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Bayesian network mediation analysis with application to the brain functional connectome
Statistics in Medicine ( IF 2 ) Pub Date : 2022-07-06 , DOI: 10.1002/sim.9488
Yize Zhao 1, 2 , Tianqi Chen 1 , Jiachen Cai 1 , Sarah Lichenstein 3 , Marc N Potenza 3, 4, 5, 6, 7, 8 , Sarah W Yip 3, 4
Affiliation  

The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.

中文翻译:

贝叶斯网络中介分析及其在脑功能连接组中的应用

大脑功能连接组是沿着功能网络的互连神经回路的集合,有助于对大脑功能的前沿理解,并有可能在暴露和结果之间的效应通路中发挥中介作用。虽然现有的中介分析方法能够提供对复杂过程的洞察,但它们主要关注单变量中介或中介向量,而没有考虑网络变量中介。为了填补方法论空白并完成这一激动人心且紧迫的应用,在本文中,我们提出了贝叶斯范式下的综合中介分析,其中网络具有中介效应。为了参数化网络测量,我们引入了具有未知块分配的单独指定的随机块模型,并且通过跨网络模块的连接权重诱导的潜在网络中介自然地桥接效应元素。为了能够识别真正活跃的中介组件,我们同时对网络中介进行了特征选择。我们展示了我们的模型在估计不同效应成分和选择主动中介网络结构方面的优越性。作为这种方法应用于网络神经科学的实际例证,我们描述了由脑功能子网络介导的治疗干预和阿片类药物戒断之间的关系。我们同时在网络中介者之间进行特征选择。我们展示了我们的模型在估计不同效应成分和选择主动中介网络结构方面的优越性。作为这种方法应用于网络神经科学的实际例证,我们描述了由脑功能子网络介导的治疗干预和阿片类药物戒断之间的关系。我们同时在网络中介者之间进行特征选择。我们展示了我们的模型在估计不同效应成分和选择主动中介网络结构方面的优越性。作为这种方法应用于网络神经科学的实际例证,我们描述了由脑功能子网络介导的治疗干预和阿片类药物戒断之间的关系。
更新日期:2022-07-07
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