ArticleIEEE transactions on bio-medical engineering2021
A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 4 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 4 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- Tracking electrophysiological signatures of Alzheimer's disease: a systematic review of multimodal studies.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Artificial Intelligence for Alzheimer's Disease Diagnosis: From Traditional Machine Learning to Large Language Models.Biosensors · 2026Review
- AI-driven magnetoencephalography biomarkers in dementia risk prediction: current evidence, challenges and future perspectives.Frontiers in dementia · 2026Review
- Detecting Mild Cognitive Impairment to Alzheimer's Disease Progression by fMRI Using Convolutional Neural Network and Long-short Term Memory.Basic and clinical neuroscience · 2025Article
- AI-powered model for accurate prediction of MCI-to-AD progression.Acta pharmaceutica Sinica. B · 2025Article
- Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories.IEEE journal of biomedical and health informatics · 2025Article
- Acupuncture modulates the microbiota-gut-brain axis: a novel therapeutic strategy for amnestic mild cognitive impairment.Frontiers in neuroscience · 2025Article
- Hyperbolic Graph Embedding of MEG Brain Networks to Study Brain Alterations in Individuals With Subjective Cognitive Decline.IEEE journal of biomedical and health informatics · 2024Article
- Article
- Topological state-space estimation of functional human brain networks.PLoS computational biology · 2024Article
- Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.NeuroImage · 2023Article
- Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline.bioRxiv : the preprint server for biology · 2023Article
- An F-ratio-based method for estimating the number of active sources in MEG.Frontiers in human neuroscience · 2023Article
- New Therapeutics in Alzheimer's Disease Longitudinal Cohort study (NTAD): study protocol.BMJ open · 2022Article
- Temporal and Spatial Analysis of Alzheimer's Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network.International journal of environmental research and public health · 2022Article
- Individual-specific networks for prediction modelling - A scoping review of methods.BMC medical research methodology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.