Alzheimer's disease classification using mutual information generated graph convolutional network for functional MRI.

ABSTRACT: BackgroundHigh-order cognitive functions depend on collaborative actions and information exchange between multiple brain regions. These inter-regional interactions can be characterized by mutual information (MI). Alzheimer's disease (AD) is known to affect many high-order cognitive functions, suggesting an alteration to inter-regional MI, which remains unstudied.ObjectiveTo examine whether inter-regional MI can effectively distinguish different stages of AD from normal control (NC) through a connectome-based graph convolutional network (GCN).MethodsMI was calculated between the mean time series of each pair of brain regions, forming the connectome which was input to a multi-level connectome based GCN (MLC-GCN) to predict the different stages of AD and NC. The spatio-temporal feature extrac

SUBMITTER: Fu Y 

PROVIDER: S-EPMC12344501 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature