Multivariate approaches to neuroimaging analysis

神经影像分析的多变量方法

• 批准号: 7383849
• 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
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-01 至 2010-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7383849
• 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

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。多变量分析技术最近受到越来越多的关注。多变量技术具有许多吸引人的特征，这些特征不是更常用的单变量体素技术容易实现的。多变量方法评估大脑各区域激活的相关性/协方差，而不是逐个体素地进行。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，协方差方法还可以产生更大的统计能力，单变量技术被迫对体素多重比较采用非常严格且往往过于保守的校正。多变量技术也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，多变量技术适合于提供关于均值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重复性检验。与这些优势形成对比的是，使用多元方法的进入门槛很高，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，尽管这些方法在算法上相似，但侧重点不同，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来保证更好的传播。研究人员应该能够以知情和可接触的方式使用它们。因此，我们提出了一系列研究，将多变量方法相互比较，并在二元报告和综合综述论文中与传统的单变量方法进行比较。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发展我们自己的协方差方法，以便能够在一个分析步骤中充分处理受试者内参数实验设计和组内差异。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。