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The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier.

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收录情况： ◇ SCIE ◇ SSCI

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机构： [a]School of Electrical Engineering, Yanshan University, Qinhuangdao, China [b]Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China [c]Biomedical Engineering Institute, Hainan University, Haikou, China [d]Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China [e]Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China [f]National Clinical Research Center for Geriatric Disorders, Beijing, China

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关键词： Phase synchronization index Directed Transfer Function Efficiency density SVM aMCI

摘要：

Amnestic mild cognitive impairment (aMCI) is an essential stage of early detection and potential intervention for Alzheimer's disease (AD). Patients with aMCI exhibit partially abnormal functional brain connectivity and it is suggested that these features may represent a new diagnostic marker of early AD.In this paper, we constructed two brain network models, a phase synchronization index (PSI) undirected network and a directed transfer function (DTF) directed network, to evaluate the cognitive function in patients with aMCI. We then built SVM classification models using the network clustering coefficient, global efficiency and average node degree as features to distinguish between aMCI patients and controls.Our results reveal a classification accuracy and AUC of 66.6±1.7% and 0.7475 and 80.0±2.2% and 0.7825, respectively, for the two network models (PSI and DTF). As the directed network model performed better than the undirected model, we introduced an improved graph theory feature, efficiency density, which resulted in an increased classification accuracy and AUC value 86.6±2.6% and 0.8295, respectively.The analysis of network models and the directionality of information flow is suitable for analysis of nonlinear EEG signals for assessment of the functional state of the brain. Compared with traditional network features, our proposed improved features more comprehensively evaluate transmission efficiency and density of the brain.In this study, we demonstrate that an improved efficiency density feature is helpful for enhancing classification the accuracy of aMCI. Moreover, directed brain network models exhibit better classification for aMCI diagnosis than undirected networks.Copyright © 2021. Published by Elsevier B.V.

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出版当年[2020]版：

大类 | 3 区医学

小类 | 3 区生化研究方法 4 区神经科学

最新[2023]版：

大类 | 4 区医学

小类 | 4 区生化研究方法 4 区神经科学

JCR分区：

出版当年[2019]版：

Q3 BIOCHEMICAL RESEARCH METHODS Q3 NEUROSCIENCES

最新[2023]版：

Q2 BIOCHEMICAL RESEARCH METHODS Q3 NEUROSCIENCES

影响因子： 2.7 最新[2023版] 2.7 最新五年平均 2.214 出版当年[2019版] 2.895 出版当年五年平均 2.785 出版前一年[2018版] 2.39 出版后一年[2020版]

第一作者：

第一作者机构： [a]School of Electrical Engineering, Yanshan University, Qinhuangdao, China [b]Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China [*1]School of Electrical Engineering, Yanshan University, Qinhuangdao, China.

通讯作者：

通讯机构： [a]School of Electrical Engineering, Yanshan University, Qinhuangdao, China [b]Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China [*1]School of Electrical Engineering, Yanshan University, Qinhuangdao, China.

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The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier.

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