ArticleHuman brain mapping2022
Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Estimation of positron emission tomography amyloid load and related biomarkers in Alzheimer's disease using evoked potential tomography EEG: development and internal validation in a cross-sectional cohort.Alzheimer's research & therapy · 2026Article
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- A dual path graph neural network framework for dementia diagnosis.Scientific reports · 2025Article
- A group based network analysis for Alzheimer's disease fMRI data.Scientific reports · 2025Article
- Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral-Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals.Brain sciences · 2025Article
- The role of quantitative EEG biomarkers in Alzheimer's disease and mild cognitive impairment: applications and insights.Frontiers in aging neuroscience · 2025Review
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.Biomedical engineering online · 2024Article
- TSANN-TG: Temporal-Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition.Brain sciences · 2024Article
- A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.BMC genomics · 2024Article
- Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Alzheimer's disease diagnosis using rhythmic power changes and phase differences: a low-density EEG study.Frontiers in aging neuroscience · 2024Article
- Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.IEEE transactions on medical imaging · 2024Article
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.Brain sciences · 2023Article
- Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA).AIMS neuroscience · 2023Article
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6 authors.
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Abstract
Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.
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