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Dynamic networks have been increasingly used to characterize brainconnectivity that varies during resting and task states. In suchcharacterizations, a connectivity network is typically measured at each timepoint for a subject over a common set of nodes representing brain regions,together with rich subject-level information. A common approach to analyzingsuch data is an edge-based method that models the connectivity between eachpair of nodes separately. However, such approach may have limited performancewhen the noise level is high and the number of subjects is limited, as it doesnot take advantage of the inherent network structure. To better understand ifand how the subject-level covariates affect the dynamic brain connectivity, weintroduce a semi-parametric dynamic network response regression that relates adynamic brain connectivity network to a vector of subject-level covariates. Akey advantage of our method is to exploit the structure of dynamic imagingcoefficients in the form of high-order tensors. We develop an efficientestimation algorithm and evaluate the efficacy of our approach throughsimulation studies. Finally, we present our results on the analysis of atask-related study on social cognition in the Human Connectome Project, wherewe identify known sex-specific effects on brain connectivity that cannot beinferred using alternative methods.

Learning Brain Connectivity in Social Cognition with Dynamic Network Regression