Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies.

翻译：非侵入性成像技术的进步促进了全脑互联网络（即脑连接）的构建。现有的脑连接分析方法常将整个网络拆解为单个边或汇总指标的向量，导致大量信息损失。受探索遗传暴露、脑连接与疾病发病时间之间作用机制的需求驱动，我们提出一个整合贝叶斯框架，在量化脑网络中介作用的同时，对每个组件之间的效应通路进行建模。为适应沿白质纤维束构建的脑连接的生物学结构，我们开发了一个包含对称矩阵变量加速失效时间模型和对称矩阵响应回归的结构建模框架，以刻画效应路径。我们进一步引入图内稀疏性和图间收缩性，以识别信息网络配置并消除噪声成分的干扰。大量仿真实验证实了该方法相较于现有替代方案的优越性。将该方法应用于具有里程碑意义的阿尔茨海默病神经影像学倡议研究，我们获得了可能为未来干预策略提供依据的神经生物学合理洞见。