McKeown Martin J, Hanlon Colleen A
Department of Medicine (Neurology), Pacific Parkinson's Research Centre, University of British Columbia (UBC), University Hospital, M31, Purdy Pavilion, UBC Site, 2221 Wesbrook Mall, Vancouver, BC, Canada V6T 2B5.
J Neurosci Methods. 2004 May 30;135(1-2):137-47. doi: 10.1016/j.jneumeth.2003.12.021.
To combine functional neuroimaging studies across subjects, anatomical and functional data are typically either transformed to a common space or averaged across regions of interest (ROIs). However, if there are (1) anatomical variations within the subject pool (as in clinical or aging populations), (2) non-Gaussian distributions of task-related activity within a typical ROI or, (3) more ROIs than subjects, neither spatial transformation of the data to a common space nor averaging across all subjects' ROIs is suitable for standard discriminant analysis. To solve these problems, we describe a post-processing method that uses voxel-based statistics representing task-related activity (pooled within ROIs) to establish combinations of ROIs that maximally differentiate tasks across all subjects. The method involves randomized resampling from multiple ROIs within each subject, multivariate linear discriminant analysis across all subjects and validation with bootstrapping techniques. When applied to experimental data from healthy subjects performing two motor tasks, the method detected some brain regions, including the supplementary motor area (SMA), that participated in a distributed network differentially active between tasks. However there was not a significant difference in SMA activity when this region was examined in isolation. We suggest this method is a practical means to combine voxel-based statistics within anatomically defined ROIs across subjects.
为了整合跨个体的功能神经影像学研究，解剖学和功能数据通常要么被转换到一个公共空间，要么在感兴趣区域（ROI）内进行平均。然而，如果存在以下情况：（1）受试者群体中存在解剖变异（如临床或老年人群体），（2）典型ROI内任务相关活动的非高斯分布，或者（3）ROI的数量多于受试者的数量，那么将数据空间转换到公共空间或对所有受试者的ROI进行平均都不适用于标准判别分析。为了解决这些问题，我们描述了一种后处理方法，该方法使用基于体素的统计量来表示任务相关活动（在ROI内汇总），以建立能够在所有受试者中最大程度区分任务的ROI组合。该方法包括在每个受试者的多个ROI内进行随机重采样、对所有受试者进行多元线性判别分析以及使用自抽样技术进行验证。当将该方法应用于健康受试者执行两项运动任务的实验数据时，该方法检测到了一些脑区，包括辅助运动区（SMA），这些脑区参与了任务之间差异活跃的分布式网络。然而，单独检查该区域时，SMA活动没有显著差异。我们认为这种方法是一种实用的手段，可以跨个体在解剖学定义的ROI内组合基于体素的统计量。
