用于推断儿童创伤性脑损伤中脑连接网络与协变量效应的贝叶斯变效应向量自回归模型
Bayesian Varying-Effects Vector Autoregressive Models for Inference of Brain Connectivity Networks and Covariate Effects in Pediatric Traumatic Brain Injury
AI总结：
提出贝叶斯变效应向量自回归模型，利用高斯过程混合先验和变分推断，同时估计组特异性脑网络并识别年龄、性别等协变量效应。
摘要：
本文开发了一种用于估计脑连接网络的分析方法，该方法考虑了受试者异质性。更具体地说，我们考虑对多受试者贝叶斯向量自回归模型进行新颖扩展，以估计组特异性有向脑连接网络，并考虑协变量对网络边的影响。我们采用一种灵活方法，通过采用高斯过程加权混合的新型贝叶斯非参数先验，允许协变量对边强度产生（可能的）非线性影响。对于后验推断，我们通过实施变分贝叶斯方案实现计算可扩展性。我们的方法能够同时估计组特异性网络并选择相关协变量效应。我们在模拟数据上展示了相较于竞争性两阶段方法的改进性能。我们将该方法应用于有创伤性脑损伤病史儿童和健康对照的静息态 fMRI 数据，以估计年龄和性别对组水平连接的影响。结果突出了父节点分布的差异，还表明年龄关系发生改变，即创伤性脑损伤（TBI）儿童的边强度达到峰值，并且男性与女性之间的有效连接强度存在差异。
英文摘要：
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.
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