Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification
This addresses the problem of efficient and accurate diagnosis of Alzheimer's disease for medical applications, representing an incremental improvement over traditional methods.
The paper tackled Alzheimer's disease classification by introducing a method based on dynamic functional connectivity and graph convolution networks, achieving an accuracy of 91.3% and an AUC of 98.4%.
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.