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.
Highlights
Mild cognitive impairment (MCI) is a precursor phase of Alzheimer’s disease (AD)
As the semi-mechanism nature of Bayesian network can provide causal relationships of markers, this paper proposes a semi-mechanism method based on the combination of Bayesian network and lasso regression for the high performance of MCI-to-AD conversion prediction and improving our understanding the mechanism of the conversion
The dataset used in this study contains 518 biomarkers (328 magnetic resonance imaging (MRI) markers and 190 plasma markers). 45 biomarkers (1 MRI marker and 44 plasma markers) are deleted during data checking due to too many missing entries. 75 biomarkers (57 MRI markers and 18 plasma markers) with significant difference between converters and non-converters are identified by analysis of variance (ANOVA) test. 34 biomarkers (25 MRI markers and 9 plasma markers) related to Alzheimer’s disease assessment scale (ADAS-cog) are selected by lasso regression. 7 biomarkers (5 MRI markers and 2 plasma markers) are eliminated during Bayesian network structure learning because they fail to connect to the Bayesian network
Summary
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 Bayesian network based on selected markers is established to predict MCI-to-AD conversion. Blood sample is more accessible and suitable for repeated collecting These make plasma-based biomarkers promising for prediction of conversion from MCI to AD. Compared with the traditional data-driven machine learning methods, Bayesian network has unique advantages that it can quantify the causal relationships between the markers, visualize these relationships by the structure of network, and conduct the prediction task based on the causal relationships[14]. These attractive characteristics make Bayesian network a semi-mechanism method. Bayesian network is especially well-suited to handle the intricacies of the prediction because it is designed for representing stochastic events and conducting prediction tasks under uncertainty[16,17]
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