In functional MRI (fMRI), effective connectivity analysis aims at inferring the causal influences that brain regions exert on one another. A common method for this type of analysis is structural equation modeling (SEM). We here propose a novel method to test the validity of a given model of structural equation. Given a structural model in the form of a directed graph, the method extracts the set of all constraints of conditional independence induced by the absence of links between pairs of regions in the model and tests for their validity in a Bayesian framework, either individually (constraint by constraint), jointly (e.g., by gathering all constraints associated with a given missing link), or globally (i.e., all constraints associated with the structural model). This approach has two main advantages. First, it only tests what is testable from observational data and does allow for false causal interpretation. Second, it makes it possible to test each constraint (or group of constraints) separately and, therefore, quantify in what measure each constraint (or, e..g., missing link) is respected in the data. We validate our approach using a simulation study and illustrate its potential benefits through the reanalysis of published data.

翻译：在功能磁共振成像（fMRI）中，有效连接分析旨在推断脑区间相互施加的因果影响。此类分析的常用方法是结构方程建模（SEM）。本文提出一种检验给定结构方程模型有效性的新方法。给定一个有向图形式的结构模型，该方法提取模型中区域对间因连接缺失而诱导的所有条件独立性约束集合，并在贝叶斯框架下检验其有效性——可单独检验（逐约束检验）、联合检验（例如，汇集与特定缺失连接相关的所有约束）或全局检验（即与结构模型相关的所有约束）。该方法具有两个主要优势：首先，它仅检验可从观测数据中检验的内容，避免了错误的因果解释；其次，它能够分别检验每个约束（或约束组），从而量化数据在何种程度上符合每个约束（或例如缺失连接）。我们通过模拟研究验证了该方法的有效性，并通过重新分析已发表数据阐明了其潜在优势。