ArticleScientific reports2017
Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 2 of them syntheses that pooled it.
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Who cites it
47 citing papers in PubMed, 2 syntheses or guidelines pooled it, 144 citations in OpenAlex.
- Somatosensory Deficits After Stroke: Insights From MRI Studies.Frontiers in neurology · 2022Pooled it
- Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review.Human brain mapping · 2021Pooled it
- A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.Scientific reports · 2021Trial
- An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.Neuroimage. Reports · 2026Article
- Alterations in Multidimensional Functional Connectivity Architecture in Preschool Children with Autism Spectrum Disorder.Brain sciences · 2026Article
- Higher-order statistics for constructing centered edge functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- Spatiotemporal evaluation of atmospheric COCarbon balance and management · 2025Article
- A group based network analysis for Alzheimer's disease fMRI data.Scientific reports · 2025Article
- Unraveling Integration-Segregation Imbalances in Schizophrenia Through Topological High-Order Functional Connectivity.Neuroinformatics · 2025Article
- Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification.Interdisciplinary sciences, computational life sciences · 2024Article
- Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.CNS neuroscience & therapeutics · 2023Article
- Intelligent diagnosis of major depression disease based on multi-layer brain network.Frontiers in neuroscience · 2023Article
- Brain effective connectivity and functional connectivity as markers of lifespan vascular exposures in middle-aged adults: The Bogalusa Heart Study.Frontiers in aging neuroscience · 2023Article
- A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder.PloS one · 2023Article
- Multiscale functional connectome abnormality predicts cognitive outcomes in subcortical ischemic vascular disease.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Interpreting models interpreting brain dynamics.Scientific reports · 2022Article
- Individual-specific networks for prediction modelling - A scoping review of methods.BMC medical research methodology · 2022Article
- Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series.Frontiers in computational neuroscience · 2022Article
- Exploring high-order correlations with deep-broad learning for autism spectrum disorder diagnosis.Frontiers in neuroscience · 2022Article
- Altered striatal functional connectivity and structural dysconnectivity in individuals with bipolar disorder: A resting state magnetic resonance imaging study.Frontiers in psychiatry · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Conventional functional connectivity (FC), referred to as low-order FC, estimates temporal correlation of the resting-state functional magnetic resonance imaging (rs-fMRI) time series between any pair of brain regions, simply ignoring the potentially high-level relationship among these brain regions. A high-order FC based on "correlation's correlation" has emerged as a new approach for abnormality detection of brain disease. However, separate construction of the low- and high-order FC networks overlooks information exchange between the two FC levels. Such a higher-level relationship could be more important for brain diseases study. In this paper, we propose a novel framework, namely "hybrid high-order FC networks" by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis. For each sliding window-based rs-fMRI sub-series, we construct a whole-brain associated high-order network, by estimating the correlations between the topographical information of the high-order FC sub-network from one brain region and that of the low-order FC sub-network from another brain region. With multi-kernel learning, complementary features from multiple time-varying FC networks constructed at different levels are fused for eMCI classification. Compared with other state-of-the-art methods, the proposed framework achieves superior diagnosis accuracy, and hence could be promising for understanding pathological changes of brain connectome.
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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.