A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks.

ABSTRACT: Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of betwe

SUBMITTER: Xu M 

PROVIDER: S-EPMC8162933 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature