ArticleFrontiers in neuroscience2021
Diagnosis of Alzheimer's Disease Using Brain Network.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI.Biomedical engineering and computational biology · 2026Article
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Artificial Intelligence in Alzheimer's Disease Diagnosis and Prognosis Using PET-MRI: A Narrative Review of High-Impact Literature Post-Tauvid Approval.Journal of clinical medicine · 2025Review
- AI-Based Classification of Mild Cognitive Impairment and Cognitively Normal Patients.Journal of clinical medicine · 2025Article
- Topological Biomarkers of Alzheimer's Disease from Functional Brain Network Analysis.Current Alzheimer research · 2025Article
- A brain structure learning-guided multi-view graph representation learning for brain network analysis.Quantitative imaging in medicine and surgery · 2024Article
- Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.Brain sciences · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- Multivariate pattern analysis of medical imaging-based Alzheimer's disease.Frontiers in medicine · 2024Article
- Beta-informativeness-diffusion multilayer graph embedding for brain network analysis.Frontiers in neuroscience · 2024Article
- Resting-State Functional Connectivity Difference in Alzheimer's Disease and Mild Cognitive Impairment Using Threshold-Free Cluster Enhancement.Diagnostics (Basel, Switzerland) · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
- BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease.Frontiers in neuroscience · 2023Review
- A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer's Disease Study.Frontiers in aging neuroscience · 2022Article
- Classification and Interpretability of Mild Cognitive Impairment Based on Resting-State Functional Magnetic Resonance and Ensemble Learning.Computational intelligence and neuroscience · 2022Article
- Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI.Frontiers in aging neuroscience · 2022Article
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2 authors.
Funding
Abstract
Recent studies suggest the brain functional connectivity impairment is the early event occurred in case of Alzheimer's disease (AD) as well as mild cognitive impairment (MCI). We model the brain as a graph based network to study these impairment. In this paper, we present a new diagnosis approach using graph theory based features from functional magnetic resonance (fMR) images to discriminate AD, MCI, and healthy control (HC) subjects using different classification techniques. These techniques include linear support vector machine (LSVM), and regularized extreme learning machine (RELM). We used pairwise Pearson's correlation-based functional connectivity to construct the brain network. We compare the classification performance of brain network using Alzheimer's disease neuroimaging initiative (ADNI) datasets. Node2vec graph embedding approach is employed to convert graph features to feature vectors. Experimental results show that the SVM with LASSO feature selection method generates better classification accuracy compared to other classification technique.
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