论文标题
阿尔茨海默氏病分类的动态功能连通性和图形卷积网络
Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification
论文作者
论文摘要
阿尔茨海默氏病（AD）是痴呆症的最普遍形式。传统方法无法实现AD的有效且准确的诊断。在本文中，我们引入了一种基于动态功能连通性（DFC）的新方法，该方法可以有效地捕获大脑的变化。我们比较并结合了四种不同类型的特征，包括低频波动（ALFF），区域同质性（REHO），DFC和受试者之间不同脑结构的邻接矩阵。我们使用图形卷积网络（GCN），这些网络考虑了患者之间大脑结构的相似性来解决非欧几里得领域的分类问题。提出的方法的准确性和接收器操作特性曲线下的面积达到91.3％和98.4％。该结果表明我们提出的方法可用于检测AD。
Alzheimer's disease (AD) is the most prevalent form of dementia. Traditional methods cannot achieve efficient and accurate diagnosis of AD. In this paper, we introduce a novel method based on dynamic functional connectivity (dFC) that can effectively capture changes in the brain. We compare and combine four different types of features including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dFC and the adjacency matrix of different brain structures between subjects. We use graph convolution network (GCN) which consider the similarity of brain structure between patients to solve the classification problem of non-Euclidean domains. The proposed method's accuracy and the area under the receiver operating characteristic curve achieved 91.3% and 98.4%. This result demonstrated that our proposed method can be used for detecting AD.
