ArticleBMC medical imaging2024
Predicting Alzheimer's progression in MCI: a DTI-based white matter network model.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.BMC medical imaging · 2026Article
- Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.Frontiers in neurology · 2025Review
- Cerebellar MRI-based radiomics models for identifying mild cognitive impairment: a retrospective multicenter study in Southeast China.Frontiers in aging neuroscience · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
objectiveThis study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients.
methodsThis study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation.
resultsAPOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers.
conclusionsA comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.
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