A growing number of publications focus on estimating Gaussian graphical models (GGM, networks of partial correlation coefficients). At the same time, generalizibility and replicability of these highly parameterized models are debated, and sample sizes typically found in datasets may not be sufficient for estimating the underlying network structure. In addition, while recent work emerged that aims to compare networks based on different samples, these studies do not take potential cross-study heterogeneity into account. To this end, this paper introduces methods for estimating GGMs by aggregating over multiple datasets. We first introduce a general maximum likelihood estimation modeling framework in which all discussed models are embedded. This modeling framework is subsequently used to introduce meta-analytic Gaussian network aggregation (MAGNA). We discuss two variants: fixed-effects MAGNA, in which heterogeneity across studies is not taken into account, and random-effects MAGNA, which models sample correlations and takes heterogeneity into account. We assess the performance of MAGNA in large-scale simulation studies. Finally, we exemplify the method using four datasets of post-traumatic stress disorder (PTSD) symptoms, and summarize findings from a larger meta-analysis of PTSD symptom.

越来越多的出版物专注于估计高斯图模型（GGM，偏相关系数网络）。同时，这些高度参数化模型的普遍性和可复制性存在争议，通常在数据集中发现的样本量可能不足以估计底层网络结构。此外，虽然最近出现了旨在比较基于不同样本的网络的工作，但这些研究并未考虑潜在的交叉研究异质性。为此，本文介绍了通过聚合多个数据集来估计 GGM 的方法。我们首先介绍一个通用的最大似然估计建模框架，其中嵌入了所有讨论的模型。该建模框架随后用于引入元分析高斯网络聚合 (MAGNA)。我们讨论了两个变体：固定效应 MAGNA，其中不考虑跨研究的异质性，以及随机效应 MAGNA，它模拟样本相关性并考虑异质性。我们评估了 MAGNA 在大规模模拟研究中的表现。最后，我们使用四个创伤后应激障碍 (PTSD) 症状数据集来举例说明该方法，并总结对 PTSD 症状进行更大荟萃分析的结果。