ArticleBMC neurology2020
Predicting MCI progression with FDG-PET and cognitive scores: a longitudinal study.
Article in BMC neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Diagnostic performance of molecular imaging methods in predicting the progression from mild cognitive impairment to dementia: an updated systematic review.European journal of nuclear medicine and molecular imaging · 2024Pooled it
- A real-world decision tree model based on ¹⁸F-FDG PET/CT Z scores and Mini-Mental State Examination for differentiating mild cognitive impairment from clinically diagnosed Alzheimer disease.Annals of nuclear medicine · 2026Article
- Moral decision-making in real and sacrificial contexts: a pilot study on social cognition in mild cognitive impairment.BMC psychology · 2026Article
- Longitudinal multi-modal data prediction model for mild cognitive impairment by deep survival analysis.BMC medical informatics and decision making · 2026Article
- Integrating imaging and omics for enhanced subtyping of mild cognitive impairment associated with Alzheimer's disease.Journal of translational medicine · 2025Article
- Federated learning with multi-cohort real-world data for predicting the progression from mild cognitive impairment to Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- The Hidden Dangers of Sedentary Living: Insights into Molecular, Cellular, and Systemic Mechanisms.International journal of molecular sciences · 2024Review
- EEG-Based Detection of Mild Cognitive Impairment Using DWT-Based Features and Optimization Methods.Diagnostics (Basel, Switzerland) · 2024Article
- Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy.Scientific reports · 2024Article
- Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection.Brain sciences · 2024Article
- Cognitive, Emotional, and Daily Functioning Domains Involved in Decision-Making among Patients with Mild Cognitive Impairment: A Systematic Review.Brain sciences · 2024Review
- Predicting conversion from mild cognitive impairment to Alzheimer's disease: a multimodal approach.Brain communications · 2024Article
- Article
- AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease.Journal of biomedical informatics · 2023Article
- Differential Diagnosis of Alzheimer Disease vs. Mild Cognitive Impairment Based on Left Temporal Lateral Lobe Hypomethabolism onDiagnostics (Basel, Switzerland) · 2022Article
- A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers.International journal of environmental research and public health · 2022Article
- Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models.Frontiers in aging neuroscience · 2022Article
- Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.Computational intelligence and neuroscience · 2021Article
- Neuroimaging statistical approaches for determining neural correlates of Alzheimer's disease via positron emission tomography imaging.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundMild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Studies on MCI progression are important for Alzheimer's disease (AD) prevention. 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) has been proven to be a powerful tool for measuring cerebral glucose metabolism. In this study, we proposed a classification framework for MCI prediction with both baseline and multiple follow-up FDG-PET scans as well as cognitive scores of 33 progressive MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
methodFirst, PET images were normalized using the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA). The average metabolic intensities of brain regions were defined as static features. Dynamic features were the intensity variation between baseline and the other three time points and change ratios with the intensity obtained at baseline considered as reference. Mini-mental State Examination (MMSE) scores and Alzheimer's disease Assessment Scale-Cognitive section (ADAS-cog) scores of each time point were collected as cognitive features. And F-score was applied for feature selection. Finally, support vector machine (SVM) with radial basis function (RBF) kernel was used for the three above features.
resultsDynamic features showed the best classification performance in accuracy of 88.61% than static features (accuracy of 78.48%). And the combination of cognitive features and dynamic features improved the classification performance in specificity of 95.65% and Area Under Curve (AUC) of 0.9308.
conclusionOur results reported that dynamic features are more representative in longitudinal research for MCI prediction work. And dynamic features and cognitive scores complementarily enhance the classification performance in specificity and AUC. These findings may predict the disease course and clinical changes in individuals with mild cognitive impairment.
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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.