A0777
Title: On network modularity statistics in connectomics and schizophrenia
Authors: Joshua Cape - University of Wisconsin, Madison (United States) [presenting]
Abstract: Modularity-based methods for structure and community discovery remain popular in the network neuroscience literature and enjoy a history of yielding meaningful neurobiological findings. All the while, the full potential of these methods remains limited in part by an absence of uncertainty quantification guarantees for use in downstream statistical inference. This direction is pursued by revisiting the classical notion of modularity maximization in the analysis of adjacency and correlation matrices. Considering certain latent space network models wherein high-dimensional matrix spectral properties can be precisely analyzed are proposed and argued. Further for the potential usefulness of several new, non-classical modularity-type network statistics. The findings are applied to an analysis of dMRI and fMRI data in the study of schizophrenia. This is based on joint work with Anirban Mitra (Statistics, University of Pittsburgh) and Konasale Prasad (Psychiatry, University of Pittsburgh).
