A Deep Learning Model of Amyloid-β Diffusion Simulation

초록

Alzheimer's disease (AD) is a severe neurodegenerative disease characterized by ongoing brain tissue decline. Since amyloid-β accumulation is supposed as a primary cause of AD progression, mathematical models have been recently employed to simulate amyloid-β diffusion in our brain. These models require parameter estimation through observed data and an iterative process to update parameters, although increased complexity can hinder their optimization. To overcome these difficulties, we converted the mathematical model of amyloid-β diffusion into a deep learning model, combining multi-layer perceptron (MLP) and graph convolutional neural network (GCN). The proposed model well predicted the change of accumulation level of amyloid-β with a high accuracy. © 2024 IEEE.

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