  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.
