  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 between-group significant alterations
across brain regions. We used MG2G to detect the intrinsic latent
dimensionality of MEG brain networks, predict the progression of patients with
mild cognitive impairment (MCI) to AD, and identify brain regions with network
alterations related to MCI.
