TY - JOUR
T1 - Evaluating dynamic bivariate correlations in resting-state fMRI
T2 - A comparison study and a new approach
AU - Lindquist, Martin A.
AU - Xu, Yuting
AU - Nebel, Mary Beth
AU - Caffo, Brain S.
N1 - Publisher Copyright:
© 2014 Elsevier Inc.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - 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.
AB - 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.
KW - Dynamic conditional correlations
KW - Dynamics
KW - FMRI
KW - Functional connectivity
KW - Resting state
UR - http://www.scopus.com/inward/record.url?scp=84907022675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907022675&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2014.06.052
DO - 10.1016/j.neuroimage.2014.06.052
M3 - Article
C2 - 24993894
AN - SCOPUS:84907022675
SN - 1053-8119
VL - 101
SP - 531
EP - 546
JO - NeuroImage
JF - NeuroImage
ER -