ArticleFrontiers in neuroscience2018
Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 22 citations in OpenAlex.
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- Estimating high-order brain functional networks by correlation-preserving embedding.Medical & biological engineering & computing · 2022Article
- Multi-View Feature Enhancement Based on Self-Attention Mechanism Graph Convolutional Network for Autism Spectrum Disorder Diagnosis.Frontiers in human neuroscience · 2022Article
- Estimating High-Order Brain Functional Networks in Bayesian View for Autism Spectrum Disorder Identification.Frontiers in neuroscience · 2022Article
- Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis.Frontiers in neuroscience · 2022Article
- Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.IEEE transactions on medical imaging · 2021Article
- A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks.Frontiers in neuroscience · 2021Article
- Hierarchical Synchronization Estimation of Low- and High-Order Functional Connectivity Based on Sub-Network Division for the Diagnosis of Autism Spectrum Disorder.Frontiers in neuroscience · 2021Article
- Modularity-Guided Functional Brain Network Analysis for Early-Stage Dementia Identification.Frontiers in neuroscience · 2021Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.
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