ArticleNeuroinformatics2017
Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification.
Article in Neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Multi-Scale Structural MRI Features Reveal Task-Based Functional Connectivity and Its Alterations in Psychiatric Disorders: A Collaborative Graph Attention Network Approach.Brain topography · 2026Article
- Topological disruption of low- and high-order functional networks in presbycusis.Brain communications · 2024Article
- Classification of female MDD patients with and without suicidal ideation using resting-state functional magnetic resonance imaging and machine learning.Frontiers in human neuroscience · 2024Article
- Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders.Frontiers in neuroscience · 2023Article
- Estimating high-order brain functional networks by correlation-preserving embedding.Medical & biological engineering & computing · 2022Article
- Multi-Modal Feature Selection with Feature Correlation and Feature Structure Fusion for MCI and AD Classification.Brain sciences · 2022Article
- Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.Human brain mapping · 2021Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification.Frontiers in cell and developmental biology · 2020Article
- Treatment-naïve first episode depression classification based on high-order brain functional network.Journal of affective disorders · 2019Article
- Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.NeuroImage · 2019Review
- Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment.Frontiers in neuroinformatics · 2018Article
- Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification.Frontiers in neuroscience · 2018Article
- Inter-subject Similarity Guided Brain Network Modeling for MCI Diagnosis.Machine learning in medical imaging. MLMI (Workshop) · 2017Article
- Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment.Connectomics in neuroimaging : First International Workshop, CNI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, proceedings. CNI (Workshop) (1st : 2017 : Quebec, Quebec) · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Conventional Functional connectivity (FC) analysis focuses on characterizing the correlation between two brain regions, whereas the high-order FC can model the correlation between two brain region pairs. To reduce the number of brain region pairs, clustering is applied to group all the brain region pairs into a small number of clusters. Then, a high-order FC network can be constructed based on the clustering result. By varying the number of clusters, multiple high-order FC networks can be generated and the one with the best overall performance can be finally selected. However, the important information contained in other networks may be simply discarded. To address this issue, in this paper, we propose to make full use of the information contained in all high-order FC networks. First, an agglomerative hierarchical clustering technique is applied such that the clustering result in one layer always depends on the previous layer, thus making the high-order FC networks in the two consecutive layers highly correlated. As a result, the features extracted from high-order FC network in each layer can be decomposed into two parts (blocks), i.e., one is redundant while the other might be informative or complementary, with respect to its previous layer. Then, a selective feature fusion method, which combines sequential forward selection and sparse regression, is developed to select a feature set from those informative feature blocks for classification. Experimental results confirm that our novel method outperforms the best single high-order FC network in diagnosis of mild cognitive impairment (MCI) subjects.
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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.