Brain Network Decomposition By Auto Encoder (Ae) And Graph Auto Encoder (Gae)
NEURAL INFORMATION PROCESSING (ICONIP 2019), PT I(2019)
摘要
Brain networks consist of nodes that are anatomically defined brain regions, and edges that connect a pair of brain regions. The diffusion-weighted magnetic resonance images and the advances in computer-aided tractography algorithms showed that human brain networks are strongly associated with cognitive functions. Brain regions dedicated to a specific cognitive function are spatially clustered and efficiently connected each other; this is called local functional segregation. However, it is not well known that such a local segregation is associated with sub-networks which may act as building blocks of brain networks. In this work, we used machine learning techniques to analyze brain networks. Specifically, using an auto-encoder and a graph auto-encoder, we decomposed brain networks into several essential building blocks, and compared their results through various measures of decomposition quality. We observed that the graph auto-encoder out-performed the auto-encoder, and that its results showed significant correlation with cognitive deterioration in Alzheimer's disease.
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关键词
Brain networks, Graph auto-encoder, Graph convolutional neural network, Alzheimer's disease
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