Partial least squares analysis of neuroimaging data: applications and advances
Partial least squares analysis of neuroimaging data: applications and advances
复制标题
DOI：
10.1016/j.neuroimage.2004.07.020
复制
发表时间：
2004-01-01
期刊：
影响因子：
5.7
通讯作者：
Lobaugh, NJ
中科院分区：
文献类型：
--
作者：
McIntosh, AR;Lobaugh, NJ
偏最小二乘（PLS）分析已被用于表征分布信号测量的神经成像方法，如正电子发射断层扫描（PET），功能磁共振成像（fMRI），事件相关电位（ERP）和脑磁图（MEG）。在PET的应用中，它已被用于提取区分认知任务的活动模式，将分布式活动与行为相关的模式，并描述大规模的区域间相互作用或功能连接。本文回顾了最近的PLS扩展到分析的时空模式中存在的功能磁共振成像，ERP和脑磁图数据。我们提出了一个基本的数学描述PLS和讨论的统计评估使用排列测试和自助回归。这两种方法提供了互补的信息提取的活动模式的统计强度（排列测试）和可靠性的区域贡献的模式（自举复位）。模拟ERP数据用于指导时空PLS结果的基本解释，并从经验ERP和fMRI数据集的例子用于进一步说明。最后，我们讨论了在使用PLS的一些警告，包括非线性，非正交性和解释困难。我们进一步讨论了它作为一个重要的工具，在多元分析方法neuroirnaging的作用。(C)2004年爱思唯尔公司All rights reserved.
Partial least squares (PLS) analysis has been used to characterize distributed signals measured by neuroimaging methods like positron emission tomography (PET), functional magnetic resonance imaging (fMRI), event-related potentials (ERP) and magnetoencephalography (MEG). In the application to PET, it has been used to extract activity patterns differentiating cognitive tasks, patterns relating distributed activity to behavior, and to describe large-scale interregional interactions or functional connections. This paper reviews the more recent extension of PLS to the analysis of spatiotemporal patterns present in fMRI, ERP, and MEG data. We present a basic mathematical description of PLS and discuss the statistical assessment using permutation testing and bootstrap resampling. These two resampling methods provide complementary information of the statistical strength of the extracted activity patterns (permutation test) and the reliability of regional contributions to the patterns (bootstrap resampling). Simulated ERP data are used to guide the basic interpretation of spatiotemporal PLS results, and examples from empirical ERP and fMRI data sets are used for further illustration. We conclude with a discussion of some caveats in the use of PLS, including nonlinearities, nonorthogonality, and interpretation difficulties. We further discuss its role as an important tool in a pluralistic analytic approach to neuroirnaging. (C) 2004 Elsevier Inc. All rights reserved.