ArticleIEEE journal of translational engineering in health and medicine2018
Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis.
Article in IEEE journal of translational engineering in health and medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application value of Rs-fMRI-based machine learning models for differentiating mild cognitive impairment from Alzheimer's disease: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Pooled it
- Application of artificial intelligence in Alzheimer's disease: a bibliometric analysis.Frontiers in neuroscience · 2025Review
- 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
- U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis.Frontiers in computational neuroscience · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning.Alzheimer's research & therapy · 2023Article
- Individual-specific functional connectivity improves prediction of Alzheimer's disease's symptoms in elderly people regardless of APOE ε4 genotype.Communications biology · 2023Article
- Wavelet-Based Fractal Analysis of rs-fMRI for Classification of Alzheimer's Disease.Sensors (Basel, Switzerland) · 2022Article
- Diagnosis of Amnesic Mild Cognitive Impairment Using MGS-WBC and VGBN-LM Algorithms.Frontiers in aging neuroscience · 2022Article
- Single-scale time-dependent window-sizes in sliding-window dynamic functional connectivity analysis: A validation study.NeuroImage · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper proposes a robust method for the Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control subject classification under size limited fMRI data samples by exploiting the brain network connectivity pattern analysis. First, we select the regions of interest (ROIs) within the default mode network and calculate the correlation coefficients between all possible ROI pairs to form a feature vector for each subject. Second, we propose a regularized linear discriminant analysis (LDA) approach to reduce the noise effect due to the limited sample size. The feature vectors are then projected onto a one-dimensional axis using the proposed regularized LDA. Finally, an AdaBoost classifier is applied to carry out the classification task. The numerical analysis demonstrates that the purposed approach can increase the classification accuracy significantly. Our analysis confirms the previous findings that the hippocampus and the isthmus of the cingulate cortex are closely involved in the development of AD and MCI.
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Registered trials
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.