Evaluating the effects of high-throughput structural neuroimaging
predictors on whole-brain functional connectome outcomes via network-based
vector-on-matrix regression
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).Comment: 20 pages, 5 figures, 2 table