Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based matrix-on-vector regression

Oregon Convention Center 

In brain research, joint analysis of multimodal neuroimaging data is vital for understanding complex brain structure-function interactions. We examine the influence of structural imaging (SI) features, such as white matter integrity and cortical thickness, on the functional connectome network. Our network-based matrix-on-vector regression model delineates FC-SI association patterns. We introduce a multi-level dense bipartite and clique subgraph extraction algorithm, pinpointing spatially specific SI features that significantly influence FC sub-networks. This method identifies correlated structural-connectomic patterns and minimizes false positives in analyzing millions of interactions. Applied to 4,242 UK Biobank participants, our method assesses the impact of whole-brain white matter integrity and cortical thickness on resting-state functional connectome. Findings show significant influences of white matter on corticospinal tracts and inferior cerebellar peduncle on sensorimotor, salience, and executive networks, with an average correlation of 0.81 (p<0.001).