It has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are typically expressed as adjacency matrices, with each cell containing a summary of connectivity between a pair of brain regions. There is an emerging statistical literature describing methods for the analysis of such multi-network data in which nodes are common across networks but the edges vary. However, there has been essentially no consideration of the important problem of outlier detection. In particular, for certain subjects, the neuroimaging data are so poor quality that the network cannot be reliably reconstructed. For such subjects, the resulting adjacency matrix may be mostly zero or exhibit a bizarre pattern not consistent with a functioning brain. These outlying networks may serve as influential points, contaminating subsequent statistical analyses. We propose a simple Outlier DetectIon for Networks (ODIN) method relying on an influence measure under a hierarchical generalized linear model for the adjacency matrices. An efficient computational algorithm is described, and ODIN is illustrated through simulations and an application to data from the UK Biobank. ODIN was successful in identifying moderate to extreme outliers. Removing such outliers can significantly change inferences in downstream applications.
翻译:在神经科学研究中,测量使用神经成像的不同个人的大脑网络已成为常规。这些网络通常以相邻矩阵的形式表现,每个单元格都载有对一对大脑区域之间连接的概况。正在出现一些统计文献,描述分析这种多网络数据的方法,其中节点在各网络之间是共同的,但边缘各有不同。然而,基本上没有考虑到异常检测这一重要问题。特别是对某些科目来说,神经成像数据质量极差,无法可靠地重建网络。对于这些科目来说,由此产生的相邻矩阵可能大多为零,或表现出与正常大脑不相符的奇异模式。这些外围网络可能起到有影响力的作用,污染随后的统计分析。我们提出网络的简单外部探测器方法,依靠对相邻矩阵的等级普遍线性模型下的影响度量度。描述了高效的计算算法,并通过模拟和英国生物银行的数据应用来说明ODIN。ODIN成功地确定了中度至极端外围的外端应用。