ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
Multi-View Graph Convolutional Network and Its Applications on Neuroimage Analysis for Parkinson's Disease.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Deep Learning-Based Automated Clinical Gait Assessment From Kinematic Data in People With Stroke: Development and Validation Study.JMIR rehabilitation and assistive technologies · 2026Article
- The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).Neuroinformatics · 2026Review
- Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables.Frontiers in neuroscience · 2026Article
- Machine learning identifies lactate metabolism biomarkers and deciphers immune infiltration landscapes in Parkinson's disease.Scientific reports · 2025Article
- Structural Connectome Analysis using a Graph-based Deep Model for Prediction of Non-Imaging Variables.bioRxiv : the preprint server for biology · 2025Article
- Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study.Scientific reports · 2025Article
- Using resting-state functional magnetic resonance imaging and contrastive learning to explore changes in the Parkinson's disease brain network and correlations with gait impairment.Quantitative imaging in medicine and surgery · 2025Article
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease.Graphs in biomedical image analysis : 6th International Workshop, GRAIL 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, Proceedings. GRAIL (Workshop) (6th : 2024 : Marrakesh, Morocco) · 2025Article
- Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.Biosensors · 2024Article
- Hybrid representation learning for cognitive diagnosis in late-life depression over 5 years with structural MRI.Medical image analysis · 2024Article
- Predicting an individual's functional connectivity from their structural connectome: Evaluation of evidence, recommendations, and future prospects.Network neuroscience (Cambridge, Mass.) · 2024Article
- covSTATIS: a multi-table technique for network neuroscience.Aperture neuro · 2024Article
- End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion.PloS one · 2024Article
- Graph Neural Networks for Parkinson's Disease Monitoring and Alerting.Sensors (Basel, Switzerland) · 2023Review
- A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants.Diagnostics (Basel, Switzerland) · 2023Article
- Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- Joint Graph Convolution for Analyzing Brain Structural and Functional Connectome.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Sparse Interpretation of Graph Convolutional Networks for Multi-Modal Diagnosis of Alzheimer's Disease.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's Disease (PD) is one of the most prevalent neurodegenerative diseases that affects tens of millions of Americans. PD is highly progressive and heterogeneous. Quite a few studies have been conducted in recent years on predictive or disease progression modeling of PD using clinical and biomarkers data. Neuroimaging, as another important information source for neurodegenerative disease, has also arisen considerable interests from the PD community. In this paper, we propose a deep learning method based on Graph Convolutional Networks (GCN) for fusing multiple modalities of brain images in relationship prediction which is useful for distinguishing PD cases from controls. On Parkinson's Progression Markers Initiative (PPMI) cohort, our approach achieved 0.9537±0.0587 AUC, compared with 0.6443±0.0223 AUC achieved by traditional approaches such as PCA.
Indexed as
Identifiers
30815157PMC6371363W2803300321What OpenQuestion holds
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.