Longitudinal Analysis of Resting-State fMRI Functional Connectivity Networks

• Activity Number: 215
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Hakmook Kang*+ and Mark Fiecas
• Companies: Vanderbilt University and University of California, San Diego
• Keywords: resting-state fMRI ; functional connectivity

Abstract

As a non-invasive diagnostic and prognostic tool, functional magnetic resonance imaging (fMRI) is evolving. In particular, longitudinal change in functional connectivity networks measured by resting-state fMRI is of great interest because it can serve as biomarkers for brain disorders. However, it has not been extensively discussed which measure(s) can efficiently and accurately detect the change in functional network. In this talk, we discuss the performance of graph-based measures, e.g., measures of centrality and modularity, and distance-based measures, e.g. L2 and L1 distance via simulations and an empirical resting-state fMRI data set. Also via simulations, we examine predictive power of each measure when it is combined with clinical outcome variables in linear mixed-effects model framework.