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

Mengjia Xu, D. L. Sanz, Pilar Garces, Fernando Maestu, Quanzheng Li, DImitrios Pantazis

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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 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.

Original languageEnglish (US)
Article number9314203
Pages (from-to)1579-1588
Number of pages10
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number5
DOIs
StatePublished - May 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Keywords

  • Alzheimer's disease
  • brain networks
  • magnetoencephalography
  • mild cognitive impairment
  • multivariate Gaussian distribution
  • stochastic graph embedding
  • uncertainty quantification

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