Dynamic Functional Connectivity and Graph Convolution Network for Alzheimer's Disease Classification
Open accessAbstract
Alzheimer's disease (AD) is the most prevalent form of dementia. Traditional methods cannot achieve efficient and accurate diagnosis of AD. This paper introduces a novel method based on dynamic functional connectivity (dFC) that can effectively capture changes in the brain. We compare and combine four different types of features including amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dFC and the adjacency matrix of different brain structures between subjects. We use graph convolution network (GCN) which consider the similarity of brain structure between patients to solve the classification problem of non-Euclidean domains. The proposed method's accuracy and the area under the receiver operating characteristic curve achieved 91.3% and 98.4%. This result demonstrated that our proposed method can be used for detecting AD.
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PPI networks combined with genomic features can predict drug sensitivity using GNNs.
“In other instances, PPI networks are combined with genomic features to predict drug sensitivity using GNNs [181].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [181]
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Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294 · see citing works
“GNNs have been used to classify the progression of Alzheimer’s disease from magnetic resonance images that are converted into graphs 181–183 .”
- Authors
- An, X., Zhou, Y., Di, Y., & Ming, D.
- Affiliations
- Tianjin University (CN)
- Venue
- 2020 7th International Conference on Biomedical and Bioinformatics Engineering
- Publisher
- ACM
- Published
- 2020-11-06
- Pages
- 1-4
- OpenAlex
- W3149479187
- Cited by
- 15
- References
- 10
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 2
- FWCI
- 1.95
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International Conference on Biomedical and Bioinformatics Engineering (ICBBE) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Key Research & Development Program of China (No.2017YFB1300302)
- National Natural Science Foundation of China (No.81630051, No.61603269)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- Alzheimer's disease classification, dynamic functional connectivity, graph convolutional networks, amplitude of low-frequency fluctuation, regional homogeneity, non-Euclidean data, dementia diagnosis, brain network features
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