Semiparametric Modeling of Time-Varying Activation and Connectivity in Task-Based fMRI Data

• Activity Number: 603 - New Development on Statistics in Imaging
• Type: Topic Contributed
• Date/Time: Thursday, August 1, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Imaging
• Author(s): Jun Young Park* and Joerg Polzehl and Snigdhansu Chatterjee and André Brechmann and Mark Fiecas
• Companies: University of Minnesota and Weierstrass Institute for Applied Analysis and Stochastics and University of Minnesota and Leibniz-Institute for Neurobiology and Univ Minnesota
• Keywords: Bootstrap; Dynamic functional connectivity; Penalized splines; Task-based fMRI; Time-varying activation

Abstract

In fMRI studies, there is a rise in evidence that the temporal change in the synchronization of brain activity, known as dynamic functional connectivity (dFC), provides additional information on brain networks not captured by measures of connectivity that is static over time. While there have been many developments for statistical models for dFC when the study participants are at rest, there remains a gap on how to simultaneously model both dFC and time-varying activation when the study participants are undergoing an experimental task designed to probe at a cognitive process of interest. We propose a method to estimate the dFC between two regions of interest (ROI) in task-based fMRI where the activation effects are also allowed to vary over time. Our method uses penalized splines to model both time-varying activation effects and connectivity, and uses the bootstrap for statistical inference. In simulations, we show that ignoring time-varying activation effects would lead to poor estimation of dFC. We give an empirical illustration of both time-varying activation and connectivity by using our proposed method to analyze two subjects in an event-related fMRI learning experiment.