This article introduces a predictor-dependent joint modeling framework for network data obtained from multiple subjects over a shared set of nodes with spatial co-ordinates and spatially correlated nodal attributes. The framework is highly flexible, allowing concurrent inference on nodes significantly associated with a predictor, spatial associations of nodal attributes and the regression relationship between a predictor and edge connecting a pair of nodes or a specific nodal attribute. Empirical results indicate a superior performance of the proposed approach due to accounting for network structure and spatial correlation in the data simultaneously. The methodology analyzes multimodal brain imaging data collected first-hand in the coauthor's Lifespan Cognitive and Motor Neuroimaging Laboratory, with a focus on integrating structural and functional information. It examines brain connectivity, represented as a connectome network across regions of interest (ROIs) derived from functional magnetic resonance imaging (fMRI), while also incorporating ROI-specific attributes obtained from structural MRI data, for each subject. Subject-specific aging-related features and spatial locations of ROIs are incorporated in the analysis. This framework facilitates robust inference on the associations between predictors and brain connectivity patterns, the spatial relationships among ROI-specific attributes, and the regression relationships involving edges or ROI-specific attributes with aging-related predictors. By integrating these diverse data sources, the approach provides a deeper understanding of the complex interplay between brain structure, function, aging-related changes, and external predictors. As a model-based Bayesian approach, it provides uncertainty quantification for all inferences, offering robust and reliable results, particularly in scenarios with limited sample size.

翻译：本文提出了一种预测依赖的联合建模框架，用于处理来自多个受试者的网络数据。这些数据共享一组具有空间坐标和空间相关节点属性的节点。该框架具有高度灵活性，可同时对以下关系进行推断：与预测变量显著相关的节点、节点属性的空间关联性，以及预测变量与连接节点对的边或特定节点属性之间的回归关系。实证结果表明，由于同时考虑了网络结构和数据中的空间相关性，所提方法表现出优越性能。该方法分析了由共同作者的生命全程认知与运动神经影像实验室一手采集的多模态脑影像数据，重点关注结构与功能信息的整合。研究以功能磁共振成像（fMRI）衍生出的感兴趣区（ROI）连接组网络表征脑连接，同时为每个受试者纳入从结构MRI数据中获取的ROI特异性属性。分析中考虑了受试者个体化的衰老相关特征及ROI空间位置。该框架稳健地实现了以下推断：预测变量与脑连接模式之间的关联、ROI特异性属性的空间关系，以及涉及边或ROI特异性属性与衰老相关预测变量间的回归关系。通过整合这些多源数据，该方法加深了对大脑结构、功能、衰老相关变化及外部预测变量间复杂交互作用的理解。作为一种基于模型的贝叶斯方法，它为所有推断提供了不确定性量化，特别是在样本量有限的情况下，能给出稳健可靠的结果。