ArticleFrontiers in computational neuroscience2018
Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls With Subnetwork Selection and Graph Kernel Principal Component Analysis Based on Minimum Spanning Tree Brain Functional Network.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Balanced deep learning on multi-omics networks identifies molecular subgroups of pathological brain aging.medRxiv : the preprint server for health sciences · 2026Article
- A group based network analysis for Alzheimer's disease fMRI data.Scientific reports · 2025Article
- Article
- Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Accumulation of network redundancy marks the early stage of Alzheimer's disease.Human brain mapping · 2023Article
- BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease.Frontiers in neuroscience · 2023Review
- Alterations of dynamic redundancy of functional brain subnetworks in Alzheimer's disease and major depression disorders.NeuroImage. Clinical · 2022Article
- Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network.Frontiers in neuroscience · 2022Article
- Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment.Interdisciplinary sciences, computational life sciences · 2021Article
- Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment.Frontiers in neuroscience · 2020Article
- Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography.Alzheimer's & dementia (Amsterdam, Netherlands) · 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
- Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease.NeuroImage. Clinical · 2019Article
- Analysis of Progression Toward Alzheimer's Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster.Frontiers in neuroscience · 2018Article
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7 authors.
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Abstract
Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its early stage (mild cognitive impairment, MCI), has attracted more and more attention recently. Researchers have constructed threshold brain function networks and extracted various features for the classification of brain diseases. However, in the construction of the brain function network, the selection of threshold is very important, and the unreasonable setting will seriously affect the final classification results. To address this issue, in this paper, we propose a minimum spanning tree (MST) classification framework to identify Alzheimer's disease (AD), MCI, and normal controls (NCs). The proposed method mainly uses the MST method, graph-based Substructure Pattern mining (gSpan), and graph kernel Principal Component Analysis (graph kernel PCA). Specifically, MST is used to construct the brain functional connectivity network; gSpan, to extract features; and subnetwork selection and graph kernel PCA, to select features. Finally, the support vector machine is used to perform classification. We evaluate our method on MST brain functional networks of 21 AD, 25 MCI, and 22 NC subjects. The experimental results show that our proposed method achieves classification accuracy of 98.3, 91.3, and 77.3%, for MCI vs. NC, AD vs. NC, and AD vs. MCI, respectively. The results show our proposed method can achieve significantly improved classification performance compared to other state-of-the-art methods.
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