2020/05/08 by Mengjia Xu, Xu, Mengjia, David López Sanz +9 · 1 citation
Environmental Science · Neuroscience · Psychology · #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Functional Brain Connectivity Studies #Health, Environment, Cognitive Aging #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Mental Health Research Topics #Neurons and Cognition (q-bio.NC) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.05784
openalex publication_date 2020/05/08 · openalex created_date 2020/05/21 · openalex updated_date 2026/07/28
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.