The joint analysis of multimodal neuroimaging data is critical in the field of brain research because it reveals complex interactive relationships between neurobiological structures and functions. In this study, we focus on investigating the effects of structural imaging (SI) features, including white matter micro-structure integrity (WMMI) and cortical thickness, on the whole brain functional connectome (FC) network. To achieve this goal, we propose a network-based vector-on-matrix regression model to characterize the FC-SI association patterns. We have developed a novel multi-level dense bipartite and clique subgraph extraction method to identify which subsets of spatially specific SI features intensively influence organized FC sub-networks. The proposed method can simultaneously identify highly correlated structural-connectomic association patterns and suppress false positive findings while handling millions of potential interactions. We apply our method to a multimodal neuroimaging dataset of 4,242 participants from the UK Biobank to evaluate the effects of whole-brain WMMI and cortical thickness on the resting-state FC. The results reveal that the WMMI on corticospinal tracts and inferior cerebellar peduncle significantly affect functional connections of sensorimotor, salience, and executive sub-networks with an average correlation of 0.81 (p<0.001).

翻译：多模态神经影像数据的联合分析在脑科学研究中至关重要，因为它能够揭示神经生物学结构与功能之间复杂的交互关系。本研究聚焦于探究结构成像特征（包括白质微观结构完整性和皮层厚度）对全脑功能连接组网络的影响。为此，我们提出一种基于网络的向量对矩阵回归模型来表征FC-SI关联模式。我们开发了一种新颖的多层次密集二分团与团子图提取方法，用于识别哪些空间特异性SI特征子集强烈影响有组织的FC子网络。所提方法能够同时识别高度相关的结构-连接组关联模式，并在处理数百万个潜在交互时抑制假阳性结果。我们将该方法应用于英国生物银行4,242名参与者的多模态神经影像数据集，以评估全脑WMMI和皮层厚度对静息态FC的影响。结果显示，皮质脊髓束和小脑下脚的WMMI显著影响感觉运动、突显和执行子网络的功能连接，平均相关性为0.81（p<0.001）。