ArticleFrontiers in cell and developmental biology2020
Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Characterizing Topological Properties of Brain Functional Networks Using Multi-Threshold Derivative for End-Stage Renal Disease with Mild Cognitive Impairment.Brain sciences · 2023Article
- GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease.Frontiers in aging neuroscience · 2022Article
- Mathematical model and genomics construction of developmental biology patterns using digital image technology.Frontiers in genetics · 2022Article
- Constructing Dynamic Brain Functional Networks via Hyper-Graph Manifold Regularization for Mild Cognitive Impairment Classification.Frontiers in neuroscience · 2021Article
- Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators.Contrast media & molecular imaging · 2021Article
- BCNet: A Novel Network for Blood Cell Classification.Frontiers in cell and developmental biology · 2021Article
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5 authors.
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Abstract
Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the
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