Khazaee Ali, Ebrahimzadeh Ata, Babajani-Feremi Abbas
Department of Electrical and Computer Engineering, Babol University of Technology, Iran.
Department of Electrical and Computer Engineering, Babol University of Technology, Iran.
Clin Neurophysiol. 2015 Nov;126(11):2132-41. doi: 10.1016/j.clinph.2015.02.060. Epub 2015 Apr 1.
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.
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.
Using the selected features, we were able to accurately classify patients with AD from healthy subjects with accuracy of 100%.
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.
Classification based on the resting-state fMRI can be used as a non-invasive and automatic tool to diagnosis of Alzheimer's disease.
基于静息态功能磁共振成像（fMRI）对脑网络进行研究，为探究因疾病导致的不同脑区之间连接性的变化提供了有前景的结果。图论能够通过计算整合与分离的度量来有效地刻画脑网络的不同方面。
在本研究中，我们将图论方法与先进的机器学习方法相结合，以研究阿尔茨海默病（AD）患者的功能性脑网络改变。支持向量机（SVM）被用于探索图度量在AD诊断中的能力。我们将我们的方法应用于20例AD患者以及20名年龄和性别匹配的健康受试者的静息态fMRI数据。对数据进行预处理，并使用自动解剖标记（AAL）图谱将全脑划分为90个不同区域，为每个受试者构建脑图谱。然后计算图度量，并将其用作判别特征。提取的基于网络的特征被输入到不同的特征选择算法中，以选择最显著的特征。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。
使用选定的特征，我们能够以100%的准确率准确地将AD患者与健康受试者区分开来。
本研究结果表明，基于静息态fMRI数据的脑网络模式识别和图谱能够有效地辅助AD的诊断。
基于静息态fMRI的分类可作为一种非侵入性的自动工具用于阿尔茨海默病的诊断。
