Work

Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification

Year: 2020

Type: conference-paper

Abstract: Alzheimer's disease (AD) is the most prevalent form of dementia. Traditional methods cannot achieve efficient and accurate diagnosis of AD. This paper introduces 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.

Source: International Conference on Biomedical and Bioinformatics Engineering (ICBBE)

DOI: 10.1145/3444884.3444885

Authors: Xingwei An, Yutao Zhou, Yang Di, Dong Ming

Institution: Tianjin University

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Natural Science Foundation of China