Meeting Banner
Abstract #3971

A Hierarchical Latent Space Network Model for Population Studies of Functional Connectivity

James David Wilson1, Skyler Cranmer2, and Zhong-Lin Lu3
1Mathematics and Statistics, University of San Francisco, San Anselmo, CA, United States, 2Political Science, The Ohio State University, Columbus, OH, United States, 3Center for Neuroscience and Department of Psychology, New York University, New York, NY, United States

Functional connectivity scans are hierarchical – heterogeneity differentiates people according to clinical diagnosis and stage of disease. Hierarchy furthermore dictates connectivity of an individual - functional networks manifest as a hierarchy of subnetworks, each with their own unique biological function. To model functional connectivity across populations of individuals, we develop the hierarchical latent space model (HLSM), a statistical model that accounts for hierarchy as a function of clinical and connectivity features describing functional images. The HLSM reveals differences in the connectivity patterns between healthy and schizophrenia groups when applied to data from the Center for Biomedical Research Excellence (COBRE) project.

This abstract and the presentation materials are available to 2020 meeting attendees and eLibrary customers only; a login is required.

Join Here