ArticleBMC bioinformatics2020
Identification of early mild cognitive impairment using multi-modal data and graph convolutional networks.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review.Frontiers in neurology · 2024Pooled it
- The Impact of Denoising Approaches on the Relationship Between Alzheimer's Disease Diagnostic Status and Network Topology Measures.Human brain mapping · 2026Article
- Artificial neural networks fighting real neural decline: a systematic review of AI in Alzheimer's research.Artificial intelligence review · 2026Article
- Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.Frontiers in neuroscience · 2025Review
- The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook.Brain sciences · 2023Review
- Texture-based brain networks for characterization of healthy subjects from MRI.Scientific reports · 2023Article
- Dynamic weighted hypergraph convolutional network for brain functional connectome analysis.Medical image analysis · 2023Article
- A joint multi-modal learning method for early-stage knee osteoarthritis disease classification.Heliyon · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- Early detection of COPD based on graph convolutional network and small and weakly labeled data.Medical & biological engineering & computing · 2022Article
- Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future.Sensors (Basel, Switzerland) · 2021Review
- Multimodal Brain Connectomics-Based Prediction of Parkinson's Disease Using Graph Attention Networks.Frontiers in neuroscience · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundThe identification of early mild cognitive impairment (EMCI), which is an early stage of Alzheimer's disease (AD) and is associated with brain structural and functional changes, is still a challenging task. Recent studies show great promises for improving the performance of EMCI identification by combining multiple structural and functional features, such as grey matter volume and shortest path length. However, extracting which features and how to combine multiple features to improve the performance of EMCI identification have always been a challenging problem. To address this problem, in this study we propose a new EMCI identification framework using multi-modal data and graph convolutional networks (GCNs). Firstly, we extract grey matter volume and shortest path length of each brain region based on automated anatomical labeling (AAL) atlas as feature representation from T1w MRI and rs-fMRI data of each subject, respectively. Then, in order to obtain features that are more helpful in identifying EMCI, a common multi-task feature selection method is applied. Afterwards, we construct a non-fully labelled subject graph using imaging and non-imaging phenotypic measures of each subject. Finally, a GCN model is adopted to perform the EMCI identification task.
resultsOur proposed EMCI identification method is evaluated on 210 subjects, including 105 subjects with EMCI and 105 normal controls (NCs), with both T1w MRI and rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed framework achieves an accuracy of 84.1% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.856 for EMCI/NC classification. In addition, by comparison, the accuracy and AUC values of our proposed framework are better than those of some existing methods in EMCI identification.
conclusionOur proposed EMCI identification framework is effective and promising for automatic diagnosis of EMCI in clinical practice.
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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.