HYPOTHESIS TESTING FOR NETWORK DATA IN FUNCTIONAL NEUROIMAGING
HYPOTHESIS TESTING FOR NETWORK DATA IN FUNCTIONAL NEUROIMAGING
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DOI：
10.1214/16-aoas1015
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发表时间：
2017-06-01
影响因子：
1.8
通讯作者：
Kolaczyk, Eric D.
中科院分区：
文献类型：
--
作者：
Ginestet, Cedric E.;Li, Jun;Kolaczyk, Eric D.
近年来，在神经科学中，使用网络来总结一组测量中的关系信息已经成为一种常见的做法，这些测量通常被认为反映了大脑中感兴趣区域之间的功能或结构关系。在分析这些数据时，最基本的任务之一是检验假设，以回答诸如“这两组受试者的网络之间是否有差异？“在经典的设置中，感兴趣的单位是标量或矢量，这些问题是通过使用熟悉的双样本测试策略来回答的。然而，网络不是欧几里德对象，因此经典方法不能直接应用。我们解决这一挑战，从几何和高维统计推断的概念和技术。我们的工作是基于一个精确的几何特征的空间图拉普拉斯矩阵和一个非参数的平均概念，由于弗雷歇。我们激励和说明我们的测试方法的背景下，从功能性神经影像学数据的人类受试者从1000个功能性连接体项目。特别是，我们表明，这种全球性的测试是更强大的统计比质量单变量的方法。此外，我们还提供了一种方法，用于可视化每个边缘对整体测试统计量的单独贡献。
In recent years, it has become common practice in neuroscience to use networks to summarize relational information in a set of measurements, typically assumed to be reflective of either functional or structural relationships between regions of interest in the brain. One of the most basic tasks of interest in the analysis of such data is the testing of hypotheses, in answer to questions such as "Is there a difference between the networks of these two groups of subjects?" In the classical setting, where the unit of interest is a scalar or a vector, such questions are answered through the use of familiar two-sample testing strategies. Networks, however, are not Euclidean objects, and hence classical methods do not directly apply. We address this challenge by drawing on concepts and techniques from geometry and high-dimensional statistical inference. Our work is based on a precise geometric characterization of the space of graph Laplacian matrices and a nonparametric notion of averaging due to Frechet. We motivate and illustrate our resulting methodologies for testing in the context of networks derived from functional neuroimaging data on human subjects from the 1000 Functional Connectomes Project. In particular, we show that this global test is more statistically powerful than a mass-univariate approach. In addition, we have also provided a method for visualizing the individual contribution of each edge to the overall test statistic.