Mechelli Andrea, Penny Will D, Price Cathy J, Gitelman Darren R, Friston Karl J
Wellcome Department of Imaging Neuroscience, Institute of Neurology, 12 Queen Square, London, WCIN 3BG, United Kingdom.
Neuroimage. 2002 Nov;17(3):1459-69. doi: 10.1006/nimg.2002.1231.
This article is about intersubject variability in the functional integration of activity in different brain regions. Previous studies of functional and effective connectivity have dealt with intersubject variability by analyzing data from different subjects separately or pretending the data came from the same subject. These approaches do not allow one to test for differences among subjects. The aim of this work was to illustrate how differences in connectivity among subjects can be addressed explicitly using structural equation modeling. This is enabled by constructing a multisubject network that comprises m regions of interest for each of the n subjects studied, resulting in a total of m x n nodes. Constructing a network of regions from different subjects may seem counterintuitive but embodies two key advantages. First, it allows one to test directly for differences among subjects by comparing models that do and do not allow a particular connectivity parameter to vary over subjects. Second, a multisubject network provides additional degrees of freedom to estimate the model's free parameters. Any neurobiological hypothesis normally addressed by single-subject or group analyses can still be tested, but with greater sensitivity. The common influence of experimental variables is modeled by connecting a virtual node, whose time course reflects stimulus onsets, to the sensory or "input" region in all subjects. Further experimental changes in task or cognitive set enter through modulation of the connections. This approach allows one to model both endogenous (or intrinsic) variance and exogenous effects induced by experimental design. We present a functional magnetic resonance imaging study that uses a multisubject network to investigate intersubject variability in functional integration in the context of single word and pseudoword reading. We tested whether the effect of word type on the reading-related coupling differed significantly among subjects. Our results showed that a number of forward and backward connections were stronger for reading pseudowords than words, and, in one case, connectivity showed significant intersubject variability. The discussion focuses on the implications of our findings and on further applications of the multisubject network analysis.
本文探讨的是不同脑区活动功能整合中的个体间变异性。以往关于功能连接和有效连接的研究，处理个体间变异性的方式是分别分析不同个体的数据，或者假定数据来自同一个体。这些方法无法检验个体间的差异。本研究的目的是说明如何使用结构方程模型明确解决个体间连接性的差异问题。通过构建一个多主体网络来实现这一点，该网络包含所研究的n个个体中每个个体的m个感兴趣区域，总共形成m×n个节点。从不同个体构建区域网络看似有违直觉，但具有两个关键优势。首先，通过比较允许和不允许特定连接参数在个体间变化的模型，能够直接检验个体间的差异。其次，多主体网络为估计模型的自由参数提供了额外的自由度。任何通常由单主体或组分析解决的神经生物学假设仍然可以进行检验，但灵敏度更高。实验变量的共同影响通过连接一个虚拟节点来建模，该虚拟节点的时间进程反映刺激开始，连接到所有个体的感觉或“输入”区域。任务或认知集的进一步实验变化通过连接的调制进入。这种方法能够对内源性（或内在）方差和实验设计引起的外源性效应进行建模。我们展示了一项功能磁共振成像研究，该研究使用多主体网络在单字和假字阅读的背景下研究功能整合中的个体间变异性。我们检验了单词类型对阅读相关耦合的影响在个体间是否存在显著差异。我们的结果表明，对于假字阅读，一些正向和反向连接比真字阅读更强，并且在一种情况下，连接性显示出显著的个体间变异性。讨论集中在我们研究结果的意义以及多主体网络分析的进一步应用上。
