ArticleHuman brain mapping2016
High-order resting-state functional connectivity network for MCI classification.
Article in Human brain mapping, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 1 of them a synthesis that pooled it.
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Who cites it
103 citing papers in PubMed, 1 synthesis or guideline pooled it, 236 citations in OpenAlex.
- Research trend of functional magnetic resonance imaging in diabetes mellitus research: a visualization and bibliometric analysis.Frontiers in neurologyPooled it
- Inter-individual differences in baseline dynamic functional connectivity are linked to cognitive aftereffects of tDCS.Scientific reports · 2022Trial
- Spatiotemporal complexity in the psychotic brain.Molecular psychiatry · 2026Article
- Higher-order statistics for constructing centered edge functional connectivity.Network neuroscience (Cambridge, Mass.) · 2026Article
- MHNet: Multi-view High-Order Network for Diagnosing Neurodevelopmental Disorders Using Resting-State fMRI.Journal of imaging informatics in medicine · 2025Article
- Brain Connectivity Estimation Network for the Identification of Dementia.Brain sciences · 2025Article
- Brain overlapping system-level architecture influenced by external magnetic stimulation and internal gene expression in AD-spectrum patients.Molecular psychiatry · 2025Article
- Analysis of dynamic network reconfiguration in HIV patients with cognitive impairment based multilayer network.Scientific reports · 2025Article
- Dynamically weighted graph neural network for detection of early mild cognitive impairment.PloS one · 2025Article
- Abnormal stability of dynamic functional architecture in drug-naïve children with attention-deficit/hyperactivity disorder.BMC psychiatry · 2024Article
- Dynamic multilayer functional connectivity detects preclinical and clinical Alzheimer's disease.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Normalized group activations based feature extraction technique using heterogeneous data for Alzheimer's disease classification.PeerJ. Computer science · 2024Article
- Topological disruption of low- and high-order functional networks in presbycusis.Brain communications · 2024Article
- Temporal Dynamic Alterations of Regional Homogeneity in Parkinson's Disease: A Resting-State fMRI Study.Biomolecules · 2023Article
- Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity.Scientific reports · 2023Article
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.CNS neuroscience & therapeutics · 2023Article
- Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification.Journal of personalized medicine · 2023Article
- State-switching and high-order spatiotemporal organization of dynamic functional connectivity are disrupted by Alzheimer's disease.Network neuroscience (Cambridge, Mass.) · 2023Article
- Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.Heliyon · 2022Article
43 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors at 1 institution in 2 countries.
Funding
Abstract
Brain functional connectivity (FC) network, estimated with resting-state functional magnetic resonance imaging (RS-fMRI) technique, has emerged as a promising approach for accurate diagnosis of neurodegenerative diseases. However, the conventional FC network is essentially low-order in the sense that only the correlations among brain regions (in terms of RS-fMRI time series) are taken into account. The features derived from this type of brain network may fail to serve as an effective disease biomarker. To overcome this drawback, we propose extraction of novel high-order FC correlations that characterize how the low-order correlations between different pairs of brain regions interact with each other. Specifically, for each brain region, a sliding window approach is first performed over the entire RS-fMRI time series to generate multiple short overlapping segments. For each segment, a low-order FC network is constructed, measuring the short-term correlation between brain regions. These low-order networks (obtained from all segments) describe the dynamics of short-term FC along the time, thus also forming the correlation time series for every pair of brain regions. To overcome the curse of dimensionality, we further group the correlation time series into a small number of different clusters according to their intrinsic common patterns. Then, the correlation between the respective mean correlation time series of different clusters is calculated to represent the high-order correlation among different pairs of brain regions. Finally, we design a pattern classifier, by combining features of both low-order and high-order FC networks. Experimental results verify the effectiveness of the high-order FC network on disease diagnosis. Hum Brain Mapp 37:3282-3296, 2016. © 2016 Wiley Periodicals, Inc.
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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.