on Mutual Information and Graph Theory for Predicting the Connection Mechanism in the Progression of Alzheimer's Disease.

Shuaizong Si,Hai Zhao,Chao Ding,Bin Wang,Xiao Liu,Chong Yu

doi:10.3390/e21030300

Abstract

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.

Highlights

Alzheimer’s disease (AD) is the primary form of dementia and the most common degenerative brain disease among older people [1]
We propose one novel brain network model named mutual information brain network model (MINM) for a better understanding of the connection mechanism of AD brain networks from two stages, i.e., the stage from normal controls (NC) to MCI, and the stage from NC to AD
The results indicated that MINM considering both network topology-based mutual information and anatomical distance could generate synthetic networks capturing all of the key topological features of real target brain networks

Summary

Introduction

Alzheimer’s disease (AD) is the primary form of dementia and the most common degenerative brain disease among older people [1]. A large amount of research has been devoted to Alzheimer’s disease, it is still a significant challenge to discover the underlying connection patterns that cause the alteration of functions in brain network of AD [5,6,7]. Addressing these problems have profound significance for a better understanding of how Entropy 2019, 21, 300; doi:10.3390/e21030300 www.mdpi.com/journal/entropy. Network modeling is viewed as a promising way to help us understand how the inter-connection mechanism affects the topological structures in complex networks

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