Basics of multivariate analysis in neuroimaging data.
Basics of multivariate analysis in neuroimaging data.
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DOI：
10.3791/1988
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发表时间：
2010-07-24
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影响因子：
--
通讯作者：
Habeck, Christian Georg
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作者：
Habeck, Christian Georg
神经成像数据的多变量分析技术最近受到越来越多的关注，因为它们有许多吸引人的特征，这些特征不能被更常用的单变量、体素技术轻易实现（1,4,5,6,7）。多变量方法评估大脑各区域激活的相关性/协方差，而不是在逐体素的基础上进行。因此，他们的结果可以更容易地解释为神经网络的特征。另一方面，单变量方法不能直接解决大脑区域间的相关性。与单变量技术相比，多变量方法也可以产生更大的统计能力，单变量技术被迫采用非常严格的体素多重比较校正。此外，多变量技术还可以更好地将一个数据集的分析结果应用于全新的数据集。因此，与单变量方法类似，多变量技术可以很好地提供有关平均差异和行为相关性的信息，具有潜在的更大的统计能力和更好的可重复性检查。与这些优势形成对比的是，多元方法的使用门槛很高，阻碍了在社区中更广泛的应用。对于熟悉多元分析技术的神经科学家来说，对该领域的初步调查可能会呈现出令人眼花缭乱的各种方法，尽管算法相似，但它们的重点不同，通常是由具有数学背景的人提出的。我们相信多变量分析技术有足够的潜力保证更好的传播。研究人员应该能够以知情和方便的方式使用它们。当前的文章是试图在一个教学介绍多元技术的新手。在概念介绍之后，对来自阿尔茨海默病神经影像学倡议（ADNI）的诊断数据集进行了非常简单的应用，清楚地展示了多变量方法的优越性能。
Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques(1,4,5,6,7). Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.