Graph Theoretical Measures for Alzheimer’s, MCI, and Normal Controls: A Comparative Study Using MRI Data

IF 1.8 Q4 NEUROSCIENCES Annals of Neurosciences Pub Date : 2023-09-01 DOI:10.1177/09727531231186503
Rakhi Sharma, S. Joshi
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Abstract

The Graph theory provides the platform that could be used to model complex brain networks mathematically, and it could play a significant role in the diagnosis of various neurodegenerative diseases such as Alzheimer’s. The main aim of our study is to perform a comparative analysis in terms of various graph theoretic measures of structural brain networks. In particular, the paper evaluates graph theoretical measures by first forming graphs using magnetic resonance imaging (MRI) data. In this paper, we study and evaluate graph theoretical measures using MRI data, namely characteristic path length, global efficiency, strength, and clustering coefficient, in a cohort of normal controls ( N = 30), a cohort of mild cognitive impairment (MCI) ( N = 30), and a cohort of Alzheimer’s disease (AD) ( N = 30). In our work, MRI data is preprocessed and cortical thickness is extracted for each brain region. The connectivity matrix is obtained, and thus a graph is formed. We have also performed receiver operating characteristic (ROC) and area under the ROC analyses of all graph theoretical measures to better elucidate and validate the results. It is observed that these measures may be used to differentiate Alzheimer’s from normal. In our study, we observed that a very random and disrupted network is obtained in the case of Alzheimer’s in comparison with the normal and MCI cases. The other observations in terms of graph theoretic measures are an increase in characteristic path length, a decrease in global efficiency, a decrease in strength, and a reduction in values of the clustering coefficient in the case of Alzheimer’s. The findings suggest that graph theoretical measures and alterations in network topology could be used as quantitative biomarkers of AD.
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阿尔茨海默病、轻度认知障碍和正常对照的图理论测量:使用MRI数据的比较研究
图论提供了一个平台,可以用来对复杂的大脑网络进行数学建模,它可以在各种神经退行性疾病(如阿尔茨海默氏症)的诊断中发挥重要作用。我们研究的主要目的是根据结构脑网络的各种图论测量进行比较分析。特别是,本文通过首先使用磁共振成像(MRI)数据形成图来评估图理论措施。在本文中,我们使用MRI数据研究和评估图理论测量,即特征路径长度,整体效率,强度和聚类系数,在正常对照组(N = 30),轻度认知障碍(MCI)队列(N = 30)和阿尔茨海默病(AD)队列(N = 30)中。在我们的工作中,对MRI数据进行预处理,并提取每个大脑区域的皮质厚度。得到连通矩阵,从而形成图。我们还对所有图理论测量进行了受试者工作特征(ROC)和ROC下面积分析,以更好地阐明和验证结果。据观察,这些措施可用于区分阿尔茨海默氏症与正常人。在我们的研究中,我们观察到,与正常和轻度认知障碍病例相比,阿尔茨海默氏症的病例中获得了一个非常随机和中断的网络。图论测量方面的其他观察结果是特征路径长度的增加,全局效率的降低,强度的降低,以及阿尔茨海默氏症中聚类系数值的降低。研究结果表明,图理论测量和网络拓扑结构的改变可以作为AD的定量生物标志物。
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来源期刊
Annals of Neurosciences
Annals of Neurosciences NEUROSCIENCES-
CiteScore
2.40
自引率
0.00%
发文量
39
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