ArticleFrontiers in neuroscience2020
Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Functional and effective EEG connectivity patterns in Alzheimer's disease and mild cognitive impairment: a systematic review.Frontiers in aging neuroscience · 2025Pooled it
- Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review.Frontiers in aging neuroscience · 2023Pooled it
- An interpretable deep learning diagnostic framework for early Alzheimer's disease based on EEG microstate spectra and multi-branch CNN.Journal of neuroengineering and rehabilitation · 2026Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- A Novel Working Memory Task-Induced EEG Response (WM-TIER) Feature Extraction Framework for Detecting Alzheimer's Disease and Mild Cognitive Impairment.Biosensors · 2025Article
- A group based network analysis for Alzheimer's disease fMRI data.Scientific reports · 2025Article
- Spectral Biomarkers of Functional Brain Network Alteration in Alzheimer's Disease.Current Alzheimer research · 2025Observational
- The spatial distribution of coupling between tau and neurodegeneration in amyloid-β positive mild cognitive impairment.Neurobiology of aging · 2024Article
- Dynamic multilayer networks reveal mind wandering.Frontiers in neuroscience · 2024Article
- Brain network correlates of affective symptoms in aMCI.Aging brain · 2024Article
- Age-dependent changes in the dynamic functional organization of the brain at rest: a cross-cultural replication approach.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine.Frontiers in neuroscience · 2023Article
- Multiplex Connectome Changes across the Alzheimer's Disease Spectrum Using Gray Matter and Amyloid Data.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Early development of sleep and brain functional connectivity in term-born and preterm infants.Pediatric research · 2022Review
- Longitudinal Changes in Hippocampal Network Connectivity in Alzheimer's Disease.Annals of neurology · 2021Article
- Dopamine Transporter Is a Master Regulator of Dopaminergic Neural Network Connectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators.Contrast media & molecular imaging · 2021Article
- Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis.Frontiers in aging neuroscience · 2021Article
- Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach.Frontiers in neuroscience · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.
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