A1078
Title: Multilayer network model for joint analysis of structural brain imaging vector and functional connectome matrix
Authors: Shuo Chen - University of Maryland (United States) [presenting]
Abstract: Assessing the association between brain structural imaging (SI) measures and functional connectome (FC) obtained from neuroimaging data is considered. In this network analysis, the outcomes are off-diagonal elements of an FC (covariance) matrix, while predictors are a multivariate vector of SI variables and other covariates. A multilayer network model is proposed to capture the systematic association patterns between subsets of SIs and FC sub-networks. The first layer network is a bipartite graph characterizing the association between all SI variables and FC outcomes, where an edge denotes a non-zero SI-FC association. A large proportion of edges are located within latent dense bipartite subgraphs, while other edges are randomly and sparsely distributed in the rest of the bipartite graph. The second layer network represents the connectomic graph, where most FC outcomes in the first layer dense subnetworks comprise dense clique subgraphs. The globally sparse and locally dense multilayer network model can reveal which FC subnetworks are systematically influenced by a selected subset of SIs. Algorithms are developed to identify the underlying multilayer sub-networks and propose a statistical inference framework to test these sub-networks. The approach is further applied to 4242 participants from UK Biobank to evaluate the effects of whole-brain white matter microstructure integrity and cortical thickness on the whole-brain FC network.
