Statistical Network Models for Integrating Functional Connectivity with sMRI and PET Brain Imaging Data

Network analysis is one of the prominent multivariate techniques used to study structural and functional connectivity of the brain. In a network model of the brain, vertices are used to represent voxels or regions of the brain, and edges between two nodes represent a physical or functional relationship between the two incident regions. Network investigations of connectivity have produced many important advances in our understanding of brain structure and function, including in domains of aging, learning and memory, cognitive control, emotion, and disease. Despite their use, network methodologies still face several important challenges. In this talk, I will focus on a particularly important challenge in the analysis of structural and functional connectivity: how does one jointly model the generative mechanisms of structural and functional connectivity with other modalities? I propose and describe a statistical network model, called the generalized exponential random graph model (GERGM), that flexibly characterizes the network topology of structural and functional connectivity and can readily integrate other modalities of data. The GERGM also directly enables the statistical testing of individual differences through the comparison of their fitted models. In applying the GERGM to the connectivity of healthy individuals from the Human Connectome Project, we find that the GERGM reveals remarkably consistent organizational properties guiding subnetwork architecture in the typically developing brain. We will discuss ongoing work of how to adapt these models to neuroimaging cohorts associated with the ADRC at the University of Pittsburgh, where the goal is to relate the dynamics of structural and functional connectivity with tau and amyloid – beta deposition in individuals across the Alzheimer’s continuum.