Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory
Identifying patients with Alzheimer's disease using resting-state fMRI and graph theory
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DOI：
10.1016/j.clinph.2015.02.060
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发表时间：
2015-11-01
影响因子：
4.7
通讯作者：
Babajani-Feremi, Abbas
中科院分区：
文献类型：
--
作者：
Khazaee, Ali;Ebrahimzadeh, Ata;Babajani-Feremi, Abbas
目的：基于静息态功能磁共振成像（fMRI）的脑网络研究为研究疾病引起的脑区之间的连接变化提供了很有前景的结果。图论可以有效地表征不同方面的大脑网络的计算措施的整合和segregation.Method：在这项研究中，我们联合收割机图形理论的方法与先进的机器学习方法来研究功能性脑网络改变阿尔茨海默病（AD）患者。采用支持向量机（SVM）对图测度在AD诊断中的应用能力进行了研究。我们将我们的方法应用于20例AD患者和20例年龄和性别匹配的健康受试者的静息状态fMRI数据。数据进行了预处理，每个受试者的图表是通过使用自动解剖标记（AAL）图谱将整个大脑分成90个不同区域来构建的。然后计算图形测量值并用作鉴别特征。提取的基于网络的特征被馈送到不同的特征选择算法，以选择最重要的特征。除了机器学习方法外，还对连接矩阵进行了统计分析，以发现AD患者中改变的连接模式。结果：使用所选特征，我们能够准确地将AD患者与健康受试者进行分类，准确率为100%。本研究的结果表明，基于静息态fMRI数据的模式识别和脑网络图可以有效地辅助AD的诊断。基于静息态功能磁共振成像的分类可以作为一种无创、自动化的诊断阿尔茨海默病的工具。(C)2015年国际临床神经生理学联合会。由Elsevier爱尔兰有限公司出版。保留所有权利。
Objective: Study of brain network on the basis of resting-state functional magnetic resonance imaging (fMRI) has provided promising results to investigate changes in connectivity among different brain regions because of diseases. Graph theory can efficiently characterize different aspects of the brain network by calculating measures of integration and segregation.Method: In this study, we combine graph theoretical approaches with advanced machine learning methods to study functional brain network alteration in patients with Alzheimer's disease (AD). Support vector machine (SVM) was used to explore the ability of graph measures in diagnosis of AD. We applied our method on the resting-state fMRI data of twenty patients with AD and twenty age and gender matched healthy subjects. The data were preprocessed and each subject's graph was constructed by parcellation of the whole brain into 90 distinct regions using the automated anatomical labeling (AAL) atlas. The graph measures were then calculated and used as the discriminating features. Extracted network-based features were fed to different feature selection algorithms to choose most significant features. In addition to the machine learning approach, statistical analysis was performed on connectivity matrices to find altered connectivity patterns in patients with AD.Results: Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%.Conclusion: Results of this study show that pattern recognition and graph of brain network, on the basis of the resting state fMRI data, can efficiently assist in the diagnosis of AD.Significance: Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease. (C) 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.