ArticleFrontiers in neuroscience2017
Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Predicting the Conversion From Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-based Features.Basic and clinical neuroscience · 2025Article
- Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia.Scientific reports · 2024Article
- Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.Cognitive neurodynamics · 2023Article
- 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
- Neuroimaging Modalities in Alzheimer's Disease: Diagnosis and Clinical Features.International journal of molecular sciences · 2022Review
- High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder.Frontiers in neuroscience · 2022Article
- Brain Function Network: Higher Order vs. More Discrimination.Frontiers in neuroscience · 2021Article
- Article
- Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks.Frontiers in neuroscience · 2020Article
- Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities.Human brain mapping · 2019Article
- Clustering of Brain Function Network Based on Attribute and Structural Information and Its Application in Brain Diseases.Frontiers in neuroinformatics · 2019Article
- Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia.Frontiers in neuroscience · 2019Article
- Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings.Frontiers in neuroscience · 2019Review
- Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification.Computational and mathematical methods in medicine · 2019Article
- Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine.Molecules (Basel, Switzerland) · 2018Review
- Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification.Frontiers in neuroscience · 2018Article
- Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment.Frontiers in neuroscience · 2018Article
- Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification.Frontiers in neuroinformatics · 2018Article
- Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging.Frontiers in neuroscience · 2018Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease.
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