McIntosh A R, Bookstein F L, Haxby J V, Grady C L
Rotman Research Institute of Baycrest Centre, University of Toronto, Ontario, Canada.
Neuroimage. 1996 Jun;3(3 Pt 1):143-57. doi: 10.1006/nimg.1996.0016.
This paper introduces a new tool for functional neuroimage analysis: partial least squares (PLS). It is unique as a multivariate method in its choice of emphasis for analysis, that being the covariance between brain images and exogenous blocks representing either the experiment design or some behavioral measure. What emerges are spatial patterns of brain activity that represent the optimal association between the images and either of the blocks. This process differs substantially from other multivariate methods in that rather than attempting to predict the individual values of the image pixels, PLS attempts to explain the relation between image pixels and task or behavior. Data from a face encoding and recognition PET rCBF study are used to illustrate two types of PLS analysis: an activation analysis of task with images and a brain-behavior analysis. The commonalities across the two analyses are suggestive of a general face memory network differentially engaged during encoding and recognition. PLS thus serves as an important extension by extracting new information from imaging data that is not accessible through other currently used univariate and multivariate image analysis tools.
偏最小二乘法（PLS）。作为一种多变量方法，它在分析重点的选择上独具特色，即关注脑图像与代表实验设计或某些行为测量的外部模块之间的协方差。由此产生的是大脑活动的空间模式，它代表了图像与任一模块之间的最佳关联。这一过程与其他多变量方法有很大不同，因为PLS不是试图预测图像像素的个体值，而是试图解释图像像素与任务或行为之间的关系。来自一项面部编码和识别PET rCBF研究的数据被用于说明两种类型的PLS分析：图像与任务的激活分析以及脑-行为分析。这两种分析的共性表明，在编码和识别过程中存在一个不同程度参与的通用面部记忆网络。因此，PLS通过从成像数据中提取新信息，成为一种重要的扩展，而这些信息是目前其他单变量和多变量图像分析工具无法获取的。
