Functional Evolving Patterns of Cortical Networks in Progression of Alzheimer's Disease: A Graph-Based Resting-State fMRI Study.

Wen Wen,The Alzheimer's Disease Neuroimaging Initiative The Alzheimer's Disease Neuroimaging Initiative,Wenliang Fan,Wei Li,Xi Chen,Xuefeng Lin,BingJie Ni

doi:10.1155/2020/7839536

Wen Wen, The Alzheimer's Disease Neuroimaging Initiative The Alzheimer's Disease Neuroimaging Initiative + Show 5 more

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https://doi.org/10.1155/2020/7839536

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Abstract

AD is a common chronic progressive neurodegenerative disorder. However, the understanding of the dynamic longitudinal change of the brain in the progression of AD is still rough and sometimes conflicting. This paper analyzed the brain networks of healthy people and patients at different stages (EMCI, LMCI, and AD). The results showed that in global network properties, most differences only existed between healthy people and patients, and few were discovered between patients at different stages. However, nearly all subnetwork properties showed significant differences between patients at different stages. Moreover, the most interesting result was that we found two different functional evolving patterns of cortical networks in progression of AD, named ‘temperature inversion' and “monotonous decline,” but not the same monotonous decline trend as the external functional assessment observed in the course of disease progression. We suppose that those subnetworks, showing the same functional evolving pattern in AD progression, may have something the same in work mechanism in nature. And the subnetworks with ‘temperature inversion' evolving pattern may play a special role in the development of AD.

Highlights

The pathogenesis of Alzheimer’s disease (AD) is concealed and affects both the brain structure and function connections [1]
The data used in this study were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset.The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD)
While in the subnetwork analysis, we found that all five attributes of each subnetwork for each progressing stage of AD (EMCI, later stage of MCI (LMCI), and AD) changed to various degrees, and most of these changes reached statistical significance, especially in default mode network (DMN) and visual network

Summary

Introduction

The pathogenesis of Alzheimer’s disease (AD) is concealed and affects both the brain structure and function connections [1]. It can be divided into three main phases: preclinical, mild cognitive impairment (MCI) which can be further divided into the early stage of MCI (EMCI) and the later stage of MCI (LMCI), and dementia [2]. Using the graph theory method, there were some differences between normal controls and patients, such as a lower value of the Neural Plasticity small-word attribute and clustering coefficient in AD [13], significant decrease of clustering coefficient, and local efficiency in the limbic network of MCI [14]. The change patterns of each network properties in the process of EMCI to LMCI and AD and how the inter- and intrasubnetworks are altered are still unknown

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