In this paper, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) non-linear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state fMRI data from children with a history of traumatic brain injury and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with traumatic brain injury (TBI), and differences in effective connectivity strength between males and females.

翻译：本文提出了一种考虑受试者异质性的脑连接网络估计分析方法。具体而言，我们开发了多受试者贝叶斯向量自回归模型的新型扩展，该模型可估计特定组别的有向脑连接网络，并考虑协变量对网络连接边的影响。我们采用灵活的方法，通过引入基于高斯过程加权混合的新型贝叶斯非参数先验，允许协变量对连接强度的（可能）非线性效应。在后验推断中，我们通过实施变分贝叶斯方案实现计算可扩展性。该方法能够同时估计组别特异性网络并筛选相关协变量效应。在模拟数据上，我们展示了相较于竞争性两阶段方法的性能提升。我们将该方法应用于具有创伤性脑损伤病史的儿童和健康对照组的静息态功能磁共振成像数据，以估计年龄和性别对组级连接性的影响。研究结果突显了父节点分布的差异，同时提示创伤性脑损伤（TBI）儿童中年龄与连接强度峰值关系的改变，以及男女有效连接强度的差异。