Anatomy and Neurobiology, University of Kentucky, Lexington, KY 40536-0098, USA.
Magn Reson Imaging. 2012 Apr;30(3):446-52. doi: 10.1016/j.mri.2011.11.001. Epub 2012 Jan 5.
Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using principal component analysis (PCA), partial least squares (PLS) or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contain more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using functional magnetic resonance imaging as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk.
基于家族史和载脂蛋白 E4 状态，使用多元判别方法比较了认知正常的女性群体的脑激活模式，这些女性处于阿尔茨海默病高风险或低风险。线性判别分析（LDA）之前使用主成分分析（PCA）、偏最小二乘法（PLS）或新的有向偏最小二乘法（OrPLS）进行降维。目的是确定一种功能连接的脑区空间模式，该模式由风险组差异表达，并产生最佳的分类准确性。当数据中特征变量的数量多于个体受试者的观察值时，在进行 LDA 之前需要进行多元降维。虽然 PCA 常用于识别神经影像学数据中的协方差模式，但这种方法仅识别总体变异性，并且无法区分组间变异性和组内变异性。PLS 和 OrPLS 通过纳入有关类结构的信息提供了更集中的降维，从而导致更简洁的判别模型。性能是根据交叉验证的错误分类率来评估的。结果支持使用功能磁共振成像作为成像生物标志物或诊断工具来区分患有疾病或高风险的个体的潜力。
