Beyond Linear Dynamic Functional Connectivity: A Vine Copula Change Point Model

Abstract

To estimate dynamic functional connectivity for functional magnetic resonance imaging (fMRI) data, two approaches have dominated: sliding window and change point methods. While computationally feasible, the sliding window approach has several limitations. In addition, the existing change point methods assume a Gaussian distribution for and linear dependencies between the fMRI time series. In this work, we introduce a new methodology called Vine Copula Change Point (VCCP) to estimate change points in the functional connectivity network structure between brain regions. It uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between brain regions including tail, symmetric and asymmetric dependence, which has not been explored before in the dynamic analysis of neuroimaging data. We apply VCCP to various simulation datasets and to two fMRI datasets: a reading task and an anxiety inducing experiment. In particular, for the former dataset, we illustrate the complexity of textual changes during the reading of Chapter 9 in Harry Potter and the Sorcerer’s Stone and find that change points across subjects are related to changes in more than one type of textual attributes. Further, the graphs created by the vine copulas indicate the importance of working beyond Gaussianity and linear dependence. Finally, the R package vccp implementing the methodology from the article is available from CRAN. Supplementary Materials for this article are available online.