ArticleMedical image analysis2021
Brain functional connectivity analysis based on multi-graph fusion.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2023Pooled it
- Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data.IEEE journal of biomedical and health informatics · 2025Article
- Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI.IEEE transactions on bio-medical engineering · 2024Article
- Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification.Biology · 2023Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.PloS one · 2023Article
- Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.Information processing & management · 2022Article
- Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis.Frontiers in human neuroscience · 2022Article
- Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e., a fully-connected FCN and a 1 nearest neighbor (1NN) FCN, whereas previous methods only focus on conducting FCN analysis from a single FCN. Specifically, our framework first conducts the graph fusion to produce the representation of the rs-fMRI data with high discriminative ability, and then employs the L1SVM to jointly conduct brain region selection and disease diagnosis. We further evaluate the effectiveness of the proposed framework on various data sets of the neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimers Disease (AD). The experimental results demonstrate that the proposed framework achieves the best diagnosis performance via selecting reasonable brain regions for the classification tasks, compared to state-of-the-art FCN analysis methods.
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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.