Madsen Sarah K, Ver Steeg Greg, Mezher Adam, Jahanshad Neda, Nir Talia M, Hua Xue, Gutman Boris A, Galstyan Aram, Thompson Paul M
Imaging Genetics Center, USC, Marina Del Rey, CA, USA.
USC Information Sciences Institute, Marina Del Rey, CA, USA.
Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:980-984. doi: 10.1109/ISBI.2015.7164035.
Cognitive decline in old age is tightly linked with brain atrophy, causing significant burden. It is critical to identify which biomarkers are most predictive of cognitive decline and brain atrophy in the elderly. In 566 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we used a novel unsupervised machine learning approach to evaluate an extensive list of more than 200 potential brain, blood and cerebrospinal fluid (CSF)-based predictors of cognitive decline. The method, called CorEx, discovers groups of variables with high multivariate mutual information and then constructs latent factors that explain these correlations. The approach produces a hierarchical structure and the predictive power of biological variables and latent factors are compared with regression. We found that a group of variables containing the well-known AD risk gene APOE and CSF tau and amyloid levels were highly correlated. This latent factor was the most predictive of cognitive decline and brain atrophy.
老年认知能力下降与脑萎缩密切相关，会造成重大负担。确定哪些生物标志物最能预测老年人的认知能力下降和脑萎缩至关重要。在来自阿尔茨海默病神经影像倡议（ADNI）的566名老年人中，我们使用了一种新颖的无监督机器学习方法，来评估200多个基于大脑、血液和脑脊液（CSF）的潜在认知能力下降预测指标的详尽列表。这种名为CorEx的方法发现具有高多变量互信息的变量组，然后构建解释这些相关性的潜在因子。该方法产生一个层次结构，并将生物变量和潜在因子的预测能力与回归进行比较。我们发现，一组包含著名的AD风险基因APOE以及脑脊液tau蛋白和淀粉样蛋白水平的变量高度相关。这个潜在因子对认知能力下降和脑萎缩的预测能力最强。
