ArticleConnectomics 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)2017
Constructing Multi-frequency High-Order Functional Connectivity Network for Diagnosis of Mild Cognitive Impairment.
Article in 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), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 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
- A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks.Frontiers in neuroscience · 2021Article
- Multiple functional connectivity networks fusion for schizophrenia diagnosis.Medical & biological engineering & computing · 2020Article
- A toolbox for brain network construction and classification (BrainNetClass).Human brain mapping · 2020Article
- Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.Frontiers in neuroscience · 2020Article
- Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment.Neuroinformatics · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
Human brain functional connectivity (FC) networks, estimated based on resting-state functional magnetic resonance imaging (rs-fMRI), has become a promising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, characterizing more complex interactions among low-order FC sub-networks that involve multiple brain regions. However, both LON and HON are usually constructed within a fixed and relatively wide frequency band, which may fail in capturing (sensitive) frequency-specific FC changes caused by pathological attacks. To address this issue, we propose a novel "multi-frequency HON construction" method. Specifically, we construct
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