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Multivariate approaches to neuroimaging analysis

神经影像分析的多变量方法

基本信息

批准号：
7564716
负责人：
Christian Georg Habeck
金额：
$ 28.4万
依托单位：
COLUMBIA UNIVERSITY HEALTH SCIENCES
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-04-01 至 2011-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7564716
关键词：
Address Attention Behavior Biological Neural Networks Brain Brain region Clinical Communities Computer Literacy Computer Simulation Computer software Data Data Analyses Data Set Development Disadvantaged Education Educational workshop Event Experimental Designs Functional Magnetic Resonance Imaging Guidelines Hand Housing Image Knowledge Laboratories Learning Least-Squares Analysis Linear Models Mathematics Methodology Modeling Multivariate Analysis Neurosciences Paper Pattern Positron-Emission Tomography Principal Component Analysis Relative (related person)Reporting Reproducibility Reproducibility of Results Research Research Personnel Series Simulate Techniques Testing Time Training Visit base cognitive neuroscience design field survey independent component analysis infancy neuroimaging prevent programs prospective research study software development theories user friendly software user-friendly

项目摘要

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.

描述（由申请人提供）：随着临床和认知神经科学的成熟，对复杂的神经成像分析的需求变得更加明显。多元分析技术最近受到越来越多的关注。多变量技术有许多吸引人的功能，不能很容易地实现更常用的单变量，体素，技术。多变量方法评估跨大脑区域的激活的相关性/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签名。另一方面，单变量方法不能直接解决大脑中的功能连接。与单变量技术相比，协方差方法还可以产生更大的统计功效，单变量技术被迫采用非常严格且通常过于保守的校正用于逐体素多重比较。多变量技术也更适合于将一个数据集的分析结果应用于全新的数据集。因此，多变量技术可以很好地提供有关平均差异和与行为相关性的信息，类似于单变量方法，具有潜在的更大的统计功效和更好的再现性检查。与这些优势形成对比的是，使用多元方法的门槛很高，阻碍了在社区中的更广泛应用。对于熟悉多变量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困惑的各种方法，尽管算法相似，但通常由具有数学背景的人提出不同的重点。我们相信，多变量分析技术有足够的潜力，保证更好的传播。研究人员应能够以知情和无障碍的方式使用这些工具。因此，我们提出了一系列的研究，相互之间的多变量方法，并与传统的单变量方法在二元报告和全面的综述论文进行比较。对于这些研究，我们将使用计算机模拟以及真实世界的神经科学数据集。我们还将进一步扩展和开发我们自己的协方差方法，以便在一个分析步骤中充分处理参数受试者内实验设计和组间差异。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功能。