ArticlePattern recognition2019
Weighted Graph Regularized Sparse Brain Network Construction for MCI Identification.
Article in Pattern recognition, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 72 citations in OpenAlex.
- Group Sparse Representation Enhances Brain Network Classification of Major Depressive Disorder in Two Chinese Cohorts.Alpha psychiatry · 2026Article
- Brain Connectivity Estimation Network for the Identification of Dementia.Brain sciences · 2025Article
- Multi-Modal Diagnosis of Alzheimer's Disease Using Interpretable Graph Convolutional Networks.IEEE transactions on medical imaging · 2025Article
- Sparsity-guided multiple functional connectivity patterns for classification of schizophrenia via convolutional network.Human brain mapping · 2023Article
- Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis.Computer modeling in engineering & sciences : CMES · 2023Article
- BrainGB: A Benchmark for Brain Network Analysis With Graph Neural Networks.IEEE transactions on medical imaging · 2023Article
- A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer's disease.Human brain mapping · 2022Article
- Temporal-spatial dynamic functional connectivity analysis in schizophrenia classification.Frontiers in neuroscience · 2022Article
- Topological clustering of multilayer networks.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI.Frontiers in neuroinformatics · 2021Article
- Multimodal Brain Network Jointly Construction and Fusion for Diagnosis of Epilepsy.Frontiers in neuroscience · 2021Article
- Effect of APOE ε4 on multimodal brain connectomic traits: a persistent homology study.BMC bioinformatics · 2020Article
- [Research on brain network for schizophrenia classification based on resting-state functional magnetic resonance imaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification.Frontiers in aging neuroscience · 2020Article
- Causality-Preserving Generative Adversarial Networks for the Diagnosis of Neurological Disorders.IET systems biologyArticle
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
Brain functional networks (BFNs) constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely applied to the analysis and diagnosis of brain diseases, such as Alzheimer's disease and its prodrome, namely mild cognitive impairment (MCI). Constructing a meaningful brain network based on, for example, sparse representation (SR) is the most essential step prior to the subsequent analysis or disease identification. However, the independent coding process of SR fails to capture the intrinsic locality and similarity characteristics in the data. To address this problem, we propose a novel weighted graph (Laplacian) regularized SR framework, based on which BFN can be optimized by considering both intrinsic correlation similarity and local manifold structure in the data, as well as sparsity prior of the brain connectivity. Additionally, the non-convergence of the graph Laplacian in the self-representation model has been solved properly. Combined with a pipeline of sparse feature selection and classification, the effectiveness of our proposed method is demonstrated by identifying MCI based on the constructed BFNs.
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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.