Multivariate approaches to neuroimaging analysis

描述（由申请人提供）：随着临床和认知神经科学的成熟，对复杂的神经影像学分析的需求变得更加明显。多元分析技术近年来受到越来越多的关注。多变量技术有许多吸引人的特征，这些特征不能被更常用的单变量、体素技术轻易地实现。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中的功能连接问题。与单变量技术相比，协方差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、通常过于保守的、针对体素的多重比较的校正。多变量技术也使自己更适合于从一个数据集的分析结果到全新数据集的前瞻性应用。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。因此，我们建议在二元报告和综合评论论文中进行一系列比较多元方法之间以及与传统的单变量方法的研究。对于这些研究，我们将使用计算机模拟以及现实世界的神经科学数据集。我们还将进一步扩展和发展我们自己的协方差方法，以便在一个分析步骤中充分处理受试者内的参数实验设计和组差异。最后，我们将提供一个软件分析包，它将以用户友好的方式集成多变量方法的最常见特征。

DESCRIPTION (provided by applicant): As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention. Multivariate techniques have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. 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 functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. 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. We therefore propose a series of studies comparing multivariate approaches amongst each other and with traditional univariate approaches in dyadic reports and comprehensive review papers. For these studies we will use computer simulations as well as real-world neuroscience data sets. We will also extend and develop our own covariance approach further to enable adequate treatment of parametric within-subjects experimental designs and group-differences in one analysis step. Finally, we will provide a software analysis package that will integrate the most common features of multivariate approaches in a user-friendly manner.