Advances in understanding the structural connectomes of human brain require improved
approaches for the construction, comparison and integration of high-dimensional whole …

The recent fusion of network science and neuroscience has catalyzed a paradigm shift in
how we study the brain and led to the field of brain network analysis. Brain network analyses …

Network data are increasingly available in various research fields, motivating statistical
analysis for populations of networks, where a network as a whole is viewed as a data point …

Brain networks have attracted increasing attention due to the potential to better characterize
brain dynamics and abnormalities in neurological and psychiatric conditions. Recent years …

Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely
used across the sciences, and in industry, to model complex data sources. Key to applying …

This article focuses on the relationship between a measure of creativity and the human brain
network for subjects in a brain connectome dataset obtained using a diffusion weighted …

We consider the problem of multiway clustering in the presence of unknown degree
heterogeneity. Such data problems arise commonly in applications such as …

Brain networks are increasingly characterized at different scales, including summary
statistics, community connectivity, and individual edges. While research relating brain …

Modeling time series of multilayer network data is challenging due to the peculiar
characteristics of real-world networks, such as sparsity and abrupt structural changes …

Importance Treatment with contemporary chemotherapy-only protocols is associated with
risk for neurocognitive impairment among survivors of childhood acute lymphoblastic …