ArticleJournal of neurology2020
A novel conversion prediction method of MCI to AD based on longitudinal dynamic morphological features using ADNI structural MRIs.
Article in Journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- White matter alterations in mild cognitive impairment revealed by meta-analysis of diffusion tensor imaging using tract-based spatial statistics.Brain imaging and behavior · 2023Pooled it
- WGTMM: WGAN with Transformer Feature Matching for Generating fMRI Data in MCI Patients.Brain sciences · 2026Article
- Multivariate pattern analysis of medical imaging-based Alzheimer's disease.Frontiers in medicine · 2024Article
- Brain Anatomy-Guided MRI Analysis for Assessing Clinical Progression of Cognitive Impairment with Structural MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network.Brain topography · 2023Article
- Classifying Alzheimer's disease and frontotemporal dementia using machine learning with cross-sectional and longitudinal magnetic resonance imaging data.Human brain mapping · 2023Article
- Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- A Machine Learning Classifier for Predicting Stable MCI Patients Using Gene Biomarkers.International journal of environmental research and public health · 2022Article
- The impact of Alzheimer's disease susceptibility loci on lateral ventricular surface morphology in older adults.Brain structure & function · 2022Article
- An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer's Disease.Sensors (Basel, Switzerland) · 2021Article
- Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
Mild cognitive impairment (MCI) is a pre-existing state of Alzheimer's disease (AD). An accurate prediction on the conversion from MCI to AD is of vital clinical significance for potential prevention and treatment of AD. Longitudinal studies received widespread attention for investigating the disease progression, though most studies did not sufficiently utilize the evolution information. In this paper, we proposed a cerebral similarity network with more progression information to predict the conversion from MCI to AD efficiently. First, we defined the new dynamic morphological feature to mine longitudinal information sufficiently. Second, based on the multiple dynamic morphological features the cerebral similarity network was constructed by sparse regression algorithm with optimized parameters to obtain better prediction performance. Then, leave-one-out cross-validation and support vector machine (SVM) were employed for the training and evaluation of the classifiers. The proposed methodology obtained a high accuracy of 92.31% (Sensitivity = 100%, Specificity = 82.86%) in a three-year ahead prediction of MCI to AD conversion. Experiment results suggest the effectiveness of the dynamic morphological feature, serving as a more sensitive biomarker in the prediction of MCI conversion.
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Registered trials
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