ArticleScientific reports2016
A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer's disease.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross-Platform Validation.Annals of clinical and translational neurology · 2026Article
- The Role of Inflammatory Biomarkers as a Diagnostic Tool for Possible Late-Life Cognitive Decline and Dementias.Advances in experimental medicine and biology · 2021Article
- Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer's disease: protocol for a rapid systematic review.Systematic reviews · 2020Article
- Article
- A hybrid computational approach for efficient Alzheimer's disease classification based on heterogeneous data.Scientific reports · 2018Article
- Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis.Scientific reports · 2017Article
- Application of Concordance Probability Estimate to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease.Biostatistics & epidemiology · 2017Article
- Toward Personalized Network Biomarkers in Alzheimer's Disease: Computing Individualized Genomic and Protein Crosstalk Maps.Frontiers in aging neuroscience · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
Mild cognitive impairment (MCI) is a precursor phase of Alzheimer's disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.
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