B1504
Title: Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain connectome outcomes
Authors: Shuo Chen - University of Maryland (United States) [presenting]
Abstract: The joint analysis of multimodal neuroimaging data is critical in brain research by revealing complex interactive relationships between neurobiological structures and functions. The effects of structural neuroimaging features are investigated, including white matter micro-structure integrity and cortical thickness on the whole brain functional connectome network. To achieve this goal, a network-based vector-on-matrix regression model is proposed to characterize the systematic association patterns between connectome networks and structural imaging variables. A novel multi-level dense bipartite and clique subgraph extraction method is developed to identify which subsets of spatially specific structural features can intensively influence organized functional connectome sub-networks. It is demonstrated that the proposed network-based vector-on-matrix regression model can simultaneously identify highly correlated structural-connectomic association patterns and suppress false positive findings while handling millions of potential interactions. The method is applied to a multimodal neuroimaging dataset of 4,242 participants from the UK Biobank to evaluate the effects of whole-brain white matter microstructure integrity and cortical thickness on the resting-state functional connectome.
