Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 379 - Single and Multi-Object Regression and Clustering with Applications in Neuro-Imaging Data
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2022 : 8:30 AM to 10:20 AM
Sponsor: Mental Health Statistics Section
Abstract #322239
Title: On Network Modularity Statistics in Connectomics and Schizophrenia
Author(s): Joshua Cape*
Companies: University of Pittsburgh
Keywords: network neuroscience; modularity; psychosis; magnetic resonance imaging; dimensionality reduction; multivariate analysis

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. Here, we pursue this direction by revisiting the classical notion of modularity maximization in the analysis of adjacency and correlation matrices. We begin by considering certain latent space network models wherein high-dimensional matrix spectral properties can be precisely analyzed. We further propose and argue for the potential usefulness of several new, non-classical modularity-type network statistics. Our findings are applied to an analysis of dMRI and fMRI data in the study of schizophrenia.

Based on joint work with Konasale Prasad and Anirban Mitra.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program