ArticleFrontiers in aging neuroscience2022
Investigation of Underlying Association Between Whole Brain Regions and Alzheimer's Disease: A Research Based on an Artificial Intelligence Model.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.Aging clinical and experimental research · 2023Pooled it
- Neurite orientation dispersion and density imaging identifies microstructural alterations in patients with cerebral microbleeds and correlations with cognitive impairment.BMC neurology · 2026Observational
- Nano-magnolol enhances the modulatory effects of magnolol on cognitive performance and BACE1-related biochemical changes in an STZ-induced rat model of Alzheimer's disease.Discover nano · 2026Article
- Phytochemical based on nanoparticles for neurodegenerative alzheimer disease management: Update review.Discover nano · 2025Review
- Early-stage diagnosis of HIV-associated neurocognitive disorders via multiple learning models based on resting-state functional magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2025Article
- Deep Learning-Based Pediatric Brain Region Segmentation and Volumetric Analysis for General Growth Pattern in Healthy Children.Journal of imaging informatics in medicine · 2025Article
- A new method for community-based intelligent screening of early Alzheimer's disease populations based on digital biomarkers of the writing process.Frontiers in computational neuroscience · 2025Article
- Assessing the impact of posture on brain volume in healthy subjects with a rotatable cryogen-free 1.5T superconducting MRI.Frontiers in neuroscience · 2025Article
- Peak width of skeletonized mean diffusivity mediates the relationship between cerebral small vessel disease burden and cognitive impairment in community-dwelling older adults.Brain communications · 2025Article
- Structural disruption in subjective cognitive decline and mild cognitive impairment.Brain imaging and behavior · 2024Article
- Correlations between cognitive reserve, gray matter, and cerebrospinal fluid volume in healthy elders and mild cognitive impairment patients.Frontiers in neurology · 2024Article
- Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer's Disease: A Systematic Review of Studies in the Field.Journal of clinical medicine · 2023Review
- Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI.Frontiers in medicine · 2023Article
- uRP: An integrated research platform for one-stop analysis of medical images.Frontiers in radiology · 2023Article
- A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction.Frontiers in aging neuroscience · 2022Article
- Magnetic resonance radiomics-based deep learning model for diagnosis of Alzheimer's disease.Digital healthArticle
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.
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