Liu Ke, Chen Kewei, Yao Li, Guo Xiaojuan
College of Information Science and Technology, Beijing Normal University Beijing, China.
Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix AZ, USA.
Front Hum Neurosci. 2017 Feb 6;11:33. doi: 10.3389/fnhum.2017.00033. eCollection 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 and 108 MCI non-converters from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (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 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.
轻度认知障碍（MCI）是从正常衰老到阿尔茨海默病（AD）的过渡阶段，且发展为AD的风险更高。因此，有必要探索和预测MCI阶段AD的发病情况。在本研究中，我们提出将独立成分分析（ICA）与多变量Cox比例风险回归模型相结合，以研究来自阿尔茨海默病神经影像倡议（ADNI）数据库的126例MCI转化者和108例MCI非转化者中与MCI转化相关的潜在风险因素。利用结构磁共振成像（MRI）和氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）数据，我们通过ICA从AD组和正常对照组中提取脑网络，然后构建包含MCI组基于网络的神经影像因素的Cox模型。我们进行了五项独立的Cox分析，双模态神经影像Cox模型识别出三个基于网络的显著风险因素，其预测性能（准确率=73.50%）高于单模态模型（准确率=68.80%）。此外，综合Cox模型的结果，包括显著的神经影像因素和临床变量，表明在基线后36个月内，结构MRI颞叶相关网络灰质体积减少的MCI个体[风险比（HR）=8.29E-05（95%置信区间（CI），5.10E-070.013）]、基于FDG-PET的后默认模式网络葡萄糖代谢低的个体[HR=0.066（95%CI，4.63E-030.928）]、载脂蛋白Eε4状态为阳性的个体[HR=1.988（95%CI，1.5312.581）]、阿尔茨海默病评估量表-认知子量表得分增加的个体[HR=1.100（95%CI，1.0591.144）]以及临床痴呆评定量表方框总和得分增加的个体[HR=1.622（95%CI，1.364~1.930）]更有可能转化为AD。与单独的神经影像因素或临床变量相比，这种综合Cox模型中的这些显著风险因素具有最佳的预测能力（准确率=84.62%，敏感性=86.51%，特异性=82.41%）。这些结果表明，ICA和Cox模型分析的结合可成功用于生存分析，并为MCI进展或AD相关研究提供基于网络的视角。
