title: Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.
abstract: To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.
authors.affiliation: Department of Biostatistics, Johns Hopkins University, USA. Electronic address: mlindqui@jhsph.edu.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Lindquist
authors.affiliation: Department of Biostatistics, Johns Hopkins University, USA.
authors.forename: Yuting
authors.initials: Y
authors.lastname: Xu
authors.affiliation: Kennedy Krieger Institute, Johns Hopkins University, USA.
authors.forename: Mary Beth
authors.initials: MB
authors.lastname: Nebel
authors.affiliation: Department of Biostatistics, Johns Hopkins University, USA.
authors.forename: Brain S
authors.initials: BS
authors.lastname: Caffo
mesh_terms: D000328:Adult; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D012146:Rest
keywords: Dynamic conditional correlations; Dynamics; Functional connectivity; Resting state; fMRI