Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model - Liu, Ke; Chen, Kewei; Yao, Li; Guo, Xiaojuan (Frontiers in Human Neuroscience, 2017)
Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of Independent Component Analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters (MCI-c) and 108 MCI non-converters (MCI-nc) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (structural MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI (hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E-07 0.013)), low glucose metabolism in the posterior default mode network based on FDG-PET (HR = 0.066 (95% CI, 4.63E-03 0.928)), positive Apolipoprotein E (APOE) ε4-status (HR = 1. 988 (95% CI, 1.531 2.581)), increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores (HR = 1.100 (95% CI, 1.059 1.144)) and Sum of Boxes of Clinical Dementia Rating scores (HR = 1.622 (95% CI, 1.364 1.930)) were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction ability (accuracy = 84.62%, sensitivity = 86.51%, specificity = 82.41%) compared to either neuroimaging factors or clinical variables alone. These results suggested that a combination of ICA and Cox model analyses could be used successfully in survival analysis and provide a network-based perspective of MCI progression or AD-related studies.