ArticlePloS one2022
Alzheimer disease stages identification based on correlation transfer function system using resting-state functional magnetic resonance imaging.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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14 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
- Associations of visceral adipose tissue metabolism with DMN-centered cross-network metabolic expression concordance and cognitive impairment in Alzheimer's disease.European journal of nuclear medicine and molecular imaging · 2026Article
- Behavioral deficits and exacerbated neural and hemodynamic odor responses during lifespan of a mouse model of late onset Alzheimer's disease expressing humanized APOEε4 and Trem2*R47H.Frontiers in aging neuroscience · 2026Article
- Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI.Biomedical engineering and computational biology · 2026Article
- Frontal, temporal, cerebellar changes link to sepsis survivors' cognitive issues: A resting state functional magnetic resonance imaging study.World journal of psychiatry · 2025Article
- Classification method based on surf and sift features for alzheimer diagnosis using diffusion tensor magnetic resonance imaging.Scientific reports · 2025Article
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- fMRI-based Alzheimer's disease detection via functional connectivity analysis: a systematic review.PeerJ. Computer science · 2024Article
- Article
- Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data.Scientific reports · 2023Article
- 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
- Hierarchical based classification method based on fusion of Gaussian map descriptors for Alzheimer diagnosis using TScientific reports · 2023Article
- Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.Journal of healthcare engineering · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
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3 authors.
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Abstract
Alzheimer's disease (AD) affects the quality of life as it causes; memory loss, difficulty in thinking, learning, and performing familiar tasks. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate and analyze different brain regions for AD identification. This study investigates the effectiveness of using correlated transfer function (CorrTF) as a new biomarker to extract the essential features from rs-fMRI, along with support vector machine (SVM) ordered hierarchically, in order to distinguish between the different AD stages. Additionally, we explored the regions, showing significant changes based on the CorrTF extracted features' strength among different AD stages. First, the process was initialized by applying the preprocessing on rs-fMRI data samples to reduce noise and retain the essential information. Then, the automated anatomical labeling (AAL) atlas was employed to divide the brain into 116 regions, where the intensity time series was calculated, and the CorrTF features were extracted for each region. The proposed framework employed the SVM classifier in two different methodologies, hierarchical and flat multi-classification schemes, to differentiate between the different AD stages for early detection purposes. The ADNI rs-fMRI dataset, employed in this study, consists of 167, 102, 129, and 114 normal, early, late mild cognitive impairment (MCI), and AD subjects, respectively. The proposed schemes achieved an average accuracy of 98.2% and 95.5% for hierarchical and flat multi-classification tasks, respectively, calculated using ten folds cross-validation. Therefore, CorrTF is considered a promising biomarker for AD early-stage identification. Moreover, the significant changes in the strengths of CorrTF connections among the different AD stages can help us identify and explore the affected brain regions and their latent associations during the progression of AD.
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