Penalized least squares regression methods and applications to neuroimaging.
Penalized least squares regression methods and applications to neuroimaging.
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DOI：
10.1016/j.neuroimage.2010.12.028
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发表时间：
2011-04-15
期刊：
影响因子：
5.7
通讯作者：
Cohen R
中科院分区：
文献类型：
--
作者：
Bunea F;She Y;Ombao H;Gongvatana A;Devlin K;Cohen R
本文的目的是回顾回归模型中最流行的预测选择方法，解释为什么当解释变量的数量P超过参与者的数量N时，一些方法会失败，并讨论在这种情况下可以采用的替代统计方法。我们专注于回归模型中的惩罚最小二乘方法，并详细讨论了两种在统计文献中建立良好的方法，LASSO和弹性网络。我们介绍了这些方法的自举增强，BE-LASSO和BE-Enet，允许用户附加一个测量的不确定性选择的每个变量。我们的工作是由一个多模态神经影像数据集，包括形态测量（体积在几个解剖区域的利益），白色物质的完整性措施，从扩散加权数据（分数各向异性，平均扩散率，轴向扩散率和径向扩散率）和临床和人口统计学变量（年龄，教育，酒精和药物史）。在这个数据集中，解释变量的数量P超过了参与者的数量N。我们使用BE-LASSO和BE-Enet来提供第一个统计分析，该分析允许从高维神经成像和临床预测因子评估神经认知性能，包括它们的相互作用。该分析的主要新奇在于，生物标志物选择和降维是为了获得对感兴趣的结果的良好预测而完成的（即，神经认知指数），不像主成分分析，其仅在独立于感兴趣的结果的预测者空间上执行。
The goal of this paper is to review the most popular methods of predictor selection in regression models, to explain why some fail when the number P of explanatory variables exceeds the number N of participants, and to discuss alternative statistical methods that can be employed in this case. We focus on penalized least squares methods in regression models, and discuss in detail two such methods that are well established in the statistical literature, the LASSO and Elastic Net. We introduce bootstrap enhancements of these methods, the BE-LASSO and BE-Enet, that allow the user to attach a measure of uncertainty to each variable selected. Our work is motivated by a multimodal neuroimaging dataset that consists of morphometric measures (volumes at several anatomical regions of interest), white matter integrity measures from diffusion weighted data (fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity) and clinical and demographic variables (age, education, alcohol and drug history). In this dataset, the number P of explanatory variables exceeds the number N of participants. We use the BE-LASSO and BE-Enet to provide the first statistical analysis that allows the assessment of neurocognitive performance from high dimensional neuroimaging and clinical predictors, including their interactions. The major novelty of this analysis is that biomarker selection and dimension reduction are accomplished with a view towards obtaining good predictions for the outcome of interest (i.e., the neurocognitive indices), unlike principal component analysis that are performed only on the predictors’ space independently of the outcome of interest.