Research Progress on Graph Neural Network Classification Methods for Alzheimer s Disease
Alzheimer's Disease(AD)is an irreversible neurodegenerative disorder that leads to gradual cognitive decline.The evolution of AD symptoms can be long,with subtle changes in biomarkers in brain regions that are detectable by different neuroimaging modalities;however,early detection is challenging.Given the high complexity of neuroimaging data and the irregularity of brain networks,traditional machine learning,and deep neural network models exhibit many shortcomings,and the development of Computer-Aided Diagnostic(CAD)models based on Graph Neural Network(GNN)can be beneficial for probing biomarkers and analyzing neuroimaging patterns in non-Euclidean space.First,a detailed investigation and overview of AD prediction based on GNN classification methods is carried out.Subsequently,an analysis is conducted from the two perspectives of single-and multi-modal data,with a focus on discussing and analyzing the processes of data extraction,brain network modeling,feature learning,and information fusion within the context of single-and multi-modal data applications.A performance evaluation is provided for certain methods.Finally,the primary challenges and future research directions for the application of GNNs in AD diagnosis are outlined to provide beneficial suggestions for further research on AD-assisted diagnosis.
Graph Neural Network(GNN)Alzheimer's Disease(AD)assisted diagnosisneuroimagingmulti-modal data
国家科技期刊平台
NSTL
万方数据
