Structural and functional neuroimaging modalities provide complementary windows into brain organization: structural imaging characterizes neural tissue anatomy and microstructure, while functional imaging captures dynamic patterns of neural activity and connectivity. Together, they offer a more complete picture than either alone. Recent multimodal neuroimaging work has focused on joint modeling of structural and functional data, often assuming a strong association between them to improve prediction and interpretability. However, relatively little attention has been given to developing statistically principled frameworks for formally testing hypotheses about these associations. Existing approaches typically rely on simple correlation-based measures or heuristic integration strategies, which may fail to capture the complex dependencies inherent in neuroimaging data, particularly when functional data are represented as brain networks and structural data as region-specific anatomical measures. We address this gap by developing an explicit Bayesian hypothesis testing framework for quantifying associations between structural and functional neuroimaging data. Our approach constructs functional brain networks from fMRI data, then integrates them with structural measurements through a hierarchical Bayesian model. The Bayesian formulation naturally accommodates two types of datasets with different structures, incorporates prior knowledge, and yields full posterior uncertainty quantification. Through extensive empirical studies, we demonstrate that the proposed method achieves excellent performance in detecting associations under a wide range of settings, including varying signal-to-noise ratios, different numbers of brain regions, and diverse sets of structural imaging measures.

翻译：结构性神经影像与功能性神经影像是揭示大脑组织的互补窗口：结构性影像表征神经组织的解剖结构与微观结构，而功能性影像则捕捉神经活动与连接的动态模式。两者结合能提供比单一模态更完整的图景。近年来多模态神经影像研究聚焦于结构性数据与功能性数据的联合建模，通常假设两者间存在强关联以提升预测能力与可解释性。然而，针对这些关联进行形式化假设检验的统计学原理框架发展相对不足。现有方法多依赖简单的基于相关性的度量或启发式整合策略，这难以捕捉神经影像数据中固有的复杂依赖关系，尤其当功能性数据以脑网络形式呈现、结构性数据以区域特异性解剖测量指标呈现时。为此，我们开发了一个显式贝叶斯假设检验框架，用于量化结构性神经影像与功能性神经影像数据间的关联。该方法首先从功能磁共振成像数据构建功能性脑网络，随后通过层次贝叶斯模型将其与结构性测量指标整合。贝叶斯框架可自然适配两类结构不同的数据集，融入先验知识，并提供完整的后验不确定性量化。通过广泛的实证研究，我们证明所提出方法在信噪比变化、脑区数量差异及多样结构性影像测量指标集等广泛场景中，均能出色地检测到关联信号。